diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index a85a42b..785b449 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -1,10 +1,10 @@
-name: Test analysis notebooks
+name: Test simulations
 
 on: [push, pull_request]
 
 jobs:
   run-analysis:
-    name: Test analysis notebooks
+    name: Test simulation notebooks
     runs-on: ubuntu-latest
 
     steps:
@@ -24,12 +24,9 @@ jobs:
           $CONDA/bin/pip install pytest
           $CONDA/bin/pip install nbval
 
-      - name: Run analysis notebooks
+      - name: Run simulation notebooks
         run: |
-          export R_HOME=`$CONDA/bin/R RHOME`
-          $CONDA/bin/pytest -v --nbval-lax --current-env Pseudomonas_tests/Pseudomonas_sample_limma.ipynb
-          $CONDA/bin/pytest -v --nbval-lax --current-env Pseudomonas_tests/Pseudomonas_sample_combat.ipynb
-          $CONDA/bin/pytest -v --nbval-lax --current-env Pseudomonas_tests/Pseudomonas_experiment_limma.ipynb
-          $CONDA/bin/pytest -v --nbval-lax --current-env Human_tests/Human_sample_limma.ipynb
-          $CONDA/bin/pytest -v --nbval-lax --current-env Human_tests/Human_experiment_limma.ipynb
-      
+          $CONDA/bin/pytest -v --nbval-lax --current-env human_tests/Human_random_sampling_simulation.ipynb
+          $CONDA/bin/pytest -v --nbval-lax --current-env human_tests/Human_latent_transform_simulation.ipynb
+          $CONDA/bin/pytest -v --nbval-lax --current-env human_tests/Human_template_simulation.ipynb
+          
\ No newline at end of file
diff --git a/Human_tests/Human_experiment_limma.ipynb b/Human_tests/Human_experiment_limma.ipynb
deleted file mode 100644
index 49eb1d3..0000000
--- a/Human_tests/Human_experiment_limma.ipynb
+++ /dev/null
@@ -1,1112 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Human experiment level analysis\n",
-    "\n",
-    "Main notebook to run experiment-level simulation experiment using human gene expression data from recount2.\n",
-    "\n",
-    "Make sure to run ```download_data.R``` download raw data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "import ast\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from plotnine import (ggplot,\n",
-    "                      labs,  \n",
-    "                      geom_line, \n",
-    "                      geom_point,\n",
-    "                      geom_errorbar,\n",
-    "                      aes, \n",
-    "                      ggsave, \n",
-    "                      theme_bw,\n",
-    "                      theme,\n",
-    "                      facet_wrap,\n",
-    "                      scale_color_manual,\n",
-    "                      guides, \n",
-    "                      guide_legend,\n",
-    "                      element_blank,\n",
-    "                      element_text,\n",
-    "                      element_rect,\n",
-    "                      element_line,\n",
-    "                      coords)\n",
-    "\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(action='ignore')\n",
-    "\n",
-    "sys.path.append(\"../\")\n",
-    "from ponyo import pipeline, utils\n",
-    "\n",
-    "from numpy.random import seed\n",
-    "randomState = 123\n",
-    "seed(randomState)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in config variables\n",
-    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
-    "config_file = os.path.abspath(os.path.join(base_dir,\n",
-    "                                           \"configs\", \n",
-    "                                           \"config_test_human_experiment_limma.tsv\"))\n",
-    "params = utils.read_config(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load parameters\n",
-    "local_dir = params[\"local_dir\"]\n",
-    "dataset_name = params['dataset_name']\n",
-    "analysis_name = params[\"simulation_type\"]\n",
-    "correction_method = params[\"correction_method\"]\n",
-    "lst_num_partitions = params[\"lst_num_partitions\"]\n",
-    "train_architecture = params['NN_architecture']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input files\n",
-    "rpkm_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"recount2_gene_RPKM_data_test.tsv\")\n",
-    "\n",
-    "metadata_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"metadata\",\n",
-    "    \"recount2_metadata.tsv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup directories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.setup_dir(config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pre-process data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output file\n",
-    "experiment_id_file = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"metadata\", \n",
-    "    \"experiment_ids.txt\")\n",
-    "\n",
-    "normalized_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"recount2_gene_normalized_data.txt.xz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input: dataset contains 50 samples and 5000 genes\n",
-      "Output: normalized dataset contains 50 samples and 5000 genes\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipeline.normalize_expression_data(base_dir,\n",
-    "                                   config_file,\n",
-    "                                   rpkm_data_file,\n",
-    "                                  normalized_data_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 3219 experiments in the compendium\n",
-      "There are 6 experiments with gene expression data\n",
-      "6 experiment ids saved to file\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipeline.create_experiment_id_file(metadata_file,\n",
-    "                                      normalized_data_file,\n",
-    "                                      experiment_id_file,\n",
-    "                                      config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train VAE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Directory containing log information from VAE training\n",
-    "vae_log_dir = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"logs\",\n",
-    "    train_architecture)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input dataset contains 50 samples and 5000 genes\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 45 samples, validate on 5 samples\n",
-      "Epoch 1/5\n",
-      "45/45 [==============================] - 4s 88ms/step - loss: 2466.1158 - val_loss: 2732.4883\n",
-      "Epoch 2/5\n",
-      "45/45 [==============================] - 4s 79ms/step - loss: 1671.1998 - val_loss: 2180.0183\n",
-      "Epoch 3/5\n",
-      "45/45 [==============================] - 4s 79ms/step - loss: 1566.8178 - val_loss: 1799.2496\n",
-      "Epoch 4/5\n",
-      "45/45 [==============================] - 4s 79ms/step - loss: 1481.5816 - val_loss: 1730.1412\n",
-      "Epoch 5/5\n",
-      "45/45 [==============================] - 4s 79ms/step - loss: 1577.6224 - val_loss: 1477.2603\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train VAE\n",
-    "pipeline.train_vae(config_file,\n",
-    "                   normalized_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation experiment without noise correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   14.2s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   14.3s remaining:   21.5s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   14.7s remaining:    9.8s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   16.4s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   16.4s finished\n",
-      "                         score\n",
-      "number of partitions          \n",
-      "1                     0.999695\n",
-      "2                     0.977723\n",
-      "6                     0.941195\n",
-      "                         score\n",
-      "number of partitions          \n",
-      "1                     0.000074\n",
-      "2                     0.004221\n",
-      "6                     0.011557\n",
-      "                          ymin      ymax\n",
-      "number of partitions                    \n",
-      "1                     0.999549  0.999841\n",
-      "2                     0.969449  0.985996\n",
-      "6                     0.918544  0.963846\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "corrected=False\n",
-    "pipeline.run_simulation(config_file,\n",
-    "                         normalized_data_file,\n",
-    "                        corrected,\n",
-    "                        experiment_id_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation with correction applied"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   10.0s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   10.2s remaining:   15.3s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   10.3s remaining:    6.9s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   12.0s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   12.0s finished\n",
-      "                         score\n",
-      "number of partitions          \n",
-      "1                     0.999695\n",
-      "2                     0.917417\n",
-      "6                     0.594720\n",
-      "                         score\n",
-      "number of partitions          \n",
-      "1                     0.000074\n",
-      "2                     0.002107\n",
-      "6                     0.006806\n",
-      "                          ymin      ymax\n",
-      "number of partitions                    \n",
-      "1                     0.999549  0.999841\n",
-      "2                     0.913288  0.921546\n",
-      "6                     0.581380  0.608059\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "corrected=True\n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_data_file,\n",
-    "                        corrected,\n",
-    "                        experiment_id_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Make figures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca_ind = [0,1,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# File directories\n",
-    "similarity_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "similarity_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "permuted_score_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_permuted.npy\")\n",
-    "\n",
-    "compendia_dir = os.path.join(\n",
-    "    local_dir,\n",
-    "    \"partition_simulated\",\n",
-    "    dataset_name + \"_\" + analysis_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "svcca_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".svg\")\n",
-    "\n",
-    "svcca_png_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".png\")\n",
-    "\n",
-    "pca_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_uncorrected_\"+correction_method+\".svg\")\n",
-    "\n",
-    "pca_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_corrected_\"+correction_method+\".svg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load pickled files\n",
-    "uncorrected_svcca = pd.read_pickle(similarity_uncorrected_file)\n",
-    "err_uncorrected_svcca = pd.read_pickle(ci_uncorrected_file)\n",
-    "corrected_svcca = pd.read_pickle(similarity_corrected_file)\n",
-    "err_corrected_svcca = pd.read_pickle(ci_corrected_file)\n",
-    "\n",
-    "permuted_score = np.load(permuted_score_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Concatenate error bars\n",
-    "uncorrected_svcca_err = pd.concat([uncorrected_svcca, err_uncorrected_svcca], axis=1)\n",
-    "corrected_svcca_err = pd.concat([corrected_svcca, err_corrected_svcca], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add group label\n",
-    "uncorrected_svcca_err['Group'] = 'uncorrected'\n",
-    "corrected_svcca_err['Group'] = 'corrected'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>score</th>\n",
-       "      <th>ymin</th>\n",
-       "      <th>ymax</th>\n",
-       "      <th>Group</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>number of partitions</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999695</td>\n",
-       "      <td>0.999549</td>\n",
-       "      <td>0.999841</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.977723</td>\n",
-       "      <td>0.969449</td>\n",
-       "      <td>0.985996</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.941195</td>\n",
-       "      <td>0.918544</td>\n",
-       "      <td>0.963846</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999695</td>\n",
-       "      <td>0.999549</td>\n",
-       "      <td>0.999841</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.917417</td>\n",
-       "      <td>0.913288</td>\n",
-       "      <td>0.921546</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.594720</td>\n",
-       "      <td>0.581380</td>\n",
-       "      <td>0.608059</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         score      ymin      ymax        Group\n",
-       "number of partitions                                           \n",
-       "1                     0.999695  0.999549  0.999841  uncorrected\n",
-       "2                     0.977723  0.969449  0.985996  uncorrected\n",
-       "6                     0.941195  0.918544  0.963846  uncorrected\n",
-       "1                     0.999695  0.999549  0.999841    corrected\n",
-       "2                     0.917417  0.913288  0.921546    corrected\n",
-       "6                     0.594720  0.581380  0.608059    corrected"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Concatenate dataframes\n",
-    "all_svcca = pd.concat([uncorrected_svcca_err, corrected_svcca_err])\n",
-    "all_svcca"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SVCCA "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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58mSMGjUKAQEBGD9+PExNTREUFKR3ntdff93oPdWMGTMGCoUCv/32m9bw5cuXAwDGjh1bqOWlpaVh586d+PTTTzF27FgEBAQgICAAf//9NwBobdvhw4cBAIMGDYK1tbXB63jrrbdgYWGhM/z06dMAAB8fHzg4OOiMHzZsGIDsZwLUmjdvDgD4z3/+g927dyM1NTXP9aqnnTBhAo4fPw6VSmVwzDkNGTIEALBhwwat4Tt37kRiYiL69OkDc3NznfmuXr2K7777DhMmTMDIkSMREBCApUuXAkCex4z6uQN9xo0bB+DF31qtqH97fZ8ZT09PANqfmYcPH+Lx48dwcXGBl5eXzjwDBw4s1HqLEkNxdezYESYmJlrD1OeHgs4t+uJwcHBAly5ddIYPGjQIQPYzAur6avWzFX5+fpDJdL8m1DXqFy5c0BknSRJ69uypd5uaN2+OmJgYjB49Gv/884/eaQqi/mwNGDBAZ/8A+j+DxVWYfacWFRWFVatWYdq0aRg9erTmPJWRkYGoqChER0frLM/V1RVvvPGG0eIuqpCQEISEhMDJyUnv8e7r6ws7OzvcvXsX4eHhOuMN/YzUq1cPlpaW2LNnD+bNm6e3Xp/oVcbeXXIxNTVF79690bt3bwBAbGwsNm3ahP/85z94+vQpJk6ciEOHDhm0LBcXF8jlcp3hVlZWAKA3GVaPS0tLK+omAAAWLFiAmTNnIiMjo1DzlURvNa6urujVqxe2bduGEydOoEOHDkhPT8fatWthbm6uSSgN8ffff6NXr1759sme8wHUR48eAQBq165dqJjz2g/qL5HcD/upqYc/fvxYMywgIAAHDx7E5s2b0bNnT5iZmeHNN9+Ej48PRo0aBRcXF82006dPx+nTp3H8+HF06tQJVlZWaN26NXr06IGAgACDez7x9vaGq6sr9u/fj+joaM0Fxe+//w4AOvs8PT0dAQEB2LhxY57LzOvB3vyOGT8/P7i4uGDjxo1YsGABrKyscPnyZVy+fBmtW7dGw4YNDdoetSpVqugMU1985fzMqBOBqlWr6l2OtbU17Ozs8nyo1RgxFFd+54eCzi364qhevbre9dja2mr2RUxMDBwdHTWfr08//RSffvppnjHqu+CsXLkylEql3ul/+ukn+Pn5YeXKlVi5ciWcnZ3RoUMH9OnTB/369dO7TbkV5TNYXIXZdwCwceNGjB07FomJiXkuMyEhQedCvyy72M1JvY/z2m5JklC9enXExsZqLoZzMvQzYmNjg+XLl2Ps2LGYMWMGZsyYAU9PT3Tq1AnDhg1D27ZtjbVJRC8ltqQXwM7ODuPGjdP0aHLs2DEkJycbNK++FqjCjC+q8+fPY9q0abCwsMDq1avx4MEDpKamQmSXN+XblaOZmVmJxJS7RXXHjh2IjIxE//79dfqez4t43kXmgwcPMG7cOFy9ehXx8fHIysqCEAKffPKJZrriKup+0NeiLJfL8X//93+4fPkyvvzyS7z55psIDAzEp59+Ck9PT5w9e1YzrY2NDY4ePYpTp05hxowZaNCgAY4ePYrJkyejbt26ebZm61unv78/MjIysGXLFgBAZGQkDhw4AFdXV51+ohcsWICNGzeiUaNG2LdvHyIiIpCeng4hhObuUV77Nb99ZWJiglGjRiExMVHTU4P6jsq7775r0LbkVFKfmbKIIa8eVQxZT0nvB3VsXl5eGDFiRJ4/+i6w8zseGjdujJs3b2L79u149913YWNjg82bN8Pf3x/t2rVDenp6sWMvTI89JeHhw4cICAhAeno6Fi1ahKCgICQnJ2vOv61btwag//NUUuffkpDffi7M8Tlo0CDcv38fy5cvR//+/REbG4tly5bBy8sL06ZNM0aoRC+tsv/Ge0mok5rMzMwitb6Vpu3btwMAvv76a4wYMULTRRkApKSk6L09WdLeeusteHh44I8//kBMTEyRyh3+/fdf/Pvvv3jjjTewdOlSNGnSBNbW1povi/v37+vMo25JvXfvnhG2AnBzcwOQ/UWsj7oFUl8raLNmzTBr1iycPHkSz549w5QpU5CQkIDJkydrTSdJEry8vPC///0PgYGBePLkCQYNGoSIiIh8WzVzUydQ6tbzzZs3Q6VSwd/fX6fFUn3MbNy4ET4+PqhcubKmlEDffi2MsWPHQiaTYfny5UhOTsaGDRtgY2NTrJKTgqgvRNV3UnJLSEgolc+xqakpAOhtUc0rtpISEhKid3h8fDxiY2Nhbm6uuWBWt4T6+flh9erV+f4UlpmZGfz8/PDrr7/izp07uHHjBho3boxz587plMTpU5zPYFEVZt/t3bsX6enp+PDDDzFp0iRN97tqxf08lYaC9nHOccbYz05OThgzZgw2b96M8PBw7Nu3DzY2NliwYEGRy6KIKgIm6c8V1Pp69+5dANlfuuX9hT4xMTEA9N9y3LJli1FamnNSJyL51U9LkoSxY8ciNTUVc+bMwZEjR1C/fv1C3c7Mb7tiYmL0liG99dZbALKTz/xuPRtKXd+8f/9+vUne+vXrAQDt2rXLdzk2Njb49ttvIUkSbty4ke+0lStX1vQbXtC0OTVv3hz16tXD6dOnERISkmepC5D/vt28ebPB69SnevXq8PHxwYULF/DZZ58hLi4OQ4YM0VvzbyzVq1eHu7s7wsPDte5UqKnvLpQ09cVCzr741QwtmzOWqKgoHDlyRGe4+g5H69atNRdv6vpr9cVbSWrYsCEmTJgAwLDjW/3Z2rJli96+ug39DBZGYfZdfp+lkydPIiIiokgxGHKeNdZ81apVQ7Vq1fDs2TO9271nzx7ExMTAw8NDp9SluCRJgo+PD3r06AEATNLplcYk/bnPP/8c06dP19vi+vjxY7z33nsAsl8Uoz7plVfqB3RWrFihVZN+8+ZNfPzxx0Zfn7rVpaCHakeOHAmlUolFixZBCFHocgcPDw/IZDIcPXpUq+wjNTUV48aN0/sgVosWLdCpUyc8ffoUY8eORVJSktb4Bw8eaB44NUStWrXQo0cPJCQkYNKkSVr799y5c1i6dCnkcrkm6QCAdevW6U0+9u3bByGEVt30L7/8guDgYJ1p9+7dCyDvGuu8DBkyBEIIfPvttzh79izq1auneTg1J/Ux88svv2gN37p1K9auXVuodeqjLndauHAhgKKVuhR1ndOmTUNcXJxm+MOHDzFnzpwSXz8AzYuali5dqvWSsqtXr+KLL74olRhy+uijj7TiCA4O1uyLnMdsy5Yt0aVLF5w5cwYTJkxAfHy8zrKuXbuG/fv3G7zu5ORk/PDDDzoXt1lZWZrlGHJ8d+zYEY0aNcKDBw/wxRdfaDU6bN++Hdu2bYOVlRVGjRplcGyGMHTfqT9L69ev1zrfPH78WHNMFoWh51ljzffBBx8AAKZOnap52R8AhIeHY/r06QCASZMmFWqZuV25cgXbtm3TKXOKjo5GYGAggMKf84gqEj44+lxiYiIWL16M+fPnw9PTEw0aNICZmRlCQ0MRGBiIjIwMeHh4aN6+WZ6NHDkS33//Pf7880/UrVsXb775JqKjo3HixAn4+fnhwoUL+d7GLKxevXphzZo1GDx4MLp27ap5uDH3retKlSqhb9++2LBhA5RKJYYPH16o9VSuXBmjR4/G8uXL0aRJE3h7e8Pc3BynTp1CZmYmAgIC9N5+X7duHTp37oyNGzfiwIED8PLyglKpxL1793D16lV8//33aNSokcFxLFu2DO3atcPatWtx4sQJtG7dGs+ePcPx48eRmZmJ77//Hk2bNtVM/8cff2D48OGoXbs2GjVqBHNzcwQHByMwMBAymQxz587VTPvLL79g/PjxaNCgAerXrw+FQoF///0X165dg5mZWaETu8GDB+Pzzz/XJN95PaQ7Y8YM7N+/HzNnzsSWLVvg6emJoKAgXLp0CR999BHmz59fqPXm1r17d1StWhWPHj3CG2+8gWbNmhVreYaYPn069uzZg/Pnz6N27dro1KkT0tLScPToUXTu3BlCCKP2xqJPp06d0KFDB5w4cQINGjRA27ZtERkZicDAQHz44YfF3q+F0apVK6Snp8PDwwPe3t7IyMjAkSNHkJycjKFDh6JPnz5a069fvx4+Pj74+eefsWHDBjRt2hRubm6Ii4vD9evX8ejRI0yaNAk+Pj4GrT89PR2TJk3CRx99hObNm2veTHrx4kU8evQINWrUMKj8TZIk/P777+jUqRO++eYbbN++HU2bNkVISAjOnDkDhUKBFStW5PvsTWEVZt/16tULDRs2xKVLl+Dh4YG2bdsiNTUVx44dQ9OmTdGmTRu9d3cK0rVrV5iZmWHhwoW4ceMG3NzcIEkSpk+fjrp16xp9vilTpuDo0aPYt28f6tSpA29vbwghcOTIESQkJMDPzw/vv/9+obcjp4cPH6Jv376wtbXFG2+8ARcXF8TGxuLkyZNISEhAz549NTX8RK8itqQ/99lnn2HdunUYOnQolEolTp06ha1bt+LmzZto0aIFvvvuO1y9etXo3ROWBEdHR1y8eBGDBw9Geno6du3ahcePH+Orr77Kt/eOourTpw8WLlyIKlWq4M8//8SKFSuwYsUKvdOqa/v79Omj6QmhMJYuXYrvv/8eNWvWxJEjR3Dq1Cm89dZbuHTpUp49Ebi7u+PixYuYM2cOqlSpgkOHDmHfvn1ITk7G+++/D19f30LFoF7etGnToFAosG3bNvz111/o3LkzDhw4gKlTp2pNP3XqVEyYMAHW1tY4deoUtm/fjqdPn2LgwIEIDAxE//79NdN+9dVXGDVqFCRJwpEjR/Dnn38iJSUFY8aMwdWrVwvd20GtWrW0vuQGDx6sd7r27dvj9OnT8Pb2xv3797F7926Ymprijz/+0GolLCq5XK5pVS6NVnQAUCqVOHToEGbMmAFLS0vs2rUL//zzD6ZNm4b/+7//Q0RERJGOwcKQJAk7d+7EuHHjIEkS9u7di+joaCxevBjz5s0r0XXnplQqcfToUQwePBjnz5/HgQMHULVqVcyfP1/vxW3lypVx9uxZ/PDDD2jQoAGuXLmCrVu34vr166hVqxbmzZuHjz76yOD1W1lZ4aeffkLPnj3x7Nkz7Nq1C0ePHoW9vT1mz56Nv/76y+C/R6NGjXD58mW8++67SExMxNatW3H79m34+fnhzJkzGDBggMFxGaIw+87U1BSnTp3C+PHjYWZmht27d+PWrVv44IMPcOjQIb3dRhrCzc0NO3fuRKtWrXD69GmsXLkSK1asKPBCs6jzyeVy7Nq1C4sXL0atWrVw4MABHDx4EHXr1sVPP/2ErVu3FvsB5latWmHu3Llo3rw5bt++jS1btuDSpUto3LgxVq5ciT/++KNYyyd62UnC2AXKRPl4++23cfDgQRw7dkxvP89UMSUnJ8Pd3R0qlQpPnjwpVJ/1JeH8+fNo3bo1fHx8sG/fvjKNhcqvBw8eoGbNmujQoQOOHz9e1uEQ0SuGLelUai5cuIBDhw6hYcOGTNBfMT/99BNiY2MxYsSIUk3Qr1y5otPV4f379zXPmAwdOrTUYiEiIioM1qRTiZs5cyZCQkKwZ88eCCHw9ddfl3VIVAqioqLw8ccfIyIiAnv37oWVlRVmzpxZqjEMGjQIcXFxaNSoERwdHRESEoK//voLaWlp6NWrV57lP0RERGWNSTqVuE2bNuHRo0eoXr06vv32W7zzzjtlHRKVgoSEBKxYsQKmpqZo1qwZ5s+fr7dbupL0wQcfYPPmzbh+/Tqio6NhZmaGpk2bYujQoZo6cSIiovKINelEREREROUMa9KJiIiIiMoZJulEREREROUMk3QiIiIionKGSToRERERUTnDJJ2IiIiIqJxhkk5EREREVM4wSc8lIyMDYWFhyMjIKOtQiIiIiOgVxSQ9l8jISPz666+IjIws61ColGRkZCAiIoIXZlQu8Hik8oTHI1HZYZJOrzyVSoVnz55BpVKVdShEPB6pXOHxSFR2mKQTEREREZUzTNKJiIiIiMoZJulEREREROUMk3QiIiIionKGSToRERERUTnDJJ2IiIiIqJxhkk5EREREVM4oyjqAkpKSkoLt27fj9u3bCAoKQmJiIiZNmoTOnTuXdWhERERERPmqsC3p8fHx2LRpE0JDQ1FiLblhAAAgAElEQVSzZs2yDoeIiIiIyGAVtiXdwcEBa9asgb29PYKCgjBt2rSyDomIiIiIyCAVtiXdxMQE9vb2ZR0GEREREVGhVdgknYiIiIjoZcUknYiIiIionKmwNekVXVZWFq5cuYLImFjYVKuHutXdYKkATGWAJEllHR4RERERFQOTdACRkZGIjIwEAERHRwMAUlNTkZKSYvR1RUdHIz4+vtjLCQ0NRUxMDAAgLvoMwmIbQZIpIJMETGXZP0oImMoAUzmgLETebmNjAwcHh2LH+LJIS0vT+k1Ulng8Unnysh2P5ubmZR0CkdEwSQfwxx9/YPny5QAAW1tbtG/fHqGhoUhKSjL6um7dugUhhNGXq3rwt+bfxT2VSpKE+vXrF3MpL5/Q0NCyDoFIg8cjlScvy/H42muvlXUIREbDJB1A37590aFDBwDZLd2HDh1ClSpV4OLiYvR12dvbG6UlPS4uDg9DQiABgNwEJtWLfmKSIKCUP2+Bh4CdrS0qOzrAQi4gfwUqZ9LS0hAaGooqVapAqVSWdTj0iuPxSOUJj0eissMkHUClSpVQqVIlAEBYWBgAwMzMrERum7m7u8Pd3d0oy6pdvwl+P/cEJ8NkcHpkDhc7Uzjbm8LZXgmlSeGeCc4EkPL8Jw7Aw4Ts4eZywEoBWCokrd9mFbD2XalU8lYplRs8Hqk84fFIVPoqdJK+e/duJCUlaerML1y4oKk99/X1haWlZVmGV2xOtuaY7FMbPZ+lYc6eMCw//wwAIAGws1LAxV4JVwdTdGpgi7puFkgTQEpm4daRkpn98yxNXaKT/VsuAZYKwEohwfJ5Im+lkGCpABSyipW8ExEREZW2Cp2k79ixA0+fPtX8/9y5czh37hwAoGPHji99kq5W20mJNQE1cOJOAmbtDsPtiDTEJKoQk6jCrUdJOHotBvYWckzv6oxBb9ojTUhIVAkkqoAkFZCoEkhSAapClMpnCiA+A4jPyDlT9r/NZDkS+Oe/rRTZrfIVrfWdiIiIqCRU6CT9t99+K+sQSlUHT2scmmSF3y9E47uDEYhJftFsHpOcif/seII156Iwy9cVHTytteYVQiAtC88T9+wEXp28Jxey9T01C0hNB6LStVvfZVAn77rlMyZsfSciIiLSqNBJ+qtIIZcworUj/JraYdGRp1hxJhKqrBfjb0ekYdCKB3irvjW+6OEKD6fsB4EkSYKZHDCTA5Vy9deYKQSSVfoT+IxCtL5nAUhQZf+oE3f1b6We1ndLBWAhB2RsfSciIqJXDJP0CsrWXI4vfV0xtKUD5uwJw6FbCVrjD99KwPHbCRjVxhGTOzvDzkKe57LkkgRrE8DaBMiueM8mhEB6rtZ39e/kzBdpuCHSsoC0dCA6V+u7hOzkPWfZjKVCglJkAKqMQqwhb6mpqcjIyEBycjKysrIKnsFAJiYmMDU1NdryiIiI6NXBJL2CU9erH7+TgNnP69XVVFnAr6ejsOVyLGZ0dcaQFg5QFKLPRUmSoJQDSjngmKv1PUsIJGfqb31PL0QeLIDn8wIROVrf0x/ehurRLcMXZICgoCCjLq9Bgwbss5eIiIiKhEn6K6KjpzW88qlX/2THE6zOo169KGSS9LzHFyBn6zsApGcJzQOrWq3vquySGEOYuNeBwrmG3nESsh9StVAAFnIJFgrA8vlvUz09U8bGxuLMmTNo27Yt7OzsCrGVBcRoYmK0ZREREdGrhUn6K0Rdr/5OEzssOhKBlWej9Nard3ler17bqWReXGEqk2BqCtib6ra+p+TR+p6WK3uXFCaQFHknwanPf6KzAKS/GK6QdB9czTRJBZDdN35F6fGHiIiIXm5M0l9BdhZyzOrphqGtHDFnTxgO56pXP3QrAcfvJD6vV68MW/O869WNSSZJmvrz3K3vGTla35NU0ErgC1NFrhJAbAYQ+/yJ16zUJKRePwsAOHHzAazr2MFEBphIyP4tk6DQ/Fs9XIKJDDrD2b0kERERGQuT9FeYh5MSa/OoV8/IFFh2KhJbLsdgepfC16sbm4lMgp0pYJer9V3oaX1X/041IHtXhd0D0lM0/06t3gBpipwPe+p7/FX/I7HqpF07edeX0Euaf+ech0k+ERERqTFJJ029+voL0ZiXq149Oim7Xn3N+SjM8nVD+zpWZRipLknKrjW30NP6rsoSSMrU3/qe+TzPlpQ5XnMtk2f/FJFKACqdPuUNT/JNciTyihwt+erhipwt+TmGmUqAjEk+ERFRhcIknQBk16sHtHaEXxM7LDwSgVW56tX/DU+D/2/B6Nogu169VqWSqVc3JoVMgq0MsDXRbX1Pzcp+22qCdW3cVSUjJiQIDg3fhKlSDlUWkJFVuD7gjSFDABmZALQSfcOSfAnIVaYDKHK02Gsn+rrD2Rc9ERFR+cIknbTYWcgxu6cbhrVyxJzdYTj8r3a9+sGbCTh2u/Tr1Y1JkiSYy7N7gKmkVMCmTjUcCQlCs8qWcHR8sT1CCKjEi4Q9Iyu728oMIbKHPR+endQLzTTq35mlmOQLZHdtmQ7kSPLzCkB3uEzKkeBrEnpJK+k3kbKHmeYq31FITPKJiIiMjUk66eXhpMTakTVw/HYCZu0Ow52n+uvVZ3R1xuA3y7ZevaRI0oskNdcYg+bPypnkayX6ORJ6zXDx/ALgxXDjvVbJkFiBNJG7F53C1eMrcrXkm0iS3mGsxyciIioYk3TKV8e61jjsYYV1gdGYf0i3Xn3m9uz+1Wf3dEM7j9KrV09PT0dGhvHeOKr+nZSUZJRlAi/eOKrbN7thCWmm0E3cM9TD9Ax/0dKfPaw0q3XU9fipWkMLX4+vndQXXI9vIgFyJvlERFQBMUmnAinkEka2cUTvpnZYcDgCq8/p1qsPXF669ep37tzBzZs3jbrMM2fOGHV5xX3jqFySIJcD2nvTsGRUCIFMkbMc50WLvXbpzvNhWbrTliZj1eMrZHhejmNYPb5Clr2fiYiIyhsm6WQwOws55vRyw7BWDvhqT3iZ1qt7enqiZs2aRllWamoqQkJCUK1aNZiZmRllmUDZvnFUkrL7d1cAgNafwfAkX12qo9LTYq+V1OsZpqqg9fi6D+eyHp+IiEoGk3QqtDqVzTT16l/uDkNQHvXqH3d1xuAWDpDLjJ/EmJqawtTUtOAJDSCTyWBiYgILCwuYm5sXPMMrwNj1+CqRnUjrq8dX5WjJV//OKsUkvzTr8V+09LMen0qOscsBMzIykJycjKws4z0poy4HJKK8MUmnIntRrx6F7w891alX/3j7E6w+F43ZPV3hVYr16lT2ZJIEUwmlUo+vytWS/7LX48uEAimWrshKlsNMlaVTo6/I0brPenzSpyTKAYOCgoy6vOKWAxK9CpikU7GYyCWMalMJvZvaYeHhpzr16rfCUzFgeTDebmCDz3u4vBT9q1PZK249fpYA0vOox9cZplPSUxJblDfdenwZoLRDYhqym/hfbJnOvLnr8XP3oKN3eI6LAtbjV0zGLAeMjY3FmTNn0LZtW9jZ2RllmUDZlgMSvSyYpJNR2FsoNPXqc/aE40iuevUDN+Nx9HYCRrd1xGTvyrB5CftXp5eDJEmQS4A5UGL1+C/6z9cd9lLV4yN33b12DzovuspkPf7LxJjlgOrer8zMzGBpaWmUZRKRYZikk1HVqWyGdSNr4Njz/tVz16v/cjISW/6Kwcdvu2DQm/YlUq9OVBwlUY+fkfuFV5rhZVyPj+xa/KLW48slGPSAraZGP2dLPkt1iIjyxSSdSkSnutbwyqNePSopEzO2Pcbqc1GY5ct6dapYiluPn5ScgrvBD1Cleg3ITM3yrLvX17NOadfjZ4rsn9QiJvm5a+xzluXo6zOf9fhE9Cphkk4lJme9+oLn9eqZOb7Mb4Zl16v7NLTB591dUJP16kSQSYBcZMJCDpibqpPQwtXj632jrZ5h5aUeP6UY/eMrcrXY65Tv5CrXUSf6rMcnovKOSTqVOHsLBb7q5YbhLR0we08Yjt5O1Bq//594HPmX9epExaWux5cDMCtGPb6+nnUMqdF/Jerxn0/LenwiKmlM0qnU1HE2w/pRNXH03+x69bvPWK9OVJ7krMfXfmOA4Um+7htttXvWSc9Zj5+rRj+zAtbj6337LUt1iMgATNKp1HnXs0a7OlZYez67Xj02RX+9+uyermhbm/XqRC8LqZj1+FlCGFR3X6Hq8Qt4wJb1+ESvLibpVCZM5BJGt62EPs3yrlfv/2t2vfoXPVxQw5H16kQVnUySoCxO//jIXXefdz2+bklPCWxQPjT1+NpboWfKwtXjK3K17rMen+jlxSSdylTOevVZu8Nw7I5uvfrRfxMw2iu7Xt3ajPXqRKRLkiTIAcjlRa/HzxS5EnwBqLJEjhKdXDX6OVv3X9J6fEWuFvvcw+KS00tiE4jIAEzSqVyo42yG30frr1dPzxRYeiISW/6KxcddneHPenUiMjJJkqB43tpcnHp8Ve6WfD0Jvb6335bHevzM2Aik3TgFAPjnn3/Qvn37UomPiLIxSadyJb969chEFaZve4xV56Iwp6cr2rBenYjKidKox1flKNfRGpaVnXgbm+rpQ82/w8PDkZWVBZlMZwOJqIQwSadyR12vru5ffc153Xr1fr8Go1tDG3zOenUiqgBKoh5flasHHXX5jm5Jj/5iGZm1IzKfhgAALC0tmaATlTIm6VRuOVgqMPcdNwxvlV2vfjxXvfq+5/2rj/FyxCTWqxPRK8po9fi5H7C1rYVHplkIvXUNzZs3L4nQiSgfvCymcs/T2QwbRtfE+pE14OGk3c6Uninw84lItJ13B78HRiMzq5S7aCAieslJkgSFTIK5XIKNiQRHpQQXMwlVLeWo61YJAGBiYlLGURK9epik00vDu541jkypgzm9XGGX662k6nr1t3+4i7P3EvNYAhEREdHLgUk6vVRM5BLGtK2E09M9MbKNI+S5jmB1vfqYdQ/xMIpdhxEREdHLiUk6vZQcLBX4+h03HJlcBx09dXt52XsjHh2+v4O5e8OQkJqpZwlERERE5ReTdHqpeTqb4fdRNbBuZHXUzq9e/QLr1YmIiOjlUaTeXcLDw7F//36cOHECV69exbNnzxAbGws7Ozs4OTmhadOm6NChA3x8fODi4mLsmIm0SJKEzvVs0L6ONdaci8L3hyMQl/Kiz8bIRBWm//EYq5/3r966FvtXJyIiovKtUEn6gQMHsHTpUuzduxeZmZkQQrtlMjk5GU+ePMG1a9ewdu1ayOVy9OjRA+PHj0fXrl2NGjhRbiZyCWO8KqFPMzvMPxSBdYHRWv2r//MkFX2XBaP7azb4vLsrqjuall2wRERERPkwKEm/dOkSPvroI5w6lf16YC8vL3Ts2BEtWrRAvXr14ODgABsbG8TFxSEmJga3bt3ChQsXcOzYMezatQu7du1C+/btMX/+fPa1SiXOwVKBb/zcMaKVI2btDsOJIO3eXvbeiMfhWwl4t10lfNjJiS8LICIionLHoPykRYsWcHZ2xjfffIMhQ4agSpUqeqdzcHCAg4MDateuDV9fXwDAo0ePsH79evzwww9o0aIFMjP5EB8VX3p6OjIyMvKdpoo18Nugyjh91wILjzxFSLT29L+feYL9VyPwbhs7NLBKR3JyMrKyjPdybRMTE5iasrWeiIiICs+gJH3hwoV47733YGZmVugVVK1aFZ988gmmTJmCZcuWFXp+In3u3LmDmzdvGjz9qCoA9F9bAhHA/Qjg/r27RolNrUGDBnjttdeMukwiIiJ6NRiUpE+aNKnYKzIzMzPKcogAwNPTEzVr1iz0fLEpmfj1VCS2XYlFZhbQslIsurpEQi4DUjOBwIzGmNjJGdUcit8Czjf0EVFZMOROo6FSU1M1v5OSkoyyTIB3GokMUaLluJmZmdi/fz969OhRkquhV5CpqWmRTvCWlsDs3jYY3DoVX+4Og2lyDNSPP5vJgf1/p+LPW6EY0doRUzpXhoMlK9aJ6OVS2DuNhjhz5oxRl8c7jUQFK5EM5OLFi1i/fj02bdqEqKgoqFSqklgNUZHVdTHDxtE1cPC6BUJunIWtLANHw+2RkikHAKw4E4Wtl2Mw2bsyAto4QqngKwWI6OVQ1DuN+qSmpiIkJATVqlUrUslrXninkahgRkvSHz58iPXr12P9+vW4c+eOpnvGhg0bGmsVRBrGup3r5WGFSIcWCDx3BveTbWFnkmOZKmDxwUfYdjEcEzs5wbuudaGWzdu5RFQWinqnUR+ZTAYTExNYWFjA3NzcKMskIsMUK0mPi4vD5s2bsW7dOpw9exZCCAghULNmTfj7+2PQoEG8nUUloiRu546p9SDPcSl3b2FPIZ8r5e1cIiIiKqpCJ+kqlQp79uzB+vXrsWfPHqSlpUEIAQcHB2RlZSEuLg737t0riViJNErydm5obDqWHHuGI/8m6p2+22s2eL99JbjY5n+7lrdziYiIqKgMTtLPnz+PdevWYfPmzYiOjoYQAkqlEr1798awYcPQvXt3eHt74+zZsyUZLxGAkr2dW9fSEj8OtUdgcBLm7AnDlUcpWtNvvJKC7X+HYmy7SpjYyQlWSrlR4iAiIiJSMyhJr1OnDu7fv6/5f7t27TB06FD0798ftra2JRYcUVlqWdMSf75fGzuvx+GbfeF4HPuiXj1VJfDDsWfYcDEGM7o6w/8NeyjkUhlGS0RERBWJQUn6vXv3IEkSXF1dsXbtWnh7e5d0XETlgkwmoXdTO/g0tMFvpyPx47FnSEx78VbSyEQVZmx7jBVnIvFlD1d0LOTDpURERET6GNSvXL169SCEwJMnT9ClSxe0bdsWS5cuRXR0dEnHR1QumJvI8EGnyjgz3RPDWjpAlqvR/HZEGgavfIAhK4JxOzy1bIIkIiKiCsOgJP3mzZu4dOkSPvzwQzg5OeHcuXOYOHEiXF1d8c4772Dr1q1IS0sr6ViJypyTtQn+18cdRybXQSdPK53xx+4kovOiIHy87TGeJRjnjX9ERET06pGEukNzA2VlZeHgwYNYu3Ytdu7ciZSUFEiSBBsbGwBAfHw8MjMzSyTY0hAWFoZff/0VY8eOhaura1mHQ6UgMzMTSUlJsLS0hFxeuIdAj99OwJy9Yfg3XPci1UopwwednDDGqxLMTfgyJDJMcY5HImPj8UhUdgqdOchkMvj4+GDDhg2IiIjAypUr0alTJyQkJCAuLg4A4ObmhilTpiAwMNDoARMZW3p6OkJCQpCenl7oeTvWtcbBD+tgXh93VLLSfsQjMS0L3+6PQPv5d7DtSiyysgp1PUyvqOIcj0TGxuORqOwUq3nPysoKAQEBOHz4MEJCQvDf//4Xr732GsLDw7F48WK0adMGHh4exoqVqFxSyCUMaemAszM88WEnJ5gptAvWH8dmYOKmR/D96R4Cg5PKKEoiIiJ6mRjtHrybmxtmzJiBa9eu4erVq5g6dSqcnZ0RHBxsrFUQlWtWSjlm+rjg5Eee6NvMTmf81dAU9P7lPt5d9xAPovgMBxEREeWtRAplGzdujPnz5yM0NBT79+8viVUQlVtV7E3xo39V7J1YGy1rWuiM33MjHh2+D8Ls3WGITX55n98gIiKikmNwkn7r1i2cPHkSISEhBU778OFDnDx5Enfu3EGXLl2KFSDRy6ppVQtse68WfhtWDTUctd+OmpEpsOxUJNrOu43fzkQiI5P16kRERPSCQUl6ZGQkWrduDX9/f5iYmBQ4vampKfz9/eHl5YXY2NhiB0n0spIkCd1fs8XxqXUwy9cVtubaH7mY5Ex8sSsMnRbcwf5/4lHIzpaIiIiogjIoSV+1ahXi4+PxzTffGNQtoaurK/773/8iOjoaq1atKnaQRC87U4UMY9tVwpnpdTGmrSMUuT559yPTMWrtQ/T/NRjXH6eUTZBERERUbhiUpO/Zswe2trYYOnSowQseMmQI7OzssGvXriIHR1TROFgqMKeXG45N9YRPQxud8WfvJ6Hbj3cxefMjhMXxZUhERESvKoOS9H/++QetWrWCQqEoeOLn5HI5WrZsiX/++afIwRFVVLWdlFg5vDr+eK8mGrmbaY0TAtj8VyzazruNeQcjkJTGh0uJiIheNQYl6XFxcXB0dCz0wh0dHTUvOCIiXa1rWWHfRA8sHlAFrjbaF8GpGQILjzyF17w72HgxGpl8GRIREdErw6Ak3c7ODtHR0YVeeHR0NGxsdG/pE9ELMpmE/s3tcWp6XUzvUhkWptofy4gEFaZtfYy3f7iLU3cTyyhKIiIiKk0GJekeHh4IDAxEZqbht91VKhXOnz+POnXqFDk4oleJhakMU95yxpnpnhj0pj0k7ReX4mZYKgYuD8bw1Q8Q9DS1bIIkIiKiUmFQku7j44PY2FgsWbLE4AUvWbIEcXFx6NatW5GDI3oVOduY4Pt+VXDwQw+087DSGX/4VgK8FwbhPzseIypRVQYREhERUUkzKEmfOHEirKysMGPGDKxfv77A6detW4cZM2bA2toaEyZMKHaQRK+ihm7m2DSmBtaNrA4PJ6XWuMwsYPW5aLT57jZ+PvEMqRlZZRQlERERlQSDknQHBwesWbMGmZmZGDFiBNq2bYslS5bg7NmzCAoKQlBQEM6ePYslS5agbdu2CAgIQFZWFtasWQMHB4eS3gaiCkuSJHSuZ4MjU+rgGz83OFjKtcYnpGVh7t5wdPj+DnZdi+XLkIiIiCoIg/tU9PPzw59//okRI0bg3LlzOH/+vN7phBBwcnLC6tWrWepCZCQmcgkBrR3Rp6kdfjz+DMtPRSI980VC/igmA+M2PMLy01GY5euK5tUtyjBaIiIiKi6DWtLVunXrhgcPHuCnn35C9+7d4ebmBqVSCaVSCTc3N3Tv3h0///wzgoODmaATlQAbczk+7eaCkx95oldjW53xf4Uko+fP9zB+QwgeRaeXQYRERERkDIa/neg5CwsLjB8/HuPHjy+JeIjIANUcTPHLkGoY45WE2bvD8FdIitb4ndfisP+feIzxqoQPOjrBxlyex5KIiIioPCpUSzoRlS9vVLfErvdr45fBVVHV3kRrXJpK4Kfjz9Bm3m2sPhcFVSbr1YmIiF4WBifp7777Lry9vXH27NkCpz137hy8vb3Z2k5UCiRJQq8mdjgxzROfdXeBtVL7Yx2dlIn/7HiCzouCcOTfeD5cSkRE9BIwKEk/cuQIVqxYAScnJ7Rp06bA6Vu3bo3KlSvj119/xcmTJ4sdJBEVzMxEhvc7OOHsjLoIaO0Aea5Pd9DTNAxb9RD+vz3AzbAU/QshIiKicsGgJH3dunWQJAlff/21wQv+5ptvIITAmjVrihwcERWeo5UC3/i54+jkOnirvrXO+FN3E9Fl8V1M2xqKiPiMMoiQiIiICmJQkn7u3Dk0atQIHh4eBi+4Vq1aaNKkCc6cOVPk4Iio6Oo4m2FtQA3837s10cDVTGucEMDGizFoO+8OFh6OQHI6X4ZERERUnhiUpIeGhsLT07PQC69Tpw5CQ0MLPR8RGU87Dysc+NAD3/dzR2Vr7Q6dktOzMO/QU7Sbdxtb/opBVhbr1YmIiMqDEu3dRZIkPqRGVA7IZRIGvemAM9M9MaVzZZiZSFrjw+JVmLQ5FN2W3MW5+4llFCURERGpGZSkOzs74+7du4VeeFBQEJydnQs9HxGVDEulHNO7OuP0R3UxoLkdJO1cHX8/TkXfZcEYtfYh7kemlU2QREREZFiS3rp1a1y/fh3//vuvwQu+desWrl27ZlBvMERUutzsTLBoQFXs+8ADbWpZ6ozf/088On5/B1/seoKYZFUZREhERPRqMyhJHzZsGLKysjB69GikpqYWOH1qaipGjx6tmZeIyqfG7ubYMrYmVg6vjlqVTLXGqbKA385Eoc13t/HrqUikq/hwKRERUWkxKEn38fFB9+7dce7cObRs2RKHDh3Kc9qDBw+iRYsWCAwMRPfu3fH2228bLVgiMj5JkuDT0AZHp9TBnF6usLeQa42PS8nCrN1h6LQgCHtvxPE5EyIiolKgKHiSbJs2bUK3bt1w5swZ+Pj4wNHREU2bNoWTkxMA4NmzZ7h69SqioqIghICXlxc2btxYYoETkXGZKmQY07YS+jWzx+KjT7HybBQyMl8k5MFR6RizLgQta1pglq8rmlSxKMNoiYiIKjaDk3QrKyscP34cCxYswKJFixAWFobDhw/rTOfq6oopU6ZgypQpkMvlepZEROWZnYUcX/q6YkRrB3y9Nxx7bsRrjQ8MTka3H++hbzM7zPRxhrudaR5LIiIioqIyOEkHALlcjunTp2PatGm4cuWKpuUcgKZlvVmzZpDJSrRnRyIqBTUclVg+rDoCg5Mwe3cYroamaI3/40os9vwdh/faV8KEjk6wUvKinIiIyFgMStJjYmJgb2+v+b9MJkPz5s3RvHnzEguMiMqHljUtsXtCbey4Fotv9kXgSVyGZlyqSmDx0WfYcCEGM7o6w/9Ne8hlUj5LIyIiIkMY1OTt6uqKvn37YseOHcjIyCh4BiKqUGQyCX2a2ePUdE984uMMS1PtU8ezRBWmb3uMLouDcPxOQhlFSUREVHEYlKSrVCps374dffv2hYuLC8aPH48zZ86UdGxEVM6Ym8jwQafKODvDE0NbOCB3o/m/4WkYvOIBhqwIxu3wgrtrJSIiIv0MStJDQ0Mxf/58NGnSBDExMVi2bBnat2+P2rVr48svv0RQUFBJx0lE5YiTtQm+6+uOw5ProKOnlc74Y3cS0XlRED7e9hjPEnj3jYiIqLAMStJdXFwwdepUXL58GTdv3sQnn3yC6tWrIzg4GHPnzkW9evXQunVr/Pzzz5oHSYmo4qvnYoYNo2vi91E1UNdZqTUuSwDrAqPRdt4d/HjsKVIy+DIkIiIiQxW6G5Z69erh66+/xv3793Hy5EmMGTMGdnZ2CAwMxAcffAA3Nze888472Lp1K9LS0koiZiIqZzrVtcahSXXwXbdJQ0QAACAASURBVB93VLLSfh49MS0L3+6PQPv5d7D9aixfhkRERGSAYvWV6OXlhWXLliE8PBzbt29H7969IZPJ8Oeff2LgwIFwcXHBu+++a6xYiagcU8glDG3pgDPTPfFhJycoFdoF649jMzBh4yP4/nQPFx4klVGURERELwejdGhuYmKiaT2PiIjAsmXL4OjoiLi4OKxcudIYqyCil4S1mRwzfVxw6iNP9GlmpzP+yqMU+C29j7HrH+JBFO+2ERER6WPUtw5duXIFs2fPxqxZszS16XzrKNGrqYq9KZb4V8WeibXRooaFzvjdf8ejw/dBmL07DHEpmWUQIRERUflVqDeO6vPw4UNs2LABv//+O27duqWpN23SpAmGDRuGwYMHFztIInp5Natqge3jamHvjXjM3RuOh9HpmnEZmQLLTkVi818xmPpWZQxv5QgTOV+GREREVKQkPTY2Fps3b8b69etx9uxZCCEghIC7uzuGDBmCoUOH4rXXXjN2rET0kpIkCT0a2eKt+tZYfTYKi44+RVzKi95eYpIz8fmuMKw6G4XPe7iia31rSBKTdSIienUZnKSnp6fjzz//xPr167Fv3z5kZGRACAFra2v06dMHw4YNQ6dOnfjFSkR5UipkeK+9E/o3t8fCI0+x5lwUVDl6ZrwfmY6Rax6ibW1LfNHDFY3czcsuWCIiojJkUJL+7rvv4o8//kBcXByEEJDL5fDx8cGwYcPg5+cHMzOzko6TiCoQB0sFvurlhoDWjpi7NxwHbsZrjT9zLwk+P95F/9ft8fHbznC1NSmjSImIiMqGQUn6ihUrAADNmzfHsGHDMGjQIDg5OZVoYERU8dV2UmLViOo4ey8Rs3aH4caTVM04IYDNf8Xgz+uxGN/BCe93cIKFqVGfdSciIiq3DPrG++STT3Dz5k1cvHgRH374IRN0IjKqNrWtsP8DDywaUAUuNtptBykZAgsOP0Xb725j08VoZGbxZUhERFTxGZSkf/3116hXr15Jx0JErzCZTMKA5vY4Pb0uPupSGeYm2s+3RCSoMHXrY/j8eBen7yaWUZRERESlw6ByF5VKhejoaCiVStja2uqMj4yMxKeffoo9e/YgMjIS7u7u6N+/Pz777DNYWVkVKbCMjAz8/vvvOH78OBITE1GjRg0MGTIEzZo1K3Deq1evYsuWLXjw4AGysrLg5uYGX19fdOrUqUixEFHpsTCVYepbzhjcwgHfHYjA//0VA5Gj8fyfJ6kYsDwYXepb47PuLqhTmc/EEBFRxWNQS/qaNWvg6uqKBQsW6IyLj49HmzZt8Ntvv+HJkydIT09HcHAw5s2bh65duyIzs2gvKVm0aBF27tyJDh06YMyYMZDJZJgzZw5u3ryZ73yBgYH48ssvkZGRgUGDBmHo0KEwNTXFwoULsXPnziLFQkSlz8XGBAv6V8HBDz3g5WGpM/7QrQR4LwzCf3Y8RlSSqgwiJCIiKjkGJeknTpwAAIwePVpn3H//+1/cvXsX5ubmWLx4Mf7++29s27YNNWrUQGBgIFatWlXooO7cuYNTp05h+PDhGDlyJHx8fDB37lw4OTlh9erV+c67Z88e2Nvb4+uvv4avry969OiBuXPnwsXFBUeOHCl0LERUthq6meP/xtTE2oDq8HBSao3LzAJWn4tG2+9uY+mJZ0jL2Z8jERHRS8ygJP3/2bvvuCrL94Hjn8M4DEFlbwVRcBDmyAG490pRM01zJqm5v2rW190yy5GWJVniyhVqmpgbc4/85ihFRdFQQBDZG87vD3+eOh1QPBwF5Hq/Xr7yPPf9PPd18IlzPfe5x7lz56hduzbVqlXTKlu1ahUKhYJZs2Yxbtw46tWrR69evQgLC0OhULB58+anDurYsWMYGBjQqVMn9TGlUkmHDh24cuUK8fHxRZ6bkZGBhYUFxsZ/L9lmaGhI5cqVUSqVTx2LEKL0KRQK2tepzIFJtfi4lzPWlQw1ylOyCvggLJZWC6+y80KyeudjIYQQorwqVpIeFxdHvXr1tI5fvnyZuLg4FAoFw4YN0yjz9vbmlVde4cKFC08d1I0bN3BxccHc3FzjuJeXFwA3b94s8tyXXnqJ27dvs27dOu7evUtMTAwbN27k+vXr9O7d+6ljEUKUHcaGCoY2t+HYVG9Gt7JFaag5ufR2Yi5vr79Nz69v8NutjFKKUgghhCi5Yk0cTU5OLnQn0d9++w2AevXqFbosY/Xq1Tl37txTB/XgwQOsrKy0jj86lpiYWOS5r7/+OnFxcWzZskXdi29iYsL06dNp1qzZU8cihCh7qpgZMrOrE0Oa2fDx7lh2XEjWKD97K4MeyyPpWb8K73d2xM1avkUTQghRvhSrJ93KyorIyEit48ePHwfglVdeKfS8/Px8nVZ3ycnJ0Riu8sij4SrZ2dlFnmtsbIyzszN+fn5MmTKFyZMnU7NmTRYvXsyVK1eeOhYhRNlVzVrJNwOrsWNMDRpVM9Mq/+l8Mi0XXuWj3bGkZOo2iV0IIYQoDcXqSW/UqBF79uzh1KlTNG3aFID09HRCQ0NRKBS0a9eu0POuXr2Ks7PzUwelVCrJzc3VOp6TkwM87BkvyooVK4iIiGDx4sUYGDx8BgkICGDs2LGsXLmSzz//XOuchIQEEhISgL976bOyssjMzHzq2EX58+ih73EPf6Jsq2dvwKZhzuz6I40F++5zJ/nv1V6y81R8FR7PhtOJTGpjTb+GlTEy1P5msKyQ+1GUJeXtfjQz035YF6K8KlaS/tZbb/HLL7/QpUsXxo0bh62tLWvWrCE+Ph5bW1t69eqldU5MTAx//PEHffr0eeqgrKysCh3S8uDBAwCsra0LPS83N5d9+/bRu3dvdYIOYGRkRKNGjdi1axe5ublavfShoaF8++23AFSpUoWWLVsSHR1Nenr6U8cuyq/o6OjSDkGUUB0zWNFNxfYr8MNFyPjHs35iRj4zd8Wz8mg8IxvBK84UOoyvrJD7UZQl5eV+9PHxKe0QhNCbYiXpvXv3ZtiwYaxatYoPP/wQAJVKhYGBAcuWLSv0yfX777+noKBAY4WW4qpRowYXL14kIyNDY/JoREQEAB4eHoWel5qaSn5+PgUF2suw5eXlUVBQUGhZnz59aNWqFfCwJ33fvn24urri6Oj41LGL8ic7O5vo6GhcXV0f+y2NKD/qeEFQu3yWhiey4Wwy+f9Y7OVWMsw4CAE1zHivoy21HcvWv7ncj6IskftRiNJTrCQd4LvvvqNbt25s3bqV+Ph43NzcGD58OH5+foXWv3nzJj179qRLly5PHZSfnx/btm1jz549BAYGAg97yQ8cOICXl5d6kmp8fDzZ2dm4uroCD3vBK1WqxMmTJ3njjTfUPeaZmZmcOXOmyF8ytra22NraAg+/AQAwNTWVr80qGBMTE/k3f4G4msGCvhaMbJHFB2Gx7L+SqlF+9EYmPVb8Rf/GVkzr5IC9pfY8mNIk96MoS+R+FOL5K3aSDg971Iu7jOHKlSt1CggeLt/o7+/PmjVrSEpKwsnJiYMHD3Lv3j3GjRunrrd48WIuXbrEjh07gIfroQcGBrJu3TqmTp1KmzZtKCgoYP/+/SQkJDB58mSdYxJClE+1HExZM8ydX6+lMW9XDH/GZKnLClTww5kHbD+fzNjWdgS1sMVcWaz59EIIIcQz9VRJ+vM0adIk1q9fT3h4OGlpabi7uzNz5swnjjfr168fDg4O7Ny5k40bN5Kbm4u7uzvTp08vstdfCPHia1nLgj3ja7Lp7AMW7I3jXurfk0szcgpYsDeOtacSea+zA71froqBQdkdry6EEOLFp1AVY2u+wiZb6kJf13mWYmJiCA4OJigoCCcnp9IORzwHmZmZREZG4unpKV/nVhDp2fl8dTiBb36NJytX+1egr4sZc7o70axGpecem9yPoiyR+1GI0lOs73U9PT1ZsWIFeXl5T65ciNzcXJYvX46np6dO5wshhD5VMjFkWkcHjk7x5rWGVbXKL9zJpPeKG4xYc4sbCeVj6TkhhBAvlmIl6TVr1mT06NG4uroyceJETp48WegqKf9UUFDAiRMnGDduHK6urowdO5ZatWrpJWghhNAH56rGfPG6G7+Mq0nzQnrNd/+RQuuFV5m98y4PMnTrpBBCCCF0Uawx6QcPHmTXrl28//77LF26VL3sYoMGDfD29sbKygpLS0tSU1NJTEwkIiKC33//nczMTFQqFS+//DIhISE6rfQihBDPmq+rGT8GebDnz1Q+DIvhRkKOuiyvAL49ep8tvyUxqZ09Q5pbozSSyaVCCCGerWJPHO3WrRvdunXj2LFjrFy5krCwMI4dO8axY8cKrW9vb0+/fv0YOXIkzZs311vAQgjxLCgUCjrXq0xbbwvWnExk0f57JGXmq8uTMvOZ/XMMISfuM6OrI53rVS7TmyEJIYQo3556dRd/f3/8/f2Bh5sLXbhwgXv37pGcnEyVKlWwt7enfv36eHl56T1YIYR41pRGBrwVYEufhlX54sA9Vp1IJPcfuyHdvJ/DiLW3aephzpzuTtR3NX/M1YQQQgjdlGgJRm9vb7y9vfUVixBClBlW5kbM6eHMkOY2fLQ7lrBLKRrlp25m0GVZJH0aVGV6ZwdcqipLKVIhhBAvIhlYKYQQj+Fha8LKN6uzbVQN6rtqL0EX+r8kWnx2lQV7YknLzi/kCkIIIcTTkyRdCCGKoalHJXa948mX/V1xqqK530NWnoolB+PxX3CV9acSyS944vYTQgghxGNJki6EEMVkYKCgdwMrjk71YnonByopNX+FxqflMXXrHTp+cZ3wq6mlFKUQQogXgSTpQgjxlMyMDRjf1p5j07wY1MQag38t8nI5Nos3voti0Pc3uRqXVTpBCiGEKNckSRdCCB3ZWxqzoI8L+ybUolUtC63ygxFptFtyjenb7pCQJpshCSGEKD5J0oUQooTqOJmy4S0P1g93x9vBRKMsvwDWnEzEb0EEyw7dIyv38bs1CyGEECBJuhBC6E0bb0v2TajFp4HO2FQy1ChLyy7gk1/iaLnwKtt+T0KlksmlQgghiiZJuhBC6JGRoYI3m9lwfJo349rYYWKkOWA9+kEu72z4ix5fRXImKr2UohRCCFHWlShJ37t3L4GBgbi4uGBiYsKIESPUZXv27GHy5MncvXu3xEEKIUR5Y2lqyHudHTkyxYvAl6tolZ/7K5OeX9/g7XW3uXU/pxQiFEIIUZbpnKRPmDCBLl268NNPP5Gamkpubq7G17dOTk4sWbKETZs26SVQIYQoj1ytlHw1oBo/v+PJK+7mWuU7LybTauFV5u2KITlTNkMSorzKyMhg6dKldOzYEScnJ0xMTLC0tKRu3boMHTqUHTt2kJ8v/4+L4tMpSV+zZg3Lli2jUaNGnDt3jpSUFK06vr6+uLm5sXPnzhIHKYQQ5V3DauZsH1WD4IHVqG6t1CjLyVfxza8J+C2IYM2pJPJkMyQhypVjx45Rs2ZNJkyYwJEjR6hVqxaBgYG0b98eIyMjVq9eTc+ePfH19S3tUEU5YqTLSV9//TVVq1Zl165d2NnZFVnP19eXixcv6hycEEK8SBQKBd19q9ChriWrjt9nyYF7pGT9vdrLg4x85u5OwLUyzOqWTrf6pigUisdcUQhR2s6dO0e7du3Izs5m6tSpzJgxg8qVK2vU+euvv1i0aBHffPNNKUUpyiOdetIvXbqEn5/fYxN0gCpVqhAXF6dTYEII8aIyMTJgVEs7jk3zZrifDUb/+k0cnQJBG2Lo9+1NLt7JLJ0ghRBPVFBQwKBBg8jOzuaDDz5gwYIFWgk6gJubG4sXL+bo0aOlEKUor3Qek16c3p27d+9iZmamaxNCCPFCs6lkxIc9nTk42YuOdS21yo9FptN52XUmbYkmNiW3FCIUQjxOWFgYly9fplq1arz33ntPrN+oUSON1wqFAnd3d3Jycpg3bx61a9fGxMSEXr16adRbu3YtAQEBVK5cGXNzc3x9ffnkk0/IytLe0bh169YoFAqioqK0yqKiolAoFLRu3Vrj+Jw5c1AoFISEhHDq1Ck6depE1apVqVy5Mh06dODkyZNP/mEIvdMpSa9Vqxbnzp0jN7foD43U1FR+//136tWrp3NwQghREdS0MyFkiDtbgjyo66g5Xl2lgk1nH+C/IIKF++LIyJHNkIQoK3bv3g3Aa6+9hqGh4RNqF66goIBevXqxYMECPD096dmzJ05OTuryt99+m8GDB/Pbb7/RokULunXrRkxMDO+//z5t27YlIyNDL+8F4Pjx47Rs2ZLo6Gi6dOmCt7c3+/fvp1WrVuzdu1dv7Yji0SlJf+2114iJiWH69OlF1nnvvfdITk6mf//+OgcnhBAVib+nBduD3JjiBw6Wmh/4mbkqFu6/R8BnEWw8k0i+TC4VotSdP38egAYNGuh8jb/++otr164RERHBrl272Lx5M19//TUAoaGhBAcH4+zszIULF9i1axdbtmzh+vXrBAQEcOLECWbNmqWX9wLw7bffMnXqVC5dusSGDRs4c+YMy5cvJycnh6FDh5KZKcPvniedkvSJEyfy0ksvsWTJEpo3b878+fMBiIyMZPHixbRs2ZLly5fToEEDRo4cqdeAhRDiRWZooKCjp4L946rzn/b2mBlrDi2MTclj8o936LzsOkevp5VSlEIIgPv37wNga2tbaPmIESMYOnSoxp/CxqV/8sknuLi4aB1funQpALNnz6ZWrVrq41WqVOGrr75CoVCwYsWKQoe96KJ69erqoS+PjB49mqZNmxITE0NoaKhe2hHFo9PqLmZmZuzfv5+hQ4eye/duTp8+DcCRI0c4cuQIAB06dGDdunUolcrHXUoIIUQhzJUG/KeDAwObWvPpnjg2//aAf2xFwR93s+j37U061LFkZjcnatqZlF6wQohCrV69Wmtt9NatWxMQEKB+rVAo6NGjh9a5ubm56rHgAwcO1Cr39fXF19eX8+fP8/vvv9OsWbMSx9unTx+MjLRTwwEDBnDq1CmOHDnCoEGDStyOKB6dknQAOzs7du3axfnz59m7dy9RUVEUFBTg6upKhw4daNKkiT7jFEKICsmxsjGLX3NlhL8N836O4Whkukb5vsupHIpI5c1mNkxub49NJZ1/rQshnpKNjQ0ACQkJhZbn5eWp/z5q1ChWrFihVcfe3h4TE+2H7Pv375OTk4OtrS2VKlUq9Pru7u6cP3+eO3fu6BK+lurVqxfZDiC7yD9nOv02v3DhAgYGBvj4+FC/fn3q16+v77iEEEL8g4+zGZtGerD/SirzdsUSGZ+tLssrgFXH7xN67gET2toz3N8Gk3+v6yiE0Lv69etz7Ngx/ve//xXa210cpqamOrf/tPsoFBTIxPPyRKff4i+//DLjx4/XdyxCCCEeQ6FQ0KFOZQ5OqsVHPZ2xMtecXJqSVcAHYbG0WniVnReSUalkcqkQz1KXLl0A2LJli9awlpKysbFBqVSSkJBAenp6oXUeLbP4z/Hsj4YZp6Vpz1n566+/HtvmrVu3Hnvc2dn5iXEL/dEpSbe2ttZYHkgIIcTzY2yoYJifDceneTO6lS1KQ83etNuJuby9/jY9v77Budv6W55NCKGpa9eu1KlTh9u3b/PJJ5/o9drGxsbqceYbN27UKr906RLnz5/HwsKCl19+WX38UX529epVrXP27dv32Da3bt1a6MPGo/b/OZZePHs6JenNmjXj4sWL+o5FCCHEU6hiZsjMrk4c/o8XPV6qolV+9lYG3b+KZMyG20Q/yCmFCIV4sRkYGLB27VpMTEyYOXMm06ZNIzk5Wave/fv3iYiIeOrrjxs3Dni42dCNGzfUx1NTUxk7diwqlYq3335bY8hMq1atAFi4cKHGGuoHDx5kyZIlj20vKiqKuXPnahwLDg7mxIkTODg40KdPn6d+D0J3OiXps2fPJiIigoULF+o7HiGEEE+puo2SFYOq8dPoGjR0097lefvvybT4/Cof744lNUu/X8kLUdE1atSI/fv34+joyGeffYaDgwOtWrViwIABBAYG8sorr+Dk5ER4eDi1a9emcePGxb523759CQoKIjo6Gh8fH7p3706/fv3w9PTk8OHDNGvWjHnz5mmcM2DAALy9vTl+/Dh16tShb9++NGvWjA4dOjB69OjHtjdy5Ejmz5+Pj48Pb7zxBk2aNOHtt9/G2NiYkJAQzM3NdfoZCd3oNHH08uXLDBo0iGnTprFu3Tq6detGtWrVipz8MHjw4BIFKYQQ4sleca/Eznc8+el8Mh//Ekv0g793hc7OU/FleDwbziQytaMDb7xijZHh0006E0IULiAggMjISFauXMmOHTu4dOkSJ06cwMTEBBcXF/r160ffvn3p3r17oUscPs6KFSsICAjgm2++4fDhw+Tl5eHp6cnEiROZNGkSZmaaD+ZmZmYcOHCAqVOn8ssvvxAWFka9evXYtGkTjRs35vPPPy+yLT8/P4YNG8bMmTP5+eefUalUtGvXjnnz5uHn56fTz0boTqHSYWaRgYEBCoVCY1JSYTOMVSoVCoVC75MpnqWYmBiCg4MJCgqScfcVRGZmJpGRkXh6emr9shPiedPX/ZiVW8DKY/dZevAeadnaKzp42Zswq5sTbbwtnnqFCFFxyO/HimHOnDnMnTuXVatWMXTo0NIOR/w/nXrSZ82aJb/UhRCiDDM1NmBsazv6N7Zi4b441p5KpOAfXTJX72UzaFUUrWpZMKubE3WcdF8GTgghhP7plKTPmTNHz2EIIYR4FmwtjPgk0IVhfjZ8EBbLgSupGuWHr6XR4YtrDHjFiqkdHbC3NC6lSIUQQvyT7HYhhBAVgJeDKWuHubNhhDt1HDV7zQtUsP70A/wXXOWLg/fIyJENT4QQorSVeP/oO3fucOzYMfWWtC4uLvj7+2ssrC+EEKJsaOVlyd6aFmw8+4AFe+KIT/t72/L0nAI+3RPHmpOJvN/ZgcCXq2JgIEMbhXjRzZkzR0ZJlEE6J+nx8fG88847bNu2TWubWYVCQZ8+ffjyyy+xs7MrcZBCCCH0x9BAwcAm1vSsX4Xl4fF8cySBrNy/B6zHJOcyblM0K4/dZ3Y3J5rVqFSK0QohRMWkU5KenJxMy5YtiYiIwMzMjI4dO+Lu7o5CoSAqKoo9e/awZcsWLly4wMmTJ6lSRXuTDSGEEKXLwsSQaZ0cGdjUmk/3xPHjuSSN8vPRmfRecYMu9Sozo6sjHrYmpRSpEEJUPDqNSZ8/fz4RERG89tprREVFsW3bNhYvXsyiRYvYunUrt27dol+/fkRERPDpp5/qO2YhhBB65FJVydLX3dg9zpNmHtq95rv/SKH1omvM3nmXBxl5hVxBCCHKhpCQEHWncXmnU5K+bds23NzcWLduXaHDWWxtbVm7di1ubm6EhoaWOEghhBDPXn1Xc0Lf9uC7wdXwsFFqlOXmq/j26H38F1zl2yMJ5OTJ5FIhhG4yMjKYM2cO4eHhpR1KmaZTkn7r1i38/f0xNi56qS5jY2P8/f25ffu2zsEJIYR4vhQKBV3qVeHQ5FrM6+FEVTNDjfKkzHxm/xxDm0XX2P1HMjrshyeEqOAyMjKYO3euJOlPoFOSbmZmRkJCwhPrJSQkyA5lQghRDimNDHgrwJZj07wYGWCDsaHmKi837+cwYs1t+qy4yYXozFKKUgihD1lZWVqLgDySnp7+nKMRj+iUpDdq1IjDhw9z9uzZIuv89ttvhIeH07hxY52DE0IIUbqszI2Y28OZQ5Nr0aVeZa3ykzfT6bzsOuM3/cXdpNxSiFCIiuPOnTuMGDECZ2dnTExM8PDwYPTo0eTk5ABw48YNXnvtNaytrTE3N6dZs2bs2rVL4xrh4eEoFAo2btzIjBkzcHFxwdzcnJSUFPV47sOHDzNmzBjs7e1xdXXVaH/48OE4ODhgYmJCvXr1+P7777XizMrKYs6cOXh5eWFqaoqTkxO9e/cmMjKSqKgo9VDpuXPnolAoUCgUGktAXrlyhb59+2JtbY2pqSmNGzdmx44dWu388ccftG3bFjMzM1xdXfnwww+LfNgoj3Ra3WXSpEkcOHCAdu3aMX78eN544w3c3d2Bh0NhNmzYwNKlS8nPz2fSpEn6jFcIIUQpqGFrwneDq3PyRjpzfo7hwh3N3vMfzyXx88VkRrWw5Z3WdlQyMSziSkIIXdy9e5cmTZqQlJREUFAQtWvX5s6dO/z4449kZGTw4MED/Pz8yMjIYPz48djY2LB69WpeffVVfvzxRwIDAzWu98EHH6BUKpkyZQrZ2dkolX/PQxkzZgx2dnbMmjVL3ZMeFxdHs2bNUCgUjB07Fjs7O3bv3s2IESNISUlh4sSJAOTn59O9e3cOHDhA//79mTBhAqmpqezbt49Lly7Rvn17vv76a0aPHk1gYCC9e/cGwNfXF3iYeD/ab2f69OlUqlSJzZs306tXL0JDQ9XvIzY2ljZt2pCXl6euFxwc/EKN4FCodBxQ+MknnzBz5swixyMqFAo++OAD3nvvvRIF+LzFxMQQHBxMUFAQTk5OpR2OeA4yMzOJjIzE09PzhfqfW5RP5eF+LChQse33JD7+JY6YZO3ec3tLI6Z2dKB/YysMZTOkcq083I+lpd3ia1yOzdL7de9++lKhx4cMGcK6des4deqU1igFlUrF5MmTWbJkCUeOHCEgIACAtLQ0fH19UalUREZGYmBgQHh4OG3atKFGjRpcunRJ4981JCSEYcOGERAQQHh4OIaGfz9sv/XWW4SFhXHx4kVsbGzUxwcMGMDu3buJiYnBzMyMVatWMXz4cBYtWqTVUatSqVAoFCQkJGBnZ8fs2bO1NlFq37499+7d48yZM5iYmKjPCwgIID4+nqtXrwIPO4yXLFnCqVOnaNKkCfBwD59atWqRnJzMzZs31R3I5ZVOw10A3nvvPU6ePMmgQYNwd3dHqVSiVCpxd3fnzTff5MSJE+UuQRdCCPFkBgYKEPCsywAAIABJREFU+jS04sgUL97t5IC5UvOj5F5qHlND79Dxi+scvppaSlEK8eIoKChg+/bt9OjRo9BhxAqFgrCwMJo0aaJO0AEsLCwICgoiKiqKP//8U+OcIUOGFPngNXLkSI0EXaVSERoaSo8ePVCpVCQkJKj/dOrUieTkZM6dOwdAaGgotra2jBs3rtA4HycxMZGDBw/Sr18/UlNT1W3cv3+fTp06ce3aNfUO92FhYTRr1kydoAPY2dkxcODAx7ZRnui84yhA48aNWb16tb5iEUIIUY6YKw2Y0NaeAa9Y8dneODaceUDBP75cvRybxYDvomhX25KZXR3xcjAtvWCFKMfi4+NJSUnBx8enyDq3bt2iadOmWsfr1KmjLv/n+R4eHkVe699l8fHxJCUlERwcTHBwcKHn3Lt3D4DIyEi8vb0xMnr6FPP69euoVCpmzpzJzJkzi2zHxcWlyPfr7e391O2WVSVK0oUQQgh7S2M+6+PKcD9b5u6K4ddraRrlB66kEn41lUFNrPlPBwdsLeSjR4jS9rjhS/8uezQZc9CgQQwZMqTQcx6NKS+JR+1MmTKFTp06FVqnZs2aJW6nvNDpN+W5c+dYt24dAwYM4JVXXim0zunTp9m4cSODBw/m5ZdfLlGQQgghyr46TqZsGOHOoYg05u6K4dq9bHVZfgGsPpnI1v8lMa6tPW/522BqrPOISyFK3YFJtZ5bW3Z2dlSuXJlLly4VWad69epERERoHb9y5Yq6vCTtW1pakp+fT/v27R9b19PTk1OnTpGbm1vkfjpFDXupUaMG8HCvnSe1U716da5du6Z1vLCfQXml02/IL7/8kuXLlz92QL6HhwfLly/nq6++0jU2IYQQ5YxCoaBtbUsOTKzF/EBnbCpprvKSml3Ax7tjabnwKtt/T5LNkIQoBgMDA3r16sXOnTsLXf5apVLRtWtXTp8+zYkTJ9TH09PTCQ4Oxt3dnbp16+rcvqGhIX369CE0NLTQB4X4+Hj13/v06UNCQgJffvlloXECmJubA5CUlKRRbm9vT+vWrVmxYgUxMTGPbadr166cPHmS06dPa5SvX7/+Kd9d2aVTT/qRI0do2LChep3LwtjZ2dGwYUMOHz6sc3BCCCHKJyNDBYOb2RD4clWWHYrn26MJZOf9nZBHP8hlzIa/+O5YArO7O9G4eqVSjFaIsu/jjz9m7969tGrViqCgIOrUqUNMTAxbtmzh6NGjTJ8+nQ0bNtClSxfGjx+PtbU1q1ev5ubNm4SGhmJgULJvrubPn8+hQ4do2rQpI0eOpG7duiQmJnLu3Dn2799PYmIiAIMHD2bNmjVMnjyZ06dP06JFC9LT09m/fz9jxoyhZ8+emJmZUbduXTZt2oSXlxfW1tb4+Pjg4+PDV199RUBAAC+99BIjR46kRo0axMXFceLECaKjozl//jwA06ZNY+3atXTu3JkJEyaol2CsXr06Fy5cKPHPuyzQKUm/c+dOkcNc/ulF+kGJF5dSqaRatWoaa8QKUVpetPvR0tSQ97s48mZTaz7ZE8v235M1yn+7ncmry2/Qw7cK/+3iSDXrF+N9vyhetPuxPHNxceHUqVPMnDmT9evXk5KSgouLC126dMHc3JyqVaty/Phx3n33XZYtW0ZWVha+vr7s3LmTbt26lbh9BwcHTp8+zbx589i6dSvLly/HxsaGevXq8emnn6rrGRoaEhYWxkcffcQPP/xAaGgoNjY26sT7kZUrVzJu3DgmTZpETk4Os2fPxsfHh7p163L27Fnmzp1LSEgI9+/fx97engYNGjBr1iz1+U5OThw6dIhx48Yxf/58bGxsGDVqFM7OzowYMaLE77cs0GmddCsrK5o3b05YWNhj63Xr1o2jR4+SnJz82HpliayTXvHIOsCiLHnR78dztzOY83MMZ29laJUpDRUM97dhQlt7qpjJZkhlwYt+PwpRlun03Ue9evU4evSo+quNwiQmJvLrr7+WaAyUEEKIF0vDaub8NLoGKwZWo5q15qSynHwV3/yagN+CCFYdv09uvoxXF0JUXDol6YMGDSItLY2+ffsSHR2tVX7nzh369etHRkbGC7WovBBCiJJTKBT08K3C4f94MbOrI5VNNT+KHmTk89+f7tJ28TX2XU6RyaVCiApJpzHpb731Fhs2bCA8PBwvLy86d+6Mp6cn8HAR+z179pCZmYm/vz+jRo3Sa8BCCCFeDCZGBoxuZUe/xlYs2n+PNSfvk1/wd3lkfDZDQm4R4FmJWd2d8HGW4RZCiIpDpyTdyMiI3bt3M378eFavXs327ds1yg0NDRk2bBhffPGFTjtOCSGEqDhsKhnxUU9nhja35sOwWPZdTtUoPxqZTqel1+nXyIp3OzngWLnwtZeFEOJFonMGbW5uzsqVK/nggw8IDw/nr7/+AsDNzY3WrVvLpEshhBBPpZa9KauHunP0+sPNkP64m6UuU6lg09kH7DifxJhWdoxuZYe5UjZDEkK8uErcze3k5MSAAQP0EYsQQghBQE0LfhlXkx/PJTH/l1jiUvPUZZm5Khbuv8f604m828mR1xpWxcCg8N0LhRCiPNN7N8Tly5f58ccfOXXqlL4vLYQQooIwNFDwemMrjk3zZnJ7e8yMNRPx2JQ8Jm2JpvOy6xyLTCulKIUQ4tnRKUnftGkTbdu21UrEp0yZgo+PD6+//jp+fn4EBgaSn5+vl0CFEEJUPOZKA6Z0cODoVG/6NbJC8a9O80t3s3gt+CZDV0dxPT67dIIUQohnQKckfd26dfz+++80aNBAfez48eMsWrQIS0tL+vfvj7u7Ozt27GD9+vV6C1YIIUTF5FTFmCX9XPllXE38PStple/9M5W2i64y46e7JKbnFXIFIYQoX3RK0i9duoSvr6/GNsFr165FoVCwefNm1q9fz5kzZ7CwsGDlypV6C1YIIUTF9pKLGZtHehAypDo1bDW3qs8rgO+P38f/swi++TWe7LyCIq4ihBDFFxISgkKhICoq6rm2q1OSfu/ePVxcXDSOHTp0CHt7ezp27AiAtbU1LVu25Pr16yWPUgghhPh/CoWCjnUrc2iyFx/2dMLK3FCjPDmzgHm7Ymm98Bo/X0iWzZCEqAAyMjKYM2cO4eHhpR2K3uiUpJuZmZGSkqJ+HRMTw9WrV2nVqpVGvapVq/LgwYOSRSiEEEIUwthQwXA/W45P82ZUS1uUhpoD1m8l5hC0/ja9vrnB//7KKKUohRDPQ0ZGBnPnzpUkvUaNGhw5coSkpCQA1q9f/7Bn4/970R+JjY3F3t6+5FEKIYQQRahiZsisbk4c/o8XPV6qolV+JiqDbl9G8s6G20Q/yCmFCIWoeLKysigoKHzIWXp6+nOOpnzSKUkfOnQoKSkpNGrUiD59+jBjxgwsLCzo2bOnuk5ubi5nz57Fy8tLb8EKIYQQRaluo2TFoGpsH12DBm5mWuXbfk+mxedX+eSXWFKzZOUxUb4MHToUd3d3reNz5sxB8Y9ljxQKBWPHjmX79u34+PhgYmJCvXr1+OWXX7TOvXPnDiNGjMDZ2RkTExM8PDwYPXo0OTl/P8zeuHGD1157DWtra8zNzWnWrBm7du3SuE54eDgKhYKNGzcyY8YMXFxcMDc3JyUlRT2e+/Dhw4wZMwZ7e3tcXV01Yhg+fDgODg7qWL///nutWLOyspgzZw5eXl6Ympri5ORE7969iYyMJCoqCjs7OwDmzp2LQqFAoVAwZ84c9flXrlyhb9++WFtbY2pqSuPGjdmxY4dWO3/88Qdt27bFzMwMV1dXPvzwwyIfNp41nTYzGjlyJIcOHSI0NJSbN29SqVIlVqxYgY2NjbrOzz//THJyMm3bttVbsEIIIcSTNHGvxM4xnvx0IZmPd8dyJylXXZadp2LZoXg2nHnA1A72DHjFGiND2QxJvFiOHj3K1q1bGTNmDJaWlixdupQ+ffpw+/Ztda529+5dmjRpQlJSEkFBQdSuXZs7d+7w448/kpGRgVKpJC4uDj8/PzIyMhg/fjw2NjasXr2aV199lR9//JHAwECNdj/44AOUSiVTpkwhOztbY4GRMWPGYGdnx6xZs9Q96XFxcTRr1kz9YGFnZ8fu3bsZMWIEKSkpTJw4EYD8/Hy6d+/OgQMH6N+/PxMmTCA1NZV9+/Zx6dIl2rdvz9dff83o0aMJDAykd+/eAPj6+gIPE29/f39cXFyYPn06lSpVYvPmzfTq1YvQ0FD1+4iNjaVNmzbk5eWp6wUHB2Nmpv3Q/1yoSuDmzZuq06dPq1JSUrTK/ve//6m2b9+uio2NLUkTz93du3dVc+bMUd29e7e0QxHPSUZGhurixYuqjIyM0g5FCLkf9SwjJ1+19GCcqtbMSyqnaRe0/rT6PEJ18Ir2Z5h4SO7HsmPIkCGq6tWrax2fPXu26p/pHKBSKpWq69evq4+dP39eBaiWLVumPjZ48GCVgYGB6syZM1rXLCgoUKlUKtXEiRNVgOrIkSPqstTUVJWHh4fK3d1dlZ+fr1KpVKpDhw6pAFWNGjW07pVVq1apAFVAQIAqLy9Po2zEiBEqJycnVUJCgsbx/v37q6pUqaK+1vfff68CVIsWLSoy1vj4eBWgmj17tladdu3aqV566SVVVlaWxnl+fn6qWrVqqY89er+nTp1SH7t3756qSpUqKkB18+ZNrWs/Szr1pD/i7u5e6FcvAC+//DIvv/xySS4vhBBClIiZsQHj2jzsMf98XxzrTiVS8I/FXq7ey2bg91G0qmXBrG5O1HEyLb1gRbly+F4+qc9gSf7uzoZPrvQE7du3x9PTU/3a19eXypUrc+PGDQAKCgrYvn07PXr0oHHjxlrnPxo+ExYWRpMmTQgICFCXWVhYEBQUxHvvvceff/6Jj4+PumzIkCFF9jqPHDkSQ8O/35tKpSI0NJR+/fqhUqlISEhQl3Xq1ImNGzdy7tw5/P39CQ0NxdbWlnHjxhUZa1ESExM5ePAg8+bNIzU1ldTUVI12Zs+ezZ07d3BxcSEsLIxmzZrRpEkTdR07OzsGDhzI8uXLH9vOs6DTmHQhhBCiPLG1MGJ+oAsHJtairbeFVvnha2l0+OIaU0OjuZeaW8gVhCg/qlWrpnXMyspKveJefHw8KSkpGgl2YW7duoW3t7fW8Tp16qjL/8nDw6PIa/27LD4+nqSkJIKDg7Gzs9P4M2zYMODhkt8AkZGReHt7Y2T09H3L169fR6VSMXPmTK12Zs+erdHOrVu3qFWrltY1CvsZPA8l6kkXQgghyhNvR1PWDfcg/Goq83bFcCU2W11WoIL1px+w/fdkxrWxY2QLW8yMpS9LlA1F9Rjn52tPgv5nj/U/qZ7xngGPG7v977JHkzEHDRrEkCFDCj3n0ZjyknjUzpQpU+jUqVOhdWrWrFnidp4FSdKFEEJUOK29LGlR04KNZx6wYG8c8Wl/j1tIzylg/p441pxM5L3ODgS+XBUDA5lcKkqXlZWVeunrf/p3b3Zx2NnZUblyZS5duvTYetWrVyciIkLr+JUrV9TlurKzs8PS0pL8/Hzat2//2Lqenp6cOnWK3NxcjI2NC61T1ENMjRo1ADA2Nn5iO9WrV+fatWtaxwv7GTwP0kUghBCiQjI0UDCwqTXHpnkxoa0dpkaaH/J3k3MZtymabl9FcuqmrOssNLWyN6S7s/7/FMXT05Pk5GQuXLigPhYTE8O2bdueOnYDAwN69erFzp07OXv2rFb5ox73rl27cvr0aU6cOKEuS09PJzg4GHd3d+rWrfvUbT9iaGhInz59CA0NLfRhIT4+Xv33Pn36kJCQwJdffllkrObm5gBaDzL29va0bt2aFStWEBMT89h2unbtysmTJzl9+rRG+fr165/y3emH9KQLIYSo0CxMDHm3kyODmloz/5c4Qv+n+SF/PjqTwG9u0NWnMv/t4oiHrUkpRSoqsv79+/Puu+8SGBjI+PHjycjI4Ouvv8bLy4tz58499fU+/vhj9u7dS6tWrQgKCqJOnTrExMSwZcsWjh49StWqVZk+fTobNmygS5cujB8/Hmtra1avXs3NmzcJDQ3FwKBkfb3z58/n0KFDNG3alJEjR1K3bl0SExM5d+4c+/fvJzExEYDBgwezZs0aJk+ezOnTp2nRogXp6ens37+fMWPG0LNnT8zMzKhbty6bNm3Cy8sLa2trfHx88PHx4auvviIgIICXXnqJkSNHUqNGDeLi4jhx4gTR0dGcP38egGnTprF27Vo6d+7MhAkT1EswVq9eXePh6HmRJF0IIYQAXKoqWdbfjbcCbJjzcwynbmZolIddSmHf5VSGNbdmYjsHqpqXfBUOIYrLxsaGbdu2MXnyZKZNm4aHhweffPIJ165d0ylJd3Fx4dSpU8ycOZP169eTkpKCi4sLXbp0UfdKOzg4cPz4cd59912WLVtGVlYWvr6+7Ny5k27dupX4PTk4OHD69GnmzZvH1q1bWb58OTY2NtSrV49PP/1UXc/Q0JCwsDA++ugjfvjhB0JDQ7GxsVEn3o+sXLmScePGMWnSJHJycpg9ezY+Pj7UrVuXs2fPMnfuXEJCQrh//z729vY0aNCAWbNmqc93cnLi0KFDjBs3jvnz52NjY8OoUaNwdnZmxIgRJX6/T0uh0mEWQXp6OpUqVXoW8ZS6mJgYgoODCQoKwsnJqbTDEc9BZmYmkZGReHp6lt6GBUL8P7kfywaVSsXuP1L4MCyWqPs5WuVW5oZMamfP4GbWKI1e3JGjcj8KUXp0+s3i7OzMqFGjCh3HJIQQQpR3CoWCrj5VCJ9ci7ndnahqptlr/iAjn1k7Y2i7+Bq7/0h+5qtmCCEqHp2SdIVCQXBwME2bNqVhw4Z88803pKSk6Ds2IYQQolQpjQwY2cKWY9O8GBlgw787zW8k5DBizW36Bt/kwp3M0glSCPFC0ilJj4mJYdWqVTRv3pzff/+dd955B2dnZ4YPH64xA1gIIYR4EViZGzG3hzOHJnvRuV5lrfITN9Lpsuw6Ezb9xd0k2QxJCFFyOiXpZmZmDBkyhKNHj3L58mUmTpyIubk5ISEhBAQE4OPjw9KlS9U7WwkhhBAvAk87E74fXJ3Qtz14ycVUo0ylgi3nkgj4PIIFe+NIz9beZEYIIYqrxLNdvL29WbhwIXfu3GHjxo20bduWy5cvM2nSJFxcXHjzzTc5cuSIPmIVQgghyoTmNSzYPbYmS193xamy5kJpWbkqlhy4h/9nV/nhdCL5BTJeXQjx9PQ2Jd3Y2Jh+/fqxZcsWJkyYgEqlIisri/Xr19O6dWvq16/Pzz//rK/mhBBCiFJlYKCgb0Mrjkz1ZlpHB8yVmh+p91LzmBJ6h05Lr/PrtbRSilIIUV7pLUk/cuQIgwcPxsXFhS+++AITExPeeOMNVq5cSfv27bl06RI9e/ZkxYoV+mpSCCGEKHXmSgMmtrPn2FQv3njFin/vTv5nTBb9V97kzVVRXI3LKp0ghRDlTomS9ISEBBYuXEidOnVo3bo169atw8XFhU8//ZTo6GjWrVvH8OHD2bNnDydOnMDS0pLPPvtMX7ELIYQQZYZDZWM+7+vKvgk1aVHTQqv8wJVU2i25xnvb73A/La8UIhRClCc67Ti6f/9+vv32W3766Sdyc3MxNDQkMDCQUaNG0b59+0LPadKkCd26dWPz5s0lClgIIYQoy+o6mbHxLXcORqQyb1cs1+5lq8vyC2D1iUS2nktifFt7RvjbYGr84m6GJITQnU5JeseOHQFwc3Nj5MiRvPXWWzg6Oj7xPDc3N1xdXXVpUgghhCg3FAoF7WpXplUtS9afTuSzfXEkpv+92ktqdgEf7Y5l9cn7/LeLI6/6VkHx73EyQogKTafH965du7Jjxw5u3rzJjBkzipWgA8yfP5+bN2/q0qQQQghR7hgZKhjS3IbjU715p7UdJkaaiXj0g1xG//AXry6P5Oyt9FKKUghRFumUpP/88890794dAwP5ik4IIYR4kspmhvy3iyO//seLnvWraJX/djuTV5ff4O31t7mdmFMKEQohyhqdsmxDQ0NGjBjxxHojR47EyEinETVCCCHEC8fNWsnXb1Rj5xhPGlUz1yrfeSGZlp9f5YOwGFIyZTMkISoynZJ0lUqFSlW8zRmKW08IIYSoKBpVN2fHmBp884YbblbGGmU5+Sq+PpyA32cRrDp+n9x8+RwVoiJ6puNVkpOTMTExeZZNCCGEEOWSQqHg1fpVOfwfL2Z0dcTSRPMjOTE9n//+dJd2i6+x/3KKdHoJUcEUeyzK7du3NV6npaVpHXskLy+PiIgI9u7di6enZ8kiFEIIIV5gpsYGjGllx+uNrFi4P461pxLJL/i7/Hp8NoNDbhFQsxKzuzlRz9ms9IIVQjw3xU7S3d3dNZaHCg0NJTQ09LHnqFQqRo4cqXt0QgghRAVhY2HEx71cGOZnwwdhsey/nKpRfvR6Oh2XXuf1RlZM6+SAY2XjIq4khHgRFDtJb9mypTpJP3z4MPb29tSuXbvQukqlEmdnZ1599VUCAwP1E6kQQghRAdSyN2XNUHeOXE9j7s8x/BmTpS5TqWDj2Qf8dD6Jd1rbMaqlHeZKWWlNiBdRsZP08PBw9d8NDAzo0qUL33///bOISQghhKjwWtS0YM/4mmz57QGf7okjLjVPXZaZq+LzffdYfyqRdzs70rdBVQwMZDMkIV4kOq2PePPmTSwsLPQdixBCCCH+wdBAQf9XrOnhW4Wvf01g+eF4snL/nkAak5LHxM3RrDyawOzuTvh7ymezEC8Knb4jq169OjY2NvqORQghhBCFqGRiyJQODhyb6k2/RlYo/tVpfuluFq8F32TY6ltExmeXTpBCCL0qVk/6mjVrAAgMDMTS0lL9urgGDx789JEJIYQQQoNTFWOW9HNluL8N836O4fiNdI3yPX+mcOBKCkOa2zCpnT3WlWRDQSHKq2L93zt06FAUCgXNmjXD0tJS/fpJVCoVCoVCknQhhBBCj3xdzNgS5MHey6l8sCuGGwk56rK8Avju2H1+PPeAiW3tGepng4mRTC4VorwpVpI+a9YsFAoFtra2Gq+fpdzcXNavX094eDhpaWm4u7szcOBAGjRoUKzzjxw5wo4dO4iKisLIyAg3NzcGDhxI/fr1n2ncQgghxPOgUCjoVLcybb0tWXPyPov23+NBRr66PDmzgLm7Ygk5kciMro509an8zD+7hRD6U6wkfc6cOY99/SwsWbKE48eP8+qrr+Lk5MTBgweZN28eH330EXXr1n3suT/88AObNm3Cz8+Pdu3akZeXx+3bt0lMTHzmcQshhBDPk7GhghH+tvRpYMUXB+/x/fH75Ob/Pbn0VmIOI9fdpom7ObO7O9HAzbwUoxVCFJdOg9WWLl2Kubk5b731lr7jAeDq1ascOXKEYcOGqddZb9u2LWPHjiUkJIQFCxYUee6VK1fYtGkTw4cPp2fPns8kPiGEEKKsqWpuyOzuTgx8xZIv9t3lwJU0jfKrd5IZuCKZzvUseaeVHY5VnrwZUlZWFrm5uWRkZFBQUPDE+sVlbGyMUqnU2/WEeBHplKT/5z//oWvXrs8sST927BgGBgZ06tRJfUypVNKhQwfWrl1LfHw8dnZ2hZ67c+dOrKys6NGjByqViqysLMzMZAtlIYQQFUNWfBQtlH/SwrfoOr8dfbprXrt2rWRB/UvdunXx8fHR6zWFeNHolKQ7Ojpiamqq71jUbty4gYuLC+bmml/JeXl5AQ/XaS8qST9//jy1a9dm586dbN68mdTUVKysrHjttdfo3r37M4tZCCGEKAu8vLzw8PAAHu5QuufPFL46HE9scp5WXetKhoxqacervlUwLGRuaVJSEseOHcPf35+qVavqLUZj4yf34gtR0emUpHfq1Indu3eTk5PzTL6uevDgAVZWVlrHHx0ramx5WloaKSkpXL58mYsXL9K/f3/s7OzYv38/wcHBGBkZ0blzZ73HK4QQQpQVSqVS47O5T5NKdG3gwMqjCSw7FE9a9t/DVpKSYNqO+3x3Ko3Z3Zxo7W2pca2srCwATE1NqVSp0vN5A0IIQMfNjD766CMMDQ0ZOHAgMTEx+o6JnJycQp+yH/3Syc4ufKOGzMxMAFJTUxk7diyBgYEEBAQwa9Ys3Nzc2Lx5s95jFUIIIco6M2MDxrWx59hUL95sao3BvxZ5iYjL5o3vo3jju5tcic0qnSCFEBp06kl/7733qF+/Plu3bmXXrl00bNiQatWqFToERqFQ8N133z3V9ZVKJbm5uVrHc3IergNrYmJS6HmPjhsZGeHn56c+bmBgQIsWLfjhhx8KHc+ekJBAQkIC8HcvfVZWljrpFy+2Rw99RT38CfE8yf0oniULI5jTxZoBDSsxf999fr2eoVEefjWNX69do1/DykxqY41R3sPP3ZycnHLxmShz0MSLRKckPSQkRP33rKwsjh8/zvHjxwutq0uSbmVlVeiQlgcPHgBgbW1d6HkWFhYolUoqVaqEoaGhRlmVKlWAh0Ni/p2kh4aG8u2336rrtWzZkujoaNLTNXdyEy+26Ojo0g5BCDW5H8WzZATMaA5nq8OKs3Ar+e+yAhVs/C2Fn86n8KZPLnUNITY2lvv375davMUlk1HFi0SnJP3QoUP6jkNDjRo1uHjxIhkZGRqTRyMiIgDUE2L+zcDAAA8PD65du0Zubq7GkJlHSX/lypW1zuvTpw+tWrVS19u3bx+urq44Ojrq7T2Jsis7O5vo6GhcXV2L/JZGiOdF7kfxPHl6Qh8/FVv+l8LiQ4ncT/97MyRU+ahSY6AqKJUm1KhR+GevEOLZ0ClJf5TQPit+fn5s27aNPXv2qNdJz83N5cCBA3h5eal7wuPj48nOzsbV1VV9bkBAABERERw8eFC9hGNOTg6HDx/Gzc0NGxsbrfZsbW3Vu6k+GmNvamoqX5tVMCYmJvJvLsoMuR/F8zS8hTn9mtjx5aF4go8kkJWnoqNTAt6VH36wotr9AAAgAElEQVSjfPnyn9StW0d2LBXiOdIpSX/WvL298ff3Z82aNSQlJal3HL137x7jxo1T11u8eDGXLl1ix44d6mOdO3dm3759rFixgrt372JnZ8ehQ4e4d+8eM2fOLI23I4QQQpR5FiaGTO/syKCm1szfEwcJsTzKyVUqFSqVSpJ0IZ6jMpmkA0yaNIn169cTHh5OWloa7u7uzJw584njzUxMTPjwww8JCQlh//79ZGVl4eHhwaxZs2jYsOFzil4IIYQon1ytlHzZ342zkRZE/XGKgqwUfHx8MDDQaUE4IYSOFCqVSqXLiRkZGSxatIiffvqJa9eukZqaWngDCgV5edobKJRVMTExBAcHExQUhJOTU2mHI56DzMxMIiMj8fT0lOEFotTJ/SjKkvv373PgwAHatWtX6HBRIcSzo1NPenJyMi1atOCPP/7A0NAQpVKJSqXCycmJ2NhYHuX91atX12uwQgghhBBCVAQ6fXc1f/58Ll26RFBQECkpKfTt2xeFQsGdO3dIT08nJCQER0dHmjZtyo0bN/QdsxBCCCGEEC80nZL07du34+zszNKlSzE1NdWYSGJqasrgwYPZv38/27ZtY+HChXoLVgghhBBCiIpApyT91q1bNGzYUL0O+aPJJP/cJbRu3bq0atVKY+MjIYQQQgghxJPpNCbd1NQUU1NT9etHGwTFxsbi5uamPm5tbc2xY8dKGKIQQgghiisnJ0ej06wksrKy1P/V5y7cxsbGKJVKvV1PiBeRTkm6m5sbf/31l/p17dq1ATh8+DCDBg0CIC8vjzNnzshscCGEEOI5unr1Kn/++ader6nvDre6des+cUllISo6nZL0Fi1asGrVKlJTU7G0tKRHjx6MHz+e8ePHk5aWhouLC9999x1RUVG88cYb+o5ZCCGEEEXw8vLCw8NDL9fKysri9u3bVKtWTeMb9JJ6NFxWCFE0nZL0/v3789tvv3H8+HE6deqEs7Mzn3zyCVOmTOGdd94BHu5O5ujoyKeffqrXgIUQQghRNKVSqbehJAYGBhgbG2Nubi7r9gvxnOmUpAcEBHDixAmNY5MnT8bf359t27bx4MEDvLy8GDZsGNbW1noJVAghhBBCiIpCpyS9KE2bNqVp06b6vKQQQgghhBAVjk5LMAohhBBCCCGenWL1pN++fbtEjVSrVq1E5wshhBBCCFGRFCtJd3d319hV9GkoFAry8vJ0OlcIIYQQQoiKqFhJesuWLXVO0oUQQgghhBBPp1hJenh4+DMOQwghhBBCCPGITBwVQgghhBCijJEkXQghhBBCiDKmWMNdfv31VwCaNGmCqamp+nVxtWzZ8ukjE0IIIYQQooIqVpLeunVrFAoFly9fxsvLS/26uPLz83UOUAghhBBCiIqmWEn64MGDUSgUVKlSReO1EEIIIYQQQv+KlaSHhIQ89rUQQgghhBBCf2TiqBBCCCGEEGWMJOlCCCGEEEKUMcUa7lKUw4cPc/jwYWJiYsjOzi60jkKh4LvvvitJM0IIIYQQQlQoOiXpiYmJBAYGcvToUVQq1WPrSpIuhBBCCCHE09EpSZ80aRJHjhyhXr16BAUFUaNGDSwsLPQdmxBCCCGEEBWSTkn6zp07cXV15cSJE5KcCyGEEEIIoWc6TRzNz8+nefPmkqALIYQQQgjxDOiUpDdq1IjY2Fh9xyKEEEIIIYRAxyR9xowZnDhxgl9++UXf8QghhBBCCFHh6TQmvW3btvzwww8MHjyYrl270qFDB1xcXDAwKDznb9myZYmCFEIIIYQQoiLReZ30tLQ0jI2NWbt2LWvXrn1s3fz8fF2bEUIIIYQQosLRKUkPCQlhxIgRqFQqGjRoIEswCiGEEEIIoUc6JekLFizAxMSEsLAwWrdureeQhBBCCCGEqNh0mjgaFRVFq1atJEEXQgghhBDiGdApSXdxccHc3FzfsQghhBBCCCHQMUkfPHgwhw4dIjExUd/xCCGEEEIIUeHplKS///77tGnThjZt2hAeHo5KpdJ3XEIIIYQQQlRYOk0crVWrFgC3bt2iXbt2GBsb4+joWOg66QqFgsjIyJJFKYQQQgghRAWiU5IeFRWl8TonJ4fbt2/rIx4hhBBCCCEqPJ2S9IKCAn3HIYQQQgghhPh/Oo1JF0IIIYQQQjw7kqQLIYQQQghRxhRruMuj8eYuLi4YGho+9fjzatWqPX1kQgghhBDi/9q797Ao6v0P4O9ZLstdbqICwgIqhqJopCIoaCKUYgRpGRVY6qE8djk+qUd/lUZp2elUptmxNPNoViqkdXooE+iCGl4yMbyl4klBRO7ITdj5/eHZiXWXZVFgR/f9ep6e/H6/85n57DI7+9nZ78ySmTKqSFepVFAoFCgoKMCAAQOgUqkgCIJRGxAEAc3NzTeVJBERERGROTGqSB87diwEQZB+ZVTTJiIiIiKizmdUkZ6Tk2OwTUREREREnYcXjhIRERERyUynFukNDQ24ePEi56ATEREREd0Eo4r0mpoa5OXl4eTJk3rHT506hZiYGDg5OcHLywuOjo546KGHUFxc3KnJEhERERGZA6OK9I0bNyIsLAxfffWVztjFixcxZswYfPfdd2huboYoimhsbMTWrVsxbtw41NfXd3rSRERERES3M6OK9O+//x4KhQKPPPKIzlhaWhouXboEFxcX7NixAzU1NTh06BBCQ0Nx6tQprFmzptOTJupM1tbW8PHxgbW1talTIeL+SLLC/ZHIdIwq0o8cOYLg4GB4eHho9avVamzZsgWCIGDZsmWIi4uDvb09QkJCkJ6eDktLS3zxxRddkjhRZ2lqasJ///tfNDU1mToVIu6PJCvcH4lMx6gi/dKlSwgMDNTpP3LkCCorK2FpaYmHHnpIa8zLywsjR47EsWPHOidTIiIiIiIzYVSRfuXKFVy9elWn/+DBgwCAIUOGwMnJSWfc29sb1dXVN5kiEREREZF5MapI79mzJ44fP67T/9NPP0EQBIwYMUJvXENDA3r06HFzGRIRERERmRmjinTNtJUvv/xS6istLUV6ejoAYOLEiXrjCgoK4Onp2QlpEhERERGZD6OK9L/+9a8QRRFTp07FY489hnnz5uGuu+5CTU0N+vbti0mTJunEnDlzBidPnsTQoUM7PWkiIiIiotuZpTELjRs3DkuWLMHSpUuxadMmCIIAURRha2uLjz76CJaWuqt5//33AQAxMTGdmzERERER0W3OqCIdAF588UVMnjwZ6enpKC0tRd++fZGUlAQ/Pz+9yyuVSjzzzDOIjY3ttGSJiIiIiMyB0UU6AAwfPhzDhw83atm0tLQbSoiIiIiIyNwZNSediIiIiIi6D4t0IiIiIiKZYZFORERERCQzLNKJiIiIiGSGRToRERERkcywSCciIiIikhkW6UREREREMsMinYiIiIhIZlikExERERHJDIt0IiIiIiKZYZFORERERCQzLNKJiIiIiGSGRToRERERkcywSCciIiIikhkW6UREREREMsMinYiIiIhIZlikExERERHJDIt0IiIiIiKZYZFORERERCQzLNKJiIiIiGSGRToRERERkcywSCciIiIikhkW6UREREREMsMinYiIiIhIZlikExERERHJDIt0IiIiIiKZYZFORERERCQzLNKJiIiIiGSGRToRERERkcywSCciIiIikhkW6UREREREMsMinYiIiIhIZlikExERERHJDIt0IiIiIiKZYZFORERERCQzLNKJiIiIiGSGRToRERERkcywSCciIiIikhkW6UREREREMmNp6gTacvXqVWzevBk5OTmora2FSqVCUlIShg0b1qH1vPDCC/j1119x7733IjU1tYuyNZ3Lly+3u4yLiwssLCw6FOPs7AxLyz93j7KyMoiiaDCmR48esLKyktrl5eVQq9UGY5ycnGBtbS21Kyoq0NLSYjDG0dERSqVSaldWVqK5udlgjIODA2xsbKR2VVUVrl69CgBobGxEXV0dysvLtdZrb28PW1tbqV1dXY2mpiaD27k+pqamBo2NjQZj7OzsYGdnJ7Vra2vR0NBgMMbW1hb29vZS+8qVK6ivrzcYY2NjAwcHB6ldV1eHuro6gzFKpRKOjo5Su76+HleuXDEYY21tDScnJ6nd0NCA2tpagzFWVlbo0aNHh2IsLS3h7OwstRsbG1FTU2MwxsLCAi4uLlK7qakJ1dXVBmMUCgVcXV2l9tWrV1FVVWUwRhAEuLm5Se3m5mZUVlYajAEAd3d36d9qtdqo16qbmxsEQZBiysvL241xdXWFQnHtHI0oiigrK2s3hscR3ZjWx5G2XH/sudWOI+1tl4i6jmyL9Lfffht79uzBlClT0KdPH2RlZeHll1/Gq6++iqCgIKPWsWfPHpw4caKLMzWtrKysdpeZPHmy1sH7hx9+aPfNKDY2VqvQys3NbfeAP2HCBK1iZt++fe0WWlFRUfDw8JDa+/fvb7eYiYiIgKenp9Q+dOhQuwXDqFGj4OPjI7V//fVXXLx4UWuZwsJCrXZoaCj8/f2l9tGjR3H+/HmD2wkJCcGAAQOk9rFjx3TWe73Bgwdr7dMnT57E77//bjAmMDAQQ4cOldqnT5/G8ePHDcYEBATgzjvvlNqFhYU4evSowRhfX1+MHDlSav/xxx84fPiwwRgvLy+Eh4dL7aKiIhw4cMBgTK9evRAZGSm1L126hH379hmMcXNzw9133y21y8rK8NNPPxmM6dGjB2JiYqR2ZWUlcnJyDMbY29tj0qRJUru2trbd151SqcR9990ntevr69uNsbCwQGJiotRuamoy6vU9depU6d8tLS1Gxdx///1SkQ4YdxyJi4vTKhy///77dgvhe+65R+tD3k8//dRu0RcdHa31QWrv3r3tfjAcN24cevbsKbXz8vLa/SA1ZswY9OnTR2ofPHiw3Q8rYWFh6Nu3r9Q+fPgwSkpKDMZcfxzJz8/HhQsXDMZcfxwpKCjAuXPnDMZcfxw5ceIETp8+bTBm4MCBGDJkiNT+/fff9b5nGvueS0SdS5bTXU6ePIkff/wRjz32GGbMmIHY2Fi88sor6NmzJzZs2GDUOpqamrB+/XokJCR0bbJERERERJ1MENv77tEEPvroI+zYsQOffPKJ1hngrVu34t///jfWrVunddZEn08//RS7du3Ce++9h6lTpxo93aW4uBhr167F7Nmztc6yyBWnu3TOdJcLFy7Ay8uL012uw+ku3T/dpb6+HqdPn4afn1+7f1OA010ATncBuna6S0VFBQICArRyIqKuJ8vpLmfOnIGXl5fWAQeA9PXf2bNnDRbppaWl2LZtG55++mmtA+rtqPUc1q6MaV1wGKt1YWOs1gWUsVoXasZqXRDW19ejoqICrq6uBt+EWheexnJ0dNQqco3h4OCgVUwbw97eXuvN1hjXv6kbw9bWtsNv1DY2NlpFSlfFKJXKDr/era2tO/x6sLKy6nCMpaVlh2MUCkW3xAiCwOMIbv44Yqxb7TiiOT4SUfeT5XSXiooKvQdZTV97Z4rWrVsHf39/jB07tkvyIyIiIiLqSrIs0puamrS+7tTQfJ1p6Gu/I0eOYO/evZg1a1aX5UdERERE1JVkOd3F2tpa7zw/zTy+tr7SbmlpwQcffICoqCj079/f6O1dvnxZml+pOUvf0NBg1HxQuvVpPvTxVmMkB9wfSU5utf2R8+bpdiLLIt3FxUXvlBbNvLi25ihmZWXhwoULeOqpp3Rui1VfX4+SkhI4OzvrFPnbt2/HBx98AODaHMOxY8fi/Pnz7V4gR7eX9m6vSNSduD+SnNwq++PgwYNNnQJRp5Flke7v74/8/HzU1dVpXdimuX+rn5+f3rjS0lI0NzdjwYIFOmPZ2dnIzs7GokWLMGrUKK2xxMRE6R7N5eXl2LVrF7y9vdG7d+/OekgkY42NjTh//jy8vb1v+wuNSf64P5KccH8kMh1ZFumjR49GRkYGvvnmG9x///0Art32bPfu3RgwYIB0Z5fS0lI0NjbC29sbADB27FitH43QWLZsGUJDQzFx4kStH4jQcHd3l+5UUFxcDODa3SX4tZl5USqV/JuTbHB/JDnh/kjU/WRZpAcGBiI8PBwbN25EZWWl9Iujly5dwty5c6Xl3nrrLRw9ehQ7d+4EAHh7e0sF+/U8PDx0zqATEREREcmRLIt0AHjuueewefNm5OTkoLa2FiqVCi+88ALnmxERERHRbU+2Rbq1tTVmzJiBGTNmtLnMsmXLjFqX5kw7EREREdGtQJb3SSciIiIiMmcs0omIiIiIZIZFOhERERGRzLBIJyIiIiKSGdleOGoqzc3NAIDLly+bOBPqLg0NDaioqMDFixdhY2Nj6nTIzHF/JDm5FfdHd3d3WFlZmToNopvGIv06lZWVAID09HQTZ0JEREQdNXv2bPTp08fUaRDdNEEURdHUSchJXV0dTp8+DWdnZ1ha8jOMOTh79ixeeOEFpKWlwc/Pz9TpkJnj/khycivujzyTTrcLVqHXsbOzQ3BwsKnToG5UVVWFqqoquLq68uwLmRz3R5IT7o9EpsMLR4mIiIiIZMZiyZIlS0ydBJGp2draIjQ0FHZ2dqZOhYj7I8kK90ci0+CcdCIiIiIimeF0FyIiIiIimWGRTkREREQkM7y7C5mlU6dOYffu3cjPz8elS5fg6OiIwMBAPPLII/Dy8jJ1ekT4/PPPsWnTJvj4+GDVqlWmTofM0OnTp7FlyxYUFBSgqakJvXv3RkxMDOLi4kydGpFZYJFOZmn79u04duwYwsPDoVKpUFFRgf/85z947rnn8MYbb8DX19fUKZIZu3z5MrZu3XrL/MIj3X5++eUXpKWlwd/fHw8++CBsbGxw8eJF/ho3UTfihaNklo4dO4Z+/fpp/eBFUVER5s6di9GjR2PevHkmzI7M3RtvvIGqqiqo1WpUV1fzTDp1q7q6OqSmpmLgwIFYuHAhFArOjCUyBb7yyCzdcccdOr9I5+npCR8fH5w/f95EWREBR48eRW5uLmbOnGnqVMhMff/996isrMSjjz4KhUKBhoYGqNVqU6dFZHY43YXof0RRRGVlJXx8fEydCpmplpYWrF27FhMnToRKpTJ1OmSmfv31V9jZ2aGsrAzLli3DhQsXYGNjg6ioKMycORPW1tamTpHILPBMOtH/5OTkoKysDBEREaZOhcxUZmYmSktLkZSUZOpUyIwVFRWhpaUFr776KoYNG4aFCxdiwoQJyMzMxDvvvGPq9IjMBs+kEwE4f/48/vWvf2HgwIEYP368qdMhM1RdXY1PPvkE06ZNQ48ePUydDpmxhoYGNDY2IjY2FrNnzwYAjB49Gs3NzcjMzERSUhI8PT1NnCXR7Y9n0snsVVRU4OWXX4adnR0WLFgACwsLU6dEZmjTpk1wcHDA5MmTTZ0KmTnNdJaxY8dq9Wvax48f7/aciMwRi3Qya1euXMGSJUuk/7u5uZk6JTJDRUVF+PbbbxEXF4fy8nKUlJSgpKQETU1NaGlpQUlJCWpqakydJpkJV1dXAICzs7NWv6ZdW1vb7TkRmSNOdyGz1dTUhLS0NBQVFSEtLY0XjJLJlJWVQa1WY+3atVi7dq3O+KxZsxAXF4dZs2aZIDsyNwEBATh8+DDKysrg7e0t9ZeVlQEAp2MRdRMW6WSWWlpasGLFCpw4cQKLFy/GwIEDTZ0SmTFfX18sWrRIp3/Tpk2or6/HrFmz0Lt3bxNkRuYoIiIC27dvx3fffYehQ4dK/bt27YKFhQUGDx5swuyIzAeLdDJL69evR15eHkaMGIGamhpkZ2drjY8bN85EmZE5cnJywqhRo3T6d+7cCQB6x4i6SkBAACZMmIDvvvsOLS0tGDx4MPLz85Gbm4sHHniA0wKJugmLdDJLZ8+eBQDk5eUhLy9PZ5xFOhGZs6eeego9e/bE7t27sW/fPvTs2RNPPPEE7rvvPlOnRmQ2BFEURVMnQUREREREf+LdXYiIiIiIZIZFOhERERGRzLBIJyIiIiKSGRbpREREREQywyKdiIiIiEhmWKQTEREREckMi3QiIiIiIplhkU5EREREJDMs0omIiIiIZIZFOpGRBEGAIAhwdnZGZWWl3mVee+01CIKAJUuWdG9yRsjJyYEgCEhJSTF1Kl1i5cqVGDRoEJRKJQRBQFRUVLsxmr+p5j+FQgFnZ2eMGTMGH374IeTyg8yCIEClUnVbHBERmR6LdKIOqqqqwj//+U9Tp0GtpKen45lnnkFxcTGmTJmC5ORkxMbGGh2fnJyM5ORkJCUlISgoCLm5uZg1axYefvjhLsz6mhv98HS7f+giIjJ3lqZOgOhWIggClEol3nnnHTz33HNwcXExdUoE4IsvvgAAbNu2DePHj+9w/IYNG7Tau3btwr333otPP/0USUlJmDx5cmekecOOHTsGKyurbosjIiLT45l0og5QKBSYPXs2qqur8Y9//MPU6dD/nD9/HgDg7+/fKeuLjo7Go48+CuDPDwCmNHDgQAQEBHRbHBERmR6LdKIOWrhwIWxtbfHuu++irKzMqJioqCgIgoDCwkKdscLCQr1zqJcsWQJBELBhwwYcPHgQ99xzD5ydneHq6opp06ZJhemVK1cwf/58qFQq2NjYYPDgwdi2bZvBfIqLi5GSkoJevXrB1tYWw4cPx8aNG9tcvry8HH//+98RFBQEW1tb9OjRA+PHj8dXX31l8PFUV1fjb3/7G/z8/GBlZYVnn322/ScLwB9//IG//OUv8PX1hVKphIeHBxISErB//369z1F2djYAwM/PT5pfnpOTY9S22jJs2DApF43i4mKsWLECkZGR8PLygrW1NXr37q03Nw2VSgVBECCKIt59910MHToUdnZ2CAkJQUpKCsaNGwcA+Pjjj7Xmx7e+ruH6ueU3Gtfa119/jejoaLi4uMDGxgaBgYFYuHCh3ustWu+L+fn5mDJlClxcXGBvb4/IyEjs2bPH4Da8vLygVCrh6emJiIgILF26VO/yRET0JxbpRB3Up08fpKamoqamBm+88UaXb+/nn39GeHg4SktLERMTAzc3N2zduhV33303qqqqMG7cOHz88ce46667EBYWhoKCAkybNg3ffPON3vWVl5dj1KhRyMzMRFRUFMaMGYP8/HwkJyfrveD15MmTCAkJwWuvvYb6+nrExMQgNDQUP//8M+Li4tr8RqG+vh6RkZHYsGEDQkJCpMKuPfn5+Rg+fDjWrl0LW1tbJCQkoH///sjIyMDo0aOxdetWadmQkBAkJyejV69eAIDExERpfnnv3r2NeHbbVlNTAwBQKpVS344dO7BgwQKUlJRgyJAhuP/+++Hp6YmMjAyEh4fj22+/bXN9qampmDdvHjw8PDBlyhT4+/sjIiICMTExAICAgAAp9+TkZISEhLS5rhuN01i+fDkmTZqEnJwc3HnnnYiPj0ddXR1ef/11jBw5EiUlJXrjDhw4gFGjRqGwsBAxMTHo378/fvjhB9x99904evSo1rKrV6/GpEmTkJ2djX79+iExMRGDBw/GuXPnZHlhNRGR7IhEZBQAooWFhSiKonjx4kXRzs5OtLe3Fy9duiQts3z5chGA+NJLL2nFRkZGigDEs2fP6qz37NmzIgAxMjJSq/+ll14SAYgAxDVr1kj9TU1N4oQJE0QAYlBQkDh+/HixtrZWGv/www9FAOLYsWO11pednS2tLzo6WismLy9PdHBwEBUKhXjw4EGpv7m5WQwODhYBiCtWrBBbWlqksVOnTol+fn6ihYWFmJ+fr/N4AIhhYWFiRUWFgWdVm1qtlrY3f/58Ua1WS2Pbtm0TFQqF6ODgIBYVFWnFGXp+DdHkqS+PsLAwEYC4ePFiqf/IkSPi0aNHdZbPzMwUra2txYCAAK2cRVEUfX19RQCiu7u73ljN3yU5Odlgnr6+vp0Sl5eXJz2P+/btk/obGhrEqVOnigDExMRErZjW++I777yjNfbss8+KAMRHH31Uq9/Hx0cUBEHcv3+/Vr9arRazs7PbzJmIiK7hmXSiG9CrVy88+eSTuHLlCl5//fUu3VZERARSU1OltpWVFebOnQsAOH78ONasWQN7e3tpPCUlBe7u7ti7dy+uXr2qsz6FQoF3331XK+auu+7CnDlzoFar8d5770n9X375JfLz85GYmIjnn38eCsWfh4x+/frhzTffREtLCz744AO9ua9cuRLOzs5GP9acnBzk5+fDx8cHr7zyCgRBkMYSExMRHx+P2tparF+/3uh1dkRLSwtOnTqFxx9/HHv37oVSqcSMGTOk8eDgYAwaNEgnLiYmBlOnTsXp06d1zihrLFiwQG9sd1u1ahXUajXmzp2LkSNHSv1KpRKrVq2Cra0tMjIytKb5aISHh+Ppp5/W6vu///s/AMAPP/yg1V9aWgpnZ2eEhoZq9Rt7e0wiInPHIp3oBi1YsAD29vZYs2ZNm9MDOsPEiRN1+jQXSKpUKgwYMEBrzMLCAr6+vrh69SouX76sExsSEoLAwECd/unTpwMAfvzxR6lPM30jISFBb25jxowBAOTl5emM9enTR6dAa49m29OmTdN7VxLNxZytc+wMmvnclpaWGDBgADZs2ABHR0ds2bJF58LLxsZG7NixA4sXL8bs2bORkpKClJQU5OfnAwBOnTqldxtTpkzp1JxvlOa5S0pK0hnz8PDAxIkToVarkZubqzOub190c3ODq6sriouLtfrvvPNOVFRU4IknnsBvv/3WSdkTEZkP3oKR6Ab17NkTc+bMwYoVK/Daa6/hrbfe6pLteHl56fQ5ODi0OdZ6vLGxUWfM19dXb4zmAsOioiKpT3Oha1JSkt6iTkPfhwEfH582l2+LZtttXeyo6b9w4UKH121IcnIygGvfMjg5OSE4OBgJCQk6c+g1F03quwBYQzOX/Xo38nx0hZt5jr29vfXGODo6ory8XKtv9erViI+Px/r167F+/Xr06tULkZGRSEhIwAMPPAALCxW1SF0AAAVzSURBVIsbfxBERGaARTrRTXj++efx3nvv4f3338f8+fNvaB1qtdrgeOspJh0Z6wya3GJjY6WLM/Vxd3fX6bOxsen0fFpPf+lM198nXR9RFDFt2jQUFhYiNTUVqamp8Pf3h4ODAwRBwKJFi7B8+fI2f6W0K56PrmDoOe7I/jZkyBAUFBQgMzMTX3/9NXJycvD555/j888/R1hYGHJycmBtbd0ZKRMR3ZZYpBPdBHd3d8ydOxfLly/H8uXL4enpqXc5TTFSW1urM6Zv7m9XOnfunMH+1o9Bc+Z05syZSExM7PLcNNtuK0fNGey2vkHoSsePH8fx48cRGhqKNWvW6IyfOXOm23O6EZ6enjh79izOnTuHoKAgnfHOfI5tbGwQHx+P+Ph4AMBvv/2Ghx9+GHv37sWHH36Ip5566qa3QUR0u+KcdKKbNG/ePDg6OmLt2rVtTsPo06cPgGu3M7zerl27ujS/6x0+fFjvvOlPP/0UwLULVTWio6MBABkZGd2Sm2aO+9atW9HS0qIzvmnTJq3lulNFRQUA/VM+KioqbvjvqPkA19zc3C1xmuduy5YtOmOlpaX45ptvIAgCwsPDO7ReYwwaNAhz5swBgDYvsCUiomtYpBPdJDc3Nzz99NNobGzEunXr9C4TGRkJAHjzzTdRV1cn9WdlZeHtt9/uljw1NHf2aJ3HwYMHsWrVKgiCgCeffFLqT0xMRFBQEDZv3oy0tDSdOe6iKCI3N1fvRYY3IioqCsHBwSgsLMSLL76oNXUkIyMD6enpcHBwwOOPP94p2+uIfv36QaFQICsrS+tDTkNDA1JTU3XmZBtL8+3BiRMnuiVuzpw5UCgUWLlyJQ4cOCD1NzU1Ye7cuaivr0dCQgL69u3bofW2VldXh5UrV+r8MJJarUZmZiYA3NT6iYjMAYt0ok4wb948ODk5ob6+Xu/49OnTERgYiD179uCOO+7AAw88gFGjRiE6OlqrKO4OkydPRkFBAQICAvDggw8iNjYWYWFhqKmpweLFi7XuyGJpaYkvvvgCfn5+ePHFF+Hj44Po6GgkJSUhJiYGvXv3RkRERJu/ttlRgiBg8+bNcHNzw7JlyzBo0CA8/PDDiIiIQEJCAhQKBdatWyd9M9GdPDw88MQTT6C6uhpDhw7F5MmTMXXqVKhUKmRlZSElJeWG1qtSqTBkyBAcOHAAI0aMwIwZMzBz5kzs3LmzS+JGjBiBtLQ0VFdXIywsDNHR0Zg+fTr69euHzz77DP3798fq1atv6LFoNDU14ZlnnoGHhwfCwsIwffp0JCYmQqVSISMjAyqVCrNnz76pbRAR3e5YpBN1AhcXF4M/eW9ra4vdu3dj+vTpqKmpwddff42WlhZ89tln0tf/3cXNzQ379u3DhAkTkJ2djZycHAQFBeGjjz5CWlqazvL9+/fHL7/8gldeeQXe3t7Yt28f0tPTcfLkSQwbNgyrV6/GI4880mn5BQcH49ChQ5g1axZqa2uxbds2nDhxAvHx8cjNzcW0adM6bVsdtWbNGrz55pvw8/PD7t278eOPP2LChAk4cOBAm3fNMcb27dsRHx+PM2fOYOPGjVi3bh0OHTrUZXGLFi3CV199hcjISOzfvx/p6elQKpWYP38+fv75Z4MXCRvDwcEBq1evRlxcHEpLS7Fz505kZWXBxcUFS5cuxcGDB+Hm5nZT2yAiut0JYlu3IiAiIiIiIpPgmXQiIiIiIplhkU5EREREJDMs0omIiIiIZIZFOhERERGRzLBIJyIiIiKSGRbpREREREQywyKdiIiIiEhmWKQTEREREckMi3QiIiIiIplhkU5EREREJDMs0omIiIiIZIZFOhERERGRzLBIJyIiIiKSmf8He2n+N6PB2BgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (8731421604577)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot\n",
-    "lst_num_partitions = list(all_svcca.index)\n",
-    "\n",
-    "threshold = pd.DataFrame(\n",
-    "    pd.np.tile(\n",
-    "        permuted_score,\n",
-    "        (len(lst_num_partitions), 1)),\n",
-    "    index=lst_num_partitions,\n",
-    "    columns=['score'])\n",
-    "\n",
-    "panel_A = ggplot(all_svcca) \\\n",
-    "    + geom_line(all_svcca,\n",
-    "                aes(x=lst_num_partitions, y='score', color='Group'),\n",
-    "                size=1.5) \\\n",
-    "    + geom_point(aes(x=lst_num_partitions, y='score'), \n",
-    "                 color ='darkgrey',\n",
-    "                size=0.5) \\\n",
-    "    + geom_errorbar(all_svcca,\n",
-    "                  aes(x=lst_num_partitions, ymin='ymin', ymax='ymax'),\n",
-    "                   color='darkgrey') \\\n",
-    "    + geom_line(threshold, \n",
-    "                aes(x=lst_num_partitions, y='score'), \n",
-    "                linetype='dashed',\n",
-    "                size=1,\n",
-    "                color=\"darkgrey\",\n",
-    "                show_legend=False) \\\n",
-    "    + labs(x = \"Number of Partitions\", \n",
-    "           y = \"Similarity score (SVCCA)\", \n",
-    "           title = \"Similarity across varying numbers of partitions\") \\\n",
-    "    + theme(\n",
-    "            plot_background=element_rect(fill=\"white\"),\n",
-    "            panel_background=element_rect(fill=\"white\"),\n",
-    "            panel_grid_major_x=element_line(color=\"lightgrey\"),\n",
-    "            panel_grid_major_y=element_line(color=\"lightgrey\"),\n",
-    "            axis_line=element_line(color=\"grey\"),\n",
-    "            legend_key=element_rect(fill='white', colour='white'),\n",
-    "            legend_title=element_text(family='sans-serif', size=15),\n",
-    "            legend_text=element_text(family='sans-serif', size=12),\n",
-    "            plot_title=element_text(family='sans-serif', size=15),\n",
-    "            axis_text=element_text(family='sans-serif', size=12),\n",
-    "            axis_title=element_text(family='sans-serif', size=15)\n",
-    "           ) \\\n",
-    "    + scale_color_manual(['#1976d2', '#b3e5fc']) \\\n",
-    "\n",
-    "\n",
-    "print(panel_A)\n",
-    "ggsave(plot=panel_A, filename=svcca_file, device=\"svg\", dpi=300)\n",
-    "ggsave(plot=panel_A, filename=svcca_png_file, device=\"svg\", dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Uncorrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(58, 5000)\n",
-      "Plotting PCA of 1 parition vs 1 partition...\n",
-      "(58, 5000)\n",
-      "[0.59602355 0.20935908]\n",
-      "Plotting PCA of 1 parition vs 2 partition...\n",
-      "(58, 5000)\n",
-      "[0.50072675 0.17944364]\n",
-      "Plotting PCA of 1 parition vs 6 partition...\n",
-      "(58, 5000)\n",
-      "[0.49352494 0.17990677]\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_partitions = [lst_num_partitions[i] for i in pca_ind]\n",
-    "\n",
-    "all_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "partition_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Partition_1_0.txt.xz\")\n",
-    "\n",
-    "partition_1 = pd.read_table(\n",
-    "    partition_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "print(partition_1.shape)\n",
-    "\n",
-    "\n",
-    "for i in lst_num_partitions:\n",
-    "    print('Plotting PCA of 1 parition vs {} partition...'.format(i))\n",
-    "    \n",
-    "    # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  partition_1.copy()\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_partitions'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added\n",
-    "    partition_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Partition_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    partition_other = pd.read_table(\n",
-    "        partition_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    print(partition_other.shape)\n",
-    "    \n",
-    "    # Simulated data with i batch effects\n",
-    "    partition_data_df =  partition_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    partition_data_df['num_partitions'] = 'multiple'\n",
-    "    \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, partition_data_df])\n",
-    "\n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space\n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_partitions'], axis=1)\n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "\n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "                                              \n",
-    "    # Variance explained\n",
-    "    print(pca.explained_variance_ratio_)  \n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_partitions'] = combined_data_df['num_partitions']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_data_df = pd.concat([all_data_df, combined_data_PCAencoded_df])     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_partitions_str = [str(i) for i in lst_num_partitions]\n",
-    "num_partitions_cat = pd.Categorical(all_data_df['num_partitions'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_data_df['comparison'], categories=lst_num_partitions_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_data_df = all_data_df.assign(num_partitions_cat = num_partitions_cat)\n",
-    "all_data_df = all_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data_df.columns = ['PC1', 'PC2', 'num_partitions', 'comparison', 'No. of partitions', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363305433317951)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_B = ggplot(all_data_df[all_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of partitions'), \n",
-    "                 alpha=0.1) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of partition 1 vs multiple partitions\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_title=element_text(family='sans-serif', size=15),\n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#b3e5fc']) \\\n",
-    "    + geom_point(data=all_data_df[all_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_B)\n",
-    "ggsave(plot=panel_B, filename=pca_uncorrected_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Corrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5000, 58)\n",
-      "Plotting PCA of 1 partition vs 1 partitions...\n",
-      "(5000, 58)\n",
-      "Plotting PCA of 1 partition vs 2 partitions...\n",
-      "(5000, 58)\n",
-      "Plotting PCA of 1 partition vs 6 partitions...\n",
-      "(5000, 58)\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_partitions = [lst_num_partitions[i] for i in pca_ind]\n",
-    "\n",
-    "all_corrected_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "partition_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Partition_corrected_1_0.txt.xz\")\n",
-    "\n",
-    "partition_1 = pd.read_table(\n",
-    "    partition_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "print(partition_1.shape)\n",
-    "\n",
-    "# Transpose data to df: sample x gene\n",
-    "partition_1 = partition_1.T\n",
-    "\n",
-    "for i in lst_num_partitions:\n",
-    "    print('Plotting PCA of 1 partition vs {} partitions...'.format(i))\n",
-    "    \n",
-    "     # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  partition_1.copy()\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    original_data_df.columns = original_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_partitions'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added and corrected\n",
-    "    partition_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Partition_corrected_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    partition_other = pd.read_table(\n",
-    "        partition_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    print(partition_other.shape)\n",
-    "    # Transpose data to df: sample x gene\n",
-    "    partition_other = partition_other.T\n",
-    "    \n",
-    "    # Simulated data with i batch effects that are corrected\n",
-    "    partition_data_df =  partition_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    partition_data_df['num_partitions'] = 'multiple'\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    partition_data_df.columns = original_data_df.columns.astype(str)\n",
-    "        \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, partition_data_df])\n",
-    "    \n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space    \n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_partitions'], axis=1)    \n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "    \n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_partitions'] = combined_data_df['num_partitions']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_corrected_data_df = pd.concat([all_corrected_data_df, combined_data_PCAencoded_df])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_partitions_str = [str(i) for i in lst_num_partitions]\n",
-    "num_partitions_cat = pd.Categorical(all_corrected_data_df['num_partitions'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_corrected_data_df['comparison'], categories=lst_num_partitions_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(num_partitions_cat = num_partitions_cat)\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_corrected_data_df.columns = ['PC1', 'PC2', 'num_partitions', 'comparison', 'No. of partitions', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (8731349790177)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_C = ggplot(all_corrected_data_df[all_corrected_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', \n",
-    "                     y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of partitions'), \n",
-    "                 alpha=0.1) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of partition 1 vs multiple partitions\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_title=element_text(family='sans-serif', size=15),\n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#1976d2']) \\\n",
-    "    + geom_point(data=all_corrected_data_df[all_corrected_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_C)\n",
-    "ggsave(plot=panel_C, filename=pca_corrected_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:simulate_expression_compendia] *",
-   "language": "python",
-   "name": "conda-env-simulate_expression_compendia-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Human_tests/Human_sample_limma.ipynb b/Human_tests/Human_sample_limma.ipynb
deleted file mode 100644
index 2332b3d..0000000
--- a/Human_tests/Human_sample_limma.ipynb
+++ /dev/null
@@ -1,1076 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Human sample level analysis\n",
-    "\n",
-    "Main notebook to run sample-level simulation experiment using human gene expression data from recount2.\n",
-    "\n",
-    "Make sure to run ```download_data.R``` download raw data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%load_ext rpy2.ipython\n",
-    "\n",
-    "from rpy2.robjects.packages import importr\n",
-    "from rpy2.robjects import pandas2ri\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "import ast\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import subprocess\n",
-    "from plotnine import (ggplot,\n",
-    "                      labs,  \n",
-    "                      geom_line, \n",
-    "                      geom_point,\n",
-    "                      geom_errorbar,\n",
-    "                      aes, \n",
-    "                      ggsave, \n",
-    "                      theme_bw,\n",
-    "                      theme,\n",
-    "                      facet_wrap,\n",
-    "                      scale_color_manual,\n",
-    "                      guides, \n",
-    "                      guide_legend,\n",
-    "                      element_blank,\n",
-    "                      element_text,\n",
-    "                      element_rect,\n",
-    "                      element_line,\n",
-    "                      coords)\n",
-    "\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(action='ignore')\n",
-    "\n",
-    "sys.path.append(\"../\")\n",
-    "from ponyo import pipeline, utils\n",
-    "\n",
-    "from numpy.random import seed\n",
-    "randomState = 123\n",
-    "seed(randomState)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in config variables\n",
-    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
-    "config_file = os.path.abspath(os.path.join(base_dir,\n",
-    "                                           \"configs\", \n",
-    "                                           \"config_test_human_sample_limma.tsv\"))\n",
-    "params = utils.read_config(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load parameters\n",
-    "local_dir = params[\"local_dir\"]\n",
-    "dataset_name = params['dataset_name']\n",
-    "analysis_name = params[\"simulation_type\"]\n",
-    "correction_method = params[\"correction_method\"]\n",
-    "lst_num_experiments = params[\"lst_num_experiments\"]\n",
-    "train_architecture = params['NN_architecture']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input files\n",
-    "rpkm_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"recount2_gene_RPKM_data_test.tsv\")\n",
-    "assert os.path.exists(rpkm_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup directories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.setup_dir(config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pre-process data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output file\n",
-    "normalized_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"recount2_gene_normalized_data_test.tsv.xz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input: dataset contains 50 samples and 5000 genes\n",
-      "Output: normalized dataset contains 50 samples and 5000 genes\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Only run pre-processind step if normalized data file is NOT created\n",
-    "#if os.path.exists(normalized_data_file) == False:\n",
-    "pipeline.normalize_expression_data(base_dir,\n",
-    "                                   config_file,\n",
-    "                                   rpkm_data_file,\n",
-    "                                   normalized_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train VAE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Directory containing log information from VAE training\n",
-    "vae_log_dir = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"logs\",\n",
-    "    train_architecture)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input dataset contains 50 samples and 5000 genes\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 45 samples, validate on 5 samples\n",
-      "Epoch 1/10\n",
-      "45/45 [==============================] - 4s 89ms/step - loss: 2466.1158 - val_loss: 2732.4883\n",
-      "Epoch 2/10\n",
-      "45/45 [==============================] - 4s 80ms/step - loss: 1671.1998 - val_loss: 2180.0183\n",
-      "Epoch 3/10\n",
-      "45/45 [==============================] - 4s 80ms/step - loss: 1566.8178 - val_loss: 1799.2496\n",
-      "Epoch 4/10\n",
-      "45/45 [==============================] - 4s 80ms/step - loss: 1481.5816 - val_loss: 1730.1412\n",
-      "Epoch 5/10\n",
-      "45/45 [==============================] - 4s 81ms/step - loss: 1577.6224 - val_loss: 1477.2603\n",
-      "Epoch 6/10\n",
-      "45/45 [==============================] - 4s 81ms/step - loss: 1444.6422 - val_loss: 1510.6864\n",
-      "Epoch 7/10\n",
-      "45/45 [==============================] - 4s 81ms/step - loss: 1475.2912 - val_loss: 1676.2584\n",
-      "Epoch 8/10\n",
-      "45/45 [==============================] - 4s 81ms/step - loss: 1398.2218 - val_loss: 1403.8243\n",
-      "Epoch 9/10\n",
-      "45/45 [==============================] - 4s 81ms/step - loss: 1352.8320 - val_loss: 1309.4790\n",
-      "Epoch 10/10\n",
-      "45/45 [==============================] - 4s 81ms/step - loss: 1411.7664 - val_loss: 1252.2395\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train VAE\n",
-    "pipeline.train_vae(config_file,\n",
-    "                   normalized_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation experiment without noise correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   12.7s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   12.8s remaining:   19.2s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   13.0s remaining:    8.6s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   13.2s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   13.2s finished\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.999876\n",
-      "5                      0.719257\n",
-      "50                     0.622500\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.000012\n",
-      "5                      0.006044\n",
-      "50                     0.009901\n",
-      "                           ymin      ymax\n",
-      "number of experiments                    \n",
-      "1                      0.999853  0.999898\n",
-      "5                      0.707411  0.731102\n",
-      "50                     0.603094  0.641906\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "corrected = False\n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_data_file,\n",
-    "                        corrected)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation with correction applied"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    7.5s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    7.8s remaining:   11.6s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:    7.9s remaining:    5.3s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    8.1s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    8.1s finished\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.999876\n",
-      "5                      0.916577\n",
-      "50                     0.396612\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.000012\n",
-      "5                      0.010577\n",
-      "50                     0.007911\n",
-      "                           ymin      ymax\n",
-      "number of experiments                    \n",
-      "1                      0.999853  0.999898\n",
-      "5                      0.895845  0.937309\n",
-      "50                     0.381106  0.412117\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "corrected = True\n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_data_file,\n",
-    "                        corrected)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Make figures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca_ind = [0,1,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# File directories\n",
-    "similarity_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "similarity_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "permuted_score_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_permuted.npy\")\n",
-    "\n",
-    "compendia_dir = os.path.join(\n",
-    "    local_dir,\n",
-    "    \"experiment_simulated\",\n",
-    "    dataset_name + \"_\" + analysis_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "svcca_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".svg\")\n",
-    "\n",
-    "svcca_png_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".png\")\n",
-    "\n",
-    "pca_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_uncorrected_\"+correction_method+\".svg\")\n",
-    "\n",
-    "pca_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_corrected_\"+correction_method+\".svg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load pickled files\n",
-    "uncorrected_svcca = pd.read_pickle(similarity_uncorrected_file)\n",
-    "err_uncorrected_svcca = pd.read_pickle(ci_uncorrected_file)\n",
-    "corrected_svcca = pd.read_pickle(similarity_corrected_file)\n",
-    "err_corrected_svcca = pd.read_pickle(ci_corrected_file)\n",
-    "\n",
-    "permuted_score = np.load(permuted_score_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Concatenate error bars\n",
-    "uncorrected_svcca_err = pd.concat([uncorrected_svcca, err_uncorrected_svcca], axis=1)\n",
-    "corrected_svcca_err = pd.concat([corrected_svcca, err_corrected_svcca], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add group label\n",
-    "uncorrected_svcca_err['Group'] = 'uncorrected'\n",
-    "corrected_svcca_err['Group'] = 'corrected'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>score</th>\n",
-       "      <th>ymin</th>\n",
-       "      <th>ymax</th>\n",
-       "      <th>Group</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>number of experiments</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999876</td>\n",
-       "      <td>0.999853</td>\n",
-       "      <td>0.999898</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.719257</td>\n",
-       "      <td>0.707411</td>\n",
-       "      <td>0.731102</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.622500</td>\n",
-       "      <td>0.603094</td>\n",
-       "      <td>0.641906</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999876</td>\n",
-       "      <td>0.999853</td>\n",
-       "      <td>0.999898</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.916577</td>\n",
-       "      <td>0.895845</td>\n",
-       "      <td>0.937309</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.396612</td>\n",
-       "      <td>0.381106</td>\n",
-       "      <td>0.412117</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                          score      ymin      ymax        Group\n",
-       "number of experiments                                           \n",
-       "1                      0.999876  0.999853  0.999898  uncorrected\n",
-       "5                      0.719257  0.707411  0.731102  uncorrected\n",
-       "50                     0.622500  0.603094  0.641906  uncorrected\n",
-       "1                      0.999876  0.999853  0.999898    corrected\n",
-       "5                      0.916577  0.895845  0.937309    corrected\n",
-       "50                     0.396612  0.381106  0.412117    corrected"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Concatenate dataframes\n",
-    "all_svcca = pd.concat([uncorrected_svcca_err, corrected_svcca_err])\n",
-    "all_svcca"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SVCCA "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363263801628444)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot\n",
-    "lst_num_partitions = list(all_svcca.index)\n",
-    "\n",
-    "threshold = pd.DataFrame(\n",
-    "    pd.np.tile(\n",
-    "        permuted_score,\n",
-    "        (len(lst_num_partitions), 1)),\n",
-    "    index=lst_num_partitions,\n",
-    "    columns=['score'])\n",
-    "\n",
-    "panel_A = ggplot(all_svcca) \\\n",
-    "    + geom_line(all_svcca,\n",
-    "                aes(x=lst_num_partitions, y='score', color='Group'),\n",
-    "                size=1.5) \\\n",
-    "    + geom_point(aes(x=lst_num_partitions, y='score'), \n",
-    "                 color ='darkgrey',\n",
-    "                size=0.5) \\\n",
-    "    + geom_errorbar(all_svcca,\n",
-    "                  aes(x=lst_num_partitions, ymin='ymin', ymax='ymax'),\n",
-    "                   color='darkgrey') \\\n",
-    "    + geom_line(threshold, \n",
-    "                aes(x=lst_num_partitions, y='score'), \n",
-    "                linetype='dashed',\n",
-    "                size=1,\n",
-    "                color=\"darkgrey\",\n",
-    "                show_legend=False) \\\n",
-    "    + labs(x = \"Number of Partitions\", \n",
-    "           y = \"Similarity score (SVCCA)\", \n",
-    "           title = \"Similarity across varying numbers of partitions\") \\\n",
-    "    + theme(\n",
-    "            plot_background=element_rect(fill=\"white\"),\n",
-    "            panel_background=element_rect(fill=\"white\"),\n",
-    "            panel_grid_major_x=element_line(color=\"lightgrey\"),\n",
-    "            panel_grid_major_y=element_line(color=\"lightgrey\"),\n",
-    "            axis_line=element_line(color=\"grey\"),\n",
-    "            legend_key=element_rect(fill='white', colour='white'),\n",
-    "            legend_title=element_text(family='sans-serif', size=15),\n",
-    "            legend_text=element_text(family='sans-serif', size=12),\n",
-    "            plot_title=element_text(family='sans-serif', size=15),\n",
-    "            axis_text=element_text(family='sans-serif', size=12),\n",
-    "            axis_title=element_text(family='sans-serif', size=15)\n",
-    "           ) \\\n",
-    "    + scale_color_manual(['#1976d2', '#b3e5fc']) \\\n",
-    "\n",
-    "\n",
-    "print(panel_A)\n",
-    "ggsave(plot=panel_A, filename=svcca_file, device=\"svg\", dpi=300)\n",
-    "ggsave(plot=panel_A, filename=svcca_png_file, device=\"svg\", dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Uncorrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting PCA of 1 experiment vs 1 experiments...\n",
-      "[0.6856755  0.19788391]\n",
-      "Plotting PCA of 1 experiment vs 5 experiments...\n",
-      "[0.23464727 0.15610842]\n",
-      "Plotting PCA of 1 experiment vs 50 experiments...\n",
-      "[0.22350863 0.06974863]\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
-    "\n",
-    "all_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "experiment_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Experiment_1_0.txt.xz\")\n",
-    "\n",
-    "experiment_1 = pd.read_table(\n",
-    "    experiment_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "\n",
-    "for i in lst_num_experiments:\n",
-    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
-    "    \n",
-    "    # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  experiment_1.copy()\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_experiments'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added\n",
-    "    experiment_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Experiment_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    experiment_other = pd.read_table(\n",
-    "        experiment_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    \n",
-    "    # Simulated data with i batch effects\n",
-    "    experiment_data_df =  experiment_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    experiment_data_df['num_experiments'] = 'multiple'\n",
-    "    \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
-    "\n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space\n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)\n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "\n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "                                              \n",
-    "    # Variance explained\n",
-    "    print(pca.explained_variance_ratio_)  \n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_data_df = pd.concat([all_data_df, combined_data_PCAencoded_df])   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
-    "num_experiments_cat = pd.Categorical(all_data_df['num_experiments'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_data_df['comparison'], categories=lst_num_experiments_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_data_df = all_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
-    "all_data_df = all_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363263801880204)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_B = ggplot(all_data_df[all_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of experiments'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#b3e5fc']) \\\n",
-    "    + geom_point(data=all_data_df[all_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_B)\n",
-    "ggsave(plot=panel_B, filename=pca_uncorrected_file, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Corrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting PCA of 1 experiment vs 1 experiments...\n",
-      "Plotting PCA of 1 experiment vs 5 experiments...\n",
-      "Plotting PCA of 1 experiment vs 50 experiments...\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
-    "\n",
-    "all_corrected_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "experiment_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Experiment_corrected_1_0.txt.xz\")\n",
-    "\n",
-    "experiment_1 = pd.read_table(\n",
-    "    experiment_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "# Transpose data to df: sample x gene\n",
-    "experiment_1 = experiment_1.T\n",
-    "\n",
-    "for i in lst_num_experiments:\n",
-    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
-    "    \n",
-    "     # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  experiment_1.copy()\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    original_data_df.columns = original_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_experiments'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added and corrected\n",
-    "    experiment_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Experiment_corrected_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    experiment_other = pd.read_table(\n",
-    "        experiment_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    \n",
-    "    # Transpose data to df: sample x gene\n",
-    "    experiment_other = experiment_other.T\n",
-    "    \n",
-    "    # Simulated data with i batch effects that are corrected\n",
-    "    experiment_data_df =  experiment_other\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    experiment_data_df.columns = experiment_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    experiment_data_df['num_experiments'] = 'multiple'\n",
-    "        \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
-    "    \n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space    \n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)    \n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "    \n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_corrected_data_df = pd.concat([all_corrected_data_df, combined_data_PCAencoded_df])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
-    "num_experiments_cat = pd.Categorical(all_corrected_data_df['num_experiments'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_corrected_data_df['comparison'], categories=lst_num_experiments_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_corrected_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (8773052691432)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_C = ggplot(all_corrected_data_df[all_corrected_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of experiments'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\",\n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    )\\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#1976d2']) \\\n",
-    "    + geom_point(data=all_corrected_data_df[all_corrected_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_C)\n",
-    "ggsave(plot=panel_C, filename=pca_corrected_file, dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:simulate_expression_compendia] *",
-   "language": "python",
-   "name": "conda-env-simulate_expression_compendia-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/Pseudomonas_tests/Pseudomonas_experiment_limma.ipynb b/Pseudomonas_tests/Pseudomonas_experiment_limma.ipynb
deleted file mode 100644
index 7a2acf1..0000000
--- a/Pseudomonas_tests/Pseudomonas_experiment_limma.ipynb
+++ /dev/null
@@ -1,1101 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pseudomonas experiment level analysis\n",
-    "\n",
-    "Main notebook to run experiment-level simulation experiment using *P. aeruginosa* gene expression data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "import ast\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from plotnine import (ggplot,\n",
-    "                      labs,  \n",
-    "                      geom_line, \n",
-    "                      geom_point,\n",
-    "                      geom_errorbar,\n",
-    "                      aes, \n",
-    "                      ggsave, \n",
-    "                      theme_bw,\n",
-    "                      theme,\n",
-    "                      facet_wrap,\n",
-    "                      scale_color_manual,\n",
-    "                      guides, \n",
-    "                      guide_legend,\n",
-    "                      element_blank,\n",
-    "                      element_text,\n",
-    "                      element_rect,\n",
-    "                      element_line,\n",
-    "                      coords)\n",
-    "\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(action='ignore')\n",
-    "\n",
-    "from ponyo import pipeline, utils\n",
-    "\n",
-    "from numpy.random import seed\n",
-    "randomState = 123\n",
-    "seed(randomState)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in config variables\n",
-    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
-    "config_file = os.path.abspath(os.path.join(base_dir,\n",
-    "                                           \"configs\", \n",
-    "                                           \"config_test_Pa_experiment_limma.tsv\"))\n",
-    "params = utils.read_config(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load parameters\n",
-    "local_dir = params[\"local_dir\"]\n",
-    "dataset_name = params['dataset_name']\n",
-    "analysis_name = params[\"simulation_type\"]\n",
-    "correction_method = params[\"correction_method\"]\n",
-    "lst_num_partitions = params[\"lst_num_partitions\"]\n",
-    "train_architecture = params['NN_architecture']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input files\n",
-    "normalized_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"train_set_normalized_test.tsv\")\n",
-    "\n",
-    "metadata_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"metadata\",\n",
-    "    \"sample_annotations.tsv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "normalized_processed_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"train_set_normalized_processed_test.txt.xz\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup directories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.setup_dir(config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Process data\n",
-    "This pipeline is expecting data to be of the form sample x gene. The downloaded data is gene x sample."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.transpose_data(normalized_data_file,\n",
-    "                       normalized_processed_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pre-process data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output file\n",
-    "experiment_id_file = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"metadata\", \n",
-    "    \"experiment_ids.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 135 experiments in the compendium\n",
-      "There are 5 experiments with gene expression data\n",
-      "5 experiment ids saved to file\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipeline.create_experiment_id_file(metadata_file,\n",
-    "                                      normalized_processed_data_file,\n",
-    "                                      experiment_id_file,\n",
-    "                                      config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train VAE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Directory containing log information from VAE training\n",
-    "vae_log_dir = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"logs\",\n",
-    "    train_architecture)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input dataset contains 11 samples and 5549 genes\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 10 samples, validate on 1 samples\n",
-      "Epoch 1/5\n",
-      "10/10 [==============================] - 1s 117ms/step - loss: 3858.4126 - val_loss: 3735.3594\n",
-      "Epoch 2/5\n",
-      "10/10 [==============================] - 1s 58ms/step - loss: 3718.5757 - val_loss: 3637.9312\n",
-      "Epoch 3/5\n",
-      "10/10 [==============================] - 1s 61ms/step - loss: 3609.0437 - val_loss: 3538.1511\n",
-      "Epoch 4/5\n",
-      "10/10 [==============================] - 1s 54ms/step - loss: 3523.7488 - val_loss: 3493.7456\n",
-      "Epoch 5/5\n",
-      "10/10 [==============================] - 1s 53ms/step - loss: 3459.9624 - val_loss: 3830.6794\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train VAE\n",
-    "pipeline.train_vae(config_file,\n",
-    "                   normalized_processed_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation experiment without noise correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   36.8s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   38.1s remaining:   57.1s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   38.6s remaining:   25.7s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   41.4s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   41.4s finished\n",
-      "                         score\n",
-      "number of partitions          \n",
-      "1                     0.999980\n",
-      "5                     0.764472\n",
-      "50                    0.833831\n",
-      "                             score\n",
-      "number of partitions              \n",
-      "1                     9.271507e-07\n",
-      "5                     5.834413e-03\n",
-      "50                    1.065429e-02\n",
-      "                          ymin      ymax\n",
-      "number of partitions                    \n",
-      "1                     0.999979  0.999982\n",
-      "5                     0.753037  0.775908\n",
-      "50                    0.812949  0.854714\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "corrected=False\n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_processed_data_file,\n",
-    "                        corrected,\n",
-    "                        experiment_id_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation with correction applied"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   18.6s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   20.4s remaining:   30.5s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   21.6s remaining:   14.4s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   24.8s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   24.8s finished\n",
-      "                         score\n",
-      "number of partitions          \n",
-      "1                     0.999980\n",
-      "5                     0.917368\n",
-      "50                    0.237713\n",
-      "                             score\n",
-      "number of partitions              \n",
-      "1                     9.356547e-07\n",
-      "5                     4.332237e-03\n",
-      "50                    6.157764e-03\n",
-      "                          ymin      ymax\n",
-      "number of partitions                    \n",
-      "1                     0.999978  0.999982\n",
-      "5                     0.908877  0.925860\n",
-      "50                    0.225644  0.249782\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction\n",
-    "corrected=True\n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_processed_data_file,\n",
-    "                        corrected,\n",
-    "                        experiment_id_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Make figures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca_ind = [0,1,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# File directories\n",
-    "similarity_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "similarity_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "permuted_score_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_permuted.npy\")\n",
-    "\n",
-    "compendia_dir = os.path.join(\n",
-    "    local_dir,\n",
-    "    \"partition_simulated\",\n",
-    "    dataset_name + \"_\" + analysis_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "svcca_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".svg\")\n",
-    "\n",
-    "svcca_png_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".png\")\n",
-    "\n",
-    "pca_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_uncorrected_\"+correction_method+\".svg\")\n",
-    "\n",
-    "pca_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_corrected_\"+correction_method+\".svg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load pickled files\n",
-    "uncorrected_svcca = pd.read_pickle(similarity_uncorrected_file)\n",
-    "err_uncorrected_svcca = pd.read_pickle(ci_uncorrected_file)\n",
-    "corrected_svcca = pd.read_pickle(similarity_corrected_file)\n",
-    "err_corrected_svcca = pd.read_pickle(ci_corrected_file)\n",
-    "\n",
-    "permuted_score = np.load(permuted_score_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Concatenate error bars\n",
-    "uncorrected_svcca_err = pd.concat([uncorrected_svcca, err_uncorrected_svcca], axis=1)\n",
-    "corrected_svcca_err = pd.concat([corrected_svcca, err_corrected_svcca], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add group label\n",
-    "uncorrected_svcca_err['Group'] = 'uncorrected'\n",
-    "corrected_svcca_err['Group'] = 'corrected'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>score</th>\n",
-       "      <th>ymin</th>\n",
-       "      <th>ymax</th>\n",
-       "      <th>Group</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>number of partitions</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999980</td>\n",
-       "      <td>0.999979</td>\n",
-       "      <td>0.999982</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.764472</td>\n",
-       "      <td>0.753037</td>\n",
-       "      <td>0.775908</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.833831</td>\n",
-       "      <td>0.812949</td>\n",
-       "      <td>0.854714</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999980</td>\n",
-       "      <td>0.999978</td>\n",
-       "      <td>0.999982</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.917368</td>\n",
-       "      <td>0.908877</td>\n",
-       "      <td>0.925860</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.237713</td>\n",
-       "      <td>0.225644</td>\n",
-       "      <td>0.249782</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         score      ymin      ymax        Group\n",
-       "number of partitions                                           \n",
-       "1                     0.999980  0.999979  0.999982  uncorrected\n",
-       "5                     0.764472  0.753037  0.775908  uncorrected\n",
-       "50                    0.833831  0.812949  0.854714  uncorrected\n",
-       "1                     0.999980  0.999978  0.999982    corrected\n",
-       "5                     0.917368  0.908877  0.925860    corrected\n",
-       "50                    0.237713  0.225644  0.249782    corrected"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Concatenate dataframes\n",
-    "all_svcca = pd.concat([uncorrected_svcca_err, corrected_svcca_err])\n",
-    "all_svcca"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SVCCA "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363276744375229)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot\n",
-    "lst_num_partitions = list(all_svcca.index)\n",
-    "\n",
-    "threshold = pd.DataFrame(\n",
-    "    pd.np.tile(\n",
-    "        permuted_score,\n",
-    "        (len(lst_num_partitions), 1)),\n",
-    "    index=lst_num_partitions,\n",
-    "    columns=['score'])\n",
-    "\n",
-    "panel_A = ggplot(all_svcca) \\\n",
-    "    + geom_line(all_svcca,\n",
-    "                aes(x=lst_num_partitions, y='score', color='Group'),\n",
-    "                size=1.5) \\\n",
-    "    + geom_point(aes(x=lst_num_partitions, y='score'), \n",
-    "                 color ='darkgrey',\n",
-    "                size=0.5) \\\n",
-    "    + geom_errorbar(all_svcca,\n",
-    "                  aes(x=lst_num_partitions, ymin='ymin', ymax='ymax'),\n",
-    "                   color='darkgrey') \\\n",
-    "    + geom_line(threshold, \n",
-    "                aes(x=lst_num_partitions, y='score'), \n",
-    "                linetype='dashed',\n",
-    "                size=1,\n",
-    "                color=\"darkgrey\",\n",
-    "                show_legend=False) \\\n",
-    "    + labs(x = \"Number of Partitions\", \n",
-    "           y = \"Similarity score (SVCCA)\", \n",
-    "           title = \"Similarity across varying numbers of partitions\") \\\n",
-    "    + theme(\n",
-    "            plot_background=element_rect(fill=\"white\"),\n",
-    "            panel_background=element_rect(fill=\"white\"),\n",
-    "            panel_grid_major_x=element_line(color=\"lightgrey\"),\n",
-    "            panel_grid_major_y=element_line(color=\"lightgrey\"),\n",
-    "            axis_line=element_line(color=\"grey\"),\n",
-    "            legend_key=element_rect(fill='white', colour='white'),\n",
-    "            legend_title=element_text(family='sans-serif', size=15),\n",
-    "            legend_text=element_text(family='sans-serif', size=12),\n",
-    "            plot_title=element_text(family='sans-serif', size=15),\n",
-    "            axis_text=element_text(family='sans-serif', size=12),\n",
-    "            axis_title=element_text(family='sans-serif', size=15)\n",
-    "           ) \\\n",
-    "    + scale_color_manual(['#1976d2', '#b3e5fc']) \\\n",
-    "\n",
-    "\n",
-    "print(panel_A)\n",
-    "ggsave(plot=panel_A, filename=svcca_file, device=\"svg\", dpi=300)\n",
-    "ggsave(plot=panel_A, filename=svcca_png_file, device=\"svg\", dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Uncorrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting PCA of 1 parition vs 1 partition...\n",
-      "[0.55408816 0.08039057]\n",
-      "Plotting PCA of 1 parition vs 5 partition...\n",
-      "[0.40020729 0.08856502]\n",
-      "Plotting PCA of 1 parition vs 50 partition...\n",
-      "[0.39292195 0.0605442 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_partitions = [lst_num_partitions[i] for i in pca_ind]\n",
-    "\n",
-    "all_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "partition_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Partition_1_0.txt.xz\")\n",
-    "\n",
-    "partition_1 = pd.read_table(\n",
-    "    partition_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "\n",
-    "for i in lst_num_partitions:\n",
-    "    print('Plotting PCA of 1 parition vs {} partition...'.format(i))\n",
-    "    \n",
-    "    # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  partition_1.copy()\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_partitions'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added\n",
-    "    partition_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Partition_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    partition_other = pd.read_table(\n",
-    "        partition_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    \n",
-    "    # Simulated data with i batch effects\n",
-    "    partition_data_df =  partition_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    partition_data_df['num_partitions'] = 'multiple'\n",
-    "    \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, partition_data_df])\n",
-    "\n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space\n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_partitions'], axis=1)\n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "\n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "                                              \n",
-    "    # Variance explained\n",
-    "    print(pca.explained_variance_ratio_)  \n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_partitions'] = combined_data_df['num_partitions']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_data_df = pd.concat([all_data_df, combined_data_PCAencoded_df])     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_partitions_str = [str(i) for i in lst_num_partitions]\n",
-    "num_partitions_cat = pd.Categorical(all_data_df['num_partitions'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_data_df['comparison'], categories=lst_num_partitions_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_data_df = all_data_df.assign(num_partitions_cat = num_partitions_cat)\n",
-    "all_data_df = all_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data_df.columns = ['PC1', 'PC2', 'num_partitions', 'comparison', 'No. of partitions', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (8760095626020)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_B = ggplot(all_data_df[all_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of partitions'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of partition 1 vs multiple partitions\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#b3e5fc']) \\\n",
-    "    + geom_point(data=all_data_df[all_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_B)\n",
-    "ggsave(plot=panel_B, filename=pca_uncorrected_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Corrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Plotting PCA of 1 partition vs 1 partitions...\n",
-      "Plotting PCA of 1 partition vs 5 partitions...\n",
-      "Plotting PCA of 1 partition vs 50 partitions...\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_partitions = [lst_num_partitions[i] for i in pca_ind]\n",
-    "\n",
-    "all_corrected_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "partition_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Partition_corrected_1_0.txt.xz\")\n",
-    "\n",
-    "partition_1 = pd.read_table(\n",
-    "    partition_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "# Transpose data to df: sample x gene\n",
-    "partition_1 = partition_1.T\n",
-    "\n",
-    "for i in lst_num_partitions:\n",
-    "    print('Plotting PCA of 1 partition vs {} partitions...'.format(i))\n",
-    "    \n",
-    "     # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  partition_1.copy()\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    original_data_df.columns = original_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_partitions'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added and corrected\n",
-    "    partition_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Partition_corrected_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    partition_other = pd.read_table(\n",
-    "        partition_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    \n",
-    "    # Transpose data to df: sample x gene\n",
-    "    partition_other = partition_other.T\n",
-    "    \n",
-    "    # Simulated data with i batch effects that are corrected\n",
-    "    partition_data_df =  partition_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    partition_data_df['num_partitions'] = 'multiple'\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    partition_data_df.columns = original_data_df.columns.astype(str)\n",
-    "        \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, partition_data_df])\n",
-    "    \n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space    \n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_partitions'], axis=1)    \n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "    \n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_partitions'] = combined_data_df['num_partitions']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_corrected_data_df = pd.concat([all_corrected_data_df, combined_data_PCAencoded_df])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_partitions_str = [str(i) for i in lst_num_partitions]\n",
-    "num_partitions_cat = pd.Categorical(all_corrected_data_df['num_partitions'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_corrected_data_df['comparison'], categories=lst_num_partitions_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(num_partitions_cat = num_partitions_cat)\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_corrected_data_df.columns = ['PC1', 'PC2', 'num_partitions', 'comparison', 'No. of partitions', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363276759350100)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_C = ggplot(all_corrected_data_df[all_corrected_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', \n",
-    "                     y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of partitions'), \n",
-    "                 alpha=0.1) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of partition 1 vs multiple partitions\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#1976d2']) \\\n",
-    "    + geom_point(data=all_corrected_data_df[all_corrected_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_C)\n",
-    "ggsave(plot=panel_C, filename=pca_corrected_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:simulate_expression_compendia] *",
-   "language": "python",
-   "name": "conda-env-simulate_expression_compendia-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Pseudomonas_tests/Pseudomonas_sample_combat.ipynb b/Pseudomonas_tests/Pseudomonas_sample_combat.ipynb
deleted file mode 100644
index f7806e9..0000000
--- a/Pseudomonas_tests/Pseudomonas_sample_combat.ipynb
+++ /dev/null
@@ -1,1059 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pseudomonas sample level analysis\n",
-    "\n",
-    "Main notebook to run sample-level simulation experiment using *P. aeruginosa* gene expression data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "import ast\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from plotnine import (ggplot,\n",
-    "                      labs,  \n",
-    "                      geom_line, \n",
-    "                      geom_point,\n",
-    "                      geom_errorbar,\n",
-    "                      aes, \n",
-    "                      ggsave, \n",
-    "                      theme_bw,\n",
-    "                      theme,\n",
-    "                      facet_wrap,\n",
-    "                      scale_color_manual,\n",
-    "                      guides, \n",
-    "                      guide_legend,\n",
-    "                      element_blank,\n",
-    "                      element_text,\n",
-    "                      element_rect,\n",
-    "                      element_line,\n",
-    "                      coords)\n",
-    "\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(action='ignore')\n",
-    "\n",
-    "from ponyo import pipeline, utils\n",
-    "\n",
-    "from numpy.random import seed\n",
-    "randomState = 123\n",
-    "seed(randomState)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in config variables\n",
-    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
-    "config_file = os.path.abspath(os.path.join(base_dir,\n",
-    "                                           \"configs\", \n",
-    "                                           \"config_test_Pa_sample_combat.tsv\"))\n",
-    "params = utils.read_config(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load parameters\n",
-    "local_dir = params[\"local_dir\"]\n",
-    "dataset_name = params['dataset_name']\n",
-    "analysis_name = params[\"simulation_type\"]\n",
-    "correction_method = params[\"correction_method\"]\n",
-    "lst_num_experiments = params[\"lst_num_experiments\"]\n",
-    "train_architecture = params['NN_architecture']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input files\n",
-    "normalized_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"train_set_normalized_test.tsv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "normalized_processed_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"train_set_normalized_processed.txt.xz\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup directories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.setup_dir(config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Process data\n",
-    "This pipeline is expecting data to be of the form sample x gene. The downloaded data is gene x sample."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.transpose_data(normalized_data_file,\n",
-    "                       normalized_processed_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train VAE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Directory containing log information from VAE training\n",
-    "vae_log_dir = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"logs\",\n",
-    "    train_architecture)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input dataset contains 11 samples and 5549 genes\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 10 samples, validate on 1 samples\n",
-      "Epoch 1/5\n",
-      "10/10 [==============================] - 1s 90ms/step - loss: 3858.4126 - val_loss: 3735.3594\n",
-      "Epoch 2/5\n",
-      "10/10 [==============================] - 0s 47ms/step - loss: 3718.5757 - val_loss: 3637.9312\n",
-      "Epoch 3/5\n",
-      "10/10 [==============================] - 0s 47ms/step - loss: 3609.0437 - val_loss: 3538.1511\n",
-      "Epoch 4/5\n",
-      "10/10 [==============================] - 0s 47ms/step - loss: 3523.7488 - val_loss: 3493.7456\n",
-      "Epoch 5/5\n",
-      "10/10 [==============================] - 0s 47ms/step - loss: 3459.9624 - val_loss: 3830.6794\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train VAE\n",
-    "pipeline.train_vae(config_file,\n",
-    "                   normalized_processed_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation experiment without noise correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   13.4s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   13.7s remaining:   20.5s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   13.7s remaining:    9.1s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   13.9s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   13.9s finished\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.999973\n",
-      "5                      0.717818\n",
-      "40                     0.434442\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.000002\n",
-      "5                      0.002665\n",
-      "40                     0.009117\n",
-      "                           ymin      ymax\n",
-      "number of experiments                    \n",
-      "1                      0.999969  0.999976\n",
-      "5                      0.712595  0.723040\n",
-      "40                     0.416572  0.452312\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_processed_data_file,\n",
-    "                        corrected=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation with correction applied"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    9.1s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    9.2s remaining:   13.8s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:    9.3s remaining:    6.2s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    9.5s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    9.5s finished\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.999972\n",
-      "5                      0.922321\n",
-      "40                     0.395008\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.000002\n",
-      "5                      0.006683\n",
-      "40                     0.009118\n",
-      "                           ymin      ymax\n",
-      "number of experiments                    \n",
-      "1                      0.999969  0.999976\n",
-      "5                      0.909222  0.935419\n",
-      "40                     0.377137  0.412879\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_processed_data_file,\n",
-    "                        corrected=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Make figures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca_ind = [0,1,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# File directories\n",
-    "similarity_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "similarity_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "permuted_score_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_permuted.npy\")\n",
-    "\n",
-    "compendia_dir = os.path.join(\n",
-    "    local_dir,\n",
-    "    \"experiment_simulated\",\n",
-    "    dataset_name + \"_\" + analysis_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "svcca_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".svg\")\n",
-    "\n",
-    "svcca_png_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".png\")\n",
-    "\n",
-    "pca_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_uncorrected_\"+correction_method+\".svg\")\n",
-    "\n",
-    "pca_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_corrected_\"+correction_method+\".svg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load pickled files\n",
-    "uncorrected_svcca = pd.read_pickle(similarity_uncorrected_file)\n",
-    "err_uncorrected_svcca = pd.read_pickle(ci_uncorrected_file)\n",
-    "corrected_svcca = pd.read_pickle(similarity_corrected_file)\n",
-    "err_corrected_svcca = pd.read_pickle(ci_corrected_file)\n",
-    "\n",
-    "permuted_score = np.load(permuted_score_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Concatenate error bars\n",
-    "uncorrected_svcca_err = pd.concat([uncorrected_svcca, err_uncorrected_svcca], axis=1)\n",
-    "corrected_svcca_err = pd.concat([corrected_svcca, err_corrected_svcca], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add group label\n",
-    "uncorrected_svcca_err['Group'] = 'uncorrected'\n",
-    "corrected_svcca_err['Group'] = 'corrected'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>score</th>\n",
-       "      <th>ymin</th>\n",
-       "      <th>ymax</th>\n",
-       "      <th>Group</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>number of experiments</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999973</td>\n",
-       "      <td>0.999969</td>\n",
-       "      <td>0.999976</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.717818</td>\n",
-       "      <td>0.712595</td>\n",
-       "      <td>0.723040</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.434442</td>\n",
-       "      <td>0.416572</td>\n",
-       "      <td>0.452312</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999972</td>\n",
-       "      <td>0.999969</td>\n",
-       "      <td>0.999976</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.922321</td>\n",
-       "      <td>0.909222</td>\n",
-       "      <td>0.935419</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.395008</td>\n",
-       "      <td>0.377137</td>\n",
-       "      <td>0.412879</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                          score      ymin      ymax        Group\n",
-       "number of experiments                                           \n",
-       "1                      0.999973  0.999969  0.999976  uncorrected\n",
-       "5                      0.717818  0.712595  0.723040  uncorrected\n",
-       "40                     0.434442  0.416572  0.452312  uncorrected\n",
-       "1                      0.999972  0.999969  0.999976    corrected\n",
-       "5                      0.922321  0.909222  0.935419    corrected\n",
-       "40                     0.395008  0.377137  0.412879    corrected"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Concatenate dataframes\n",
-    "all_svcca = pd.concat([uncorrected_svcca_err, corrected_svcca_err])\n",
-    "all_svcca"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SVCCA "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (8780146757163)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot\n",
-    "lst_num_partitions = list(all_svcca.index)\n",
-    "\n",
-    "threshold = pd.DataFrame(\n",
-    "    pd.np.tile(\n",
-    "        permuted_score,\n",
-    "        (len(lst_num_partitions), 1)),\n",
-    "    index=lst_num_partitions,\n",
-    "    columns=['score'])\n",
-    "\n",
-    "panel_A = ggplot(all_svcca) \\\n",
-    "    + geom_line(all_svcca,\n",
-    "                aes(x=lst_num_partitions, y='score', color='Group'),\n",
-    "                size=1.5) \\\n",
-    "    + geom_point(aes(x=lst_num_partitions, y='score'), \n",
-    "                 color ='darkgrey',\n",
-    "                size=0.5) \\\n",
-    "    + geom_errorbar(all_svcca,\n",
-    "                  aes(x=lst_num_partitions, ymin='ymin', ymax='ymax'),\n",
-    "                   color='darkgrey') \\\n",
-    "    + geom_line(threshold, \n",
-    "                aes(x=lst_num_partitions, y='score'), \n",
-    "                linetype='dashed',\n",
-    "                size=1,\n",
-    "                color=\"darkgrey\",\n",
-    "                show_legend=False) \\\n",
-    "    + labs(x = \"Number of Partitions\", \n",
-    "           y = \"Similarity score (SVCCA)\", \n",
-    "           title = \"Similarity across varying numbers of partitions\") \\\n",
-    "    + theme(\n",
-    "            plot_background=element_rect(fill=\"white\"),\n",
-    "            panel_background=element_rect(fill=\"white\"),\n",
-    "            panel_grid_major_x=element_line(color=\"lightgrey\"),\n",
-    "            panel_grid_major_y=element_line(color=\"lightgrey\"),\n",
-    "            axis_line=element_line(color=\"grey\"),\n",
-    "            legend_key=element_rect(fill='white', colour='white'),\n",
-    "            legend_title=element_text(family='sans-serif', size=15),\n",
-    "            legend_text=element_text(family='sans-serif', size=12),\n",
-    "            plot_title=element_text(family='sans-serif', size=15),\n",
-    "            axis_text=element_text(family='sans-serif', size=12),\n",
-    "            axis_title=element_text(family='sans-serif', size=15)\n",
-    "           ) \\\n",
-    "    + scale_color_manual(['#1976d2', '#b3e5fc']) \\\n",
-    "\n",
-    "\n",
-    "print(panel_A)\n",
-    "ggsave(plot=panel_A, filename=svcca_file, device=\"svg\", dpi=300)\n",
-    "ggsave(plot=panel_A, filename=svcca_png_file, device=\"svg\", dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Uncorrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(50, 5549)\n",
-      "Plotting PCA of 1 experiment vs 1 experiments...\n",
-      "(50, 5549)\n",
-      "[0.51984538 0.10192906]\n",
-      "Plotting PCA of 1 experiment vs 5 experiments...\n",
-      "(50, 5549)\n",
-      "[0.21325085 0.21000224]\n",
-      "Plotting PCA of 1 experiment vs 40 experiments...\n",
-      "(50, 5549)\n",
-      "[0.04986153 0.040133  ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
-    "\n",
-    "all_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "experiment_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Experiment_1_0.txt.xz\")\n",
-    "\n",
-    "experiment_1 = pd.read_table(\n",
-    "    experiment_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "print(experiment_1.shape)\n",
-    "\n",
-    "\n",
-    "for i in lst_num_experiments:\n",
-    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
-    "    \n",
-    "    # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  experiment_1.copy()\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_experiments'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added\n",
-    "    experiment_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Experiment_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    experiment_other = pd.read_table(\n",
-    "        experiment_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    print(experiment_other.shape)\n",
-    "    # Simulated data with i batch effects\n",
-    "    experiment_data_df =  experiment_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    experiment_data_df['num_experiments'] = 'multiple'\n",
-    "    \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
-    "\n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space\n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)\n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "\n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "                                              \n",
-    "    # Variance explained\n",
-    "    print(pca.explained_variance_ratio_)  \n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_data_df = pd.concat([all_data_df, combined_data_PCAencoded_df])   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
-    "num_experiments_cat = pd.Categorical(all_data_df['num_experiments'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_data_df['comparison'], categories=lst_num_experiments_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_data_df = all_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
-    "all_data_df = all_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363256708149318)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_B = ggplot(all_data_df[all_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of experiments'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_title=element_text(family='sans-serif', size=15),\n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#b3e5fc']) \\\n",
-    "    + geom_point(data=all_data_df[all_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_B)\n",
-    "ggsave(plot=panel_B, filename=pca_uncorrected_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Corrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5549, 50)\n",
-      "Plotting PCA of 1 experiment vs 1 experiments...\n",
-      "(5549, 50)\n",
-      "Plotting PCA of 1 experiment vs 5 experiments...\n",
-      "(5549, 50)\n",
-      "Plotting PCA of 1 experiment vs 40 experiments...\n",
-      "(5549, 50)\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
-    "\n",
-    "all_corrected_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "experiment_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Experiment_corrected_1_0.txt.xz\")\n",
-    "\n",
-    "experiment_1 = pd.read_table(\n",
-    "    experiment_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "print(experiment_1.shape)\n",
-    "\n",
-    "# Transpose data to df: sample x gene\n",
-    "experiment_1 = experiment_1.T\n",
-    "\n",
-    "for i in lst_num_experiments:\n",
-    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
-    "    \n",
-    "     # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  experiment_1.copy()\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    original_data_df.columns = original_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_experiments'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added and corrected\n",
-    "    experiment_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Experiment_corrected_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    experiment_other = pd.read_table(\n",
-    "        experiment_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    \n",
-    "    print(experiment_other.shape)\n",
-    "    \n",
-    "    # Transpose data to df: sample x gene\n",
-    "    experiment_other = experiment_other.T\n",
-    "    \n",
-    "    # Simulated data with i batch effects that are corrected\n",
-    "    experiment_data_df =  experiment_other\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    experiment_data_df.columns = experiment_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    experiment_data_df['num_experiments'] = 'multiple'\n",
-    "        \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
-    "    \n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space    \n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)    \n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "    \n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_corrected_data_df = pd.concat([all_corrected_data_df, combined_data_PCAencoded_df])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
-    "num_experiments_cat = pd.Categorical(all_corrected_data_df['num_experiments'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_corrected_data_df['comparison'], categories=lst_num_experiments_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_corrected_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363256708397273)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_C = ggplot(all_corrected_data_df[all_corrected_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of experiments'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\",\n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_title=element_text(family='sans-serif', size=15),\n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    )\\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#1976d2']) \\\n",
-    "    + geom_point(data=all_corrected_data_df[all_corrected_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_C)\n",
-    "ggsave(plot=panel_C, filename=pca_corrected_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:simulate_expression_compendia] *",
-   "language": "python",
-   "name": "conda-env-simulate_expression_compendia-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Pseudomonas_tests/Pseudomonas_sample_limma.ipynb b/Pseudomonas_tests/Pseudomonas_sample_limma.ipynb
deleted file mode 100644
index f883eee..0000000
--- a/Pseudomonas_tests/Pseudomonas_sample_limma.ipynb
+++ /dev/null
@@ -1,1059 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pseudomonas sample level analysis\n",
-    "\n",
-    "Main notebook to run sample-level simulation experiment using *P. aeruginosa* gene expression data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "import ast\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from plotnine import (ggplot,\n",
-    "                      labs,  \n",
-    "                      geom_line, \n",
-    "                      geom_point,\n",
-    "                      geom_errorbar,\n",
-    "                      aes, \n",
-    "                      ggsave, \n",
-    "                      theme_bw,\n",
-    "                      theme,\n",
-    "                      facet_wrap,\n",
-    "                      scale_color_manual,\n",
-    "                      guides, \n",
-    "                      guide_legend,\n",
-    "                      element_blank,\n",
-    "                      element_text,\n",
-    "                      element_rect,\n",
-    "                      element_line,\n",
-    "                      coords)\n",
-    "\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(action='ignore')\n",
-    "\n",
-    "from ponyo import pipeline, utils\n",
-    "\n",
-    "from numpy.random import seed\n",
-    "randomState = 123\n",
-    "seed(randomState)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read in config variables\n",
-    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
-    "config_file = os.path.abspath(os.path.join(base_dir,\n",
-    "                                           \"configs\", \n",
-    "                                           \"config_test_Pa_sample_limma.tsv\"))\n",
-    "params = utils.read_config(config_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load parameters\n",
-    "local_dir = params[\"local_dir\"]\n",
-    "dataset_name = params['dataset_name']\n",
-    "analysis_name = params[\"simulation_type\"]\n",
-    "correction_method = params[\"correction_method\"]\n",
-    "lst_num_experiments = params[\"lst_num_experiments\"]\n",
-    "train_architecture = params['NN_architecture']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input files\n",
-    "normalized_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"train_set_normalized_test.tsv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "normalized_processed_data_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"data\",\n",
-    "    \"input\",\n",
-    "    \"train_set_normalized_processed.txt.xz\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup directories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.setup_dir(config_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Process data\n",
-    "This pipeline is expecting data to be of the form sample x gene. The downloaded data is gene x sample."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipeline.transpose_data(normalized_data_file,\n",
-    "                       normalized_processed_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train VAE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Directory containing log information from VAE training\n",
-    "vae_log_dir = os.path.join(\n",
-    "    base_dir, \n",
-    "    dataset_name,\n",
-    "    \"logs\",\n",
-    "    train_architecture)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input dataset contains 11 samples and 5549 genes\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
-      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 10 samples, validate on 1 samples\n",
-      "Epoch 1/5\n",
-      "10/10 [==============================] - 1s 96ms/step - loss: 3858.4126 - val_loss: 3735.3594\n",
-      "Epoch 2/5\n",
-      "10/10 [==============================] - 0s 49ms/step - loss: 3718.5757 - val_loss: 3637.9312\n",
-      "Epoch 3/5\n",
-      "10/10 [==============================] - 0s 49ms/step - loss: 3609.0437 - val_loss: 3538.1511\n",
-      "Epoch 4/5\n",
-      "10/10 [==============================] - 0s 49ms/step - loss: 3523.7488 - val_loss: 3493.7456\n",
-      "Epoch 5/5\n",
-      "10/10 [==============================] - 0s 48ms/step - loss: 3459.9624 - val_loss: 3830.6794\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train VAE\n",
-    "pipeline.train_vae(config_file,\n",
-    "                   normalized_processed_data_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation experiment without noise correction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   17.8s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   18.6s remaining:   27.8s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   18.8s remaining:   12.5s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   20.5s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   20.5s finished\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.999973\n",
-      "5                      0.709038\n",
-      "50                     0.562674\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.000002\n",
-      "5                      0.004112\n",
-      "50                     0.007102\n",
-      "                           ymin      ymax\n",
-      "number of experiments                    \n",
-      "1                      0.999969  0.999977\n",
-      "5                      0.700978  0.717098\n",
-      "50                     0.548753  0.576595\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_processed_data_file,\n",
-    "                        corrected=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run simulation with correction applied"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
-      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   13.4s\n",
-      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   14.0s remaining:   20.9s\n",
-      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   15.9s remaining:   10.6s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   16.8s remaining:    0.0s\n",
-      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   16.8s finished\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.999970\n",
-      "5                      0.947964\n",
-      "50                     0.391582\n",
-      "                          score\n",
-      "number of experiments          \n",
-      "1                      0.000004\n",
-      "5                      0.003015\n",
-      "50                     0.008496\n",
-      "                           ymin      ymax\n",
-      "number of experiments                    \n",
-      "1                      0.999963  0.999978\n",
-      "5                      0.942055  0.953872\n",
-      "50                     0.374930  0.408235\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run simulation without correction \n",
-    "pipeline.run_simulation(config_file,\n",
-    "                        normalized_processed_data_file,\n",
-    "                        corrected=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Make figures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca_ind = [0,1,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# File directories\n",
-    "similarity_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_uncorrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "similarity_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_svcca_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "ci_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_ci_corrected_\" + correction_method + \".pickle\")\n",
-    "\n",
-    "permuted_score_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    \"saved_variables\",\n",
-    "    dataset_name + \"_\" + analysis_name + \"_permuted.npy\")\n",
-    "\n",
-    "compendia_dir = os.path.join(\n",
-    "    local_dir,\n",
-    "    \"experiment_simulated\",\n",
-    "    dataset_name + \"_\" + analysis_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Output files\n",
-    "svcca_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".svg\")\n",
-    "\n",
-    "svcca_png_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".png\")\n",
-    "\n",
-    "pca_uncorrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_uncorrected_\"+correction_method+\".svg\")\n",
-    "\n",
-    "pca_corrected_file = os.path.join(\n",
-    "    base_dir,\n",
-    "    dataset_name,\n",
-    "    \"results\",\n",
-    "    dataset_name +\"_\"+analysis_name+\"_pca_corrected_\"+correction_method+\".svg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load pickled files\n",
-    "uncorrected_svcca = pd.read_pickle(similarity_uncorrected_file)\n",
-    "err_uncorrected_svcca = pd.read_pickle(ci_uncorrected_file)\n",
-    "corrected_svcca = pd.read_pickle(similarity_corrected_file)\n",
-    "err_corrected_svcca = pd.read_pickle(ci_corrected_file)\n",
-    "\n",
-    "permuted_score = np.load(permuted_score_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Concatenate error bars\n",
-    "uncorrected_svcca_err = pd.concat([uncorrected_svcca, err_uncorrected_svcca], axis=1)\n",
-    "corrected_svcca_err = pd.concat([corrected_svcca, err_corrected_svcca], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add group label\n",
-    "uncorrected_svcca_err['Group'] = 'uncorrected'\n",
-    "corrected_svcca_err['Group'] = 'corrected'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>score</th>\n",
-       "      <th>ymin</th>\n",
-       "      <th>ymax</th>\n",
-       "      <th>Group</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>number of experiments</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999973</td>\n",
-       "      <td>0.999969</td>\n",
-       "      <td>0.999977</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.709038</td>\n",
-       "      <td>0.700978</td>\n",
-       "      <td>0.717098</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.562674</td>\n",
-       "      <td>0.548753</td>\n",
-       "      <td>0.576595</td>\n",
-       "      <td>uncorrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.999970</td>\n",
-       "      <td>0.999963</td>\n",
-       "      <td>0.999978</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.947964</td>\n",
-       "      <td>0.942055</td>\n",
-       "      <td>0.953872</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.391582</td>\n",
-       "      <td>0.374930</td>\n",
-       "      <td>0.408235</td>\n",
-       "      <td>corrected</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                          score      ymin      ymax        Group\n",
-       "number of experiments                                           \n",
-       "1                      0.999973  0.999969  0.999977  uncorrected\n",
-       "5                      0.709038  0.700978  0.717098  uncorrected\n",
-       "50                     0.562674  0.548753  0.576595  uncorrected\n",
-       "1                      0.999970  0.999963  0.999978    corrected\n",
-       "5                      0.947964  0.942055  0.953872    corrected\n",
-       "50                     0.391582  0.374930  0.408235    corrected"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Concatenate dataframes\n",
-    "all_svcca = pd.concat([uncorrected_svcca_err, corrected_svcca_err])\n",
-    "all_svcca"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SVCCA "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363257047658012)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot\n",
-    "lst_num_partitions = list(all_svcca.index)\n",
-    "\n",
-    "threshold = pd.DataFrame(\n",
-    "    pd.np.tile(\n",
-    "        permuted_score,\n",
-    "        (len(lst_num_partitions), 1)),\n",
-    "    index=lst_num_partitions,\n",
-    "    columns=['score'])\n",
-    "\n",
-    "panel_A = ggplot(all_svcca) \\\n",
-    "    + geom_line(all_svcca,\n",
-    "                aes(x=lst_num_partitions, y='score', color='Group'),\n",
-    "                size=1.5) \\\n",
-    "    + geom_point(aes(x=lst_num_partitions, y='score'), \n",
-    "                 color ='darkgrey',\n",
-    "                size=0.5) \\\n",
-    "    + geom_errorbar(all_svcca,\n",
-    "                  aes(x=lst_num_partitions, ymin='ymin', ymax='ymax'),\n",
-    "                   color='darkgrey') \\\n",
-    "    + geom_line(threshold, \n",
-    "                aes(x=lst_num_partitions, y='score'), \n",
-    "                linetype='dashed',\n",
-    "                size=1,\n",
-    "                color=\"darkgrey\",\n",
-    "                show_legend=False) \\\n",
-    "    + labs(x = \"Number of Partitions\", \n",
-    "           y = \"Similarity score (SVCCA)\", \n",
-    "           title = \"Similarity across varying numbers of partitions\") \\\n",
-    "    + theme(\n",
-    "            plot_background=element_rect(fill=\"white\"),\n",
-    "            panel_background=element_rect(fill=\"white\"),\n",
-    "            panel_grid_major_x=element_line(color=\"lightgrey\"),\n",
-    "            panel_grid_major_y=element_line(color=\"lightgrey\"),\n",
-    "            axis_line=element_line(color=\"grey\"),\n",
-    "            legend_key=element_rect(fill='white', colour='white'),\n",
-    "            legend_title=element_text(family='sans-serif', size=15),\n",
-    "            legend_text=element_text(family='sans-serif', size=12),\n",
-    "            plot_title=element_text(family='sans-serif', size=15),\n",
-    "            axis_text=element_text(family='sans-serif', size=12),\n",
-    "            axis_title=element_text(family='sans-serif', size=15)\n",
-    "           ) \\\n",
-    "    + scale_color_manual(['#1976d2', '#b3e5fc']) \\\n",
-    "\n",
-    "\n",
-    "print(panel_A)\n",
-    "ggsave(plot=panel_A, filename=svcca_file, device=\"svg\", dpi=300)\n",
-    "ggsave(plot=panel_A, filename=svcca_png_file, device=\"svg\", dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Uncorrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(50, 5549)\n",
-      "Plotting PCA of 1 experiment vs 1 experiments...\n",
-      "(50, 5549)\n",
-      "[0.59082602 0.10040417]\n",
-      "Plotting PCA of 1 experiment vs 5 experiments...\n",
-      "(50, 5549)\n",
-      "[0.21191965 0.20670454]\n",
-      "Plotting PCA of 1 experiment vs 50 experiments...\n",
-      "(50, 5549)\n",
-      "[0.05665387 0.02453082]\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
-    "\n",
-    "all_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "experiment_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Experiment_1_0.txt.xz\")\n",
-    "\n",
-    "experiment_1 = pd.read_table(\n",
-    "    experiment_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "print(experiment_1.shape)\n",
-    "\n",
-    "\n",
-    "for i in lst_num_experiments:\n",
-    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
-    "    \n",
-    "    # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  experiment_1.copy()\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_experiments'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added\n",
-    "    experiment_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Experiment_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    experiment_other = pd.read_table(\n",
-    "        experiment_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    print(experiment_other.shape)\n",
-    "    # Simulated data with i batch effects\n",
-    "    experiment_data_df =  experiment_other\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    experiment_data_df['num_experiments'] = 'multiple'\n",
-    "    \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
-    "\n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space\n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)\n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "\n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "                                              \n",
-    "    # Variance explained\n",
-    "    print(pca.explained_variance_ratio_)  \n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_data_df = pd.concat([all_data_df, combined_data_PCAencoded_df])   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
-    "num_experiments_cat = pd.Categorical(all_data_df['num_experiments'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_data_df['comparison'], categories=lst_num_experiments_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_data_df = all_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
-    "all_data_df = all_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (8779806817607)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_B = ggplot(all_data_df[all_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of experiments'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\", \n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_title=element_text(family='sans-serif', size=15),\n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    ) \\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#b3e5fc']) \\\n",
-    "    + geom_point(data=all_data_df[all_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_B)\n",
-    "ggsave(plot=panel_B, filename=pca_uncorrected_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Corrected PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5549, 50)\n",
-      "Plotting PCA of 1 experiment vs 1 experiments...\n",
-      "(5549, 50)\n",
-      "Plotting PCA of 1 experiment vs 5 experiments...\n",
-      "(5549, 50)\n",
-      "Plotting PCA of 1 experiment vs 50 experiments...\n",
-      "(5549, 50)\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
-    "\n",
-    "all_corrected_data_df = pd.DataFrame()\n",
-    "\n",
-    "# Get batch 1 data\n",
-    "experiment_1_file = os.path.join(\n",
-    "    compendia_dir,\n",
-    "    \"Experiment_corrected_1_0.txt.xz\")\n",
-    "\n",
-    "experiment_1 = pd.read_table(\n",
-    "    experiment_1_file,\n",
-    "    header=0,\n",
-    "    index_col=0,\n",
-    "    sep='\\t')\n",
-    "\n",
-    "print(experiment_1.shape)\n",
-    "\n",
-    "# Transpose data to df: sample x gene\n",
-    "experiment_1 = experiment_1.T\n",
-    "\n",
-    "for i in lst_num_experiments:\n",
-    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
-    "    \n",
-    "     # Simulated data with all samples in a single batch\n",
-    "    original_data_df =  experiment_1.copy()\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    original_data_df.columns = original_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    original_data_df['num_experiments'] = '1'\n",
-    "    \n",
-    "    # Get data with additional batch effects added and corrected\n",
-    "    experiment_other_file = os.path.join(\n",
-    "        compendia_dir,\n",
-    "        \"Experiment_corrected_\"+str(i)+\"_0.txt.xz\")\n",
-    "\n",
-    "    experiment_other = pd.read_table(\n",
-    "        experiment_other_file,\n",
-    "        header=0,\n",
-    "        index_col=0,\n",
-    "        sep='\\t')\n",
-    "    \n",
-    "    print(experiment_other.shape)\n",
-    "    \n",
-    "    # Transpose data to df: sample x gene\n",
-    "    experiment_other = experiment_other.T\n",
-    "    \n",
-    "    # Simulated data with i batch effects that are corrected\n",
-    "    experiment_data_df =  experiment_other\n",
-    "    \n",
-    "    # Match format of column names in before and after df\n",
-    "    experiment_data_df.columns = experiment_data_df.columns.astype(str)\n",
-    "    \n",
-    "    # Add grouping column for plotting\n",
-    "    experiment_data_df['num_experiments'] = 'multiple'\n",
-    "        \n",
-    "    # Concatenate datasets together\n",
-    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
-    "    \n",
-    "    # PCA projection\n",
-    "    pca = PCA(n_components=2)\n",
-    "\n",
-    "    # Encode expression data into 2D PCA space    \n",
-    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)    \n",
-    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
-    "\n",
-    "    \n",
-    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
-    "                                               index=combined_data_df.index,\n",
-    "                                               columns=['PC1', 'PC2']\n",
-    "                                              )\n",
-    "    \n",
-    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
-    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
-    "    \n",
-    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
-    "    # is represented by label = 5)\n",
-    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
-    "    \n",
-    "    # Concatenate ALL comparisons\n",
-    "    all_corrected_data_df = pd.concat([all_corrected_data_df, combined_data_PCAencoded_df])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert 'num_experiments' into categories to preserve the ordering\n",
-    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
-    "num_experiments_cat = pd.Categorical(all_corrected_data_df['num_experiments'], categories=['1', 'multiple'])\n",
-    "\n",
-    "# Convert 'comparison' into categories to preserve the ordering\n",
-    "comparison_cat = pd.Categorical(all_corrected_data_df['comparison'], categories=lst_num_experiments_str)\n",
-    "\n",
-    "# Assign to a new column in the df\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
-    "all_corrected_data_df = all_corrected_data_df.assign(comparison_cat = comparison_cat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_corrected_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<ggplot: (-9223363257047871470)>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Plot all comparisons in one figure\n",
-    "panel_C = ggplot(all_corrected_data_df[all_corrected_data_df['Comparison'] != '1'],\n",
-    "                 aes(x='PC1', y='PC2')) \\\n",
-    "    + geom_point(aes(color='No. of experiments'), \n",
-    "                 alpha=0.2) \\\n",
-    "    + facet_wrap('~Comparison') \\\n",
-    "    + labs(x = \"PC 1\",\n",
-    "           y = \"PC 2\", \n",
-    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
-    "    + theme_bw() \\\n",
-    "    + theme(\n",
-    "        legend_title_align = \"center\",\n",
-    "        plot_background=element_rect(fill='white'),\n",
-    "        legend_key=element_rect(fill='white', colour='white'), \n",
-    "        legend_title=element_text(family='sans-serif', size=15),\n",
-    "        legend_text=element_text(family='sans-serif', size=12),\n",
-    "        plot_title=element_text(family='sans-serif', size=15),\n",
-    "        axis_text=element_text(family='sans-serif', size=12),\n",
-    "        axis_title=element_text(family='sans-serif', size=15)\n",
-    "    )\\\n",
-    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
-    "    + scale_color_manual(['#bdbdbd', '#1976d2']) \\\n",
-    "    + geom_point(data=all_corrected_data_df[all_corrected_data_df['Comparison'] == '1'],\n",
-    "                 alpha=0.1, \n",
-    "                 color='#bdbdbd')\n",
-    "\n",
-    "print(panel_C)\n",
-    "ggsave(plot=panel_C, filename=pca_corrected_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:simulate_expression_compendia] *",
-   "language": "python",
-   "name": "conda-env-simulate_expression_compendia-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Pseudomonas_tests/data/input/train_set_normalized_test.tsv b/Pseudomonas_tests/data/input/train_set_normalized_test.tsv
deleted file mode 100644
index 965af3d..0000000
--- a/Pseudomonas_tests/data/input/train_set_normalized_test.tsv
+++ /dev/null
@@ -1,5550 +0,0 @@
-Gene_symbol	05_PA14000-4-2_5-10-07_S2.CEL	54375-4-05.CEL	AKGlu_plus_nt_7-8-09_s1.CEL	anaerobic_NO3_1.CEL	anaerobic_NO3_2.CEL	control1aerobic_Pae_G1a.CEL	control1_anaerobic_Pae_G1a.CEL	control2aerobic_Pae_G1a.CEL	control2_anaerobic_Pae_G1a.CEL	control3aerobic_Pae_G1a.CEL	control3_anaerobic_Pae_G1a.CEL
-PA0001	0.853357438	0.778789658	0.78915519	0.716320169	0.65801519	0.365511525	0.689253533	0.353000012	0.674305247	0.399186209	0.66038591
-PA0002	0.725280404	0.767872684	0.729507718	0.585078632	0.592172421	0.572967189	0.723461199	0.580625598	0.680740384	0.653977308	0.741509115
-PA0003	0.640616819	0.61485889	0.725912595	0.390210627	0.410330663	0.417762099	0.510094209	0.302544416	0.515478239	0.328754596	0.494089832
-PA0004	0.811465328	0.907865442	0.718988541	0.193247812	0.245503909	0.663095579	0.801569235	0.639623521	0.747121428	0.692894653	0.818501245
-PA0005	0.694459786	0.398800433	0.530159671	0.279456119	0.312027586	0.424846198	0.619373626	0.281793035	0.656701381	0.48248214	0.684195702
-PA0006	0.533958424	0.460849107	0.466326564	0.301780573	0.305851593	0.606589788	0.937262411	0.687342723	0.910653708	0.618445982	0.969857478
-PA0007	0.158864958	0.113876488	0.07950684	0.513546667	0.513499361	0.378900347	0.138600359	0.462644636	0.149743745	0.308135426	0.229250269
-PA0008	0.889579417	0.761350988	0.731642606	0.342051488	0.336722679	0.224257084	0.298615405	0	0.262492218	0.191401173	0.26137868
-PA0009	0.884945493	0.801740139	0.827707481	0.41566753	0.33422627	0.16158193	0.306533449	0.107960936	0.263527973	0.20116961	0.232540322
-PA0010	0.176558497	0.222709376	0.241847032	0.125914006	0.162964751	0.074461422	0.032482111	0.093162991	0.019909804	0.020374768	0.088296298
-PA0011	0.508393075	0.53610587	0.49220005	0.55087788	0.397272044	0.747978286	0.824547198	0.721733844	0.906766801	0.813768277	0.762278839
-PA0012	0.574737012	0.424370688	0.666643273	0.789358235	0.801253349	0.288118429	0.191857778	0.260258035	0.370564527	0.127692717	0.340626412
-PA0013	0.211950914	0.069501166	0.166325452	0.117096463	0.133774213	0.173339062	0.260225847	0.22402107	0.270486437	0.175170072	0.381425347
-PA0014	0.346369497	0.20730239	0.268976858	0.205795697	0.223304834	0.209706999	0.354312352	0.3544999	0.427878073	0.3551742	0.429040845
-PA0015	0.289759451	0.395063846	0.256625741	0.298559345	0.3189291	0.59924599	0.571666833	0.57661085	0.594987384	0.563701952	0.774440298
-PA0016	0.760732539	0.720094153	0.877866074	0.564832155	0.547799737	0.389525099	0.330777991	0.375679452	0.41692093	0.29714039	0.426178498
-PA0017	0.968098267	0.74778871	0.835161334	0.374777235	0.325020007	0.585709681	0.527076708	0.616812623	0.519636236	0.62876018	0.543068404
-PA0018	0.913531751	0.785984311	0.854847996	0.404156074	0.441348977	0.286578504	0.382935925	0.310681005	0.286142939	0.276813621	0.316358742
-PA0019	0.822313857	0.791657787	0.837594737	0.517857526	0.56645818	0.670157849	0.871529142	0.76742823	0.839926939	0.732636553	0.84884591
-PA0020	0.69483489	0.700445559	0.823804791	0.617106302	0.647513293	0.357632607	0.420371037	0.319133411	0.374124729	0.249592739	0.446204434
-PA0021	0.358382216	0.368386623	0.194719853	0.318760255	0.293973182	0.419163771	0.388536037	0.30033759	0.373371507	0.419442076	0.546023579
-PA0022	0.736800594	0.549842614	0.743330584	0.461914164	0.471748759	0.568764962	0.563277742	0.543532176	0.643838661	0.52606639	0.85692962
-PA0023	0.552906126	0.603189959	0.56355646	0.545560401	0.508874548	0.29667604	0.35111391	0.282723419	0.186169398	0.38134651	0.184236333
-PA0024	0.570323329	0.748181799	0.50675664	0.790391944	0.751610009	0.559969265	0.593775729	0.631901343	0.625513136	0.613396434	0.527815821
-PA0025	0.342567319	0.564246878	0.186186918	0.360287191	0.31501829	0.650262106	0.708474036	0.723393701	0.746962761	0.695786119	0.73278451
-PA0026	0.36938479	0.466329385	0.507798068	0.775932026	0.706265733	0.256078605	0.122400993	0.258767565	0.217848457	0.212732417	0.25263638
-PA0027	0.273316278	0.24360049	0.305884448	0.56405551	0.470302465	0.34358	0.239886984	0.16026996	0.322942164	0.212381032	0.204768736
-PA0028	0.270007521	0.238079121	0.37257603	0.481650322	0.484179396	0.341412044	0.222784508	0.301995421	0.293383065	0.28040622	0.242092526
-PA0029	0.242972259	0.286132336	0.188099102	0.242310531	0.1933266	0.324835094	0.485442215	0.432622083	0.445944542	0.323699567	0.398027722
-PA0030	0.229370142	0.256387831	0.24328471	0.296201324	0.283726499	0.313395987	0.317772763	0.372606428	0.320114407	0.273775237	0.407694268
-PA0031	0.252786852	0.360320227	0.211676482	0.274258561	0.338320845	0.239164504	0.330300553	0.180304103	0.240439729	0.29274175	0.276753522
-PA0032	0.420577998	0.397702434	0.356439138	0.269629581	0.269809358	0.112143718	0.264324844	0.295426091	0.258059911	0.161933664	0.366327839
-PA0033	0.464643552	0.24925093	0.515961606	0.096354162	0.173320957	0.362074527	0.306236182	0.381592326	0.502989667	0.373649943	0.483631722
-PA0034	0.767763031	0.236939616	0.704828144	0.16401737	0.126331179	0.156462568	0.223246072	0.246359396	0.272727016	0.172477477	0.238796402
-PA0035	0.807119514	0.436012128	0.892770715	0.352641605	0.284632979	0.211995483	0.212249185	0.143915002	0.237727645	0.254167254	0.218014258
-PA0036	0.745765334	0.358798293	0.870661858	0.242014241	0.248495479	0.21309074	0.119641768	0.239165356	0.145189411	0.240031101	0.185227181
-PA0037	0.423191006	0.274617023	0.41697515	0.215022464	0.167695752	0.312418051	0.337044063	0.308428537	0.287994303	0.242817023	0.405756469
-PA0038	0.228167083	0.377909447	0.350350909	0.7794098	0.796652363	0.611064496	0.369817653	0.665596315	0.403693709	0.559787143	0.379673224
-PA0039	0.592613985	0.526707962	0.601658515	0.899312849	0.909207081	0.536117353	0.64229858	0.583876184	0.604707621	0.613856373	0.643013076
-PA0040	0.608415508	0.486052028	0.4049466	0.221536042	0.217934609	0.291436631	0.214499352	0.303295318	0.30538926	0.297156031	0.24878414
-PA0041	0.369066946	0.425199241	0.245218412	0.257016057	0.209019038	0.298877757	0.32196088	0.222708667	0.325269423	0.263687828	0.366874063
-PA0042	0.533867992	0.457909312	0.580435832	0.456526626	0.456473025	0.272449359	0.384659547	0.25203745	0.454416738	0.366738879	0.477639329
-PA0043	0.30553738	0.343294976	0.258217775	0.406474603	0.410246255	0.208283709	0.097105536	0.223218694	0.143143461	0.160704571	0.166787414
-PA0044	0.168331816	0.210322467	0.537793882	0.209955438	0.172854511	0.109590199	0.062247408	0.054684899	0.02287076	0.103633899	0.03830586
-PA0045	0.87341452	0.781744622	0.504374876	0.224686438	0.185012068	0.221799387	0.283777264	0.257555334	0.280624457	0.258093922	0.279806078
-PA0046	0.828195863	0.705740309	0.569403205	0.216438105	0.227252807	0.115613819	0.045835306	0.05606301	0.119821264	0.086783315	0.117755854
-PA0047	0.805505789	0.576304141	0.393061026	0.16059391	0.107075615	0.137804214	0.111817508	0.148751106	0.135331776	0.145799924	0.122888003
-PA0048	0.135155027	0.214366182	0.126823281	0.202547369	0.211550557	0.161789928	0.206434442	0.078723164	0.117087532	0.13166289	0.278079854
-PA0049	0.187971149	0.338164039	0.143365172	0.195489828	0.19566823	0.187512915	0.173462334	0.133243319	0.188215589	0.169242481	0.154188864
-PA0050	0.082886057	0.141904917	0.08479584	0.489730052	0.408987177	0.425465252	0.328807131	0.529577576	0.416608871	0.485509853	0.390361628
-PA0051	0.238403382	0.321620777	0.125894115	0.295301055	0.31106344	0.28392541	0.326374081	0.420438666	0.38750771	0.465934528	0.292909382
-PA0052	0.180845306	0.190228437	0.107093722	0.995725559	1	0.319789395	0.130614086	0.391845807	0.117329895	0.346574681	0.16084896
-PA0053	0.278162192	0.783704109	0.255720919	0.282118403	0.30996385	0.243803061	0.337818484	0.480664869	0.244853406	0.290246398	0.335921293
-PA0054	0.75898675	0.462320601	0.813446227	0.312980163	0.347277211	0.330235092	0.259445565	0.1386991	0.349664409	0.387164136	0.278069834
-PA0055	0.724817437	0.717862427	0.812693046	0.711699537	0.706436815	0.635175449	0.642298367	0.659655929	0.627857439	0.630678446	0.584619269
-PA0056	0.360318862	0.231590243	0.515561805	0.241699941	0.287331546	0.196526405	0.306617549	0.165831282	0.177201533	0.18007657	0.19788725
-PA0057	0.161278931	0.179150705	0.177213706	0.141339462	0.086264949	0.129012861	0.133779381	0.193731344	0.197676604	0.097506734	0.147112191
-PA0058	0.288670476	0.206704543	0.327967706	0.307619977	0.30846746	0.274109252	0.148882341	0.355892795	0.188834638	0.14315649	0.230365475
-PA0059	0.10958132	0.288039473	0.342778698	0.680709517	0.739631614	0.459186017	0.285236542	0.52302831	0.326352289	0.467035255	0.338330006
-PA0060	0.50521446	0.536142221	0.646190736	0.600594145	0.674685017	0.34010342	0.26014849	0.433767232	0.313062648	0.247133045	0.20847403
-PA0061	0.182653586	0.273256055	0.298796399	0.47563549	0.47698078	0.418317452	0.572628377	0.413741299	0.747680655	0.431475995	0.476967525
-PA0062	0.134376473	0.282103157	0.441191564	0.260148827	0.304986825	0.492697344	0.463958359	0.5119487	0.48320936	0.485239696	0.471733057
-PA0063	0.699076797	0.629822469	0.679800859	0.445639516	0.493828409	0.517000191	0.679365929	0.547840848	0.633282814	0.501134617	0.574379367
-PA0064	0.601377304	0.245145674	0.393269314	0.356342643	0.371746178	0.283234061	0.261867269	0.10292307	0.282120976	0.04031015	0.202597544
-PA0065	0.674982947	0.582301081	0.78257995	0.544128794	0.541036016	0.272739894	0.285583135	0.065482044	0.382813908	0.425464217	0.219320916
-PA0066	0.908246629	0.744001133	0.791112407	0.813097043	0.83858652	0.408802852	0.37523659	0.344597316	0.553652204	0.526864918	0.468244937
-PA0067	0.751584112	0.818294971	0.702442112	0.566234725	0.615157659	0.482304653	0.332397395	0.465539429	0.341663323	0.447780457	0.373892296
-PA0068	0.556615783	0.582010639	0.614834088	0.283189988	0.293067643	0.586303755	0.464769375	0.45979958	0.433682049	0.518506895	0.33739855
-PA0069	0.169880894	0.115989579	0.120459089	0.084531854	0.132699449	0.183633804	0.171406928	0.168839424	0.155932316	0.18240637	0.091455079
-PA0070	0.658111187	0.863638461	0.391001234	0.420832416	0.359139249	0.539183011	0.543134194	0.510774671	0.545745149	0.531707451	0.524825847
-PA0071	0.62051099	0.655948357	0.349088311	0.372190051	0.366645735	0.375616755	0.562719233	0.268952981	0.401523014	0.496813541	0.392768039
-PA0072	0.570396533	0.491372843	0.25793276	0.249371301	0.265389113	0.413032872	0.351963359	0.12441263	0.416875823	0.282015739	0.375232771
-PA0073	0.468571971	0.466591393	0.263160691	0.156864595	0.170765539	0.219030577	0.217672642	0.156111419	0.223004248	0.193359191	0.22822144
-PA0074	0.659236524	0.687207104	0.37567688	0.183765412	0.097303308	0.415747098	0.403883007	0.352046862	0.326293722	0.380409845	0.435625864
-PA0075	0.666695842	0.773030467	0.453209634	0.253734757	0.277293126	0.446155001	0.506897459	0.478683243	0.571366389	0.51021829	0.454142967
-PA0076	0.635836707	0.673340726	0.302526018	0.202060437	0.148308567	0.328609507	0.403426555	0.466547486	0.260185229	0.266774552	0.327203201
-PA0077	0.565592124	0.612873494	0.220571881	0.258733279	0.204403338	0.491043112	0.488856164	0.478384295	0.517223858	0.50687449	0.491785158
-PA0078	0.572990448	0.568852707	0.303180548	0.189955368	0.219767578	0.587800282	0.562408447	0.507056933	0.584140204	0.559688542	0.479431009
-PA0079	0.681192052	0.728332404	0.372631865	0.362273218	0.330551841	0.31781014	0.229303503	0.205179954	0.303297626	0.316016173	0.266079689
-PA0080	0.55515847	0.597418381	0.286096845	0.255111068	0.288310065	0.134594784	0.208184282	0.226817629	0.401214253	0.271387287	0.101994839
-PA0081	0.583280073	0.618069381	0.397384375	0.546254144	0.477075362	0.279751738	0.363593876	0.275117723	0.464389042	0.287671188	0.396620771
-PA0082	0.623091267	0.446574267	0.229954736	0.149564547	0.124055795	0.347040833	0.50268319	0.348447684	0.534429172	0.329607974	0.496438016
-PA0083	0.632133232	0.714704687	0.318526818	0.152363535	0.11882787	0.305609911	0.463293887	0.283946264	0.443649537	0.318146708	0.406936151
-PA0084	0.640034278	0.729578444	0.32993567	0.195001918	0.134218804	0.424308911	0.520444957	0.363889777	0.555001643	0.46169901	0.492913481
-PA0085	0.569365931	0.647691854	0.246838125	0.184238973	0.224338116	0.495452345	0.571561723	0.342158554	0.527918442	0.478752778	0.550004635
-PA0086	0.493985808	0.496497497	0.135946268	0.086061407	0.102609781	0.400781965	0.478751185	0.252972239	0.471586268	0.341777005	0.40450349
-PA0087	0.508815095	0.46263495	0.19499163	0.159326914	0.152013855	0.456998313	0.399210468	0.371096082	0.481198865	0.333476764	0.449396715
-PA0088	0.527302147	0.457347369	0.247571127	0.241034401	0.196804848	0.392523444	0.412784131	0.285064376	0.428716857	0.381658452	0.394653748
-PA0089	0.481224221	0.480676656	0.093439504	0.069370905	0.085955462	0.359385756	0.428934177	0.369029552	0.449667629	0.507271732	0.442807584
-PA0090	0.488442335	0.548916021	0.197563115	0.126171917	0.146507211	0.414298111	0.406520933	0.349919866	0.407177257	0.417235042	0.353888465
-PA0091	0.304398369	0.375984375	0.128071963	0.190064329	0.17618887	0.479164285	0.501860099	0.448678725	0.462901074	0.509373545	0.570369273
-PA0092	0.152219997	0.520528195	0.013509129	0.039127765	0.125776666	0.364553613	0.569124128	0.313439598	0.629720177	0.456513004	0.524494844
-PA0093	0.626156778	0.451158469	0.309210475	0.253510335	0.220877501	0.394005487	0.286194163	0.405052735	0.354996895	0.34086058	0.268428438
-PA0094	0.672319382	0.63463469	0.472468046	0.539380878	0.556744263	0.196869196	0.19477028	0.091435459	0.148123871	0.181975954	0.174285281
-PA0095	0.349707495	0.36859924	0.41408387	0.708846669	0.649605087	0.572592332	0.453138215	0.613357252	0.540092734	0.610598339	0.385157541
-PA0096	0.215691437	0.317877566	0.077547491	0.404711324	0.37541249	0.361638593	0.396868056	0.512485636	0.248967777	0.458031442	0.279356172
-PA0097	0.336791162	0.340335104	0.158392627	0.443797089	0.455162751	0.209798085	0.172433906	0.361603853	0.229413679	0.341537715	0.304868028
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-PA0991	0.221475106	0.058673371	0.247809751	0.226153054	0.206590769	0.645091057	0.556140083	0.631453987	0.442352167	0.734769629	0.539882055
-PA0992	0.263173969	0.263840768	0.297784469	0.222291165	0.245714387	0.407331219	0.380849468	0.334528765	0.319921094	0.446029643	0.30018705
-PA0993	0.080753161	0.086717796	0.126180541	0.054766875	0.055984741	0.404364216	0.394679845	0.313912814	0.361501591	0.269098323	0.457515777
-PA0994	0.413516084	0.271832014	0.373145663	0.405545297	0.389917129	0.274827063	0.489472475	0.411510565	0.372692688	0.340038683	0.425706428
-PA0995	0.391797379	0.432568597	0.438653792	0.368518359	0.38987239	0.795737082	0.772666852	0.785245141	0.730324894	0.839239383	0.787800953
-PA0996	0.752062216	0.363283179	0.283871226	0.533041708	0.425617749	0.522522913	0.728806928	0.487383174	0.738201956	0.550716796	0.760755849
-PA0997	0.82748369	0.31119968	0.225578709	0.56137846	0.467292774	0.487107146	0.552289686	0.285177466	0.584729399	0.484370194	0.515493496
-PA0998	0.828918499	0.265750176	0.219879625	0.500315419	0.436327035	0.334576152	0.478262606	0.268679777	0.528833518	0.381753109	0.455536319
-PA0999	0.851238056	0.329776651	0.273102194	0.560394825	0.475540049	0.521270577	0.597600645	0.37003627	0.651576281	0.583605331	0.637686316
-PA1000	0.756318733	0.155936517	0.180365132	0.373077985	0.300547837	0.546710079	0.602017828	0.357404632	0.679539548	0.517833699	0.646395368
-PA1001	0.83135822	0.155920166	0.159779419	0.375224251	0.416837211	0.42751986	0.529339024	0.241711871	0.607134553	0.466373253	0.517062038
-PA1002	0.618535202	0.167852665	0.229438902	0.407665888	0.394314578	0.301017397	0.467520457	0.300602085	0.475571135	0.335266031	0.437628086
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-PA1004	0.84302152	0.680074509	0.745493359	0.667167026	0.628986384	0.651390064	0.677445169	0.685540316	0.761456033	0.59096648	0.686556671
-PA1005	0.57052336	0.618728141	0.769992309	0.27940836	0.310952957	0.665131499	0.616454569	0.699139444	0.538105439	0.719882569	0.560047286
-PA1006	0.701791453	0.49259973	0.663852069	0.342496668	0.346297659	0.407433288	0.533501434	0.459528759	0.553488729	0.52302289	0.521510242
-PA1007	0.52457722	0.250943333	0.380270346	0.208070153	0.244146248	0.340894712	0.554569237	0.317160261	0.49828823	0.42986164	0.586940435
-PA1008	0.585622356	0.554330365	0.581025973	0.387835353	0.390188245	0.537333037	0.684326346	0.499950826	0.654094123	0.543321865	0.638289596
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-PA1010	0.840142718	0.944706221	0.791724321	0.553941063	0.569974643	0.462229919	0.657966049	0.474770139	0.625262162	0.57908572	0.695890245
-PA1011	0.796528173	0.622551563	0.778237756	0.27803511	0.220261763	0.498032516	0.732027686	0.441347446	0.688055602	0.394764955	0.702569558
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-PA1013	0.718347668	0.875306442	0.780011972	0.608927588	0.621216091	0.615748797	0.728882391	0.519466762	0.720277573	0.662453941	0.735816086
-PA1014	0.762815907	0.278026196	0.785117818	0.471033548	0.549144216	0.135753064	0.211655039	0.196891485	0.161313513	0.141781821	0.134206445
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-PA1016	0.250752894	0.187754993	0.377035433	0.194111715	0.200624695	0.216460034	0.109568225	0.179623999	0.135264014	0.175702355	0.090953972
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-PA1021	0.09413281	0.106115045	0.23782906	0.116239194	0.096075224	0.094194463	0.052216382	0.030615026	0.075914538	0.128447666	0.068727793
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-PA1881	0.104676631	0.175661744	0.326021931	0.735251181	0.731156708	0.08391619	0.083853392	0.110692884	0.016468919	0.159091057	0.072220891
-PA1882	0.251459302	0.248792024	0.242601119	0.279353261	0.28220162	0.287590476	0.386898401	0.497942615	0.423456968	0.32418081	0.33311772
-PA1883	0.16566924	0.211629242	0.163555986	0.227413843	0.186477351	0.254702212	0.251973873	0.335034202	0.331163452	0.210890864	0.332209795
-PA1884	0.114701877	0.130689293	0.071907874	0.167588252	0.121690002	0.270601439	0.341279118	0.128257954	0.421654953	0.366780886	0.361742797
-PA1885	0.155707104	0.221381372	0.141198291	0.416777187	0.441279146	0.265726426	0.09978329	0.229911441	0.157404608	0.12093557	0.006676146
-PA1886	0.426730126	0.299464635	0.441018527	0.558331615	0.578851327	0.259400263	0.21441306	0.145285779	0.108183454	0.139891649	0.469424863
-PA1887	0.13229675	0.115186004	0.190499194	0.545567664	0.617330993	0.132687077	0.118785381	0.148200745	0.138784309	0.176728505	0.122425184
-PA1888	0.088391619	0.136125215	0.215242571	0.652649934	0.653697114	0.109358115	0.135085231	0.06980197	0.115047176	0.146102405	0.097970478
-PA1889	0.424341432	0.463193907	0.554250316	0.361015959	0.374814897	0.374923298	0.49639772	0.441233135	0.471256668	0.422959564	0.548456846
-PA1890	0.390617807	0.201658371	0.352362014	0.331326658	0.32635238	0.696892577	0.566616321	0.578479316	0.670104044	0.556622969	0.572028037
-PA1891	0.111950741	0.065004197	0.085723042	0.112515386	0.095432501	0.083757121	0.130173496	0.089109859	0.112951611	0.183098213	0.160526413
-PA1892	0.119711972	0.041228585	0.108852863	0.101557922	0.094595015	0.142229652	0.119428822	0.098617936	0.145755162	0.100024019	0.11888895
-PA1893	0.171854111	0.088355913	0.115882611	0.126354218	0.086419132	0.138524067	0.151880234	0.133229972	0.095912278	0.166967849	0.09476766
-PA1894	0.206753089	0.085491913	0.232837426	0.073972871	0.071470137	0.194537795	0.196915807	0.235797622	0.192453273	0.246061429	0.181458423
-PA1895	0.111374575	0.070147833	0.187462779	0.104591225	0.094295175	0.164956911	0.044933031	0.209571464	0.083332042	0.157580982	0.107349781
-PA1896	0.102492572	0.077254003	0.154017247	0.07980162	0.043618896	0.127575495	0.062430986	0.04513537	0.052599228	0.144991042	0.033726238
-PA1897	0.195601427	0.06009196	0.259676468	0.137563781	0.099677417	0.250884316	0.107328027	0.110638141	0.116890931	0.177979779	0.151445967
-PA1898	0.1539959	0.12800034	0.175654931	0.204504464	0.202416836	0.474713592	0.393330174	0.475983471	0.440236435	0.48179328	0.373660626
-PA1901	0.293678734	0.24812512	0.229402169	0.573439977	0.483447293	0.453148556	0.328478008	0.431158067	0.395552118	0.469883245	0.369073108
-PA1902	0.181036884	0.126996645	0.208800228	0.398713298	0.326703366	0.532097665	0.302288512	0.462415912	0.431904843	0.512466923	0.381870413
-PA1903	0.251044775	0.116256594	0.188458808	0.463402359	0.396786659	0.50582808	0.364602046	0.446786048	0.425225429	0.50379057	0.405650224
-PA1904	0.178594285	0.099111464	0.183570391	0.45870421	0.381230657	0.373331782	0.295428294	0.310362857	0.313867615	0.369641338	0.211489633
-PA1905	0.229557081	0.113494348	0.217544268	0.542797856	0.49227985	0.538024205	0.442405333	0.427290341	0.522937885	0.503245621	0.420283438
-PA1906	0.296272808	0.217496428	0.281806668	0.330067472	0.338069401	0.399377594	0.473080512	0.278327609	0.421239164	0.430129622	0.526444571
-PA1907	0.195764491	0.107246058	0.121204222	0.134689991	0.131754529	0.25119339	0.228329848	0.341225456	0.148102838	0.186904958	0.333914367
-PA1908	0.194278388	0.145047621	0.168000347	0.202691432	0.182409279	0.255636732	0.207157395	0.362668916	0.288031861	0.196121272	0.4003118
-PA1909	0.206298696	0.168161749	0.19029178	0.221912935	0.259753168	0.071380257	0.143012846	0.362297209	0.308642139	0.12284123	0.030272678
-PA1910	0.239829331	0.210800938	0.283061557	0.122516262	0.1881351	0.213116743	0.186635321	0.171640712	0.202938862	0.155155125	0.2220155
-PA1911	0.201960536	0.135501473	0.163944772	0.104912304	0.098211997	0.061830975	0.095351554	0.081580419	0.202827197	0.112368442	0.155293904
-PA1912	0.282484246	0.159750542	0.374782695	0.101931156	0	0.09315303	0.073727394	0.062140458	0.264651378	0.087870443	0.161486488
-PA1913	0.826805533	0.600663204	0.598784763	0.331837334	0.354051038	0.186706901	0.159999905	0.202740898	0.254835708	0.136215603	0.292512521
-PA1914	0.167261976	0.15764906	0.17245518	1	0.982489821	0.276538766	0.2046446	0.298934491	0.251957278	0.224797313	0.278102375
-PA1915	0.130808833	0.153453041	0.185209988	0.307637673	0.345519224	0.337206248	0.246881267	0.4103798	0.220444894	0.383079126	0.227587812
-PA1916	0.287717546	0.245631551	0.225959166	0.298584421	0.289730846	0.470043563	0.58282218	0.374004166	0.546407118	0.400162465	0.656325818
-PA1917	0.14508979	0.217880486	0.248132596	0.295379418	0.229866896	0.455385119	0.524360292	0.484330573	0.433682016	0.517680773	0.460377002
-PA1918	0.281308935	0.259956699	0.22042608	0.369710832	0.239170694	0.381253572	0.511097054	0.379269869	0.382595121	0.462303716	0.442375554
-PA1919	0.249527768	0.263254331	0.257040027	0.679298935	0.614418314	0.556486713	0.468856369	0.466248668	0.50142656	0.559191275	0.494701359
-PA1920	0.216755128	0.516274763	0.175256295	0.660756745	0.51882173	0.767106633	0.410259237	0.695715706	0.323291852	0.827201377	0.370912261
-PA1921	0.115683769	0.200422721	0.936154528	0.939246608	1	0.248519544	0.159178961	0.255920203	0.194634819	0.203352486	0.256033213
-PA1922	0.206656659	0.186571108	0.784783984	0.386124031	0.411377567	0.204849133	0.174345746	0.290680835	0.153392369	0.171658335	0.150079319
-PA1923	0.228022859	0.167500155	0.666361552	0.301940762	0.350240432	0.243751768	0.279445216	0.309552748	0.304244858	0.163791326	0.238690207
-PA1924	0.151548972	0.128055277	0.680613693	0.27197086	0.294379603	0.177058047	0.116952875	0.254341315	0.185853624	0.203612923	0.097796193
-PA1925	0.108696298	0.101709458	0.674229344	0.239159419	0.261526498	0.165814653	0.17563544	0.106516978	0.220807312	0.212061141	0.131822038
-PA1926	0.630641086	0.561017517	0.779812988	0.289190383	0.240512211	0.483105031	0.510789426	0.385616573	0.432202291	0.500971818	0.53406036
-PA1927	0.108211527	0.077387546	0.084053227	0.450426992	0.629331741	0.521547873	0.70845772	0.243031574	0.815554446	0.565565666	0.757129571
-PA1928	0.283511554	0.315204178	0.324388168	0.345518031	0.378782621	0.513232709	0.780141652	0.353243755	0.85203117	0.505385897	0.784094775
-PA1929	0.240572835	0.262769991	0.32377419	0.451003577	0.463677839	0.313380709	0.518938073	0.189652706	0.70403319	0.421127125	0.522442152
-PA1930	0.071946097	0.026011024	0.124066531	0.981167835	0.954025232	0.402161066	0.102336118	0.623236108	0.191341642	0.402149784	0.23501398
-PA1931	0.197541479	0.21805088	0.220884966	0.842623742	0.859932782	0.423302765	0.29891361	0.534100448	0.280465533	0.455943293	0.280119874
-PA1932	0.253877899	0.312404456	0.320138159	0.738321283	0.758940651	0.331077229	0.301422888	0.34411904	0.176344796	0.323088303	0.291616328
-PA1933	0.287424945	0.253482341	0.337321857	0.632176639	0.661542141	0.515435536	0.311611257	0.487649465	0.353671651	0.526338294	0.346217772
-PA1934	0.348260346	0.428634208	0.299665062	0.269085576	0.302878663	0.129075331	0.208355663	0.209392923	0.220780631	0.181364094	0.212804281
-PA1935	0.299641783	0.390244763	0.257510151	0.292985728	0.150267426	0.295275868	0.285385877	0.500535299	0.410226277	0.395839787	0.321572939
-PA1936	0.139072453	0.135683348	0.076173274	0.087601393	0.077473724	0.200976856	0.218800028	0.245618499	0.190220511	0.132770422	0.319686009
-PA1939	0.105966069	0.430561698	0.121353801	0.216065892	0.126847987	0.192224718	0.151377733	0.190079151	0.223968932	0.125221338	0.108815919
-PA1940	0.379002976	0.341343698	0.455790392	0.339177982	0.336188614	0.245575929	0.332596783	0.380036557	0.333120802	0.412068168	0.352413438
-PA1941	0.623541716	0.454124206	0.650956241	0.422388577	0.475681526	0.481628427	0.379302497	0.440373629	0.355702429	0.394151109	0.348038368
-PA1942	0.17404044	0.185363913	0.368009463	0.330604053	0.332782852	0.435162202	0.327598032	0.527890101	0.454541449	0.396153034	0.332369901
-PA1943	0.380566007	0.5051748	0.397675017	0.744091547	0.641196178	0.222698558	0.095752888	0.587472876	0.118365056	0.244447275	0.191966529
-PA1944	0.222860243	0.351390083	0.425375981	0.63677226	0.653909279	0.417442428	0.243702328	0.433621624	0.371130427	0.409312782	0.183682876
-PA1945	0.167462683	0.101706538	0.228750419	0.432940005	0.371993057	0.449199178	0.39971676	0.352961037	0.316235271	0.270564638	0.332098602
-PA1946	0.227868379	0.419641395	0.180140404	0.285339703	0.301350031	0.424756365	0.323389848	0.43056951	0.390410015	0.387367274	0.299031303
-PA1947	0.216573888	0.223094382	0.113622036	0.235803159	0.230346205	0.243890156	0.19977113	0.199831169	0.16815247	0.117414764	0.156940158
-PA1948	0.332427545	0.154379348	0.178687506	0.333609506	0.337687717	0.480784997	0.370411813	0.444646394	0.382371472	0.350067464	0.342605227
-PA1949	0.42118388	0.458468061	0.401599414	0.416734235	0.385327729	0.416118993	0.322993174	0.297985452	0.453073102	0.408633344	0.359421397
-PA1950	0.356606209	0.404168148	0.324915089	0.487213955	0.432753706	0.53684131	0.432454171	0.471399714	0.535491595	0.526672614	0.437732265
-PA1951	0.090698346	0.152561137	0.144351581	0.520956818	0.517332402	0.174116231	0.214648081	0.431969439	0.181746245	0.221208804	0.188609517
-PA1952	0.231432909	0.195971546	0.198612745	0.286346733	0.256405607	0.526065874	0.458997556	0.322513877	0.392877655	0.302362084	0.391267086
-PA1953	0.265164125	0.220194012	0.210769061	0.328835624	0.232988769	0.343251859	0.354438495	0.352583826	0.45378262	0.307511802	0.418728289
-PA1954	0.126242654	0.149561012	0.127976003	0.172428914	0.204666992	0.088281481	0.056952132	0.121853	0.071903743	0.233856482	0.120052642
-PA1955	0.183805738	0.1382553	0.149945536	0.226004698	0.216483489	0.16364075	0.152770764	0.143149898	0.143627514	0.2287683	0.171905724
-PA1956	0.203506069	0.231356349	0.200966335	0.202134319	0.189746082	0.221066178	0.206381642	0.323938768	0.210345239	0.463798114	0.253343481
-PA1957	0.375580348	0.272586405	0.409325496	0.285767669	0.302357695	0.410015685	0.5050756	0.346974783	0.50368301	0.413476433	0.414296802
-PA1958	0.567904828	0.32263817	0.637584787	0.16793826	0.225368008	0.363940949	0.506085687	0.488320569	0.374979453	0.705024187	0.541964804
-PA1959	0.725398366	0.4822425	0.612303156	0.094756298	0.107415616	0.285807828	0.480095908	0.338526565	0.354853642	0.277223387	0.465862697
-PA1960	0.444591456	0.186759752	0.357144889	0.345290784	0.359988008	0.183441294	0.314457202	0.11080867	0.303887235	0.231936786	0.229821441
-PA1961	0.29504737	0.287693607	0.370279305	0.235975064	0.149714484	0.249644521	0.103675395	0.342797689	0.29503492	0.189909127	0.387955159
-PA1962	0.165081317	0.081112606	0.118946057	0.134447552	0.127945708	0.112533807	0.106772871	0.074843944	0.115440218	0.059931782	0.162366265
-PA1963	0.520956874	0.44703404	0.672323797	0.64178594	0.712536647	0.358314906	0.384733427	0.31287762	0.364874662	0.445663704	0.330341621
-PA1964	0.80117205	0.775689753	0.611012762	0.36913427	0.326286788	0.667889859	0.622942008	0.516111279	0.671386807	0.628281665	0.629121556
-PA1965	0.451425903	0.491356036	0.52624752	0.249568976	0.249228963	0.52167608	0.941287873	0.475829978	0.853071203	0.618462866	0.86736646
-PA1966	0.740699559	0.387446675	0.610240647	0.490605061	0.596743958	0.398433931	0.536679919	0.494724556	0.505298018	0.35390017	0.471432949
-PA1967	0.32953152	0.453829156	0.370769003	0.666775579	0.821480436	0.291680596	0.202940406	0.231590461	0.202194909	0.19860188	0.368134937
-PA1968	0.485941969	0.546929736	0.567595863	0.513402286	0.520502524	0.597492304	0.718160465	0.648213038	0.716130982	0.560630252	0.589914351
-PA1969	0.604244137	0.716450062	0.611105875	0.409940284	0.440681866	0.273415342	0.396898636	0.295773227	0.290323542	0.302046278	0.256967524
-PA1970	0.252931145	0.197545418	0.327073991	0.208378073	0.197942907	0.328830213	0.397055109	0.30033012	0.337803594	0.400212379	0.408548814
-PA1971	0.530882877	0.298995787	0.628134487	0.214475864	0.137325378	0.47809626	0.598341843	0.340445171	0.60843042	0.226374562	0.485341642
-PA1972	0.156746247	0.166422917	0.11648796	0.147077409	0.146863178	0.175940342	0.203182158	0.147366837	0.232422175	0.215303538	0.221814917
-PA1973	0.486435378	0.480268659	0.471545287	0.387931557	0.404119831	0.332989716	0.431547952	0.417488222	0.390004109	0.349137411	0.334187045
-PA1974	0.120398157	0.133994476	0.105801096	0.232268372	0.178583538	0.23891711	0.106623268	0.201523401	0.139446775	0.16306428	0.179207462
-PA1975	0.277113179	0.236022845	0.228743813	0.255903514	0.273624545	0.341903095	0.361609444	0.290463997	0.283240012	0.216067161	0.414544102
-PA1976	0.139287551	0.205854018	0.145186176	0.225261703	0.208759743	0.170672303	0.163546368	0.250333998	0.231578399	0.244939815	0.20586634
-PA1977	0.25514767	0.210816921	0.152681216	0.243081437	0.289300902	0.358570623	0.371408203	0.391256978	0.396686094	0.252056586	0.466499218
-PA1978	0.185583873	0.145559383	0.353650923	0.132800486	0.160407211	0.097488778	0.119404929	0.082126056	0.146945885	0.12449556	0.098937786
-PA1979	0.063980607	0.072089901	0.082300958	0.239113387	0.162476736	0.129269289	0.092059853	0.163811261	0.144270223	0.110952378	0.157239915
-PA1980	0.162490638	0.150340333	0.169558318	0.274236108	0.252744909	0.265004053	0.238295546	0.233807507	0.204749583	0.211060202	0.221309351
-PA1981	0.151385061	0.144780017	0.267248205	0.214117357	0.15924762	0.030056633	0.032948414	0.088509915	0.065567943	0.047388443	0.06055111
-PA1982	0.179038367	0.132673301	0.188427546	0.193166691	0.209961509	0.133461249	0.152376206	0.205699678	0.14191787	0.173076461	0.148875989
-PA1983	0.080148177	0.109508662	0.245517494	0.342219555	0.358746952	0.149855486	0.079660581	0.099671273	0.09916476	0.105959485	0.112559059
-PA1984	0.50166145	0.231897606	0.250380826	0.664070187	0.535669614	0.302517956	0.620268507	0.206176594	0.558632517	0.321488285	0.578545062
-PA1985	0.09290564	0.144464585	0.29146509	0.536346961	0.531010676	0.469464463	0.405554702	0.409831897	0.568320491	0.464725271	0.361640317
-PA1986	0.152600877	0.19934875	0.250332976	0.411295965	0.411735748	0.2683574	0.246281352	0.239853091	0.242343172	0.205604869	0.288868327
-PA1987	0.10377653	0.131004225	0.267877773	0.41944726	0.367162584	0.263258936	0.169140604	0.379302099	0.242528789	0.281947555	0.175209446
-PA1988	0.143853717	0.101551078	0.456063923	0.530981154	0.492968455	0.202582221	0.090319129	0.178191046	0.150937611	0.162054121	0.209476419
-PA1989	0.079743364	0.121475467	0.207248071	0.299660968	0.316013657	0.211934029	0.1103583	0.117548434	0.20972707	0.195942612	0.207080917
-PA1990	0.195015031	0.137881038	0.083975886	0.227024088	0.267334604	0.269844014	0.101933606	0.156862285	0.202049931	0.125966158	0.143416567
-PA1991	0.423892206	0.198554018	0.485519498	0.365187926	0.43738528	0.341884206	0.363968699	0.384736242	0.255348215	0.260249296	0.302068446
-PA1992	0.260417974	0.143495763	0.449573073	0.30666365	0.252309606	0.362663546	0.268805858	0.224422431	0.3168718	0.220861619	0.235178129
-PA1993	0.258943712	0.197588575	0.345978423	0.079357147	0.086901574	0.245887238	0.320695274	0.288854672	0.224879827	0.279360401	0.398102212
-PA1994	0.449401165	0.290307373	0.491979776	0.581179851	0.632944641	0.722768192	0.726831541	0.790694849	0.836635505	0.772589778	0.414480557
-PA1995	0.668107847	0.565878115	0.635083481	0.907572008	0.889605584	0.575090042	0.641785728	0.540177394	0.590957	0.479263125	0.622467907
-PA1996	0.663907385	0.541950103	0.850494595	0.839403802	0.832711772	0.387392465	0.50013492	0.485424618	0.467535045	0.664496392	0.616622188
-PA1997	0.342079871	0.314861537	0.397733959	0.547994439	0.564413018	0.21698365	0.143569149	0.315411693	0.19549883	0.23370587	0.023425326
-PA1998	0.37001535	0.201770158	0.237511197	0.292059913	0.252320647	0.433572385	0.229632034	0.395448993	0.266288697	0.346559326	0.271936335
-PA1999	0.686083464	0.269956652	0.228868099	0.535780575	0.337834727	0.677558676	0.509439293	0.703773898	0.496192653	0.669301399	0.494929599
-PA2000	0.617799588	0.173654954	0.129190455	0.399262116	0.21238616	0.528738269	0.350734283	0.552242643	0.279699107	0.47650541	0.363457997
-PA2001	0.676838712	0.280834317	0.266774856	0.508834395	0.440161884	0.359996575	0.259214144	0.394776648	0.242534936	0.288969283	0.254101961
-PA2002	0.466679645	0.137003142	0.17224686	0.199984108	0.189180304	0.370460427	0.294929228	0.24518563	0.266951645	0.275109175	0.248776445
-PA2003	0.226814936	0.257168718	0.169863512	0.865621133	0.891392064	0.395084474	0.186901917	0.508373269	0.166219856	0.418968844	0.090854462
-PA2004	0.257075331	0.193181299	0.154172299	1	0.942007089	0.608681201	0.182610756	0.656598138	0.16736497	0.534926137	0.146286986
-PA2005	0.452697766	0.340624738	0.477476362	0.2644591	0.103645141	0.365234155	0.119710884	0.140077701	0.251656183	0	0.255010453
-PA2006	0.543353724	0.278707089	0.25716616	0.321598659	0.347465666	0.285407883	0.434226912	0.325125883	0.366652127	0.204982569	0.356105872
-PA2007	0.580150401	0.31795499	0.233614246	0.431641076	0.496314897	0.833786571	0.797012974	0.843791532	0.879239598	0.795640446	0.840494872
-PA2008	0.645226839	0.426205208	0.314039991	0.483853782	0.532691559	0.536891355	0.542020424	0.533610002	0.574139192	0.523094076	0.545121715
-PA2009	0.698967509	0.505563016	0.202400058	0.621996187	0.615868087	0.614681137	0.57840783	0.653972106	0.566603929	0.604531425	0.645142997
-PA2010	0.442165702	0.255571111	0.396088101	0.331900783	0.370210242	0.58749477	0.654068004	0.679893741	0.670310253	0.650518304	0.560130636
-PA2011	0.327198446	0.232548046	0.363277841	0.17536871	0.112189045	0.624379957	0.612141366	0.554913242	0.636226743	0.615899019	0.550816176
-PA2012	0.201254769	0.163455394	0.20460597	0.183076258	0.207457975	0.285291813	0.214467934	0.238837415	0.205296673	0.275553237	0.133222258
-PA2013	0.381573651	0.412970733	0.385066506	0.326034131	0.287391917	0.28439892	0.284711492	0.283428312	0.297524135	0.334226831	0.297584823
-PA2014	0.384548095	0.290228517	0.301943503	0.189493782	0.150041386	0.432165787	0.403445959	0.354383384	0.404549441	0.480534802	0.294792427
-PA2015	0.412195295	0.31759469	0.43435487	0.455862377	0.434847757	0.588425173	0.581442588	0.667612766	0.579169068	0.641598828	0.599300466
-PA2016	0.480125119	0.417688679	0.468481854	0.611259047	0.585217626	0.926098402	0.812810177	0.955989576	0.850616656	0.889295221	0.818941749
-PA2017	0.167597957	0.1686129	0.151932614	0.384150856	0.370081548	0.485515331	0.506587831	0.467057893	0.530504812	0.440561228	0.515221872
-PA2018	0.131248504	0.160295164	0.085697511	0.127582903	0.118754375	0.433015455	0.610670609	0.356534615	0.592451088	0.480923216	0.558756398
-PA2019	0.238912474	0.284003724	0.075856935	0.106573651	0.099758577	0.362739942	0.466689303	0.293720023	0.555043442	0.329869088	0.480458864
-PA2020	0.235521073	0.156960916	0.285049677	0.268307425	0.229119154	0.678822319	0.655249367	0.747152281	0.69819345	0.661557962	0.664703456
-PA2021	0.061174482	0.166493439	0.13754013	0.249313992	0.241589768	0.642806732	0.563765908	0.717789564	0.612117657	0.696939199	0.581620471
-PA2022	0.112992675	0.143085815	0.083988324	0.084302832	0.07548094	0.225478538	0.338333408	0.300278069	0.288953116	0.221644792	0.305877165
-PA2023	0.694539521	0.539245346	0.69405336	0.508039611	0.519698599	0.881625514	0.952084876	0.835448663	0.912953812	0.87151661	0.949051439
-PA2024	0.080544835	0.123602881	0.148963274	0.79956051	0.797999875	0.390750424	0.284283574	0.472836787	0.245847473	0.456875481	0.237725731
-PA2025	0.335908009	0.442913792	0.336332104	0.745127424	0.687850713	0.598459897	0.53958238	0.65288197	0.568636584	0.635824496	0.585624967
-PA2026	0.22464551	0.788495039	0.220241913	0.423316732	0.355929549	0.23272308	0.193921872	0.168743415	0.190673807	0.227297778	0.169990246
-PA2027	0.143236587	0.149556668	0.502373557	0.190378535	0.166283011	0.250976812	0.101718154	0.136098199	0.12838736	0.253273752	0.1773847
-PA2028	0.294797114	0.273947224	0.261752678	0.243389772	0.297751826	0.52155355	0.551864308	0.564918607	0.591397069	0.752086678	0.502279137
-PA2029	0.485109926	0.202328021	0.66185363	0.579247923	0.548845108	0.973419747	0.659607567	0.811077991	0.801150094	0.86307302	0.810466672
-PA2030	0.176151838	0.111354051	0.462831859	0.738213482	0.686549404	0.600327874	0.382614042	0.48746665	0.484675495	0.532638414	0.343496169
-PA2031	0.40243256	0.26670977	0.564684104	0.754913231	0.720776422	0.559765647	0.402682824	0.469154003	0.463756362	0.44638714	0.438418799
-PA2032	0.525087588	0.28706784	0.711150184	0.577143403	0.556410155	0.398702641	0.490931645	0.305472928	0.504975135	0.380572294	0.498097174
-PA2033	0.128154965	0.069627373	0.148575754	0.097722551	0.127459115	0.087606777	0.112216478	0.042991972	0.090238048	0.071192949	0.097900743
-PA2034	0.14992134	0.15222016	0.197879503	0.212132355	0.173970308	0.178782239	0.228899829	0.271324653	0.210694331	0.188275887	0.208748616
-PA2035	0.220121233	0.260499886	0.188273822	0.31804019	0.345528509	0.215182907	0.152311902	0.354908741	0.181393289	0.170873138	0.225843435
-PA2036	0.196589303	0.182396651	0.209914813	0.21070108	0.212686438	0.445105013	0.378344338	0.493901392	0.438697072	0.528651414	0.468195132
-PA2037	0.179131879	0.184134068	0.23087366	0.171497226	0.18584349	0.721403628	0.7812712	0.778096376	0.738753265	0.772398186	0.714675293
-PA2038	0.455672602	0.367590543	0.436691387	0.43531642	0.478290727	0.777208781	1	0.718105305	0.944559813	0.805083257	0.957201572
-PA2039	0.549050292	0.786554542	0.322948174	0.501082308	0.414405061	0.679148094	0.586755871	0.362188077	0.672497377	0.568591926	0.65177757
-PA2041	0.167053171	0.16152339	0.107184307	0.148060113	0.10846262	0.308056953	0.361628159	0.377203316	0.386826928	0.361545224	0.427293861
-PA2042	0.777883067	0.469200792	0.511582408	0.279243197	0.304622626	0.394264715	0.56106651	0.354465041	0.499635917	0.4516747	0.55165783
-PA2043	0.396007325	0.222937885	0.229103315	0.152398668	0.175226375	0.277076943	0.32403222	0.358121246	0.223633482	0.275671577	0.304895186
-PA2044	0.777860607	0.522883637	0.774146451	0.208834245	0.13638373	0.220751038	0.305950397	0.141905651	0.386587177	0.382639511	0.347342696
-PA2045	0.690821044	0.659709722	0.723637697	0.758631249	0.681638525	0.730066736	0.645506212	0.661711395	0.471455551	0.712902255	0.571638761
-PA2046	0.257153453	0.156867199	0.150023119	0.316788169	0.271631223	0.42099796	0.646543184	0.590171919	0.464561311	0.42341706	0.688476927
-PA2047	0.101346359	0.227014097	0.400422716	0.334907598	0.355106875	0.061208155	0.073594853	0.206182351	0.041377007	0.18437232	0.128372126
-PA2048	0.145136867	0.129542539	0.303152508	0.433837473	0.444294474	0.132375656	0.209903505	0.137831447	0.187489103	0.119906747	0.15109842
-PA2049	0.329963462	0.382890171	0.290892191	0.138763745	0.185243439	0.418856464	0.382036612	0.365273627	0.311056401	0.352911039	0.363240247
-PA2050	0.173473059	0.132711733	0.20217841	0.176100443	0.155237395	0.289126858	0.31986036	0.301286259	0.241490257	0.230580698	0.316907082
-PA2051	0.155532186	0.167162939	0.127491227	0.197936471	0.189241836	0.191083005	0.201560717	0.27928654	0.220394844	0.273573069	0.345319969
-PA2052	0.198912909	0.22504102	0.277598886	0.173782146	0.201838972	0.497277555	0.41871904	0.426624544	0.588658615	0.664225818	0.488191067
-PA2053	0.191923866	0.237963323	0.172646096	0.31466117	0.309809834	0.273894379	0.231318968	0.228912459	0.173593175	0.282789309	0.2651668
-PA2054	0.266275446	0.255692198	0.257334416	0.330216702	0.320734833	0.399911247	0.341581424	0.445772381	0.366554485	0.352476211	0.369710982
-PA2055	0.266459389	0.204040031	0.144928208	0.253725612	0.243400923	0.374951127	0.254814566	0.256212652	0.269675494	0.288910053	0.375300114
-PA2056	0.41404791	0.310068377	0.187473308	0.267377232	0.179849035	0.219454673	0.191893874	0.274049384	0.286473228	0.299835842	0.144813493
-PA2057	0.212844363	0.248795886	0.144379148	0.379490435	0.377487346	0.43914246	0.39696529	0.735453169	0.190200856	0.34809283	0.52541728
-PA2058	0.250840192	0.187470034	0.220326771	0.213954463	0.201300231	0.475524911	0.615216519	0.514304682	0.411978349	0.406304762	0.484105389
-PA2059	0.223863846	0.318223277	0.210598889	0.330619156	0.222958821	0.270768564	0.222647775	0.265095671	0.138132197	0.296479469	0.150549666
-PA2060	0.2849335	0.222832972	0.209108922	0.201197892	0.18710411	0.345833965	0.327563157	0.442489753	0.337953435	0.331266299	0.473894605
-PA2061	0.199204658	0.192133681	0.252220292	0.246404163	0.228219998	0.292248271	0.270739313	0.426231767	0.317424552	0.387078168	0.321819491
-PA2062	0.235716451	0.269224246	0.368552054	0.447356824	0.449833275	0.269897186	0.189135623	0.239588861	0.220832098	0.20147418	0.238918443
-PA2063	0.408586368	0.234313263	0.393852795	0.168459999	0.142049488	0.315775522	0.292836445	0.322765051	0.282457004	0.22978327	0.277576113
-PA2064	0.221950888	0.213471911	0.217302965	0.220645692	0.237322654	0.314530933	0.192655958	0.360140715	0.272301134	0.303243582	0.224855368
-PA2065	0.162605774	0.169142824	0.182685926	0.211693031	0.169358285	0.290481354	0.17522437	0.228952441	0.173732764	0.207423088	0.206215803
-PA2066	0.312528315	0.308905063	0.298308906	0.601189867	0.568327412	0.968555717	0.634698281	0.851366904	0.762919432	1	0.618646093
-PA2067	0.200453156	0.175123949	0.238764246	0.669790299	0.64846708	0.636511399	0.376640014	0.443573534	0.50962917	0.612302117	0.284484844
-PA2068	0.178389559	0.120013312	0.087068315	0.252895865	0.233600083	0.675599786	0.256743752	0.676416049	0.367531398	0.684423032	0.369840871
-PA2069	0.191821326	0.188435202	0.225507522	0.529092846	0.468673922	0.58956481	0.326476889	0.509661176	0.374717137	0.586813835	0.359936306
-PA2070	0.356535693	0.312244534	0.247394317	0.307201956	0.311458667	0.529077458	0.359949666	0.609709637	0.51816389	0.444324189	0.450112088
-PA2071	0.277097447	0.36489697	0.467979147	0.992899014	0.830778948	0.735260897	0.500878505	0.780946321	0.506884038	0.723063268	0.422286204
-PA2072	0.226720945	0.206266715	0.253359036	1	0.946828413	0.409242705	0.319374655	0.642101085	0.364811665	0.459995122	0.326488459
-PA2073	0.179600448	0.193771981	0.079576948	0.196088706	0.175697345	0.299090678	0.334546223	0.416006122	0.309878362	0.30533651	0.256883109
-PA2074	0.329037432	0.430471846	0.286106123	0.347847163	0.304503259	0.578269201	0.453197302	0.610128785	0.427778426	0.505713702	0.415224926
-PA2075	0.189411318	0.224674987	0.221980137	0.335753146	0.284211057	0.368240626	0.377207454	0.40296402	0.158259302	0.262446848	0.413774441
-PA2076	0.485100174	0.3470303	0.548001159	0.405562355	0.379657581	0.494301626	0.455770073	0.532521101	0.550333557	0.448096495	0.523188074
-PA2077	0.192180832	0.169271279	0.132974261	0.271826969	0.231789143	0.214925408	0.119378233	0.234169167	0.145544815	0.163566896	0.151922493
-PA2078	0.220464482	0.219053445	0.164071987	0.235318937	0.212402101	0.278867365	0.141758006	0.29900464	0.257863768	0.322357192	0.249955991
-PA2079	0.260205969	0.157650496	0.292156499	0.199101504	0.205618232	0.424677385	0.353725631	0.401498249	0.423697803	0.485743574	0.403395468
-PA2080	0.493259006	0.41230425	0.521945086	0.236407928	0.221052727	0.187457659	0.163631433	0.153490595	0.233129819	0.229589073	0.193367831
-PA2081	0.597776528	0.600541083	0.540230753	0.436346444	0.448219718	0.411768132	0.322112072	0.366248291	0.346568909	0.323107002	0.389540388
-PA2082	0.606364116	0.661761667	0.712266751	0.511870981	0.53207386	0.676158043	0.797137633	0.675070216	0.775171014	0.691535037	0.729927442
-PA2083	0.275890023	0.242524027	0.354105326	0.252506921	0.243238169	0.222108946	0.22063489	0.190511267	0.178023003	0.201005787	0.171695065
-PA2084	0.234561566	0.175373221	0.255963753	0.195252647	0.22615226	0.251052318	0.18214554	0.154826211	0.193376487	0.188465616	0.225758281
-PA2085	0.238138106	0.185170484	0.281505097	0.224550767	0.219865043	0.223041776	0.209691914	0.174994033	0.128717751	0.24298946	0.306757076
-PA2086	0.204318942	0.166947954	0.200953147	0.144762367	0.149336014	0.160750156	0.120947037	0.162735886	0.144263011	0.22587518	0.143902535
-PA2087	0.162121755	0.119210201	0.15931551	0.128017379	0.124072872	0.16788954	0.136045052	0.136151305	0.165430697	0.178515159	0.189797122
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-PA3975	0.845757353	0.798261754	0.68714079	0.640205337	0.639052812	0.309518544	0.207319429	0.098529514	0.381917182	0.153908856	0.234313539
-PA3976	0.792591759	0.834695166	0.739929483	0.683204366	0.6494008	0.327608777	0.353211104	0.127278163	0.299876859	0.297765823	0.220709198
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-PA4973	0.76488649	0.234696923	0.282937471	0.26044622	0.305270889	0.407388436	0.295791459	0.335217865	0.325259971	0.446118747	0.390184651
-PA4974	0.848421952	0.733238887	0.924390384	0.601968874	0.669067822	0.410541526	0.480451217	0.396771741	0.587075223	0.428114239	0.612561573
-PA4975	0.214088892	0.208658519	0.325895196	0.408239995	0.400317596	0.263184601	0.379927894	0.061234178	0.506231229	0.321868929	0.451207454
-PA4976	0.36976873	0.44383534	0.40358087	0.69553512	0.73437042	0.229917383	0.333031223	0.258790548	0.31867341	0.203289388	0.265223112
-PA4977	0.300531558	0.265033516	0.424552401	0.37044059	0.375059522	0.246986457	0.285090504	0.364826701	0.248713279	0.224541893	0.22598467
-PA4978	0.212185898	0.205638862	0.10726993	0.211828599	0.249193801	0.200439908	0.139032235	0.174755212	0.186449	0.129063939	0.194095366
-PA4979	0.19440966	0.183916849	0.182023646	0.104212838	0.112776832	0.125164272	0.163182613	0.213013424	0.095652466	0.146910365	0.104946661
-PA4980	0.340339225	0.344393692	0.229274877	0.247373966	0.239804652	0.252490032	0.158623472	0.148539805	0.129532827	0.337643686	0.178343958
-PA4981	0.120895083	0.137812799	0.16005583	0.233711325	0.198928637	0.173609821	0.27464186	0.267939379	0.265541934	0.188755923	0.297327039
-PA4982	0.294142961	0.230951903	0.133635416	0.170192665	0.142391846	0.387106515	0.405809971	0.264661991	0.327580543	0.261744805	0.448147559
-PA4983	0.564433346	0.422868828	0.782365022	0.8091197	0.83640314	0.183153742	0.343098198	0.397142603	0.42278029	0.258188989	0.411944654
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-PA4986	0.453747651	0.325152823	0.273567231	0.330691764	0.42704032	0.436052779	0.422597436	0.482846623	0.383904209	0.500820373	0.350175797
-PA4987	0.316199494	0.236122589	0.33439393	0.151030671	0.167258738	0.108809523	0.225086473	0.169919339	0.277449508	0.118511415	0.20808868
-PA4988	0.6347878	0.476449353	0.424409537	0.443923949	0.414925465	0.25076906	0.183732265	0.199837581	0.215370867	0.29405725	0.320743128
-PA4989	0.204324109	0.285762304	0.088047828	0.223126714	0.274097067	0.052903561	0.170709181	0.073398981	0.192248151	0.09422384	0.198530284
-PA4990	0.221975474	0.325498193	0.228649626	0.200836585	0.194331848	0.476208624	0.296067837	0.302536862	0.375640703	0.35382403	0.29976283
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-PA4994	0.202519955	0.189098611	0.253519384	0.221561211	0.264707912	0.261296178	0.207404277	0.321363709	0.27080371	0.256041944	0.23432193
-PA4995	0.238787751	0.200944089	0.111465534	0.198121489	0.130926648	0.18731036	0.145885366	0.271554283	0.196293796	0.197269275	0.330240122
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-PA4997	0.629088292	0.606941822	0.529586502	0.025119456	0.020605566	0.514205241	0.634994248	0.441161462	0.589414779	0.47884067	0.59491655
-PA4998	0.848668203	0.800711251	0.748155876	0.291423067	0.373166892	0.492828185	0.575109484	0.39174181	0.645490051	0.551209592	0.512152346
-PA4999	0.142796691	0.789653835	0.119450539	0.082018856	0.123184665	0.405132574	0.611797246	0.491476809	0.763900515	0.36279815	0.745636766
-PA5000	0.571862156	0.403917739	0.545089312	0.532437057	0.550114155	0.568814439	0.688924616	0.625240699	0.655793642	0.669939163	0.708679167
-PA5001	0.812716423	0.657962465	0.742032355	0.516204585	0.542742584	0.720255662	0.90718951	0.703013633	0.875323017	0.735412859	0.980788396
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-PA5003	0.719455199	0.52240508	0.607295223	0.18656927	0.180565935	0.317287791	0.57976063	0.269693919	0.631200122	0.478364983	0.523514359
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-PA5006	0.994132045	0.555725421	0.853206713	0.293305099	0.399012609	0.817788039	0.615397565	0.513180239	0.578976378	0.601679793	0.475041063
-PA5007	0.82245072	0.689164537	0.882104141	0.22275583	0.313224309	0.551635646	0.579970026	0.413669086	0.628458353	0.585855314	0.508699857
-PA5008	0.85597854	0.69625305	0.796841086	0.361534299	0.450516171	0.519233318	0.50647987	0.371419385	0.619934023	0.636316611	0.486631316
-PA5009	0.976186227	0.617959619	0.888137558	0.144779487	0.222964383	0.650417537	0.614524693	0.673912966	0.50175351	0.695983591	0.52012185
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-PA5015	0.841226711	0.866852761	0.827453549	0.561918059	0.560924285	0.478796923	0.523668828	0.402025132	0.463974058	0.45950069	0.452437851
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-PA5017	0.539403224	0.623942447	0.629688225	0.488844898	0.538374656	0.606814877	0.389119684	0.683412643	0.300085374	0.55940318	0.398726788
-PA5018	0.623729157	0.752535815	0.498128284	0.731272521	0.729340224	0.457221826	0.323312225	0.421611643	0.365822931	0.404969426	0.331834197
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-PA5021	0.601979744	0.57705609	0.431057293	0.150328556	0.141098502	0.238332637	0.283677438	0.107622865	0.218486687	0.082041113	0.241866224
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-PA5023	0.390803487	0.244490099	0.239198812	0.463332279	0.415524449	0.543677116	0.440429975	0.487985013	0.592147834	0.534223918	0.412393737
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-PA5025	0.345459622	0.624003028	0.48950302	0.508585331	0.43597472	0.258893792	0.259266668	0.320252017	0.270274634	0.282468677	0.385173159
-PA5026	0.308039603	0.599074154	0.343863465	0.52754987	0.579513519	0.366725352	0.18658236	0.326154188	0.23611847	0.276384704	0.188599352
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-PA5028	0.479138608	0.472318864	0.623134189	0.698034557	0.643422429	0.799312153	0.680156357	0.845818077	0.682207044	0.843384434	0.621855129
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-PA5048	0.622841185	0.451631386	0.495944638	0.444822232	0.409674426	0.516670034	0.538206529	0.479338599	0.558003546	0.324648749	0.505441524
-PA5049	0.777039781	0.616624424	0.590215911	0.518194536	0.531965292	0.622246548	0.689841362	0.512440798	0.675133416	0.595513763	0.669317971
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-PA5051	0.755824433	0.889786307	0.757469293	0.920953897	0.925299092	0.603328763	0.550759058	0.622084947	0.570875041	0.551571327	0.593657806
-PA5052	0.87466573	0.922911499	0.91066837	0.772293882	0.800750895	0.646139523	0.576688071	0.620274235	0.602356569	0.534803928	0.644956023
-PA5053	0.562635846	0.763358161	0.620254942	0.230854268	0.263763107	0.435070694	0.352127382	0.345092258	0.354507281	0.380235881	0.340663698
-PA5054	0.589180309	0.754047008	0.834125894	0.33157894	0.322115541	0.332864149	0.302295547	0.325935719	0.25236438	0.250136232	0.293387404
-PA5055	0.582677765	0.697726323	0.70761611	0.375039168	0.422172296	0.462122633	0.591045834	0.443400026	0.497007579	0.536051377	0.44198543
-PA5056	0.384434718	0.359121995	0.631218435	0.715663667	0.731566725	0.52388096	0.413482023	0.515482067	0.509908658	0.536335137	0.450967084
-PA5057	0.235078659	0.235200083	0.461694496	0.539947361	0.585959344	0.392098981	0.301569474	0.476340538	0.329454737	0.510613451	0.30322758
-PA5058	0.093457941	0.106439709	0.263164152	0.776094569	0.738702064	0.361547708	0.109568987	0.419855598	0.069704508	0.332469108	0.157848596
-PA5059	0.200510112	0.217765749	0.334937251	0.783115153	0.802208894	0.46686288	0.353498384	0.460979682	0.340256295	0.413538017	0.376073984
-PA5060	0.549512958	0.827372249	0.652019626	0.909966185	0.915630691	0.525801397	0.548423527	0.57796833	0.532056528	0.571119859	0.498473245
-PA5061	0.220162799	0.355145833	0.369302918	0.877036446	0.827669524	0.302696545	0.359494697	0.324369229	0.315978537	0.28120964	0.31554288
-PA5062	0.491496235	0.478892203	0.549748976	0.307695237	0.415828043	0.762247518	0.694963964	0.70633162	0.772011238	0.699473639	0.737113506
-PA5063	0.830862039	0.773180792	0.807671827	0.794336175	0.816647075	0.385870876	0.605872018	0.345946162	0.563142951	0.460357398	0.440605483
-PA5064	0.684939905	0.723010547	0.718731815	0.524704743	0.512294159	0.549925874	0.514652429	0.394130843	0.54390218	0.471290887	0.501303261
-PA5065	0.735264231	0.778483881	0.651016167	0.259996173	0.192055704	0.813805532	0.842819491	0.700051602	0.871425345	0.827448211	0.928209807
-PA5066	0.616519428	0.731203363	0.588429738	0.498429929	0.550985599	0.630565914	0.625851032	0.605601667	0.613198457	0.617864705	0.617166429
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-PA5068	0.748547362	0.829572419	0.722084238	0.729311959	0.736761104	0.839267797	0.810182606	0.832673086	0.761998313	0.8606114	0.808382551
-PA5069	0.790374817	0.910682393	0.883657139	0.730445792	0.752837115	0.44163667	0.615137563	0.547174424	0.631233872	0.474861998	0.518051734
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-PA5566	0.164156644	0.110301181	0.172569567	0.205509876	0.155548057	0.25090494	0.421389237	0.328478018	0.458622305	0.456465075	0.333367664
-PA5567	0.970470154	0.619553707	0.797147596	0.504766777	0.562926516	0.362160951	0.392671169	0.29849283	0.426766598	0.348577354	0.350236484
-PA5568	0.88747179	0.747655583	0.753784563	0.105662	0.049738113	0.487777004	0.686055353	0.254059275	0.701313711	0.503229181	0.707673064
-PA5569	0.900484365	0.749892936	0.85625322	0.363408967	0.388931332	0.729434041	0.911163049	0.588305462	0.886073914	0.718768149	0.884961447
-PA5570	0.880011746	0.805374483	0.811099487	0.54478005	0.548813793	0.483383602	0.585208304	0.406311441	0.556363347	0.474649256	0.599753809
diff --git a/Pseudomonas_tests/data/metadata/experiment_ids.txt b/Pseudomonas_tests/data/metadata/experiment_ids.txt
deleted file mode 100644
index 9241616..0000000
--- a/Pseudomonas_tests/data/metadata/experiment_ids.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-	experiment_id
-0	E-MEXP-1051
-1	E-MEXP-1591
-2	E-MEXP-2593
-3	E-MEXP-2867
-4	E-MEXP-3764
diff --git a/Pseudomonas_tests/data/metadata/sample_annotations.tsv b/Pseudomonas_tests/data/metadata/sample_annotations.tsv
deleted file mode 100644
index 07b4cce..0000000
--- a/Pseudomonas_tests/data/metadata/sample_annotations.tsv
+++ /dev/null
@@ -1,1218 +0,0 @@
-experiment	sample_name	ml_data_source	description	nucleic_acid	medium	genotype	od	growth_setting_1	growth_setting_2	strain	temperature	treatment	additional_notes	variant_phenotype	abx_marker	biotic_int_lv_2	biotic_int_lv_1
-E-GEOD-46947	GSM1141730 1	GSM1141730_PA01_ZnO_PZO_.CEL	Pseudomonas aeruginosa PAO1 LB aerated  5 h with ZnO nanoparticles	RNA	LB			planktonic	aerated	PAO1	37	1 mM ZnO nanoparticles	Grown for 5h				
-E-GEOD-46947	GSM1141729 1	GSM1141729_PA01_none_PC_.CEL	Pseudomonas aeruginosa PAO1 LB aerated  5 h	RNA	LB			planktonic	aerated	PAO1	37		Grown for 5h				
-E-GEOD-65882	GSM1608059 1	GSM1608059_Planktonic_1.CEL	PAO1 WT. Planktonic. Rep1	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65882	GSM1608060 1	GSM1608060_Planktonic_2.CEL	PAO1 WT. Planktonic. Rep2	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65882	GSM1608061 1	GSM1608061_Planktonic_3.CEL	PAO1 WT. Planktonic. Rep3	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65882	GSM1608062 1	GSM1608062_Planktonic_4.CEL	PAO1 WT. Planktonic. Rep4	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65882	GSM1608067 1	GSM1608067_Biofilm+12hrcipro_rep1.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 1.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 1"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with ciprofloxacin at 1.0 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65882	GSM1608068 1	GSM1608068_Biofilm+12hrcipro_rep2.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 1.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 2"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with ciprofloxacin at 1.0 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65882	GSM1608069 1	GSM1608069_Biofilm+12hrcipro_rep3.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 1.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 3"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with ciprofloxacin at 1.0 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65882	GSM1608070 1	GSM1608070_Biofilm+12hrtobra_rep1.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 10.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 1"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with tobramycin at 10 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65882	GSM1608071 1	GSM1608071_Biofilm+12hrtobra_rep2.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 10.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 2"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with tobramycin at 10 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65882	GSM1608072 1	GSM1608072_Biofilm+12hrtobra_rep3.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 10.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 3"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with tobramycin at 10 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65870	GSM1608067 1	GSM1608067_Biofilm+12hrcipro_rep1.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 1.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 1"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with ciprofloxacin at 1.0 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65870	GSM1608068 1	GSM1608068_Biofilm+12hrcipro_rep2.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 1.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 2"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with ciprofloxacin at 1.0 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65870	GSM1608069 1	GSM1608069_Biofilm+12hrcipro_rep3.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 1.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 3"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with ciprofloxacin at 1.0 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65870	GSM1608070 1	GSM1608070_Biofilm+12hrtobra_rep1.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 10.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 1"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with tobramycin at 10 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65870	GSM1608071 1	GSM1608071_Biofilm+12hrtobra_rep2.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 10.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 2"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with tobramycin at 10 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65870	GSM1608072 1	GSM1608072_Biofilm+12hrtobra_rep3.CEL	"P. aeruginosa PAO1, cells grown 72 hours in a drip flow reactor and then treated with 10.0 μg/ml ciprofloxacin for 12 hours in the reactor, replicate 3"	RNA	PBM supplemented with 0.2 g/L glucose	WT		Biofilm	drip-flow biofilm reactor	PAO1	37	12h treatment with tobramycin at 10 μg/ml	12 hours; stainless steel slides; 10o incline; 60 ml/hr flow rate				
-E-GEOD-65869	GSM1608059 1	GSM1608059_Planktonic_1.CEL	PAO1 WT. Planktonic. Rep1	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65869	GSM1608060 1	GSM1608060_Planktonic_2.CEL	PAO1 WT. Planktonic. Rep2	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65869	GSM1608061 1	GSM1608061_Planktonic_3.CEL	PAO1 WT. Planktonic. Rep3	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-65869	GSM1608062 1	GSM1608062_Planktonic_4.CEL	PAO1 WT. Planktonic. Rep4	RNA	PBM plus 1 g / L glucose. 	WT	0.26	Planktonic 	Aerated	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-54245	GSM1310895 1	GSM1310895_PA14_wild-type.CEL	PA14 wild-type without H2O2	RNA	LB	WT	1.5	Planktonic	Aerated	PA14	37						
-E-GEOD-54245	GSM1310896 1	GSM1310896_PA14_lasR_rhlR.CEL	PA14 lasR rhlR without H2O2.	RNA	LB	∆lasR∆rhR	1.5	Planktonic	Aerated	PA14	37						
-E-GEOD-54245	GSM1310897 1	GSM1310897_PA14_wild-type_+H2O2.CEL	PA14 wild-type with H2O2.	RNA	LB	WT	1.5	Planktonic	Aerated	PA14	37	80 mM H2O2	30 min exposure				
-E-GEOD-54245	GSM1310898 1	GSM1310898_PA14_lasR_rhlR_+H2O2.CEL	PA14 lasR rhlR with H2O2.	RNA	LB	∆lasR∆rhR	1.5	Planktonic	Aerated	PA14	37	80 mM H2O2	30 min exposure				
-E-MTAB-2540	Overhage13001	Overhage13001_(Pae_G1a).CEL	"PA14 wt 1  was spotted on BM2-swarm plates containing 0.1% (wt/vol) CAA and 0.5% (wt/vol) agar) and incubated for 20 h at 37 °C, replicate 1"	RNA	BM2	WT	20 hours	Swarm	swarm plates	PA14	37		BM2-swarm plates containing 0.1 % (wt/vol) casaminoacids and 0.5 % (wt/vol) agar				
-E-MTAB-2540	Overhage13002	Overhage13002_(Pae_G1a).CEL	"PA14 wt 2  was spotted on BM2-swarm plates containing 0.1% (wt/vol) CAA and 0.5% (wt/vol) agar) and incubated for 20 h at 37 °C, replicate 2"	RNA	BM2	WT	20 hours	Swarm	swarm plates	PA14	37		BM2-swarm plates containing 0.1 % (wt/vol) casaminoacids and 0.5 % (wt/vol) agar				
-E-MTAB-2540	Overhage13003	Overhage13003_(Pae_G1a).CEL	"PA14 PA4398 mutant strain was spotted on BM2-swarm plates containing 0.1% (wt/vol) CAA and 0.5% (wt/vol) agar) and incubated for 20 h at 37 °C, replicate 1"	RNA	BM2	ΔPA4398	20 hours	Swarm	swarm plates	PA14	37		BM2-swarm plates containing 0.1 % (wt/vol) casaminoacids and 0.5 % (wt/vol) agar				
-E-MTAB-2540	Overhage13004	Overhage13004_(Pae_G1a).CEL	"PA14 PA4398 mutant strain was spotted on BM2-swarm plates containing 0.1% (wt/vol) CAA and 0.5% (wt/vol) agar) and incubated for 20 h at 37 °C, replicate 2"	RNA	BM2	ΔPA4398	20 hours	Swarm	swarm plates	PA14	37		BM2-swarm plates containing 0.1 % (wt/vol) casaminoacids and 0.5 % (wt/vol) agar				
-E-GEOD-51491	GSM1246481 1	GSM1246481_PA14bvlR_pME6032_rep1.CEL	PAO1 ∆bvlR plus empty vector. Planktonic. Rep1	RNA	LB	bvlR::tn5 pME6032	0.7	Planktonic 	Aerated	PA14	37		Grown shaking at 150rpm 				
-E-GEOD-51491	GSM1246482 1	GSM1246482_PA14bvlR_pME6032_rep2.CEL	PAO1 ∆bvlR plus empty vector. Planktonic. Rep2	RNA	LB	bvlR::tn5 pME6032	0.7	Planktonic 	Aerated	PA14	37		Grown shaking at 150rpm 				
-E-GEOD-51491	GSM1246483 1	GSM1246483_PA14bvlR_pME6032_rep3.CEL	PAO1 ∆bvlR plus empty vector. Planktonic. Rep3	RNA	LB	bvlR::tn5 pME6032	0.7	Planktonic 	Aerated	PA14	37		Grown shaking at 150rpm 				
-E-GEOD-51491	GSM1246484 1	GSM1246484_PA14bvlR_pMEbvlR_rep1.CEL	PAO1 ∆bvlR plus bvlR expression construct. Planktonic. Rep1	RNA	LB	bvlR::tn5 pMEbvlR	0.7	Planktonic 	Aerated	PA14	37		Grown shaking at 150rpm 				
-E-GEOD-51491	GSM1246485 1	GSM1246485_PA14bvlR_pMEbvlR_rep2.CEL	PAO1 ∆bvlR plus bvlR expression construct. Planktonic. Rep2	RNA	LB	bvlR::tn5 pMEbvlR	0.7	Planktonic 	Aerated	PA14	37		Grown shaking at 150rpm 				
-E-GEOD-51491	GSM1246486 1	GSM1246486_PA14bvlR_pMEbvlR_rep3.CEL	PAO1 ∆bvlR plus bvlR expression construct. Planktonic. Rep3	RNA	LB	bvlR::tn5 pMEbvlR	0.7	Planktonic 	Aerated	PA14	37		Grown shaking at 150rpm 				
-E-GEOD-47173	GSM1146022 1	GSM1146022_pJN105_1.CEL	PAO1 pJN105; control; replicate 1	RNA	MOPS + 0.5% CAA + 0.5% glucose	WT	1	Planktonic		PAO1	37		pH 7.2; exponential phase cells				
-E-GEOD-47173	GSM1146023 1	GSM1146023_pJN105_2.CEL	PAO1 pJN105; control; replicate 2	RNA	MOPS + 0.5% CAA + 0.5% glucose	WT	1	Planktonic		PAO1	37		pH 7.2; exponential phase cells				
-E-GEOD-47173	GSM1146024 1	GSM1146024_phaF_1.CEL	PAO1 pJN105-phaF; replicate 1	RNA	MOPS + 0.5% CAA + 0.5% glucose	phaF OE	1	Planktonic		PAO1	37		pH 7.2; exponential phase cells				
-E-GEOD-47173	GSM1146025 1	GSM1146025_phaF_2.CEL	PAO1 pJN105-phaF; replicate 2	RNA	MOPS + 0.5% CAA + 0.5% glucose	phaF OE	1	Planktonic		PAO1	37		pH 7.2; exponential phase cells				
-E-GEOD-58862	GSM1420990 1	GSM1420990_PAO1_control1.CEL	"Gene expression data from microaerobically grown P. aeruginosa PAO1ut before exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	WT	0.3	Planktonic	1 l jar fermenter	PAO1	37						
-E-GEOD-58862	GSM1420991 1	GSM1420991_PAO1_control2.CEL	"Gene expression data from microaerobically grown P. aeruginosa PAO1ut before exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	WT	0.3	Planktonic	1 l jar fermenter	PAO1	37						
-E-GEOD-58862	GSM1420992 1	GSM1420992_PAO1_NO1.CEL	"Gene expression data from microaerobically grown P. aeruginosa PAO1ut after exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	WT	0.3	Planktonic	1 l jar fermenter	PAO1	37	20 μM NO was added					
-E-GEOD-58862	GSM1420993 1	GSM1420993_PAO1_NO2.CEL	"Gene expression data from microaerobically grown P. aeruginosa PAO1ut after exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	WT	0.3	Planktonic	1 l jar fermenter	PAO1	37	20 μM NO was added					
-E-GEOD-58862	GSM1420994 1	GSM1420994_anr_control1.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant before exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr	0.3	Planktonic	1 l jar fermenter	Rmanr (PAO1)	37						
-E-GEOD-58862	GSM1420995 1	GSM1420995_anr_control2.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant before exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr	0.3	Planktonic	1 l jar fermenter	Rmanr (PAO1)	37						
-E-GEOD-58862	GSM1420996 1	GSM1420996_anr_NO1.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant after exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr	0.3	Planktonic	1 l jar fermenter	Rmanr (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1420997 1	GSM1420997_anr_NO2.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant after exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr	0.3	Planktonic	1 l jar fermenter	Rmanr (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1420998 1	GSM1420998_dnr_control1.CEL	"Gene expression data from microaerobically grown P. aeruginosa dnr mutant before exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆dnr	0.3	Planktonic	1 l jar fermenter	RMdnr (PAO1)	37						
-E-GEOD-58862	GSM1420999 1	GSM1420999_dnr_control2.CEL	"Gene expression data from microaerobically grown P. aeruginosa dnr mutant before exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆dnr	0.3	Planktonic	1 l jar fermenter	RMdnr (PAO1)	37						
-E-GEOD-58862	GSM1421000 1	GSM1421000_dnr_NO1.CEL	"Gene expression data from microaerobically grown P. aeruginosa dnr mutant after exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆dnr	0.3	Planktonic	1 l jar fermenter	RMdnr (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1421001 1	GSM1421001_dnr_NO2.CEL	"Gene expression data from microaerobically grown P. aeruginosa dnr mutant after exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆dnr	0.3	Planktonic	1 l jar fermenter	RMdnr (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1421002 1	GSM1421002_EXdnr_control1.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant expressing DNR before exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr +dnr	0.3	Planktonic	1 l jar fermenter	RManrEXdnr (PAO1)	37						
-E-GEOD-58862	GSM1421003 1	GSM1421003_EXdnr_control2.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant expressing DNR before exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr +dnr	0.3	Planktonic	1 l jar fermenter	RManrEXdnr (PAO1)	37						
-E-GEOD-58862	GSM1421004 1	GSM1421004_EXdnr_NO1.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant expressing DNR after exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr +dnr	0.3	Planktonic	1 l jar fermenter	RManrEXdnr (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1421005 1	GSM1421005_EXdnr_NO2.CEL	"Gene expression data from microaerobically grown P. aeruginosa anr mutant expressing DNR after exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆anr +dnr	0.3	Planktonic	1 l jar fermenter	RManrEXdnr (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1421006 1	GSM1421006_fhpR_control1.CEL	"Gene expression data from microaerobically grown P. aeruginosa fhpR mutant before exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆fhpR	0.3	Planktonic	1 l jar fermenter	PDM2665 (PAO1)	37						
-E-GEOD-58862	GSM1421007 1	GSM1421007_fhpR_control2.CEL	"Gene expression data from microaerobically grown P. aeruginosa fhpR mutant before exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆fhpR	0.3	Planktonic	1 l jar fermenter	PDM2665 (PAO1)	37						
-E-GEOD-58862	GSM1421008 1	GSM1421008_fhpR_NO1.CEL	"Gene expression data from microaerobically grown P. aeruginosa fhpR mutant after exposure to nitric oxide. It is the first of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆fhpR	0.3	Planktonic	1 l jar fermenter	PDM2665 (PAO1)	37	20 μM NO was added					
-E-GEOD-58862	GSM1421009 1	GSM1421009_fhpR_NO2.CEL	"Gene expression data from microaerobically grown P. aeruginosa fhpR mutant after exposure to nitric oxide. It is the second of two biological replicates used in this experiment, each from separate cultures."	RNA	LB with a constant supply of 98% N2 and 2% O2	∆fhpR	0.3	Planktonic	1 l jar fermenter	PDM2665 (PAO1)	37	20 μM NO was added					
-E-GEOD-63588	GSM1553187 1	GSM1553187_Persisters_C1.CEL	PAO1 WT; persister cells; control; replicate 1	RNA	PBS	WT		Planktonic	Aerated	PAO1	37		1 hour; 200 rpm; stationary phase cells	Persister cell			
-E-GEOD-63588	GSM1553188 1	GSM1553188_Persisters_T1.CEL	PAO1 WT; persister cells; GM-CSF; replicate 1	RNA	PBS	WT		Planktonic	Aerated	PAO1	37	0.17 pM GM-CSF	1 hour; 200 rpm; stationary phase cells	Persister cell			
-E-GEOD-63588	GSM1553189 1	GSM1553189_Persisters_C2.CEL	PAO1 WT; persister cells; control; replicate 2	RNA	PBS	WT		Planktonic	Aerated	PAO1	37		1 hour; 200 rpm; stationary phase cells	Persister cell			
-E-GEOD-63588	GSM1553190 1	GSM1553190_Persisters_T2.CEL	PAO1 WT; persister cells; GM-CSF; replicate 2	RNA	PBS	WT		Planktonic	Aerated	PAO1	37	0.17 pM GM-CSF	1 hour; 200 rpm; stationary phase cells	Persister cell			
-E-GEOD-63588	GSM1553191 1	GSM1553191_Normal_C1.CEL	PAO1 WT; control; replicate 1	RNA	PBS	WT		Planktonic	Aerated	PAO1	37		1 hour; 200 rpm; stationary phase cells				
-E-GEOD-63588	GSM1553192 1	GSM1553192_Normal_T1.CEL	PAO1 WT; GM-CSF; replicate 1	RNA	PBS	WT		Planktonic	Aerated	PAO1	37	0.17 pM GM-CSF	1 hour; 200 rpm; stationary phase cells				
-E-GEOD-63588	GSM1553193 1	GSM1553193_Normal_C2.CEL	PAO1 WT; control; replicate 2	RNA	PBS	WT		Planktonic	Aerated	PAO1	37		1 hour; 200 rpm; stationary phase cells				
-E-GEOD-63588	GSM1553194 1	GSM1553194_Normal_T2.CEL	PAO1 WT; GM-CSF; replicate 2	RNA	PBS	WT		Planktonic	Aerated	PAO1	37	0.17 pM GM-CSF	1 hour; 200 rpm; stationary phase cells				
-E-MTAB-2160	Pseudomonas aeruginosa cbrA (1)	Overhage3004_(Pae_G1a).CEL	"Overnight cultures of Pseudomonas aeruginosa  cbrA mutant grown in LB medium at 37 degrees C and 170 rpm were washed twice and resuspended in PBS buffer and adjusted to an optical density of OD600 of 2.0. Of these bacterial cell suspensions, 10 ul were mixed with washed amoeba cells of 2-days old D. discoideum cultures in a ratio of 3:1 bacteria to amoebae. These mixtures were subsequently plated on M9 agar plates and incubated for 48 h at 22.5 degrees C, replicate 1"	RNA	M9 agar	∆cbrA		colony	lawn on plate	PA14	22.5					Dictyostelium discoideum	amoeba
-E-MTAB-2160	Pseudomonas aeruginosa cbrA (2)	Overhage3006_(Pae_G1a).CEL	"Overnight cultures of Pseudomonas aeruginosa  cbrA mutant grown in LB medium at 37 degrees C and 170 rpm were washed twice and resuspended in PBS buffer and adjusted to an optical density of OD600 of 2.0. Of these bacterial cell suspensions, 10 ul were mixed with washed amoeba cells of 2-days old D. discoideum cultures in a ratio of 3:1 bacteria to amoebae. These mixtures were subsequently plated on M9 agar plates and incubated for 48 h at 22.5 degrees C, replicate 2"	RNA	M9 agar	∆cbrA		colony	lawn on plate	PA14	22.5					Dictyostelium discoideum	amoeba
-E-MTAB-2160	Pseudomonas aeruginosa PA14 (1)	Overhage3002_(Pae_G1a).CEL	"Overnight cultures of Pseudomonas aeruginosa PA14 wild type grown in LB medium at 37 degrees C and 170 rpm were washed twice and resuspended in PBS buffer and adjusted to an optical density of OD600 of 2.0. Of these bacterial cell suspensions, 10 ul were mixed with washed amoeba cells of 2-days old D. discoideum cultures in a ratio of 3:1 bacteria to amoebae. These mixtures were subsequently plated on M9 agar plates and incubated for 48 h at 22.5 degrees C, replicate 1"	RNA	M9 agar	WT		colony	lawn on plate	PA14	22.5					Dictyostelium discoideum	amoeba
-E-MTAB-2160	Pseudomonas aeruginosa PA14 (2)	Overhage3005_(Pae_G1a).CEL	"Overnight cultures of Pseudomonas aeruginosa PA14 wild type grown in LB medium at 37 degrees C and 170 rpm were washed twice and resuspended in PBS buffer and adjusted to an optical density of OD600 of 2.0. Of these bacterial cell suspensions, 10 ul were mixed with washed amoeba cells of 2-days old D. discoideum cultures in a ratio of 3:1 bacteria to amoebae. These mixtures were subsequently plated on M9 agar plates and incubated for 48 h at 22.5 degrees C, replicate 2"	RNA	M9 agar	WT		colony	lawn on plate	PA14	22.5					Dictyostelium discoideum	amoeba
-E-MTAB-1983	Source_1	120330-10978D_22_(Pae_G1a).CEL	MPAO1 bfms mutant grown planktonically in minimal medium with 0.2% glucose with shaking for 48 hours to an OD of 1.0 at 37C; replicate 1	RNA	minimal media with 0.2% glucose	bfms mutant	1	planktonic	aerated	MPAO1	37		"minimal medium (6g Na2HPO4, 3g KH2PO4, 0.5g NaCl, 0.24g MgSO4, 0.5g Glutamate per liter; pH7.4) "				
-E-MTAB-1983	Source_2	120330-10978D_23_(Pae_G1a).CEL	MPAO1 bfms mutant grown planktonically in minimal medium with 0.2% glucose with shaking for 48 hours to an OD of 1.0 at 37C; replicate 2	RNA	minimal media with 0.2% glucose	bfms mutant	1	planktonic	aerated	MPAO1	37		"minimal medium (6g Na2HPO4, 3g KH2PO4, 0.5g NaCl, 0.24g MgSO4, 0.5g Glutamate per liter; pH7.4) "				
-E-MTAB-1983	Source_3	120330-10978D_24_(Pae_G1a).CEL	MPAO1 bfms mutant grown planktonically in minimal medium with 0.2% glucose with shaking for 48 hours to an OD of 1.0 at 37C; replicate 3	RNA	minimal media with 0.2% glucose	bfms mutant	1	planktonic	aerated	MPAO1	37		"minimal medium (6g Na2HPO4, 3g KH2PO4, 0.5g NaCl, 0.24g MgSO4, 0.5g Glutamate per liter; pH7.4) "				
-E-MTAB-1983	Source_4	120330-10978D_M1_(Pae_G1a).CEL	MPAO1 WT  grown planktonically in minimal medium with 0.2% glucose with shaking for 48 hours to an OD of 1.0 at 37C; replicate 1	RNA	minimal media with 0.2% glucose	WT	1	planktonic	aerated	MPAO1	37		"minimal medium (6g Na2HPO4, 3g KH2PO4, 0.5g NaCl, 0.24g MgSO4, 0.5g Glutamate per liter; pH7.4) "				
-E-MTAB-1983	Source_5	120330-10978D_M3_(Pae_G1a).CEL	MPAO1 WT  grown planktonically in minimal medium with 0.2% glucose with shaking for 48 hours to an OD of 1.0 at 37C; replicate 2	RNA	minimal media with 0.2% glucose	WT	1	planktonic	aerated	MPAO1	37		"minimal medium (6g Na2HPO4, 3g KH2PO4, 0.5g NaCl, 0.24g MgSO4, 0.5g Glutamate per liter; pH7.4) "				
-E-MTAB-1983	Source_6	120330-10978D_M4_(Pae_G1a).CEL	MPAO1 WT  grown planktonically in minimal medium with 0.2% glucose with shaking for 48 hours to an OD of 1.0 at 37C; replicate 3	RNA	minimal media with 0.2% glucose	WT	1	planktonic	aerated	MPAO1	37		"minimal medium (6g Na2HPO4, 3g KH2PO4, 0.5g NaCl, 0.24g MgSO4, 0.5g Glutamate per liter; pH7.4) "				
-E-GEOD-61925	GSM1517347 1	GSM1517347_MK-1.CEL	PAO1 WT. Planktonic plus mouse fascia homogenate. Rep1	RNA	TY	WT		Planktonic 		PAO1	37					Fascia homogenate	Mouse
-E-GEOD-61925	GSM1517348 1	GSM1517348_MK-2.CEL	PAO1 WT. Planktonic plus mouse fascia homogenate. Rep2	RNA	TY	WT		Planktonic 		PAO1	37					Fascia homogenate	Mouse
-E-GEOD-61925	GSM1517349 1	GSM1517349_MK-3.CEL	PAO1 WT. Planktonic plus mouse fascia homogenate. Rep3	RNA	TY	WT		Planktonic 		PAO1	37					Fascia homogenate	Mouse
-E-GEOD-61925	GSM1517350 1	GSM1517350_MK-4.CEL	PAO1 WT. Planktonic. Rep1	RNA	TY	WT		Planktonic 		PAO1	37						
-E-GEOD-61925	GSM1517351 1	GSM1517351_MK-5.CEL	PAO1 WT. Planktonic. Rep2	RNA	TY	WT		Planktonic 		PAO1	37						
-E-GEOD-61925	GSM1517352 1	GSM1517352_MK-6.CEL	PAO1 WT. Planktonic. Rep3	RNA	TY	WT		Planktonic 		PAO1	37						
-E-GEOD-32572	GSM807322 1	GSM807322.CEL	NCTC8626; control; replicate 1	RNA	MHB	WT		Planktonic		NCTC8626	37		4 hours				
-E-GEOD-32572	GSM807323 1	GSM807323.CEL	NCTC8626; control; replicate 2	RNA	MHB	WT		Planktonic		NCTC8626	37		4 hours				
-E-GEOD-32572	GSM807324 1	GSM807324.CEL	NCTC8626; control; replicate 3	RNA	MHB	WT		Planktonic		NCTC8626	37		4 hours				
-E-GEOD-32572	GSM807325 1	GSM807325.CEL	NCTC8626 ; honey treated; replicate 1	RNA	MHB	WT		Planktonic		NCTC8626	37	10 % manuka honey	4 hours				
-E-GEOD-32572	GSM807326 1	GSM807326.CEL	NCTC8626 ; honey treated; replicate 2	RNA	MHB	WT		Planktonic		NCTC8626	37	10 % manuka honey	4 hours				
-E-GEOD-32572	GSM807327 1	GSM807327.CEL	NCTC8626 ; honey treated; replicate 3	RNA	MHB	WT		Planktonic		NCTC8626	37	10 % manuka honey	4 hours				
-E-GEOD-58758	GSM1419138 1	GSM1419138_PAO.CEL	PAO1 WT. Planktonic. Rep1	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37		Grown shaking in baffled flask (10 mL culture in 50 mL flask) 				
-E-GEOD-58758	GSM1419139 1	GSM1419139_PAO2.CEL	PAO1 WT. Planktonic. Rep2	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37		Grown shaking in baffled flask (10 mL culture in 50 mL flask) 				
-E-GEOD-58758	GSM1419140 1	GSM1419140_PAO3.CEL	PAO1 WT. Planktonic. Rep3	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37		Grown shaking in baffled flask (10 mL culture in 50 mL flask) 				
-E-GEOD-58758	GSM1419141 1	GSM1419141_BC.CEL	PAO1 ∆creBC. Planktonic. Rep1	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37		Grown shaking in baffled flask (10 mL culture in 50 mL flask) 				
-E-GEOD-58758	GSM1419142 1	GSM1419142_BC2.CEL	PAO1 ∆creBC. Planktonic. Rep2	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37		Grown shaking in baffled flask (10 mL culture in 50 mL flask) 				
-E-GEOD-58758	GSM1419143 1	GSM1419143_BC3.CEL	PAO1 ∆creBC. Planktonic. Rep3	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37		Grown shaking in baffled flask (10 mL culture in 50 mL flask) 				
-E-GEOD-58758	GSM1419144 1	GSM1419144_PAO_FOX.CEL	PAO1 WT. Planktonic plus cefoxitin. Rep1	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37	 50 μg/ml cefoxitin	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419145 1	GSM1419145_PAO2_FOX.CEL	PAO1 WT. Planktonic plus cefoxitin. Rep2	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37	 50 μg/ml cefoxitin	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419146 1	GSM1419146_PAO3_FOX.CEL	PAO1 WT. Planktonic plus cefoxitin. Rep3	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37	 50 μg/ml cefoxitin	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419147 1	GSM1419147_BC_FOX.CEL	PAO1 ∆creBC. Planktonic plus cefoxitin. Rep1	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37	 50 μg/ml cefoxitin	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419148 1	GSM1419148_BC2_FOX.CEL	PAO1 ∆creBC. Planktonic plus cefoxitin. Rep2	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37	 50 μg/ml cefoxitin	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419149 1	GSM1419149_BC3_FOX.CEL	PAO1 ∆creBC. Planktonic plus cefoxitin. Rep3	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37	 50 μg/ml cefoxitin	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419150 1	GSM1419150_PAO_CAZ.CEL	PAO1 WT. Planktonic plus ceftazidime. Rep1	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37	0.5 µg/ml ceftazidime	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419151 1	GSM1419151_PAO2_CAZ.CEL	PAO1 WT. Planktonic plus ceftazidime. Rep2	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37	0.5 µg/ml ceftazidime	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419152 1	GSM1419152_PAO3_CAZ.CEL	PAO1 WT. Planktonic plus ceftazidime. Rep3	RNA	LB	WT	1	Planktonic 	Aerated	PAO1	37	0.5 µg/ml ceftazidime	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419153 1	GSM1419153_BC_CAZ.CEL	PAO1 ∆creBC. Planktonic plus ceftazidime Rep1	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37	0.5 µg/ml ceftazidime	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419154 1	GSM1419154_BC2_CAZ.CEL	PAO1 ∆creBC. Planktonic plus ceftazidime Rep2	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37	0.5 µg/ml ceftazidime	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-GEOD-58758	GSM1419155 1	GSM1419155_BC3_CAZ.CEL	PAO1 ∆creBC. Planktonic plus ceftazidime Rep3	RNA	LB	creBC::lox	1	Planktonic 	Aerated	PAO1	37	0.5 µg/ml ceftazidime	"Grown shaking in baffled flask (10 mL culture in 50 mL flask) , 3 h exposure"				
-E-MTAB-2536	20mM_KT2440	20mM_KT2440.CEL	P. putida KT2440. Planktonic plus 20 mM ammonium chloride. 	RNA	M9 plus 2mM glucose			Planktonic 	Aerated	P. putida KT2440	30	20 mM ammonium chloride	Grown shaking at 160rpm 				
-E-MTAB-2536	2mM_KT2440	2mM_KT2440.CEL	P. putida KT2440. Planktonic plus 2 mM ammonium chloride. 	RNA	M9 plus 2mM glucose			Planktonic 	Aerated	P. putida KT2440	30	2 mM ammonium chloride	Grown shaking at 160rpm 				
-E-MTAB-2535	20mM_CF600	20mM_CF600.CEL	"Pseudomonas putida CF600_grown in M9 plus 20 mM ammonium chloride as nitrogen source, rep1"	RNA	M9 with 20 mM ammonium chloride	WT		Planktonic	shaking	CF600	30		unpublished data				
-E-MTAB-2535	2mM_CF600	2mM_CF600.CEL	"Pseudomonas putida CF600_grown in M9 plus 2 mM ammonium chloride as nitrogen source, rep1"	RNA	M9 with 2 mM ammonium chloride	WT		Planktonic	shaking	CF600	30						
-E-GEOD-29879	GSM740019 1	GSM740019.CEL	PA14 WT; biofilm; control	RNA	LB	WT		Biofilm	Shaking- glass wool	PA14	37		7 hours; shaking at 250 rpm				
-E-GEOD-29879	GSM740020 1	GSM740020.CEL	ΔPA2444; biofilm	RNA	LB	ΔPA2444 (glyA2)		Biofilm	Shaking- glass wool	PA14	37		7 hours; shaking at 250 rpm	RSCV phenotype			
-E-MTAB-2528	0.1_12hr_CSV86	0.1_12hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 0.1% NH4NO3, 12 hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	0.1% NH4N03					
-E-MTAB-2528	0.1_2hr_CSV86	0.1_2hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 0.1% NH4NO3, 2hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	0.1% NH4N03					
-E-MTAB-2528	0.1_6hr_CSV86	0.1_6hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 0.1% NH4NO3, 6hr"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	0.1% NH4N03					
-E-MTAB-2528	0.1_7hr_CSV86	0.1_7hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 0.1% NH4NO3, 7hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	0.1% NH4N03					
-E-MTAB-2528	0.1_9h_CSV86	0.1_9hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 0.1% NH4NO3, 9hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	0.1% NH4N03					
-E-MTAB-2528	1_12hr_CSV86	1_12hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 1% NH4NO3, 12 hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	1% NH4NO3					
-E-MTAB-2528	1_2hr_CSV86	1_2hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 1% NH4NO3, 2hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	1% NH4NO3					
-E-MTAB-2528	1_6hr_CSV86	1_6hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 1% NH4NO3, 6hr"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	1% NH4NO3					
-E-MTAB-2528	1_7hr_CSV86	1_7hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 1% NH4NO3, 7hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	1% NH4NO3					
-E-MTAB-2528	1_9h_CSV86	1_9hr_CSV86(Pae_G1a).CEL	"Pseudomonas putida in mineral salt medium (MSM) + 1% NH4NO3, 9hrs"	RNA	mineral salt medium (MSM) 	WT		Planktonic	Shaking	CSV86	30	1% NH4NO3					
-E-MTAB-2528	Glu_6hr_CSV86	Glu_6Hour_(Pae_G1a).CEL	Pseudomonas putida in 1XLB + 0.25% glucose	RNA	LB+ 0.25% glucose	WT		Planktonic	Shaking	CSV86	30						
-E-GEOD-54032	GSM1306125 1	GSM1306125_Nomura_PAO1_NS-1_Pae_G1a_.CEL	"P. aeruginosa PAO1 was grown in nutrient broth (Oxoid number 2) control treatment, replicate 1"	RNA	nutrient broth no. 2 (Oxoid)	WT	0.3	Planktonic	Shaking	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-54032	GSM1306126 1	GSM1306126_Nomura_PAO1_NS-2_Pae_G1a_.CEL	"P. aeruginosa PAO1 was grown in nutrient broth (Oxoid number 2) control treatment, replicate 2"	RNA	nutrient broth no. 2 (Oxoid)	WT	0.3	Planktonic	Shaking	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-54032	GSM1306127 1	GSM1306127_Nomura_PAO1_NS-3_Pae_G1a_.CEL	"P. aeruginosa PAO1 was grown in nutrient broth (Oxoid number 2) control treatment, replicate 3"	RNA	nutrient broth no. 2 (Oxoid)	WT	0.3	Planktonic	Shaking	PAO1	37		Grown shaking at 200rpm				
-E-GEOD-54032	GSM1306128 1	GSM1306128_Nomura_PAO1_KG-1_Pae_G1a_.CEL	"P. aeruginosa PAO1 was grown in nutrient broth (Oxoid number 2) and induced with 20 mM 2-oxoglutarate. At 30 min post induction, replicate 1"	RNA	nutrient broth no. 2 (Oxoid)	WT	0.3	Planktonic	Shaking	PAO1	37	20 mM 2-oxoglutarate	Grown shaking at 200rpm				
-E-GEOD-54032	GSM1306129 1	GSM1306129_Nomura_PAO1_KG-2_Pae_G1a_.CEL	"P. aeruginosa PAO1 was grown in nutrient broth (Oxoid number 2) and induced with 20 mM 2-oxoglutarate. At 30 min post induction, replicate 2"	RNA	nutrient broth no. 2 (Oxoid)	WT	0.3	Planktonic	Shaking	PAO1	37	20 mM 2-oxoglutarate	Grown shaking at 200rpm				
-E-GEOD-54032	GSM1306130 1	GSM1306130_Nomura_PAO1_KG-3_Pae_G1a_.CEL	"P. aeruginosa PAO1 was grown in nutrient broth (Oxoid number 2) and induced with 20 mM 2-oxoglutarate. At 30 min post induction, replicate 3"	RNA	nutrient broth no. 2 (Oxoid)	WT	0.3	Planktonic	Shaking	PAO1	37	20 mM 2-oxoglutarate	Grown shaking at 200rpm				
-E-GEOD-47031	GSM1143375 1	GSM1143375_A1_Pae_G1a_.CEL	"PAO1 PAO1161 RifR parA1-39::smh (parAnull) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 1"	RNA	LB	PAO1161 RifR parA1-39::smh (parAnull)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143376 1	GSM1143376_A2_Pae_G1a_.CEL	"PAO1 PAO1161 RifR parA1-39::smh (parAnull) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 2"	RNA	LB	PAO1161 RifR parA1-39::smh (parAnull)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143377 1	GSM1143377_A3_Pae_G1a_.CEL	"PAO1 PAO1161 RifR parA1-39::smh (parAnull) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 3"	RNA	LB	PAO1161 RifR parA1-39::smh (parAnull)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143378 1	GSM1143378_B1_Pae_G1a_.CEL	"PAO1 PAO1161 RifR parB1-18::TcR (parBnull) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 1"	RNA	LB	PAO1161 RifR parB1-18::TcR (parBnull)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143379 1	GSM1143379_B2_Pae_G1a_.CEL	"PAO1 PAO1161 RifR parB1-18::TcR (parBnull) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 2"	RNA	LB	PAO1161 RifR parB1-18::TcR (parBnull)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143380 1	GSM1143380_B3_Pae_G1a_.CEL	"PAO1 PAO1161 RifR parB1-18::TcR (parBnull) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 3"	RNA	LB	PAO1161 RifR parB1-18::TcR (parBnull)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143381 1	GSM1143381_WT1_Pae_G1a_.CEL	"PAO1 WT (PAO1161) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 1"	RNA	LB	WT (PAO1161)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143382 1	GSM1143382_WT2_Pae_G1a_.CEL	"PAO1 WT (PAO1161) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 2"	RNA	LB	WT (PAO1161)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-47031	GSM1143383 1	GSM1143383_WT3_Pae_G1a_.CEL	"PAO1 WT (PAO1161) grown planktonically in LB with aeration at 37 degrees to an OD of 0.5, replicate 3"	RNA	LB	WT (PAO1161)	0.5	Planktonic	aerated	PAO1	37				Rifampin		
-E-GEOD-39867	GSM980539 1	GSM980539_MHH1372.CEL	"P. aeruginosa TBCF10839nrtR mutant cells harvested at late exponential phase, 37°C LB, replicate 1"	RNA	LB	TBCF10839nrtR::Tn5		Planktonic	Shaking	TBCF10839	37		late exponential phase				
-E-GEOD-39867	GSM980540 1	GSM980540_MHH1373.CEL	"P. aeruginosa TBCF10839 nrtR mutant cells harvested at late exponential phase, 37°C LB, replicate 2"	RNA	LB	TBCF10839nrtR::Tn5		Planktonic	Shaking	TBCF10839	37		late exponential phase				
-E-GEOD-39867	GSM980541 1	GSM980541_MHH1376.CEL	"P. aeruginosa TBCF10839 cells harvested at late exponential phase, 37°C LB, replicate 1"	RNA	LB	WT		Planktonic	Shaking	TBCF10839	37		late exponential phase				
-E-GEOD-39867	GSM980542 1	GSM980542_MHH1377.CEL	"P. aeruginosa TBCF10839 cells harvested at late exponential phase, 37°C LB, replicate 2"	RNA	LB	WT		Planktonic	Shaking	TBCF10839	37		late exponential phase				
-E-GEOD-51409	GSM1244967 1	GSM1244967_PAO1-22-replicate-01.CEL	"PAO1 WT grown at 22C in LB with shaking to mid-log phase, replicate 1"	RNA	LB 	WT	0.5	planktonic	aerated	PAO1	22	ciprofloxacin (1 ug/ml;~8x MIC)					
-E-GEOD-51409	GSM1244968 1	GSM1244968_PAO1-22-replicate-02.CEL	"PAO1 WT grown at 22C in LB with shaking to mid-log phase, replicate 2"	RNA	LB 	WT	0.5	planktonic	aerated	PAO1	22	ciprofloxacin (1 ug/ml;~8x MIC)					
-E-GEOD-51409	GSM1244969 1	GSM1244969_PAO1-22-replicate-03.CEL	"PAO1 WT grown at 22C in LB with shaking to mid-log phase, replicate 3"	RNA	LB 	WT	0.5	planktonic	aerated	PAO1	22	ciprofloxacin (1 ug/ml;~8x MIC)					
-E-GEOD-51409	GSM1244970 1	GSM1244970_PAO1-37-replicate-01.CEL	"PAO1 WT grown at 37C in LB with shaking to mid-log phase, replicate 1"	RNA	LB 	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)					
-E-GEOD-51409	GSM1244971 1	GSM1244971_PAO1-37-replicate-02.CEL	"PAO1 WT grown at 37C in LB with shaking to mid-log phase, replicate 2"	RNA	LB 	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)					
-E-GEOD-51409	GSM1244972 1	GSM1244972_PAO1-37-replicate-03.CEL	"PAO1 WT grown at 37C in LB with shaking to mid-log phase, replicate 3"	RNA	LB 	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)					
-E-GEOD-51076	GSM1237734 1	GSM1237734_M9_H103_2_51059600653056031211409890238138.CEL	H103 (PAO1) WT; control; replicate 1	RNA	M9 minimal medium + 0.2% glucose	WT	0.4	Planktonic	Aerated	H103 (PAO1)	37		OD580; mid log phase cells; 180 rpm				
-E-GEOD-51076	GSM1237735 1	GSM1237735_M9_H103_3_51059600653061050411410396750551.CEL	H103 (PAO1) WT; control; replicate 2	RNA	M9 minimal medium + 0.2% glucose	WT	0.4	Planktonic	Aerated	H103 (PAO1)	37		OD580; mid log phase cells; 180 rpm				
-E-GEOD-51076	GSM1237736 1	GSM1237736_M9_H103_4_51059600653061050411410396750502.CEL	H103 (PAO1) WT; control; replicate 3	RNA	M9 minimal medium + 0.2% glucose	WT	0.4	Planktonic	Aerated	H103 (PAO1)	37		OD580; mid log phase cells; 180 rpm				
-E-GEOD-51076	GSM1237737 1	GSM1237737_M9_PAOSX_1_51059600653060050411410396750419.CEL	PAOSX (PAO1) ΔsigX; replicate 1	RNA	M9 minimal medium + 0.2% glucose	ΔsigX	0.4	Planktonic	Aerated	PAOSX (PAO1)	37		OD580; mid log phase cells; 180 rpm				
-E-GEOD-51076	GSM1237738 1	GSM1237738_M9_PAOSX_3_51059600653060050411410396750359.CEL	PAOSX (PAO1) ΔsigX; replicate 2	RNA	M9 minimal medium + 0.2% glucose	ΔsigX	0.4	Planktonic	Aerated	PAOSX (PAO1)	37		OD580; mid log phase cells; 180 rpm				
-E-GEOD-51076	GSM1237739 1	GSM1237739_M9_PAOSX_4_51059600653061050411410396750526.CEL	PAOSX (PAO1) ΔsigX; replicate 3	RNA	M9 minimal medium + 0.2% glucose	ΔsigX	0.4	Planktonic	Aerated	PAOSX (PAO1)	37		OD580; mid log phase cells; 180 rpm				
-E-MEXP-3970	Treated_1	Overhage11002_Pae_G1a.CEL	PAO1 WT; LL-37; replicate 1	RNA	MHB	WT		Planktonic	Aerated	H103 (PAO1)	37	20 ug/ml LL-37	2 hours; mid log phase cells; 170 rpm				
-E-MEXP-3970	Treated_2	Overhage5002_Pae_G1a.CEL	PAO1 WT; LL-37; replicate 2	RNA	MHB	WT		Planktonic	Aerated	H103 (PAO1)	37	20 ug/ml LL-37	2 hours; mid log phase cells; 170 rpm				
-E-MEXP-3970	Untreated_1	Overhage11001_Pae_G1a.CEL	PAO1 WT; control; replicate 1	RNA	MHB	WT		Planktonic	Aerated	H103 (PAO1)	37		2 hours; mid log phase cells; 170 rpm				
-E-MEXP-3970	Untreated_2	Overhage5001_Pae_G1a.CEL	PAO1 WT; control; replicate 2	RNA	MHB	WT		Planktonic	Aerated	H103 (PAO1)	37		2 hours; mid log phase cells; 170 rpm				
-E-GEOD-33871	GSM838206 1	GSM838206_OXYR-LB-1_Pae_G1a.CEL	"PAO1 ΔoxyR grown planktonically in LB for 12 hours at 37 degrees, replicate 1"	RNA	LB	ΔoxyR	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838207 1	GSM838207_OXYR-LB-2_Pae_G1a.CEL	"PAO1 ΔoxyR grown planktonically in LB for 12 hours at 37 degrees, replicate 2"	RNA	LB	ΔoxyR	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838208 1	GSM838208_OXYR-LB-4_Pae_G1a.CEL	"PAO1 ΔoxyR grown planktonically in LB for 12 hours at 37 degrees, replicate 3"	RNA	LB	ΔoxyR	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838209 1	GSM838209_OXYR-PM-1_Pae_G1a.CEL	"PAO1 ΔoxyR grown planktonically in King's A medium for 12 hours at 37 degrees, replicate 1"	RNA	King's A Medium	ΔoxyR	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838210 1	GSM838210_OXYR-PM-3_Pae_G1a.CEL	"PAO1 ΔoxyR grown planktonically in King's A medium for 12 hours at 37 degrees, replicate 2"	RNA	King's A Medium	ΔoxyR	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838211 1	GSM838211_OXYR-PM-4_Pae_G1a.CEL	"PAO1 ΔoxyR grown planktonically in King's A medium for 12 hours at 37 degrees, replicate 3"	RNA	King's A Medium	ΔoxyR	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838212 1	GSM838212_PAO1-LB-1_Pae_G1a.CEL	"PAO1 WT grown planktonically in LB for 12 hours at 37 degrees, replicate 1"	RNA	LB	WT	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838213 1	GSM838213_PAO1-LB-2_Pae_G1a.CEL	"PAO1 WT grown planktonically in LB for 12 hours at 37 degrees, replicate 2"	RNA	LB	WT	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838214 1	GSM838214_PAO1-LB-3_Pae_G1a.CEL	"PAO1 WT grown planktonically in LB for 12 hours at 37 degrees, replicate 3"	RNA	LB	WT	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838215 1	GSM838215_PAO1-PM-1_Pae_G1a.CEL	"PAO1 WT grown planktonically in King's A medium for 12 hours at 37 degrees, replicate 1"	RNA	King's A Medium	WT	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838216 1	GSM838216_PAO1-PM-3_Pae_G1a.CEL	"PAO1 WT grown planktonically in King's A medium for 12 hours at 37 degrees, replicate 2"	RNA	King's A Medium	WT	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838217 1	GSM838217_PAO1-PM-4_Pae_G1a.CEL	"PAO1 WT grown planktonically in King's A medium for 12 hours at 37 degrees, replicate 3"	RNA	King's A Medium	WT	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838218 1	GSM838218_PAO1-5344-1_Pae_G1a.CEL	"PAO1 oxyR overexpression strain grown planktonically in LB for 12 hours at 37 degrees, replicate 1s"	RNA	LB	oxyR overexpression	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838219 1	GSM838219_PAO1-5344-3_Pae_G1a.CEL	"PAO1 oxyR overexpression strain grown planktonically in LB for 12 hours at 37 degrees, replicate 2"	RNA	LB	oxyR overexpression	12 hrs	Planktonic		PAO1	37						
-E-GEOD-33871	GSM838220 1	GSM838220_PAO1-5344-4_Pae_G1a.CEL	"PAO1 oxyR overexpression strain grown planktonically in LB for 12 hours at 37 degrees, replicate 3"	RNA	LB	oxyR overexpression	12 hrs	Planktonic		PAO1	37						
-E-GEOD-24038	GSM591496 1	GSM591496.CEL	"Pseudomonas aeruginosa PA14 WT, grown in LB, no treatment control, replicate 1"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	no treatment					
-E-GEOD-24038	GSM591601 1	GSM591601.CEL	"Pseudomonas aeruginosa PA14 WT, grown in LB, no treatment control, replicate 2"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	no treatment					
-E-GEOD-24038	GSM591602 1	GSM591602.CEL	"Pseudomonas aeruginosa PA14 WT, grown in LB, no treatment control, replicate 3"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	no treatment					
-E-GEOD-24038	GSM591624 1	GSM591624.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB, no treatment control, replicate 1"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	no treatment		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24038	GSM591625 1	GSM591625.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB, no treatment control, replicate 2"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	no treatment		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24038	GSM591627 1	GSM591627.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB, no treatment control, replicate 3"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	no treatment		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-52445	GSM1267086 1	GSM1267086_HZI1981_G1a.CEL	PAO1 WT. Planktonic. 0 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 0 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267087 1	GSM1267087_HZI1950_Pae_G1a.CEL	PAO1 WT. Planktonic. 5 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 5 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267088 1	GSM1267088_HZI1951_Pae_G1a.CEL	PAO1 WT. Planktonic. 10 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 10 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267089 1	GSM1267089_HZI1952_Pae_G1a.CEL	PAO1 WT. Planktonic. 15 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 15 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267090 1	GSM1267090_HZI1953a_Pae_G1a.CEL	PAO1 WT. Planktonic. 20 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 20 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267091 1	GSM1267091_HZI1954_Pae_G1a.CEL	PAO1 WT. Planktonic. 25 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 25 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267092 1	GSM1267092_HZI1955_Pae_G1a.CEL	PAO1 WT. Planktonic. 30 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 30 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267093 1	GSM1267093_HZI1956_Pae_G1a.CEL	PAO1 WT. Planktonic. 35 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 35 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267094 1	GSM1267094_HZI1958_Pae_G1a.CEL	PAO1 WT. Planktonic. 40 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 40 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267095 1	GSM1267095_HZI1959_Pae_G1a.CEL	PAO1 WT. Planktonic. 50 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 50 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267096 1	GSM1267096_HZI1960a_Pae_G1a.CEL	PAO1 WT. Planktonic. 60 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 60 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267097 1	GSM1267097_HZI1961_Pae_G1a.CEL	PAO1 WT. Planktonic. 70 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 70 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267098 1	GSM1267098_HZI1962_Pae_G1a.CEL	PAO1 WT. Planktonic. 80 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 80 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267099 1	GSM1267099_HZI1963_Pae_G1a.CEL	PAO1 WT. Planktonic. 90 mins after the high (100%)- to low (0.5%)-oxygen transition. 	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 90 mins during the high- to low-oxygen transition 				
-E-GEOD-52445	GSM1267100 1	GSM1267100_HZI1966_Pae_G1a.CEL	PAO1 WT. Planktonic. 0 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 0 min during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267101 1	GSM1267101_HZI1967_Pae_G1a.CEL	PAO1 WT. Planktonic. 5 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 5 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267102 1	GSM1267102_HZI1968_Pae_G1a.CEL	PAO1 WT. Planktonic. 10 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 10 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267103 1	GSM1267103_HZI1969_Pae_G1a.CEL	PAO1 WT. Planktonic. 15 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 15 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267104 1	GSM1267104_HZI1970_Pae_G1a.CEL	PAO1 WT. Planktonic. 20 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 20 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267105 1	GSM1267105_HZI1971_Pae_G1a.CEL	PAO1 WT. Planktonic. 25 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 25 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267106 1	GSM1267106_HZI1972_Pae_G1a.CEL	PAO1 WT. Planktonic. 30 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 30 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267107 1	GSM1267107_HZI1973_Pae_G1a.CEL	PAO1 WT. Planktonic. 35 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 35 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267108 1	GSM1267108_HZI1974_G1a.CEL	PAO1 WT. Planktonic. 40 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 40 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267109 1	GSM1267109_HZI1975_G1a.CEL	PAO1 WT. Planktonic. 50 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 50 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267110 1	GSM1267110_HZI1976_G1a.CEL	PAO1 WT. Planktonic. 60 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 60 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267111 1	GSM1267111_HZI1977_G1a.CEL	PAO1 WT. Planktonic. 70 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 70 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267112 1	GSM1267112_HZI1978_G1a.CEL	PAO1 WT. Planktonic. 80 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 80 mins during the low- to high-oxygen transition 				
-E-GEOD-52445	GSM1267113 1	GSM1267113_HZI1979_G1a.CEL	PAO1 WT. Planktonic. 90 mins after the low (0.5%)- to high (100%)-oxygen transition.  	RNA	"Medium C (per liter, 1.5 g glucose, 6 g of yeast extract, 0.6 g of (NH4)2SO4, 2 g of Na2HPO4, 0.3 g of MgSO4 7H2O, and pH adjusted to 7.0 with 1 M NaOH)."	WT		Planktonic 	Stirred bioreactor	PAO1	37		Stirred bioreactor at 90 mins during the low- to high-oxygen transition 				
-E-GEOD-48982	GSM1191066 1	GSM1191066_sphR-pyr-1.CEL	Pseudomonas aeruginosa sphR mutant in MOPS pyruvate	RNA	MOPS 20mM pyruvate	∆sphR		Planktonic	aerated	PAO1	37		18 h				
-E-GEOD-48982	GSM1191067 1	GSM1191067_sphR-pyr-2.CEL	Pseudomonas aeruginosa sphR mutant in MOPS pyruvate	RNA	MOPS 20mM pyruvate	∆sphR		Planktonic	aerated	PAO1	37		18 h				
-E-GEOD-48982	GSM1191068 1	GSM1191068_sphR-surf-1.CEL	Pseudomonas aeruginosa sphR mutant in MOPS pyruvate plus lung surfactant	RNA	MOPS 20mM pyruvate	∆sphR		Planktonic	aerated	PAO1	37	Survanta 	18 h				
-E-GEOD-48982	GSM1191069 1	GSM1191069_sphR-surf-2.CEL	Pseudomonas aeruginosa sphR mutant in MOPS pyruvate plus lung surfactant	RNA	MOPS 20mM pyruvate	∆sphR		Planktonic	aerated	PAO1	37	Survanta 	18 h				
-E-GEOD-48982	GSM1191070 1	GSM1191070_WT-pyr-1.CEL	Pseudomonas aeruginosa WT in MOPS pyruvate	RNA	MOPS 20mM pyruvate	WT		Planktonic	aerated	PAO1	37		18 h				
-E-GEOD-48982	GSM1191071 1	GSM1191071_WT-pyr-2.CEL	Pseudomonas aeruginosa WT in MOPS pyruvate	RNA	MOPS 20mM pyruvate	WT		Planktonic	aerated	PAO1	37		18 h				
-E-GEOD-48982	GSM1191072 1	GSM1191072_WT-surf-1.CEL	Pseudomonas aeruginosa WT in MOPS pyruvate plus lung surfactant	RNA	MOPS 20mM pyruvate	WT		Planktonic	aerated	PAO1	37	Survanta 	18 h				
-E-GEOD-48982	GSM1191073 1	GSM1191073_WT-surf-2.CEL	Pseudomonas aeruginosa WT in MOPS pyruvate plus lung surfactant	RNA	MOPS 20mM pyruvate	WT		Planktonic	aerated	PAO1	37	Survanta 	18 h				
-E-GEOD-49759	GSM1206643 1	GSM1206643_WT-PYR-1.CEL	"PA14 WT; control, replicate 1"	RNA	MOPS + 20 mM pyruvate	WT	0.45	Planktonic	Aerated	PA14	37		4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206644 1	GSM1206644_WT-PYR-2.CEL	"PA14 WT; control, replicate 2"	RNA	MOPS + 20 mM pyruvate	WT	0.45	Planktonic	Aerated	PA14	37		4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206645 1	GSM1206645_WT-CHO-1.CEL	PA14 WT; choline; replicate 1	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM choline	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206646 1	GSM1206646_WT-CHO-2.CEL	PA14 WT; choline; replicate 2	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM choline	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206647 1	GSM1206647_WT-DTC-1.CEL	PA14 WT; diethylcholine; replicate 1	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM diethylcholine	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206648 1	GSM1206648_WT-DTC-2.CEL	PA14 WT; diethylcholine; replicate 2	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM diethylcholine	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206649 1	GSM1206649_WT-ETC-1.CEL	PA14 WT; ethylcholine; replicate 1	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM ethylcholine	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206650 1	GSM1206650_WT-ETC-2.CEL	PA14 WT; ethylcholine; replicate 2	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM ethylcholine	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206651 1	GSM1206651_WT-PRC-1.CEL	PA14 WT; choline and propargylcholine; replicate 1	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM choline + 250 uM propargylcholine	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206652 1	GSM1206652_WT-PRC-2.CEL	PA14 WT; choline and propargylcholine; replicate 2	RNA	MOPS + 20 mM pyruvate 	WT	0.45	Planktonic	Aerated	PA14	37	250 uM choline + 250 uM propargylcholine	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206653 1	GSM1206653_dgcA-CHO-1.CEL	PA14 ΔdgcA; choline; replicate 1	RNA	MOPS + 20 mM pyruvate 	ΔdgcA	0.45	Planktonic	Aerated	PA14	37	250 uM choline	4 hours; horizontal shaker				
-E-GEOD-49759	GSM1206654 1	GSM1206654_dgcA-CHO-2.CEL	PA14 ΔdgcA; choline; replicate 2	RNA	MOPS + 20 mM pyruvate 	ΔdgcA	0.45	Planktonic	Aerated	PA14	37	250 uM choline	4 hours; horizontal shaker				
-E-GEOD-48587	GSM1181847 1	GSM1181847_gc_WT1_1.CEL	Gene expression data from WT strain PAO1 grown under cyanogenic conditions_rep1	RNA	synthetic glycine minimal medium (MMC)	WT	1	Planktonic	shaking	PAO1	37	under oxygen limitation					
-E-GEOD-48587	GSM1181848 1	GSM1181848_gc_WT2_3.CEL	Gene expression data from WT strain PAO1 grown under cyanogenic conditions_rep2	RNA	synthetic glycine minimal medium (MMC)	WT	1	Planktonic	shaking	PAO1	37	under oxygen limitation					
-E-GEOD-48587	GSM1181849 1	GSM1181849_gc_Delta1_2.CEL	Gene expression data from HCN mutant strain PAO6344 grown under cyanogenic conditions_rep1	RNA	synthetic glycine minimal medium (MMC)	ΔhcnB	1	Planktonic	shaking	PAO6344 (PAO1)	37	under oxygen limitation					
-E-GEOD-48587	GSM1181850 1	GSM1181850_gc_Delta2_4.CEL	Gene expression data from HCN mutant strain PAO6344 grown under cyanogenic conditions_rep2	RNA	synthetic glycine minimal medium (MMC)	ΔhcnB	1	Planktonic	shaking	PAO6344 (PAO1)	37	under oxygen limitation					
-E-MEXP-3764	Control_Aerobic_1	control1aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic control, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	aerobic control					
-E-MEXP-3764	Control_Aerobic_2	control2aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic control, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	aerobic control					
-E-MEXP-3764	Control_Aerobic_3	control3aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic control, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	aerobic control					
-E-MEXP-3764	Control_Anaerobic_1	control1_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic control, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	anaerobic control					
-E-MEXP-3764	Control_Anaerobic_2	control2_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic control, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	anaerobic control					
-E-MEXP-3764	Control_Anaerobic_3	control3_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic control, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	anaerobic control					
-E-MEXP-3764	Fos_Aerobic_1	fos1aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 1 mg/l fosfomycin was added, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 1 mg/l fosfomycin was added"					
-E-MEXP-3764	Fos_Aerobic_2	fos2aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 1 mg/l fosfomycin was added, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 1 mg/l fosfomycin was added"					
-E-MEXP-3764	Fos_Aerobic_3	Fos3_aerobic_wdh_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 1 mg/l fosfomycin was added, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 1 mg/l fosfomycin was added"					
-E-MEXP-3764	Fos_Anaerobic_1	fos1_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 1 mg/l fosfomycin was added, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 1 mg/l fosfomycin was added"					
-E-MEXP-3764	Fos_Anaerobic_2	fos2_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 1 mg/l fosfomycin was added, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 1 mg/l fosfomycin was added"					
-E-MEXP-3764	Fos_Anaerobic_3	fos3_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 1 mg/l fosfomycin was added, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 1 mg/l fosfomycin was added"					
-E-MEXP-3764	FT_Aerobic_1	FT1aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added"					
-E-MEXP-3764	FT_Aerobic_2	FT2_aerobic_wdh_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added"					
-E-MEXP-3764	FT_Aerobic_3	FT3aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added"					
-E-MEXP-3764	FT_Anaerobic_1	FT1_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added"					
-E-MEXP-3764	FT_Anaerobic_2	FT2_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added"					
-E-MEXP-3764	FT_Anaerobic_3	FT3_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 1 mg/l fosfomycin and  0.25 mg/l tobramycin was added"					
-E-MEXP-3764	Tob_Aerobic_1	Tob1_aerobic_wdh_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 0.25 mg/l tobramycin was added, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 0.25 mg/l tobramycin was added"					
-E-MEXP-3764	Tob_Aerobic_2	Tob2_aerobic_wdh_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 0.25 mg/l tobramycin was added, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 0.25 mg/l tobramycin was added"					
-E-MEXP-3764	Tob_Aerobic_3	Tob3aerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, aerobic, 0.25 mg/l tobramycin was added, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"aerobic, 0.25 mg/l tobramycin was added"					
-E-MEXP-3764	Tob_Anaerobic_1	Tob1_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 0.25 mg/l tobramycin was added, rep1"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 0.25 mg/l tobramycin was added"					
-E-MEXP-3764	Tob_Anaerobic_2	Tob2_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 0.25 mg/l tobramycin was added, rep2"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 0.25 mg/l tobramycin was added"					
-E-MEXP-3764	Tob_Anaerobic_3	Tob3_anaerobic_Pae_G1a.CEL	"Pseudomonas aeruginosa clinical isolate CM6, anaerobic, 0.25 mg/l tobramycin was added, rep3"	RNA	MHB plus 1% (100 mM) potassium nitrate (KNO3)	clinical isolate	0.4	Planktonic	shaking	CM6	37	"anaerobic, 0.25 mg/l tobramycin was added"					
-E-GEOD-48429	GSM1177842 1	GSM1177842_PAO1-1.CEL	PAO1 WT colony biofilm on Pseudomonas isolation agar. Rep1	RNA	PIA	WT		Biofilm	Colony biofilm	PAO1	37		18 h colony biofilms				
-E-GEOD-48429	GSM1177843 1	GSM1177843_PAO1-2.CEL	PAO1 WT colony biofilm on Pseudomonas isolation agar. Rep2	RNA	PIA	WT		Biofilm	Colony biofilm	PAO1	37		18 h colony biofilms				
-E-GEOD-48429	GSM1177844 1	GSM1177844_PAO1-3.CEL	PAO1 WT colony biofilm on Pseudomonas isolation agar. Rep3	RNA	PIA	WT		Biofilm	Colony biofilm	PAO1	37		18 h colony biofilms				
-E-GEOD-48429	GSM1177845 1	GSM1177845_PAO1-AMV-1.CEL	PAO1 WT colony biofilm on Pseudomonas isolation agar plus ammonium metavanadate. Rep1	RNA	PIA	WT		Biofilm	Colony biofilm	PAO1	37	Ammonium metavanadate	18 h colony biofilms				
-E-GEOD-48429	GSM1177846 1	GSM1177846_PAO1-AMV-2.CEL	PAO1 WT colony biofilm on Pseudomonas isolation agar plus ammonium metavanadate. Rep2	RNA	PIA	WT		Biofilm	Colony biofilm	PAO1	37	Ammonium metavanadate	18 h colony biofilms				
-E-GEOD-48429	GSM1177847 1	GSM1177847_PAO1-AMV-3.CEL	PAO1 WT colony biofilm on Pseudomonas isolation agar plus ammonium metavanadate. Rep3	RNA	PIA	WT		Biofilm	Colony biofilm	PAO1	37	Ammonium metavanadate	18 h colony biofilms				
-E-GEOD-33160	GSM821084 1	GSM821084_HZI0139_Pae_G1a.CEL	P. aeruginosa PAO1_aerobic_AUM_rep 1	RNA	artificial urine medium 		18 hrs	Colony biofilms	Oxic	PAO1	37		aerobically grown				
-E-GEOD-33160	GSM821085 1	GSM821085_HZI0140_Pae_G1a.CEL	P. aeruginosa PAO1_aerobic_AUM_rep 2	RNA	artificial urine medium 		18 hrs	Colony biofilms	Oxic	PAO1	37		aerobically grown				
-E-GEOD-33160	GSM821086 1	GSM821086_HZI0141_Pae_G1a.CEL	P. aeruginosa PAO1_aerobic_AUM_rep 3	RNA	artificial urine medium 		18 hrs	Colony biofilms	Oxic	PAO1	37		aerobically grown				
-E-GEOD-33160	GSM821087 1	GSM821087_HZI0142_Pae_G1a.CEL	P. aeruginosa PAO1_aerobic_10-fold diluted LB_rep 1	RNA	10% LB		18 hrs	Colony biofilms	Oxic	PAO1	37		aerobically grown				
-E-GEOD-33160	GSM821088 1	GSM821088_HZI0143_Pae_G1a.CEL	P. aeruginosa PAO1_aerobic_10-fold diluted LB_rep 2	RNA	10% LB		18 hrs	Colony biofilms	Oxic	PAO1	37		aerobically grown				
-E-GEOD-33160	GSM821089 1	GSM821089_HZI0144_Pae_G1a.CEL	P. aeruginosa PAO1_aerobic_10-fold diluted LB_rep 3	RNA	10% LB		18 hrs	Colony biofilms	Oxic	PAO1	37		aerobically grown				
-E-GEOD-33160	GSM821090 1	GSM821090_HZI0145_Pae_G1a.CEL	P. aeruginosa PAO1_anaerobic_AUM_rep 1	RNA	artificial urine medium 		2 days	Colony biofilms	Anaerobic	PAO1	37	50 mM KNO3	anaerobically grown				
-E-GEOD-33160	GSM821091 1	GSM821091_HZI0146_Pae_G1a.CEL	P. aeruginosa PAO1_anaerobic_AUM_rep 2	RNA	artificial urine medium 		2 days	Colony biofilms	Anaerobic	PAO1	37	50 mM KNO3	anaerobically grown				
-E-GEOD-33160	GSM821092 1	GSM821092_HZI0148_Pae_G1a.CEL	P. aeruginosa PAO1_anaerobic_10-fold diluted LB_rep 1	RNA	10% LB		2 days	Colony biofilms	Anaerobic	PAO1	37	50 mM KNO3	anaerobically grown				
-E-GEOD-33160	GSM821093 1	GSM821093_HZI0149_Pae_G1a.CEL	P. aeruginosa PAO1_anaerobic_10-fold diluted LB_rep 2	RNA	10% LB		2 days	Colony biofilms	Anaerobic	PAO1	37	50 mM KNO3	anaerobically grown				
-E-GEOD-33160	GSM821094 1	GSM821094_HZI0147_Pae_G1a.CEL	P. aeruginosa PAO1_anaerobic_AUM_rep 3	RNA	artificial urine medium 		2 days	Colony biofilms	Anaerobic	PAO1	37	50 mM KNO3	anaerobically grown				
-E-GEOD-33160	GSM821095 1	GSM821095_HZI0150_Pae_G1a.CEL	P. aeruginosa PAO1_anaerobic_10-fold diluted LB_rep 3	RNA	10% LB		2 days	Colony biofilms	Anaerobic	PAO1	37	50 mM KNO3	anaerobically grown				
-E-GEOD-46603	GSM1133186 1	GSM1133186_Weiqing_He_L-Glu-1_021810.CEL	"P. aeruginosa PAO1 cultures were grown aerobically in pyruvate minimal medium P in the presence of 10 mM L-Glutamate, replicate 1"	RNA	Pyruvate minimal media. 	WT	0.5	Planktonic	Aerated	PAO1	37	10mM L-Glutamate	Grown shaking				
-E-GEOD-46603	GSM1133187 1	GSM1133187_Weiqing_He_L-Arg-1_021810.CEL	"P. aeruginosa PAO1 cultures were grown aerobically in pyruvate minimal medium P in the presence of 10 mM L-Arginine, replicate 1"	RNA	Pyruvate minimal media. 	WT	0.5	Planktonic	Aerated	PAO1	37	10mM L-Arginine	Grown shaking				
-E-GEOD-46603	GSM1133188 1	GSM1133188_Weiqing_He_D-Glu-1_021810.CEL	"P. aeruginosa PAO1 cultures were grown aerobically in pyruvate minimal medium P in the presence of 10 mM D-Glutamate, replicate 1"	RNA	Pyruvate minimal media. 	WT	0.5	Planktonic	Aerated	PAO1	37	10mM D-Glutamate	Grown shaking				
-E-GEOD-46603	GSM1133189 1	GSM1133189_Weiqing_He_L-Glu-2_022410.CEL	"P. aeruginosa PAO1 cultures were grown aerobically in pyruvate minimal medium P in the presence of 10 mM L-Glutamate, replicate 2"	RNA	Pyruvate minimal media. 	WT	0.5	Planktonic	Aerated	PAO1	37	10mM L-Glutamate	Grown shaking				
-E-GEOD-46603	GSM1133190 1	GSM1133190_Weiqing_He_L-Arg-2_022410.CEL	"P. aeruginosa PAO1 cultures were grown aerobically in pyruvate minimal medium P in the presence of 10 mM L-Arginine, replicate 2"	RNA	Pyruvate minimal media. 	WT	0.5	Planktonic	Aerated	PAO1	37	10mM L-Arginine	Grown shaking				
-E-GEOD-46603	GSM1133191 1	GSM1133191_Weiqing_He_D-Glu-2_022410.CEL	"P. aeruginosa PAO1 cultures were grown aerobically in pyruvate minimal medium P in the presence of 10 mM D-Glutamate, replicate 2"	RNA	Pyruvate minimal media. 	WT	0.5	Planktonic	Aerated	PAO1	37	10mM D-Glutamate	Grown shaking				
-E-MTAB-1381	Pseudomonas_aeruginosa_BAL6_1	Van_Delden_Kohler_0311_BAL6_1.CEL	Pseudomonas aeruginosa BAL6 (BAL30072 resistant PT5)_untreated_rep1	RNA	LB	selected for growth on 4 mg/l Siderophore Monosulfactam BAL30072	2	Planktonic	shaking	BAL6 (PAO1)	37						
-E-MTAB-1381	Pseudomonas_aeruginosa_BAL6_2	Van_Delden_Kohler_0311_BAL6_2.CEL	Pseudomonas aeruginosa BAL6 (BAL30072 resistant PT5)_untreated_rep2	RNA	LB	selected for growth on 4 mg/l Siderophore Monosulfactam BAL30073	2	Planktonic	shaking	BAL6 (PAO1)	37						
-E-MTAB-1381	Pseudomonas_aeruginosa_BAL6_3	Van_Delden_Kohler_0311_BAL6_3.CEL	Pseudomonas aeruginosa BAL6 (BAL30072 resistant PT5)_untreated_rep3	RNA	LB	selected for growth on 4 mg/l Siderophore Monosulfactam BAL30074	2	Planktonic	shaking	BAL6 (PAO1)	37						
-E-MTAB-1381	Pseudomonas_aeruginosa_BAL6+_1	Van_Delden_Kohler_0311_BAL6+_1.CEL	Pseudomonas aeruginosa BAL6 (BAL30072 resistant PT5)_treated with 4mg/l BAL30072_rep1	RNA	LB	selected for growth on 4 mg/l Siderophore Monosulfactam BAL30075	2	Planktonic	shaking	BAL6 (PAO1)	37	4 mg/l Siderophore Monosulfactam BAL30072					
-E-MTAB-1381	Pseudomonas_aeruginosa_BAL6+_2	Van_Delden_Kohler_0311_BAL6+_2.CEL	Pseudomonas aeruginosa BAL6 (BAL30072 resistant PT5)_treated with 4mg/l BAL30072_rep2	RNA	LB	selected for growth on 4 mg/l Siderophore Monosulfactam BAL30076	2	Planktonic	shaking	BAL6 (PAO1)	37	4 mg/l Siderophore Monosulfactam BAL30072					
-E-MTAB-1381	Pseudomonas_aeruginosa_BAL6+_3	Van_Delden_Kohler_0311_BAL6+_3.CEL	Pseudomonas aeruginosa BAL6 (BAL30072 resistant PT5)_treated with 4mg/l BAL30072_rep3	RNA	LB	selected for growth on 4 mg/l Siderophore Monosulfactam BAL30077	2	Planktonic	shaking	BAL6 (PAO1)	37	4 mg/l Siderophore Monosulfactam BAL30072					
-E-MTAB-1381	Pseudomonas_aeruginosa_PT5_1	Van_Delden_Kohler_0311_PT5_1.CEL	Pseudomonas aeruginosa PT5 (mexT nonfunctional PAO1)_untreated_rep1	RNA	LB	mexT nonfunctional	2	Planktonic	shaking	PT5 (PAO1)	37						
-E-MTAB-1381	Pseudomonas_aeruginosa_PT5_2	Van_Delden_Kohler_0311_PT5_2.CEL	Pseudomonas aeruginosa PT5 (mexT nonfunctional PAO1)_untreated_rep2	RNA	LB	mexT nonfunctional	2	Planktonic	shaking	PT5 (PAO1)	37						
-E-MTAB-1381	Pseudomonas_aeruginosa_PT5_3	Van_Delden_Kohler_0311_PT5_3.CEL	Pseudomonas aeruginosa PT5 (mexT nonfunctional PAO1)_untreated_rep3	RNA	LB	mexT nonfunctional	2	Planktonic	shaking	PT5 (PAO1)	37						
-E-GEOD-45695	GSM1112046 1	GSM1112046_PGAS_25_1A.CEL	"P. aeruginosa PAO1, gene expression data from cells growth in PPGAS media at 25C_rep1"	RNA	PPGAS media	WT	1.5	Planktonic	shaking	PAO1	25						
-E-GEOD-45695	GSM1112047 1	GSM1112047_PGAS_25_2A.CEL	"P. aeruginosa PAO1, gene expression data from cells growth in PPGAS media at 25C_rep2"	RNA	PPGAS media	WT	1.5	Planktonic	shaking	PAO1	25						
-E-GEOD-45695	GSM1112048 1	GSM1112048_PGAS_25_4A.CEL	"P. aeruginosa PAO1, gene expression data from cells growth in PPGAS media at 25C_rep3"	RNA	PPGAS media	WT	1.5	Planktonic	shaking	PAO1	25						
-E-GEOD-45695	GSM1112049 1	GSM1112049_PGAS_37_1B.CEL	"P. aeruginosa PAO1, gene expression data from cells growth in PPGAS media at 37C_rep1"	RNA	PPGAS media	WT	1.5	Planktonic	shaking	PAO1	37						
-E-GEOD-45695	GSM1112050 1	GSM1112050_PGAS_37_2B.CEL	"P. aeruginosa PAO1, gene expression data from cells growth in PPGAS media at 37C_rep2"	RNA	PPGAS media	WT	1.5	Planktonic	shaking	PAO1	37						
-E-GEOD-45695	GSM1112051 1	GSM1112051_PGAS_37_3A.CEL	"P. aeruginosa PAO1, gene expression data from cells growth in PPGAS media at 37C_rep3"	RNA	PPGAS media	WT	1.5	Planktonic	shaking	PAO1	37						
-E-GEOD-28194	GSM698080 1	GSM698080.CEL	P. aeruginosa PAO1 in LB for 7h biofilm cells	RNA	LB	WT		Biofilm	shaking - glass wool	PAO1	37						
-E-GEOD-28194	GSM698081 1	GSM698081.CEL	P. aeruginosa PAO1 LB 1% Streptomyces 230 for 7h biofilm cells	RNA	LB	WT		Biofilm	shaking - glass wool	PAO1	37	Streptomyces 230 strain culture media was added in at 1%				Streptomyces 230 strain supernatant	Bacteria
-E-GEOD-43641	GSM1067456 1	GSM1067456_PAO1_CK_1.CEL	PAO1 WT; control; replicate 1	RNA	LB + 7.5 mM nitriloacetic acid	WT	1.5	Planktonic	Aerated	PAO1	37		4 hours; 260 rpm				
-E-GEOD-43641	GSM1067457 1	GSM1067457_PAO1_PAA_1.CEL	PAO1 WT; treated; replicate 1	RNA	LB + 7.5 mM nitriloacetic acid	WT	1.5	Planktonic	Aerated	PAO1	37	1 mM Phenylacetic acid	4 hours; 260 rpm				
-E-GEOD-43641	GSM1067458 1	GSM1067458_PAO1_CK_2.CEL	PAO1 WT; control; replicate 2	RNA	LB + 7.5 mM nitriloacetic acid	WT	1.5	Planktonic	Aerated	PAO1	37		4 hours; 260 rpm				
-E-GEOD-43641	GSM1067459 1	GSM1067459_PAO1_PAA_2.CEL	PAO1 WT; treated; replicate 2	RNA	LB + 7.5 mM nitriloacetic acid	WT	1.5	Planktonic	Aerated	PAO1	37	1 mM Phenylacetic acid	4 hours; 260 rpm				
-E-GEOD-35632	GSM871963 1	GSM871963.CEL	"P. aeruginosa PAO1 with empty vector, grown in LB to mid-exponential phase_rep1"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO1	37		Grown shaking at 200rpm		GentR		
-E-GEOD-35632	GSM871964 1	GSM871964.CEL	"P. aeruginosa PAO1 with empty vector, grown in LB to mid-exponential phase_rep2"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO1	37		Grown shaking at 200rpm		GentR		
-E-GEOD-35632	GSM871965 1	GSM871965.CEL	"P. aeruginosa PAO1 with empty vector, grown in LB to mid-exponential phase_rep3"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO1	37		Grown shaking at 200rpm		GentR		
-E-GEOD-35632	GSM871966 1	GSM871966.CEL	"P. aeruginosa PAO1 with empty vector, grown in LB to mid-exponential phase_rep4"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO1	37		Grown shaking at 200rpm		GentR		
-E-GEOD-35632	GSM871967 1	GSM871967.CEL	"P. aeruginosa PAO1 with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep1"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO1	37		IPTG induced RpoN molecular roadblock		GentR		
-E-GEOD-35632	GSM871968 1	GSM871968.CEL	"P. aeruginosa PAO1 with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep2"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO1	37		IPTG induced RpoN molecular roadblock		GentR		
-E-GEOD-35632	GSM871969 1	GSM871969.CEL	"P. aeruginosa PAO1 with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep3"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO1	37		IPTG induced RpoN molecular roadblock		GentR		
-E-GEOD-35632	GSM871970 1	GSM871970.CEL	"P. aeruginosa PAO1 with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep4"	RNA	LB supplemented with 30 mg/L gentamicin	WT + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO1	37		IPTG induced RpoN molecular roadblock		GentR		
-E-GEOD-35632	GSM871971 1	GSM871971.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with empty vector, grown in LB to mid-exponential phase_rep1"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		Grown shaking at 200rpm		Km + GentR		
-E-GEOD-35632	GSM871972 1	GSM871972.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with empty vector, grown in LB to mid-exponential phase_rep2"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		Grown shaking at 200rpm		Km + GentR		
-E-GEOD-35632	GSM871973 1	GSM871973.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with empty vector, grown in LB to mid-exponential phase_rep3"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		Grown shaking at 200rpm		Km + GentR		
-E-GEOD-35632	GSM871974 1	GSM871974.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with empty vector, grown in LB to mid-exponential phase_rep4"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL344 (empty plasmid)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		Grown shaking at 200rpm		Km + GentR		
-E-GEOD-35632	GSM871975 1	GSM871975.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep1"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		IPTG induced RpoN molecular roadblock		Km + GentR		
-E-GEOD-35632	GSM871976 1	GSM871976.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep2"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		IPTG induced RpoN molecular roadblock		Km + GentR		
-E-GEOD-35632	GSM871977 1	GSM871977.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep3"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		IPTG induced RpoN molecular roadblock		Km + GentR		
-E-GEOD-35632	GSM871978 1	GSM871978.CEL	"P. aeruginosa PAO6359 (ΔrpoN) with plasmid expressing RpoN molecular roadblock (IPTG induced), grown in LB to mid-exponential phase_rep4"	RNA	LB supplemented with 30 mg/L gentamicin	ΔrpoN + pBRL348 (RpoN*)	0.2	Planktonic	shaking	PAO6359 (PAO1)	37		IPTG induced RpoN molecular roadblock		Km + GentR		
-E-GEOD-41926	GSM1027584 1	GSM1027584_062508WT.CEL	"PAO1 WT in MOPS lung surfactant, 37 C"	RNA	MOPS defined medium	WT	0.4	Planktonic	aerated	PAO1	37	3% Survanta	4 hours; mid exponential phase cells; shaking				
-E-GEOD-41926	GSM1027585 1	GSM1027585_071008WT.CEL	"PAO1 WT in MOPS lung surfactant, 37 C"	RNA	MOPS defined medium	WT	0.4	Planktonic	aerated	PAO1	37	3% Survanta	4 hours; mid exponential phase cells; shaking				
-E-GEOD-41926	GSM1027586 1	GSM1027586_062508plcHR.CEL	"PAO1 ∆plcHR in MOPS lung surfactant, 37 C"	RNA	MOPS defined medium	∆plcHR	0.4	Planktonic	aerated	PAO1	37	3% Survanta	4 hours; mid exponential phase cells; shaking				
-E-GEOD-41926	GSM1027587 1	GSM1027587_071008plcHR.CEL	"PAO1 ∆plcHR in MOPS lung surfactant, 37 C"	RNA	MOPS defined medium	∆plcHR	0.4	Planktonic	aerated	PAO1	37	3% Survanta	4 hours; mid exponential phase cells; shaking				
-E-GEOD-41926	GSM1027588 1	GSM1027588_062508gbdR.CEL	"PAO1 ∆gbdR in MOPS lung surfactant, 37 C"	RNA	MOPS defined medium	∆gbdR	0.4	Planktonic	aerated	PAO1	37	3% Survanta	4 hours; mid exponential phase cells; shaking				
-E-GEOD-41926	GSM1027589 1	GSM1027589_071008gbdR.CEL	"PAO1 ∆gbdR in MOPS lung surfactant, 37 C"	RNA	MOPS defined medium	∆gbdR	0.4	Planktonic	aerated	PAO1	37	3% Survanta	4 hours; mid exponential phase cells; shaking				
-E-GEOD-23367	GSM573322 1	GSM573322.CEL	"Pseudomonas aeruginosa PAO1 PA2206::Tn5 mutant strain, grown shaking to OD 0.5 then exposed to H2O2 for ten minutes, replicate 1"	RNA	M9 2% glucose	PA2206::Tn5 mutant	0.5	Planktonic	Shaking	PAO1	37	H2O2_10mins			TetR		
-E-GEOD-23367	GSM573323 1	GSM573323.CEL	"Pseudomonas aeruginosa PAO1 PA2206::Tn5 mutant strain, grown shaking to OD 0.5 then exposed to H2O2 for ten minutes, replicate 2"	RNA	M9 2% glucose	PA2206::Tn5 mutant	0.5	Planktonic	Shaking	PAO1	37	H2O2_10mins			TetR		
-E-GEOD-23367	GSM573324 1	GSM573324.CEL	"Pseudomonas aeruginosa PAO1 PA2206::Tn5 strain complemented with pBR1-pa2206c grown shaking to OD 0.5 then exposed to H2O2 for ten minutes, replicate 1"	RNA	M9 2% glucose	PA2206 complemented strain	0.5	Planktonic	Shaking	PAO1	37	H2O2_10mins			"Tet, Gent"		
-E-GEOD-23367	GSM573325 1	GSM573325.CEL	"Pseudomonas aeruginosa PAO1 PA2206::Tn5 strain complemented with pBR1-pa2206c grown shaking to OD 0.5 then exposed to H2O2 for ten minutes, replicate 2"	RNA	M9 2% glucose	PA2206 complemented strain	0.5	Planktonic	Shaking	PAO1	37	H2O2_10mins			"Tet, Gent"		
-E-GEOD-23367	GSM573326 1	GSM573326.CEL	"Pseudomonas aeruginosa PAO1 PA2206::Tn5 strain complemented with pBR1-pa2206c grown shaking to OD 0.5 then exposed to H2O2 for ten minutes, replicate 3"	RNA	M9 2% glucose	PA2206 complemented strain	0.5	Planktonic	Shaking	PAO1	37	H2O2_10mins			"Tet, Gent"		
-E-GEOD-23367	GSM573327 1	GSM573327.CEL	"Pseudomonas aeruginosa PAO1 PA2206::Tn5 mutant strain, grown shaking to OD 0.5 then exposed to H2O2 for ten minutes, replicate 3"	RNA	M9 2% glucose	PA2206::Tn5 mutant	0.5	Planktonic	Shaking	PAO1	37	H2O2_10mins			TetR		
-E-GEOD-25945	GSM637573 1	GSM637573.CEL	In vitro P. aeruginosa gene expression from sessile growth p10 LB 1 HZI1050 p10_LB_a.CEL Re-analysis of Sample GSM567666	RNA	LB		early stationary	Planktonic	static	p10							
-E-GEOD-25945	GSM637574 1	GSM637574.CEL	In vitro P. aeruginosa gene expression from sessile growth p10 LB 2 HZI1052 p10_LB_b.CEL Re-analysis of Sample GSM567667	RNA	LB		early stationary	Planktonic	static	p10							
-E-GEOD-25945	GSM637575 1	GSM637575.CEL	In vitro P. aeruginosa gene expression from sessile growth p11 LB 1 GBF0928 p11_LB_a.CEL Re-analysis of Sample GSM567676	RNA	LB		early stationary	Planktonic	static	p11							
-E-GEOD-25945	GSM637576 1	GSM637576.CEL	In vitro P. aeruginosa gene expression from sessile growth p11 LB 2 GBF0929 p11_LB_b.CEL Re-analysis of Sample GSM567677	RNA	LB		early stationary	Planktonic	static	p11							
-E-GEOD-25945	GSM637577 1	GSM637577.CEL	In vitro P. aeruginosa gene expression from sessile growth p17 LB 1 HZI1548 p17_lb_a.CEL Re-analysis of Sample GSM567686	RNA	LB		early stationary	Planktonic	static	p17							
-E-GEOD-25945	GSM637578 1	GSM637578.CEL	In vitro P. aeruginosa gene expression from sessile growth p17 LB 2 HZI1549 p17_lb_b.CEL Re-analysis of Sample GSM567687	RNA	LB		early stationary	Planktonic	static	p17							
-E-GEOD-25945	GSM637579 1	GSM637579.CEL	In vitro P. aeruginosa gene expression from sessile growth CF1-4 LB 1 HZI1733_Pae_G1a.CEL	RNA	LB		early stationary	Planktonic	static	"CF1, CF2, CF3, CF4"							
-E-GEOD-25945	GSM637580 1	GSM637580.CEL	In vitro P. aeruginosa gene expression from sessile growth CF1-4 LB 2 HZI1737_Pae_G1a.CEL	RNA	LB		early stationary	Planktonic	static	"CF1, CF2, CF3, CF4"							
-E-GEOD-25945	GSM637581 1	GSM637581.CEL	In vitro P. aeruginosa gene expression from sessile growth CFCZ LB 1 HZI2436_Pae_G1a.CEL	RNA	LB		early stationary	Planktonic	static	CFCZ							
-E-GEOD-25945	GSM637582 1	GSM637582.CEL	In vitro P. aeruginosa gene expression from sessile growth CFCZ LB 2 HZI2437_Pae_G1a.CEL	RNA	LB		early stationary	Planktonic	static	CFCZ							
-E-GEOD-25945	GSM637583 1	GSM637583.CEL	Ex vivo P. aeruginosa gene expression from burn wound p10 burn 1 HZI1051 p10_clinical_a.CEL Re-analysis of Sample GSM567668	RNA	Ex vivo					p10	37					Burn wound	human
-E-GEOD-25945	GSM637584 1	GSM637584.CEL	Ex vivo P. aeruginosa gene expression from burn wound p10 burn 2 HZI1053 p10_clinical_b.CEL Re-analysis of Sample GSM567669	RNA	Ex vivo					p10	37					Burn wound	human
-E-GEOD-25945	GSM637585 1	GSM637585.CEL	Ex vivo P. aeruginosa gene expression from burn wound p11 burn 1 GBF0930 p11_clinical_a.CEL Re-analysis of Sample GSM567678	RNA	Ex vivo					p11	37					Burn wound	human
-E-GEOD-25945	GSM637586 1	GSM637586.CEL	Ex vivo P. aeruginosa gene expression from burn wound p11 burn 2 GBF0931 p11_clinical_b.CEL Re-analysis of Sample GSM567679	RNA	Ex vivo					p11	37					Burn wound	human
-E-GEOD-25945	GSM637587 1	GSM637587.CEL	Ex vivo P. aeruginosa gene expression from burn wound p17 burn 1 HZI1544 p17_clinicA.CEL Re-analysis of Sample GSM567688	RNA	Ex vivo					p17	37					Burn wound	human
-E-GEOD-25945	GSM637588 1	GSM637588.CEL	Ex vivo P. aeruginosa gene expression from burn wound p17 burn 2 HZI1545 p17_clinic_B.CEL Re-analysis of Sample GSM567689	RNA	Ex vivo					p17	37					Burn wound	human
-E-GEOD-25945	GSM637589 1	GSM637589.CEL	Ex vivo P. aeruginosa gene expression from CF lung infection CF1-4 CF 1 HZI1734_Pae_G1a.CEL	RNA	Ex vivo					"CF1, CF2, CF3, CF4"	37					"Lung, CF sputum"	human
-E-GEOD-25945	GSM637590 1	GSM637590.CEL	Ex vivo P. aeruginosa gene expression from CF lung infection CF1-4 CF 2 HZI1738_Pae_G1a.CEL	RNA	Ex vivo					"CF1, CF2, CF3, CF4"	37					"Lung, CF sputum"	human
-E-GEOD-25945	GSM637591 1	GSM637591.CEL	Ex vivo P. aeruginosa gene expression from CF lung infection CFCZ CF 1 HZI2391_Pae_G1a.CEL	RNA	Ex vivo					CFCZ	37					"Lung, CF sputum"	human
-E-GEOD-25945	GSM637592 1	GSM637592.CEL	Ex vivo P. aeruginosa gene expression from CF lung infection CFCZ CF 2 HZI2392_Pae_G1a.CEL	RNA	Ex vivo					CFCZ	37					"Lung, CF sputum"	human
-E-GEOD-25945	GSM637593 1	GSM637593.CEL	Ex vivo P. aeruginosa gene expression from murine tumor infection p10 tumor 1 p10T23_Pae_G1a.CEL Re-analysis of Sample GSM567672	RNA	Ex vivo		24 hrs			p10	37					tumor	mouse
-E-GEOD-25945	GSM637594 1	GSM637594.CEL	Ex vivo P. aeruginosa gene expression from murine tumor infection p10 tumor 2 p10T23a_Pae_G1a.CEL Re-analysis of Sample GSM567673	RNA	Ex vivo		24 hrs			p10	37					tumor	mouse
-E-GEOD-25945	GSM637595 1	GSM637595.CEL	Ex vivo P. aeruginosa gene expression from murine tumor infection p11 tumor 1 HZI1558 p11_tumor_a.CEL Re-analysis of Sample GSM567682	RNA	Ex vivo		24 hrs			p11	37					tumor	mouse
-E-GEOD-25945	GSM637596 1	GSM637596.CEL	Ex vivo P. aeruginosa gene expression from murine tumor infection p11 tumor 2 HZI1559 p11_tumor_b.CEL Re-analysis of Sample GSM567683	RNA	Ex vivo		24 hrs			p11	37					tumor	mouse
-E-GEOD-25945	GSM637597 1	GSM637597.CEL	Ex vivo P. aeruginosa gene expression from murine tumor infection p17 tumor 1 p17T34_Pae_G1a.CEL Re-analysis of Sample GSM567692	RNA	Ex vivo		24 hrs			p17	37					tumor	mouse
-E-GEOD-25945	GSM637598 1	GSM637598.CEL	Ex vivo P. aeruginosa gene expression from murine tumor infection p17 tumor 2 p17T34a_Pae_G1a.CEL Re-analysis of Sample GSM567693	RNA	Ex vivo		24 hrs			p17	37					tumor	mouse
-E-GEOD-39044	GSM954576 1	GSM954576_Nomura_PA01-1_Pae_G1a_.CEL	"PAO1 WT grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 1"	RNA	Peptone browth	WT (PAO1-UW)	0.5	planktonic	aerated	PAO1	37						
-E-GEOD-39044	GSM954577 1	GSM954577_Nomura_PA01-2_Pae_G1a_.CEL	"PAO1 WT grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 2"	RNA	Peptone browth	WT (PAO1-UW)	0.5	planktonic	aerated	PAO1	37						
-E-GEOD-39044	GSM954578 1	GSM954578_Nomura_PA01-3_Pae_G1a_.CEL	"PAO1 WT grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 3"	RNA	Peptone browth	WT (PAO1-UW)	0.5	planktonic	aerated	PAO1	37						
-E-GEOD-39044	GSM954579 1	GSM954579_Nomura_PA01-4_Pae_G1a_.CEL	"PAO1 WT grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 3"	RNA	Peptone browth	WT (PAO1-UW)	0.5	planktonic	aerated	PAO1	37						
-E-GEOD-39044	GSM954580 1	GSM954580_Nomura_PA2449-1_Pae_G1a_.CEL	"PAO1 PA2449-E03::ISphoA/hah (PW5126) grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 1"	RNA	Peptone browth	PA2449-E03::ISphoA/hah (PW5126)	0.5	planktonic	aerated	PAO1	37				Gentamicin		
-E-GEOD-39044	GSM954581 1	GSM954581_Nomura_PA2449-2_Pae_G1a_.CEL	"PAO1 PA2449-E03::ISphoA/hah (PW5126) grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 2"	RNA	Peptone browth	PA2449-E03::ISphoA/hah (PW5126)	0.5	planktonic	aerated	PAO1	37				Gentamicin		
-E-GEOD-39044	GSM954582 1	GSM954582_Nomura_PA2449-3_Pae_G1a_.CEL	"PAO1 PA2449-E03::ISphoA/hah (PW5126) grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 3"	RNA	Peptone browth	PA2449-E03::ISphoA/hah (PW5126)	0.5	planktonic	aerated	PAO1	37				Gentamicin		
-E-GEOD-39044	GSM954583 1	GSM954583_Nomura_PA2449-4_Pae_G1a_.CEL	"PAO1 PA2449-E03::ISphoA/hah (PW5126) grown planktonically in peptone broth with aeration to an OD of 0.5 at 37 degrees, replicate 4"	RNA	Peptone browth	PA2449-E03::ISphoA/hah (PW5126)	0.5	planktonic	aerated	PAO1	37				Gentamicin		
-E-GEOD-40461	GSM994496 1	GSM994496_PAO1-replicate-01.CEL	PAO1 WT. Planktonic. Rep1	RNA	LB	WT		Planktonic	Aerated	PAO1	37		Grown shaking				
-E-GEOD-40461	GSM994497 1	GSM994497_PAO1-replicate-02.CEL	PAO1 WT. Planktonic. Rep2	RNA	LB	WT		Planktonic	Aerated	PAO1	37		Grown shaking				
-E-GEOD-40461	GSM994498 1	GSM994498_PAO1-replicate-03.CEL	PAO1 WT. Planktonic. Rep3	RNA	LB	WT		Planktonic	Aerated	PAO1	37		Grown shaking				
-E-GEOD-40461	GSM994499 1	GSM994499_HM4-replicate-01.CEL	PAHM4 WT. Planktonic. Rep1	RNA	LB	WT		Planktonic	Aerated	PAHM4	37		Grown shaking				
-E-GEOD-40461	GSM994500 1	GSM994500_HM4-replicate-02.CEL	PAHM4 WT. Planktonic. Rep2	RNA	LB	WT		Planktonic	Aerated	PAHM4	37		Grown shaking				
-E-GEOD-40461	GSM994501 1	GSM994501_HM4-replicate-03.CEL	PAHM4 WT. Planktonic. Rep3	RNA	LB	WT		Planktonic	Aerated	PAHM4	37		Grown shaking				
-E-GEOD-36647	GSM897995 1	GSM897995_MHH0094_Pae_G1a.CEL	TBCF10839; replicate 1	RNA	LB		1.5	Planktonic		TBCF10839	37		early exponential phase cells				
-E-GEOD-36647	GSM897996 1	GSM897996_MHH0095_Pae_G1a.CEL	TBCF10839; replicate 2	RNA	LB		1.5	Planktonic		TBCF10839	37		early exponential phase cells				
-E-GEOD-36647	GSM897997 1	GSM897997_MHH0106_Pae_G1a.CEL	TBCF121838; replicate 1	RNA	LB		1.5	Planktonic		TBCF121838	37		early exponential phase cells				
-E-GEOD-36647	GSM897998 1	GSM897998_MHH0107_Pae_G1a.CEL	TBCF121838; replicate 2	RNA	LB		1.5	Planktonic		TBCF121838	37		early exponential phase cells				
-E-GEOD-36647	GSM897999 1	GSM897999_MHH0110_Pae_G1a.CEL	TBCF10839; replicate 1	RNA	LB		3	Planktonic		TBCF10839	37		late exponential phase cells				
-E-GEOD-36647	GSM898000 1	GSM898000_MHH0111_Pae_G1a.CEL	TBCF10839; replicate 2	RNA	LB		3	Planktonic		TBCF10839	37		late exponential phase cells				
-E-GEOD-36647	GSM898001 1	GSM898001_MHH0122_Pae_G1a.CEL	TBCF121838; replicate 1	RNA	LB		3	Planktonic		TBCF121838	37		late exponential phase cells				
-E-GEOD-36647	GSM898002 1	GSM898002_MHH0123_Pae_G1a.CEL	TBCF121838; replicate 2	RNA	LB		3	Planktonic		TBCF121838	37		late exponential phase cells				
-E-GEOD-34836	GSM856031 1	GSM856031_PA01.CEL	"PAO1 WT anaerobically grown in LB with 1% KNO3 for 12 hours, single replicate"	RNA	LBN (LB+1% KNO3)	WT	12 hrs	Planktonic		PAO1	37	anaerobic growth					
-E-GEOD-34836	GSM856032 1	GSM856032_PA01+V12.CEL	"PAO1 WT anaerobically grown in LB with 1% KNO3 and 1 μM vitamin B12 for 12 hours, single replicate"	RNA	LBN (LB+1% KNO3) with 1 μ vitamin B12	WT	12 hrs	Planktonic		PAO1	37	anaerobic growth					
-E-MEXP-3117	pak1	MBU_PAER_pak1.CEL	P. aeruginosa PAO1 delta pppA-ppkA control replicate 1	RNA	M9	ΔpppA-ppkA	0.8	Planktonic		PAO1	37						
-E-MEXP-3117	pak2	MBU_PAER_pak2.CEL	P. aeruginosa PAO1 delta pppA-ppkA control replicate 2	RNA	M9	ΔpppA-ppkA	0.8	Planktonic		PAO1	37						
-E-MEXP-3117	pakA1	MBU_JGOLD_pakA1.CEL	P. aeruginosa PAO1 delta pppA-ppkA + H2O2 for 15 min replicate 1	RNA	M9	ΔpppA-ppkA	0.8	Planktonic		PAO1	37	10mM H2O2					
-E-MEXP-3117	pakA2	MBU_JGOLD_pakA2.CEL	P. aeruginosa PAO1 delta pppA-ppkA + H2O2 for 15 min replicate 2	RNA	M9	ΔpppA-ppkA	0.8	Planktonic		PAO1	37	10mM H2O2					
-E-MEXP-3117	PAO11	MBU_JGOLD_PAO11.CEL	P. aeruginosa PAO1 WT + H2O2 for 15 min replicate 1	RNA	M9	WT	0.8	Planktonic		PAO1	37	10mM H2O2					
-E-MEXP-3117	PAO12	MBU_JGOLD_PAO12.CEL	P. aeruginosa PAO1 WT + H2O2 for 15 min replicate 2	RNA	M9	WT	0.8	Planktonic		PAO1	37	10mM H2O2					
-E-MEXP-3117	PT51	MBU_PAER_PT51.CEL	P. aeruginosa PAO1 WT control replicate 1	RNA	M9	WT	0.8	Planktonic		PAO1	37						
-E-MEXP-3117	PT52	MBU_PAER_PT52.CEL	P. aeruginosa PAO1 WT control replicate 2	RNA	M9	WT	0.8	Planktonic		PAO1	37						
-E-GEOD-27674	GSM685447 1	GSM685447.CEL	"P. aeruginosa, MPAO1_treated with 125μM Protoanemonin_rep1"	RNA	ABT minimal medium + 0.5% casamino acids	WT	2	Planktonic	shaking	MPAO1	37	125μM Protoanemonin	Grown shaking at 250rpm 				
-E-GEOD-27674	GSM685448 1	GSM685448.CEL	"P. aeruginosa, MPAO1_treated with 125μM Protoanemonin_rep2"	RNA	ABT minimal medium + 0.5% casamino acids	WT	2	Planktonic	shaking	MPAO1	37	125μM Protoanemonin	Grown shaking at 250rpm 				
-E-GEOD-27674	GSM685449 1	GSM685449.CEL	"P. aeruginosa, MPAO1_untreated control_rep1"	RNA	ABT minimal medium + 0.5% casamino acids	WT	2	Planktonic	shaking	MPAO1	37		Grown shaking at 250rpm 				
-E-GEOD-27674	GSM685450 1	GSM685450.CEL	"P. aeruginosa, MPAO1_untreated control_rep2"	RNA	ABT minimal medium + 0.5% casamino acids	WT	2	Planktonic	shaking	MPAO1	37		Grown shaking at 250rpm 				
-E-GEOD-29665	GSM735748 1	GSM735748_PA14_WT.CEL	P. aeruginosa PA14 WT in LB  grown for 7 hr in a static biofilm	RNA	LB	WT		Biofilm	Static biofilm	PA14	37						
-E-GEOD-29665	GSM735749 1	GSM735749_PA14_WT_adenosine.CEL	"P. aeruginosa PA14 WT in LB with 10 mM adenosine, grown for 7 hr in a static biofilm"	RNA	LB	WT		Biofilm	Static biofilm	PA14	37	10 mM adenosine					
-E-GEOD-29946	GSM741203 1	GSM741203.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY, replicate 1"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37						
-E-GEOD-29946	GSM741204 1	GSM741204.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY, replicate 2"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37						
-E-GEOD-29946	GSM741205 1	GSM741205.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY, replicate 3"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37						
-E-GEOD-29946	GSM741206 1	GSM741206.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 25 mM potassium phosphate and 200uM of U50,488 (kappa opioid), replicate 1"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	"25mM potassium phosphate and 200 uM U-50,488"					
-E-GEOD-29946	GSM741207 1	GSM741207.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 25 mM potassium phosphate and 200uM of U50,488 (kappa opioid), replicate 2"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	"25mM potassium phosphate and 200 uM U-50,488"					
-E-GEOD-29946	GSM741208 1	GSM741208.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 25 mM potassium phosphate and 200uM of U50,488 (kappa opioid), replicate 3"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	"25mM potassium phosphate and 200 uM U-50,488"					
-E-GEOD-29946	GSM741209 1	GSM741209.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 200 uM U-50,488 (kappa opioid) replicate 1"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	"200 uM U-50,488"					
-E-GEOD-29946	GSM741210 1	GSM741210.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 200 uM U-50,488 (kappa opioid) replicate 2"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	"200 uM U-50,488"					
-E-GEOD-29946	GSM741211 1	GSM741211.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 200 uM U-50,488 (kappa opiod) replicate 3"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	"200 uM U-50,488"					
-E-GEOD-29946	GSM741212 1	GSM741212.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 25 mM potassium phosphate, replicate 1"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	25mM potassium phosphate					
-E-GEOD-29946	GSM741213 1	GSM741213.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 25 mM potassium phosphate, replicate 2"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	25mM potassium phosphate					
-E-GEOD-29946	GSM741214 1	GSM741214.CEL	"P. aeruginosa PAO1 WT grown for 7 hrs at 37 in 0.1xTY + 25 mM potassium phosphate, replicate 3"	RNA	TY (0.1x)	WT	0.25	Planktonic	Shaking	MPAO1	37	25mM potassium phosphate					
-E-GEOD-34762	GSM854792 1	GSM854792.CEL	PAO1 WT; top 50 um; replicate 1	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854793 1	GSM854793.CEL	PAO1 WT; bottom 50 um; replicate 1	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854794 1	GSM854794.CEL	PAO1 WT; top 50 um; replicate 2	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854795 1	GSM854795.CEL	PAO1 WT; bottom 50 um; replicate 2	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854796 1	GSM854796.CEL	PAO1 WT; top 50 um; replicate 3	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854797 1	GSM854797.CEL	PAO1 WT; bottom 50 um; replicate 3	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854798 1	GSM854798.CEL	PAO1 WT; top 50 um; replicate 4	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854799 1	GSM854799.CEL	PAO1 WT; bottom 50 um; replicate 4	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854800 1	GSM854800.CEL	PAO1 WT; top 50 um; replicate 5	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-34762	GSM854801 1	GSM854801.CEL	PAO1 WT; bottom 50 um; replicate 5	RNA	TSA	WT		Colony	Biofilm	PAO1	37		52 hours; polycarbonate membrane; 50 um of biofilm				
-E-GEOD-35286	GSM717419 1	GSM717419.CEL	"P. aeruginosa PAO1 grown in a flow-through bioflm, rep1"	RNA	1/20 diluted LB and/or VBMM w/citrate	WT	6 days	Biofilm	flow-through system	PAO1	22						
-E-GEOD-35286	GSM717420 1	GSM717420.CEL	"P. aeruginosa PAO1 grown in a flow-through bioflm, rep2"	RNA	1/20 diluted LB and/or VBMM w/citrate	WT	6 days	Biofilm	flow-through system	PAO1	22						
-E-GEOD-35286	GSM717421 1	GSM717421.CEL	"P. aeruginosa PAO1 grown in a flow-through bioflm, rep3"	RNA	1/20 diluted LB and/or VBMM w/citrate	WT	6 days	Biofilm	flow-through system	PAO1	22						
-E-GEOD-35286	GSM865129 1	GSM865129_schurr_5511_Pae_G1a_.CEL	PAO1 ΔmifR grown as a biofilm in a flow cell with single flow through minimal media with 10 mM glutamate grown for 6 days at room temperature	RNA	minimal media with 10mM glutamate	ΔmifR (PA5511)	6 days	biofilm	flow cell	PAO1	25						
-E-GEOD-35286	GSM865130 1	GSM865130_schurr_5511_1_Pae_G1a_.CEL	PAO1 ΔmifR grown as a biofilm in a flow cell with single flow through minimal media with 10 mM glutamate grown for 6 days at room temperature	RNA	minimal media with 10mM glutamate	ΔmifR (PA5511)	6 days	biofilm	flow cell	PAO1	25						
-E-GEOD-35286	GSM865131 1	GSM865131_schurr_5511_3_Pae_G1a_.CEL	PAO1 ΔmifR grown as a biofilm in a flow cell with single flow through minimal media with 10 mM glutamate grown for 6 days at room temperature	RNA	minimal media with 10mM glutamate	ΔmifR (PA5511)	6 days	biofilm	flow cell	PAO1	25						
-E-GEOD-32032	GSM794226 1	GSM794226_A27.CEL	"P. aeruginosa PA14 WT cells were grown in Kadouri drip-fed biofilms on polystyrene plates in M63 medium supplemented with 0.4% arginine for 48 hours, replicate 1"	RNA	M63 + 0.4% arginine and 1 mM MgSO4	WT		Biofilm	Kadouri drip-fed	PA14	37		48 hours				
-E-GEOD-32032	GSM794227 1	GSM794227_A28.CEL	"P. aeruginosa PA14 WT cells were grown in Kadouri drip-fed biofilms on polystyrene plates in M63 medium supplemented with 0.4% arginine for 48 hours, replicate 2"	RNA	M63 + 0.4% arginine and 1 mM MgSO4	WT		Biofilm	Kadouri drip-fed	PA14	37		48 hours				
-E-GEOD-32032	GSM794228 1	GSM794228_A29.CEL	"P. aeruginosa PA14 WT cells were grown in Kadouri drip-fed biofilms on polystyrene plates in M63 medium supplemented with 0.4% arginine for 48 hours, replicate 3"	RNA	M63 + 0.4% arginine and 1 mM MgSO4	WT		Biofilm	Kadouri drip-fed	PA14	37		48 hours				
-E-GEOD-32032	GSM794229 1	GSM794229_A30.CEL	"P. aeruginosa PA14 ndvB knockout cells were grown in Kadouri drip-fed biofilms on polystyrene plates in M63 medium supplemented with 0.4% arginine for 48 hours, replicate 1"	RNA	M63 + 0.4% arginine and 1 mM MgSO4	ΔndvB		Biofilm	Kadouri drip-fed	PA14	37		48 hours				
-E-GEOD-32032	GSM794230 1	GSM794230_A31.CEL	"P. aeruginosa PA14 ndvB knockout cells were grown in Kadouri drip-fed biofilms on polystyrene plates in M63 medium supplemented with 0.4% arginine for 48 hours, replicate 2"	RNA	M63 + 0.4% arginine and 1 mM MgSO4	ΔndvB		Biofilm	Kadouri drip-fed	PA14	37		48 hours				
-E-GEOD-32032	GSM794231 1	GSM794231_A32.CEL	"P. aeruginosa PA14 ndvB knockout cells were grown in Kadouri drip-fed biofilms on polystyrene plates in M63 medium supplemented with 0.4% arginine for 48 hours, replicate 3"	RNA	M63 + 0.4% arginine and 1 mM MgSO4	ΔndvB		Biofilm	Kadouri drip-fed	PA14	37		48 hours				
-E-GEOD-33245	GSM822689 1	GSM822689_wtBSM_A.CEL	PAO1 WT; control; replicate 1	RNA	BSM w/ 40 mM succinate	WT	1.6	Planktonic	Aerated	PAO1	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822690 1	GSM822690_wtBSM_B.CEL	PAO1 WT; control; replicate 2	RNA	BSM w/ 40 mM succinate	WT	1.6	Planktonic	Aerated	PAO1	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822691 1	GSM822691_delta_crcBSM_A.CEL	PAO1 Δcrc; replicate 1	RNA	BSM w/ 40 mM succinate	Δcrc (PAO6673)	1.6	Planktonic	Aerated	PAO6673 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822692 1	GSM822692_delta_crcBSM_B.CEL	PAO1 Δcrc; replicate 2	RNA	BSM w/ 40 mM succinate	Δcrc (PAO6673)	1.6	Planktonic	Aerated	PAO6673 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822693 1	GSM822693_delta_cbrBBSM_A.CEL	PAO1 ΔcbrB; replicate 1	RNA	BSM w/ 40 mM succinate	ΔcbrB (PAO66711)	1.6	Planktonic	Aerated	PAO66711 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822694 1	GSM822694_delta_cbrBBSM_B.CEL	PAO1 ΔcbrB; replicate 2	RNA	BSM w/ 40 mM succinate	ΔcbrB (PAO66711)	1.6	Planktonic	Aerated	PAO66711 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822695 1	GSM822695_delta_crcZBSM_A.CEL	PAO1 ΔcbcZ; replicate 1	RNA	BSM w/ 40 mM succinate	ΔcrcZ (PAO6679)	1.6	Planktonic	Aerated	PAO6679 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822696 1	GSM822696_delta_crcZBSM_B.CEL	PAO1 ΔcbcZ; replicate 2	RNA	BSM w/ 40 mM succinate	ΔcrcZ (PAO6679)	1.6	Planktonic	Aerated	PAO6679 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33245	GSM822708 1	GSM822708_wtLB_A.CEL	"PAO1 WT grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 1"	RNA	LB	WT	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33245	GSM822709 1	GSM822709_wtLB_B.CEL	"PAO1 WT grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 2"	RNA	LB	WT	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33245	GSM822710 1	GSM822710_delta_crcLB_A.CEL	"PAO1 Δcrc grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 1"	RNA	LB	Δcrc (PAO6673)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33245	GSM822711 1	GSM822711_delta_crcLB_B.CEL	"PAO1 Δcrc grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 2"	RNA	LB	Δcrc (PAO6673)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33245	GSM822712 1	GSM822712_delta_cbrBLB_A.CEL	"PAO1 ΔcbrB grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 1"	RNA	LB	ΔcbrB (PAO66711)	1.6	Planktonic	aerated	PAO1	37		There is a typo in GEO. PAO6711 is labeled PAO66711				
-E-GEOD-33245	GSM822713 1	GSM822713_delta_cbrBLB_B.CEL	"PAO1 ΔcbrB grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 2"	RNA	LB	ΔcbrB (PAO66711)	1.6	Planktonic	aerated	PAO1	37		There is a typo in GEO. PAO6711 is labeled PAO66711				
-E-GEOD-33245	GSM822714 1	GSM822714_delta_crcZLB_A.CEL	"PAO1 ΔcrcZ grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 1"	RNA	LB	ΔcrcZ (PAO6679)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33245	GSM822715 1	GSM822715_delta_crcZLB_B.CEL	"PAO1 ΔcrcZ grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 2"	RNA	LB	ΔcrcZ (PAO6679)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33275	GSM823341 1	GSM823341.CEL	P. aeruginosa isolate CJ-1d grown on Nematode Growth Medium (NGM) (replicate A)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25		No information provided to tie Samples to any run.				
-E-GEOD-33275	GSM823342 1	GSM823342.CEL	P. aeruginosa isolate CJ-1d grown on Nematode Growth Medium (NGM) (replicate B)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33275	GSM823343 1	GSM823343.CEL	P. aeruginosa isolate CJ-2009a grown on Nematode Growth Medium (NGM) (replicate A)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33275	GSM823344 1	GSM823344.CEL	P. aeruginosa isolate CJ-2009a grown on Nematode Growth Medium (NGM) (replicate B)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33275	GSM823345 1	GSM823345.CEL	P. aeruginosa isolate BBa grown on Nematode Growth Medium (NGM) (replicate A)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33275	GSM823346 1	GSM823346.CEL	P. aeruginosa isolate BBa grown on Nematode Growth Medium (NGM) (replicate B)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33275	GSM823347 1	GSM823347.CEL	P. aeruginosa isolate BB2009a grown on Nematode Growth Medium (NGM) (replicate A)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33275	GSM823348 1	GSM823348.CEL	P. aeruginosa isolate BB2009a grown on Nematode Growth Medium (NGM) (replicate B)	RNA	NGM agar			colony	lawn on plate	CF clinical isolate	25						
-E-GEOD-33244	GSM822708 1	GSM822708_wtLB_A.CEL	"PAO1 WT grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 1"	RNA	LB	WT	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33244	GSM822709 1	GSM822709_wtLB_B.CEL	"PAO1 WT grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 2"	RNA	LB	WT	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33244	GSM822710 1	GSM822710_delta_crcLB_A.CEL	"PAO1 Δcrc grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 1"	RNA	LB	Δcrc (PAO6673)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33244	GSM822711 1	GSM822711_delta_crcLB_B.CEL	"PAO1 Δcrc grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 2"	RNA	LB	Δcrc (PAO6673)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33244	GSM822712 1	GSM822712_delta_cbrBLB_A.CEL	"PAO1 ΔcbrB grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 1"	RNA	LB	ΔcbrB (PAO66711)	1.6	Planktonic	aerated	PAO1	37		There is a typo in GEO. PAO6711 is labeled PAO66711				
-E-GEOD-33244	GSM822713 1	GSM822713_delta_cbrBLB_B.CEL	"PAO1 ΔcbrB grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 2"	RNA	LB	ΔcbrB (PAO66711)	1.6	Planktonic	aerated	PAO1	37		There is a typo in GEO. PAO6711 is labeled PAO66711				
-E-GEOD-33244	GSM822714 1	GSM822714_delta_crcZLB_A.CEL	"PAO1 ΔcrcZ grown planktonicall with aeration at 37 degrees to an OD of 1.6 inLB, replicate 1"	RNA	LB	ΔcrcZ (PAO6679)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33244	GSM822715 1	GSM822715_delta_crcZLB_B.CEL	"PAO1 ΔcrcZ grown planktonicall with aeration at 37 degrees to an OD of 1.6 in LB, replicate 2"	RNA	LB	ΔcrcZ (PAO6679)	1.6	Planktonic	aerated	PAO1	37						
-E-GEOD-33241	GSM822689 1	GSM822689_wtBSM_A.CEL	PAO1 WT; control; replicate 1	RNA	BSM w/ 40 mM succinate	WT	1.6	Planktonic	Aerated	PAO1	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822690 1	GSM822690_wtBSM_B.CEL	PAO1 WT; control; replicate 2	RNA	BSM w/ 40 mM succinate	WT	1.6	Planktonic	Aerated	PAO1	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822691 1	GSM822691_delta_crcBSM_A.CEL	PAO1 Δcrc; replicate 1	RNA	BSM w/ 40 mM succinate	Δcrc (PAO6673)	1.6	Planktonic	Aerated	PAO6673 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822692 1	GSM822692_delta_crcBSM_B.CEL	PAO1 Δcrc; replicate 2	RNA	BSM w/ 40 mM succinate	Δcrc (PAO6673)	1.6	Planktonic	Aerated	PAO6673 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822693 1	GSM822693_delta_cbrBBSM_A.CEL	PAO1 ΔcbrB; replicate 1	RNA	BSM w/ 40 mM succinate	ΔcbrB (PAO66711)	1.6	Planktonic	Aerated	PAO66711 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822694 1	GSM822694_delta_cbrBBSM_B.CEL	PAO1 ΔcbrB; replicate 2	RNA	BSM w/ 40 mM succinate	ΔcbrB (PAO66711)	1.6	Planktonic	Aerated	PAO66711 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822695 1	GSM822695_delta_crcZBSM_A.CEL	PAO1 ΔcbcZ; replicate 1	RNA	BSM w/ 40 mM succinate	ΔcrcZ (PAO6679)	1.6	Planktonic	Aerated	PAO6679 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33241	GSM822696 1	GSM822696_delta_crcZBSM_B.CEL	PAO1 ΔcbcZ; replicate 2	RNA	BSM w/ 40 mM succinate	ΔcrcZ (PAO6679)	1.6	Planktonic	Aerated	PAO6679 (PAO1)	37	40 mM succinate	shaking				
-E-GEOD-33188	GSM821495 1	GSM821495.CEL	"P. aeruginosa PAO1, gene expression data from wild type cells unexposed to ß-lactams_rep1"	RNA	LB	WT	0.6	Planktonic	Aerated	PAO1	37		2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821496 1	GSM821496.CEL	"P. aeruginosa PAO1, gene expression data from wild type cells unexposed to ß-lactams_rep2"	RNA	LB	WT	0.6	Planktonic	Aerated	PAO1	37		2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821497 1	GSM821497.CEL	"P. aeruginosa PAO1, gene expression data from wild type cells unexposed to ß-lactams_rep3"	RNA	LB	WT	0.6	Planktonic	Aerated	PAO1	37		2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821498 1	GSM821498.CEL	"P. aeruginosa PAO1, gene expression data from wild type cells exposed to sub-MIC ß-lactams_rep1"	RNA	LB	WT	0.6	Planktonic	Aerated	PAO1	37	2-hour ß-lactam (100μg/ml penicillin G potassium salt) exposure	2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821499 1	GSM821499.CEL	"P. aeruginosa PAO1, gene expression data from wild type cells exposed to sub-MIC ß-lactams_rep2"	RNA	LB	WT	0.6	Planktonic	Aerated	PAO1	37	2-hour ß-lactam (100μg/ml penicillin G potassium salt) exposure	2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821500 1	GSM821500.CEL	"P. aeruginosa PAO1, gene expression data from wild type cells exposed to sub-MIC ß-lactams_rep3"	RNA	LB	WT	0.6	Planktonic	Aerated	PAO1	37	2-hour ß-lactam (100μg/ml penicillin G potassium salt) exposure	2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821501 1	GSM821501.CEL	"P. aeruginosa PKM315 (ΔampR), gene expression data from mutant cells unexposed to ß-lactams_rep1"	RNA	LB	ΔampR	0.6	Planktonic	Aerated	PKM315 (PAO1)	37		2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821502 1	GSM821502.CEL	"P. aeruginosa PKM315 (ΔampR), gene expression data from mutant cells unexposed to ß-lactams_rep2"	RNA	LB	ΔampR	0.6	Planktonic	Aerated	PKM315 (PAO1)	37		2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821503 1	GSM821503.CEL	"P. aeruginosa PKM315 (ΔampR), gene expression data from mutant cells unexposed to ß-lactams_rep3"	RNA	LB	ΔampR	0.6	Planktonic	Aerated	PKM315 (PAO1)	37		2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821504 1	GSM821504.CEL	"P. aeruginosa PKM315 (ΔampR), gene expression data from mutant cells exposed to sub-MIC ß-lactams_rep1"	RNA	LB	ΔampR	0.6	Planktonic	Aerated	PKM315 (PAO1)	37	2-hour ß-lactam (100μg/ml penicillin G potassium salt) exposure	2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821505 1	GSM821505.CEL	"P. aeruginosa PKM315 (ΔampR), gene expression data from mutant cells exposed to sub-MIC ß-lactams_rep2"	RNA	LB	ΔampR	0.6	Planktonic	Aerated	PKM315 (PAO1)	37	2-hour ß-lactam (100μg/ml penicillin G potassium salt) exposure	2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-33188	GSM821506 1	GSM821506.CEL	"P. aeruginosa PKM315 (ΔampR), gene expression data from mutant cells exposed to sub-MIC ß-lactams_rep3"	RNA	LB	ΔampR	0.6	Planktonic	Aerated	PKM315 (PAO1)	37	2-hour ß-lactam (100μg/ml penicillin G potassium salt) exposure	2 hours; baffled flask; mid log pahse cells; 300 rpm				
-E-GEOD-36753	GSM900177 1	GSM900177_F1_Pae_G1a_.CEL	"PAO1 WT grown planktonically at 37 degrees in LB then treated with Ciprofloxacin to isolate persister cells. Persister cells were treated with 1 μg/mL of BF8 (Furanone) for 1 hour before harvesting, replicate 1"	RNA	LB	WT	18 hrs	planktonic	aerated	PAO1	37	200 μg/mL Ciprofloxacin	Ciprofloxacin treatment used to isolate persister cells	persister cells			
-E-GEOD-36753	GSM900178 1	GSM900178_C1_Pae_G1a_.CEL	"PAO1 WT grown planktonically at 37 degrees in LB then treated with Ciprofloxacin to isolate persister cells. Persister cells were treated with the equivalent of 1 μg/mL EtOH as a control for 1 hour before harvesting, replicate 1"	RNA	LB	WT	18 hrs	planktonic	aerated	PAO1	37	200 μg/mL Ciprofloxacin	Ciprofloxacin treatment used to isolate persister cells	persister cells			
-E-GEOD-36753	GSM900179 1	GSM900179_F2_Pae_G1a_.CEL	"PAO1 WT grown planktonically at 37 degrees in LB then treated with Ciprofloxacin to isolate persister cells. Persister cells were treated with 1 μg/mL of BF8 (Furanone) for 1 hour before harvesting, replicate 2"	RNA	LB	WT	18 hrs	planktonic	aerated	PAO1	37	200 μg/mL Ciprofloxacin	Ciprofloxacin treatment used to isolate persister cells	persister cells			
-E-GEOD-36753	GSM900180 1	GSM900180_C2_Pae_G1a_.CEL	"PAO1 WT grown planktonically at 37 degrees in LB then treated with Ciprofloxacin to isolate persister cells. Persister cells were treated with the equivalent of 1 μg/mL EtOH as a control for 1 hour before harvesting, replicate 2"	RNA	LB	WT	18 hrs	planktonic	aerated	PAO1	37	200 μg/mL Ciprofloxacin	Ciprofloxacin treatment used to isolate persister cells	persister cells			
-E-GEOD-36753	GSM900181 1	GSM900181_F3_Pae_G1a_.CEL	"PAO1 WT grown planktonically at 37 degrees in LB then treated with Ciprofloxacin to isolate persister cells. Persister cells were treated with 1 μg/mL of BF8 (Furanone) for 1 hour before harvesting, replicate 3"	RNA	LB	WT	18 hrs	planktonic	aerated	PAO1	37	200 μg/mL Ciprofloxacin	Ciprofloxacin treatment used to isolate persister cells	persister cells			
-E-GEOD-36753	GSM900182 1	GSM900182_C3_Pae_G1a_.CEL	"PAO1 WT grown planktonically at 37 degrees in LB then treated with Ciprofloxacin to isolate persister cells. Persister cells were treated with the equivalent of 1 μg/mL EtOH as a control for 1 hour before harvesting, replicate 3"	RNA	LB	WT	18 hrs	planktonic	aerated	PAO1	37	200 μg/mL Ciprofloxacin	Ciprofloxacin treatment used to isolate persister cells	persister cells			
-E-GEOD-35248	GSM864516 1	GSM864516.CEL	Pseudomonas aeruginosa PAO1 ΔkinB grown 16 hrs on PIA supplemented with 150 µg/ml gentamicin at 37°C_rep1	RNA	PIA supplemented with 150 µg/ml gentamicin	ΔkinB		colony	streaked on plate	PAO1	37				GentR		
-E-GEOD-35248	GSM864517 1	GSM864517.CEL	Pseudomonas aeruginosa PAO1 ΔkinB grown 16 hrs on PIA supplemented with 150 µg/ml gentamicin at 37°C_rep2	RNA	PIA supplemented with 150 µg/ml gentamicin	ΔkinB		colony	streaked on plate	PAO1	37				GentR		
-E-GEOD-35248	GSM864518 1	GSM864518.CEL	Pseudomonas aeruginosa PAO1 ΔkinB grown 16 hrs on PIA supplemented with 150 µg/ml gentamicin at 37°C_rep3	RNA	PIA supplemented with 150 µg/ml gentamicin	ΔkinB		colony	streaked on plate	PAO1	37				GentR		
-E-GEOD-35248	GSM864519 1	GSM864519.CEL	Pseudomonas aeruginosa PAO1 ΔkinB ΔrpoN grown 16 hrs on PIA supplemented with 150 µg/ml gentamicin at 37°C_rep1	RNA	PIA supplemented with 150 µg/ml gentamicin	ΔkinB ΔrpoN		colony	streaked on plate	PAO1	37				GentR		
-E-GEOD-35248	GSM864520 1	GSM864520.CEL	Pseudomonas aeruginosa PAO1 ΔkinB ΔrpoN grown 16 hrs on PIA supplemented with 150 µg/ml gentamicin at 37°C_rep2	RNA	PIA supplemented with 150 µg/ml gentamicin	ΔkinB ΔrpoN		colony	streaked on plate	PAO1	37				GentR		
-E-GEOD-35248	GSM864521 1	GSM864521.CEL	Pseudomonas aeruginosa PAO1 ΔkinB ΔrpoN grown 16 hrs on PIA supplemented with 150 µg/ml gentamicin at 37°C_rep3	RNA	PIA supplemented with 150 µg/ml gentamicin	ΔkinB ΔrpoN		colony	streaked on plate	PAO1	37				GentR		
-E-MEXP-3459	Fe02 A	Pae_Fe02_13.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). ?M FeCl2 was added to the cultures. Iron concentrations were calculated by weight, assuming 100% purity of) stocks. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen); replicate 1"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37	100 µM Fe(II)	30 min exposure				
-E-MEXP-3459	Fe02 B	Pae_Fe02_14.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). ?M FeCl2 was added to the cultures. Iron concentrations were calculated by weight, assuming 100% purity of) stocks. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen) replicate 2"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37	100 µM Fe(II)	30 min exposure				
-E-MEXP-3459	Fe02 C	Pae_Fe02_15.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). ?M FeCl2 was added to the cultures. Iron concentrations were calculated by weight, assuming 100% purity of) stocks. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen); replicate 3"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37	100 µM Fe(II)	30 min exposure				
-E-MEXP-3459	Fe03 A	Pae_Fe03_16.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). ?M FeCl3 was added to the cultures. Iron concentrations were calculated by weight, assuming 100% purity of) stocks. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen); replicate 1"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37	100 µM Fe(III)	30 min exposure				
-E-MEXP-3459	Fe03 B	Pae_Fe03_17.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). ?M FeCl3 was added to the cultures. Iron concentrations were calculated by weight, assuming 100% purity of) stocks. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen); replicate 2"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37	100 µM Fe(III)	30 min exposure				
-E-MEXP-3459	Fe03 C	Pae_Fe03_18.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). ?M FeCl3 was added to the cultures. Iron concentrations were calculated by weight, assuming 100% purity of) stocks. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen) replicate 3"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37	100 µM Fe(III)	30 min exposure				
-E-MEXP-3459	noFe A	Pae_noFe_10.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). Water was added to the cultures. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen; );  replicate 1"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37		30 min exposure				
-E-MEXP-3459	noFe B	Pae_noFe_11.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). Water was added to the cultures. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen; );  replicate 2"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37		30 min exposure				
-E-MEXP-3459	noFe C	Pae_noFe_12.CEL	"P. aeruginosa PA14 was grown anaerobically in MMM (minimal metal medium) at 37 °C until they reached exponential phase (OD500 of 0.3), at which time they were removed from the incubator and were taken into the anaerobic chamber containing a gas mix of 5% H2 and 95% N2 (Coy Laboratory Products). Water was added to the cultures. The cultures were vortexed every 5 min over a 30 min period, after which 5 mL of culture was removed and mixed with 10 mL of Bacterial RNAprotect (Qiagen; );  replicate 3"	RNA	"Minimal metal medium with 50 mM glutamate, 1% glycerol, 100 mM KNO3"	WT	0.3	Planktonic	anaerobic	PA14	37		30 min exposure				
-E-GEOD-24036	GSM591496 1	GSM591496.CEL	"Pseudomonas aeruginosa PA14 WT, grown in LB, no treatment control, replicate 1"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	no treatment					
-E-GEOD-24036	GSM591601 1	GSM591601.CEL	"Pseudomonas aeruginosa PA14 WT, grown in LB, no treatment control, replicate 2"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	no treatment					
-E-GEOD-24036	GSM591602 1	GSM591602.CEL	"Pseudomonas aeruginosa PA14 WT, grown in LB, no treatment control, replicate 3"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	no treatment					
-E-GEOD-24036	GSM591603 1	GSM591603.CEL	"Pseudomonas aeruginosa PA14 WT,  grown in LB with 2-AA (3mM), replicate 1"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	2-AA (3mM)					
-E-GEOD-24036	GSM591622 1	GSM591622.CEL	"Pseudomonas aeruginosa PA14 WT,  grown in LB with 2-AA (3mM), replicate 2"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	2-AA (3mM)					
-E-GEOD-24036	GSM591623 1	GSM591623.CEL	"Pseudomonas aeruginosa PA14 WT,  grown in LB with 2-AA (3mM), replicate 3"	RNA	LB	WT	2	Planktonic	Shaking	PA14	37	2-AA (3mM)					
-E-GEOD-24036	GSM591624 1	GSM591624.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB, no treatment control, replicate 1"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	no treatment		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24036	GSM591625 1	GSM591625.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB, no treatment control, replicate 2"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	no treatment		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24036	GSM591627 1	GSM591627.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB, no treatment control, replicate 3"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	no treatment		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24036	GSM597216 1	GSM597216.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB with 2-AA (3mM), replicate 1"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	2-AA (3mM)		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24036	GSM597217 1	GSM597217.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB with 2-AA (3mM), replicate 2"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	2-AA (3mM)		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24036	GSM597218 1	GSM597218.CEL	"Pseudomonas aeruginosa PA14 topA mutant,  grown in LB with 2-AA (3mM), replicate 3"	RNA	LB	topA::TnM	2	Planktonic	Shaking	PA14	37	2-AA (3mM)		Lacks Topoisomerase I	Gentamicin		
-E-GEOD-24036	GSM597219 1	GSM597219.CEL	"Pseudomonas aeruginosa PA14 pqsA mutant,  grown in LB, no treatment control, replicate 1"	RNA	LB	ΔpqsA	2	Planktonic	Shaking	PA14	37	no treatment		No HAQs production			
-E-GEOD-24036	GSM597220 1	GSM597220.CEL	"Pseudomonas aeruginosa PA14 pqsA mutant,  grown in LB, no treatment control, replicate 2"	RNA	LB	ΔpqsA	2	Planktonic	Shaking	PA14	37	no treatment		No HAQs production			
-E-GEOD-24036	GSM597221 1	GSM597221.CEL	"Pseudomonas aeruginosa PA14 pqsA mutant,  grown in LB, no treatment control, replicate 3"	RNA	LB	ΔpqsA	2	Planktonic	Shaking	PA14	37	no treatment		No HAQs production			
-E-GEOD-24036	GSM597329 1	GSM597329.CEL	"Pseudomonas aeruginosa PA14 pqsA mutant,  grown in LB with 2-AA (3mM), replicate 1"	RNA	LB	ΔpqsA	2	Planktonic	Shaking	PA14	37	2-AA (3mM)		No HAQs production			
-E-GEOD-24036	GSM597330 1	GSM597330.CEL	"Pseudomonas aeruginosa PA14 pqsA mutant,  grown in LB with 2-AA (3mM), replicate 2"	RNA	LB	ΔpqsA	2	Planktonic	Shaking	PA14	37	2-AA (3mM)		No HAQs production			
-E-GEOD-24036	GSM597331 1	GSM597331.CEL	"Pseudomonas aeruginosa PA14 pqsA mutant,  grown in LB with 2-AA (3mM), replicate 3"	RNA	LB	ΔpqsA	2	Planktonic	Shaking	PA14	37	2-AA (3mM)		No HAQs production			
-E-GEOD-28719	GSM711446 1	GSM711446.CEL	PAO1 ΔPA1006; control; replicate 1	RNA	NY	ΔPA1006	1.2	Planktonic	Aerated	PAO1	37		Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711447 1	GSM711447.CEL	PAO1 ΔPA1006; control; replicate 2	RNA	NY	ΔPA1006	1.2	Planktonic	Aerated	PAO1	37		Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711448 1	GSM711448.CEL	PAO1 WT; control; replicate 1	RNA	NY	WT	1.2	Planktonic	Aerated	PAO1	37		Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711449 1	GSM711449.CEL	PAO1 WT; control; replicate 2	RNA	NY	WT	1.2	Planktonic	Aerated	PAO1	37		Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711450 1	GSM711450.CEL	PAO1 ΔPA1006; 100 mM KNO3; replicate 1	RNA	NY	ΔPA1006	1.2	Planktonic	Aerated	PAO1	37	100 mM KNO3	Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711451 1	GSM711451.CEL	PAO1 ΔPA1006; 100 mM KNO3; replicate 2	RNA	NY	ΔPA1006	1.2	Planktonic	Aerated	PAO1	37	100 mM KNO3	Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711452 1	GSM711452.CEL	PAO1 ΔPA1006; 100 mM KNO3; replicate 3	RNA	NY	ΔPA1006	1.2	Planktonic	Aerated	PAO1	37	100 mM KNO3	Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711453 1	GSM711453.CEL	PAO1 WT; 100 mM KNO3; replicate 1	RNA	NY	WT	1.2	Planktonic	Aerated	PAO1	37	100 mM KNO3	Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711454 1	GSM711454.CEL	PAO1 WT; 100 mM KNO3; replicate 2	RNA	NY	WT	1.2	Planktonic	Aerated	PAO1	37	100 mM KNO3	Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-28719	GSM711455 1	GSM711455.CEL	PAO1 WT; 100 mM KNO3; replicate 3	RNA	NY	WT	1.2	Planktonic	Aerated	PAO1	37	100 mM KNO3	Early stationary phase cells; shaking at 250 rpm				
-E-GEOD-34141	GSM842839 1	GSM842839_Wright_1_WT-log-1_Pae_G1a.CEL	PAO1 WT; control; replicate 1	RNA	Iron poor CAA	WT		Planktonic		PAO1	37		24 hours; early stationary phase cells				
-E-GEOD-34141	GSM842840 1	GSM842840_Wright_2_WT-log-2_Pae_G1a.CEL	PAO1 WT; control; replicate 2	RNA	Iron poor CAA	WT		Planktonic		PAO1	37		24 hours; early stationary phase cells				
-E-GEOD-34141	GSM842841 1	GSM842841_Wright_3_WT-log-3_Pae_G1a.CEL	PAO1 WT; control; replicate 3	RNA	Iron poor CAA	WT		Planktonic		PAO1	37		24 hours; early stationary phase cells				
-E-GEOD-34141	GSM842842 1	GSM842842_Wright_4_WT-stat-1_Pae_G1a.CEL	PAO1 WT; control; replicate 1	RNA	Iron poor CAA	WT		Planktonic		PAO1	37		48 hours; late stationary phase cells				
-E-GEOD-34141	GSM842843 1	GSM842843_Wright_5_WT-stat-2_Pae_G1a.CEL	PAO1 WT; control; replicate 2	RNA	Iron poor CAA	WT		Planktonic		PAO1	37		48 hours; late stationary phase cells				
-E-GEOD-34141	GSM842844 1	GSM842844_Wright_6_WT-stat-3_Pae_G1a.CEL	PAO1 WT; control; replicate 3	RNA	Iron poor CAA	WT		Planktonic		PAO1	37		48 hours; late stationary phase cells				
-E-GEOD-34141	GSM842845 1	GSM842845_Wright_7_4203-log-1_Pae_G1a.CEL	PAO-SCV; replicate 1	RNA	Iron poor CAA	PAO-SCV		Planktonic		PAO1	37		24 hours; early stationary phase cells	"SCV, QS + motility defective"	GentR		
-E-GEOD-34141	GSM842846 1	GSM842846_Wright_8_4203-log-2_Pae_G1a.CEL	PAO-SCV; replicate 2	RNA	Iron poor CAA	PAO-SCV		Planktonic		PAO1	37		24 hours; early stationary phase cells	"SCV, QS + motility defective"	GentR		
-E-GEOD-34141	GSM842847 1	GSM842847_Wright_9_4203-log-3_Pae_G1a.CEL	PAO-SCV; replicate 3	RNA	Iron poor CAA	PAO-SCV		Planktonic		PAO1	37		24 hours; early stationary phase cells	"SCV, QS + motility defective"	GentR		
-E-GEOD-34141	GSM842848 1	GSM842848_Wright_10_4203-stat-1_Pae_G1a.CEL	PAO-SCV; replicate 1	RNA	Iron poor CAA	PAO-SCV		Planktonic		PAO1	37		48 hours; late stationary phase cells	"SCV, QS + motility defective"	GentR		
-E-GEOD-34141	GSM842849 1	GSM842849_Wright_11_4203-stat-2_Pae_G1a.CEL	PAO-SCV; replicate 2	RNA	Iron poor CAA	PAO-SCV		Planktonic		PAO1	37		48 hours; late stationary phase cells	"SCV, QS + motility defective"	GentR		
-E-GEOD-34141	GSM842850 1	GSM842850_Wright_12_4203-stat-3_Pae_G1a.CEL	PAO-SCV; replicate 3	RNA	Iron poor CAA	PAO-SCV		Planktonic		PAO1	37		48 hours; late stationary phase cells	"SCV, QS + motility defective"	GentR		
-E-GEOD-26932	GSM663165 1	GSM663165_Mix_label_cDNA_5-13-08_s2.CEL	E. coli MG1655 and P. aeruginosa PAO1 culture rep1	RNA	MOPS minimal media with N-acetyl glucosamine	WT	0.5	Planktonic	shaking	E. coli MG1655	37	"mixed with P. aeruginosa PA01, ratio 1:1"	analyzed with Affymetrix E. coli Genome 2.0 Array			P. aeruginosa PA01	Bacteria
-E-GEOD-26932	GSM663166 1	GSM663166_BM-Mix-Ec2_6-6-08_s1.CEL	E. coli MG1655 and P. aeruginosa PAO1 culture rep2	RNA	MOPS minimal media with N-acetyl glucosamine	WT	0.5	Planktonic	shaking	E. coli MG1655	37	"mixed with P. aeruginosa PA01, ratio 1:1"	analyzed with Affymetrix E. coli Genome 2.0 Array			P. aeruginosa PA01	Bacteria
-E-GEOD-26932	GSM663167 1	GSM663167_Ec_label_cDNA_5-13-08_s2.CEL	E. coli MG1655 culture rep1	RNA	MOPS minimal media with N-acetyl glucosamine	WT	0.5	Planktonic	shaking	E. coli MG1655	37		analyzed with Affymetrix E. coli Genome 2.0 Array				
-E-GEOD-26932	GSM663168 1	GSM663168_BM-Mix-Pa_1_6-6-08_s1.CEL	P. aeruginosa PAO1 and E. coli MG1655 culture rep1	RNA	MOPS minimal media with N-acetyl glucosamine	WT	0.5	Planktonic	shaking	PAO1	37	"mixed with E. coli MG1655, ratio 1:1"				E. coli MG1655	Bacteria
-E-GEOD-26932	GSM663169 1	GSM663169_BM-Mix-Pa_2_6-6-08_s1.CEL	P. aeruginosa PAO1 and E. coli MG1655 culture rep2	RNA	MOPS minimal media with N-acetyl glucosamine	WT	0.5	Planktonic	shaking	PAO1	37	"mixed with E. coli MG1655, ratio 1:1"				E. coli MG1655	Bacteria
-E-GEOD-26932	GSM663170 1	GSM663170_Pa_label_cDNA_5-13-08_s2.CEL	P. aeruginosa PAO1 pure culture rep1	RNA	MOPS minimal media with N-acetyl glucosamine	WT	0.5	Planktonic	shaking	PAO1	37						
-E-GEOD-26142	GSM641845 1	GSM641845.CEL	"Pseudomonas aeruginosa, MPAO1 (PA3726::ISlacZ/hah)_untreated_rep1"	RNA	 M63  + 0.5% casamino acids and 0.3% glucose	PA3726::lacZ (UW-14479)	0.3	Planktonic	shaking	MPAO1 [SAH108]	37						
-E-GEOD-26142	GSM641846 1	GSM641846.CEL	"Pseudomonas aeruginosa, MPAO1 (PA3726::ISlacZ/hah)_untreated_rep2"	RNA	 M63  + 0.5% casamino acids and 0.3% glucose	PA3726::lacZ (UW-14479)	0.3	Planktonic	shaking	MPAO1 [SAH108]	37						
-E-GEOD-26142	GSM641847 1	GSM641847.CEL	"Pseudomonas aeruginosa, MPAO1 (WT [SAH502])_untreated_rep1"	RNA	 M63  + 0.5% casamino acids and 0.3% glucose	WT	0.3	Planktonic	shaking	MPAO1 [SAH502]	37						
-E-GEOD-26142	GSM641848 1	GSM641848.CEL	"Pseudomonas aeruginosa, MPAO1 (WT [SAH087])_untreated_rep1"	RNA	 M63  + 0.5% casamino acids and 0.3% glucose	WT	0.3	Planktonic	shaking	MPAO1 [SAH087]	37						
-E-GEOD-26142	GSM641849 1	GSM641849.CEL	"Pseudomonas aeruginosa, MPAO1 (PA0311::phoA)_untreated_rep1"	RNA	 M63  + 0.5% casamino acids and 0.3% glucose	PA0311::phoA (UW-50363)	0.3	Planktonic	shaking	MPAO1 [SAH084]	37		"allegedly a WT, but in strain list not declared as WT"				
-E-GEOD-26931	GSM663157 1	GSM663157_02_mix_2w1_e_coli_5-10-07_S2.CEL	E. coli ZK126 and P. aeruginosa PAO1 culture rep1	RNA	LB	WT	0.3	Planktonic	shaking	E.coli ZK126	37	"mixed with P. aeruginosa PA01, ratio 1:1"	analyzed with Affymetrix E. coli Genome 2.0 Array			P. aeruginosa PA01	Bacteria
-E-GEOD-26931	GSM663158 1	GSM663158_E_coli_mix2_5-22-07_S2.CEL	E. coli ZK126 and P. aeruginosa PAO1 culture rep2	RNA	LB	WT	0.3	Planktonic	shaking	E. coli ZK126	37	"mixed with P. aeruginosa PA01, ratio 1:1"	analyzed with Affymetrix E. coli Genome 2.0 Array			P. aeruginosa PA01	Bacteria
-E-GEOD-26931	GSM663159 1	GSM663159_01_e_coli_5-10-07_S2.CEL	E. coli ZK126 pure culture rep1	RNA	LB	WT	0.3	Planktonic	shaking	E.coli ZK126	37		analyzed with Affymetrix E. coli Genome 2.0 Array				
-E-GEOD-26931	GSM663160 1	GSM663160_E_coli_5-22-07_S2.CEL	E. coli ZK126 pure culture rep2	RNA	LB	WT	0.3	Planktonic	shaking	E. coli ZK126	37		analyzed with Affymetrix E. coli Genome 2.0 Array				
-E-GEOD-26931	GSM663161 1	GSM663161_03_mix_1wP_Pae_5-10-07_S2.CEL	P. aeruginosa PAO1 and E. coli ZK126 culture rep1	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37	"mixed with E. coli ZK126, ratio 1:1"				E. coli ZK126	Bacteria
-E-GEOD-26931	GSM663162 1	GSM663162_Pae-Mix-1_5-22-07_S2.CEL	P. aeruginosa PAO1 and E. coli ZK126 culture rep2	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37	"mixed with E. coli ZK126, ratio 1:1"				E. coli ZK126	Bacteria
-E-GEOD-26931	GSM663163 1	GSM663163_04_Pae_5-10-07_S2.CEL	P. aeruginosa PAO1 pure culture rep1	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37						
-E-GEOD-26931	GSM663164 1	GSM663164_Pae_5-22-07_S2.CEL	P. aeruginosa PAO1 pure culture rep2	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37						
-E-GEOD-25481	GSM625973 1	GSM625973.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient I, isolate I1NM, mutidrug resistant"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	I1NM	37		amikacin and ticarcillin resistant	"Non-mucoid, mutator"			
-E-GEOD-25481	GSM625974 1	GSM625974.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient I, isolate I2M1, mutidrug resistant"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	I2M1	37		"ciprofloxacin, amikacin and ticarcillin resistant"	"Mucoid, mutator, "			
-E-GEOD-25481	GSM625975 1	GSM625975.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient I, isolate I3M1"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	I3M1	37		amikacin resistant	Mucoid			
-E-GEOD-25481	GSM625976 1	GSM625976.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient I, isolate I3M2, mutidrug resistant"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	I3M2	37		"ciprofloxacin, amikacin and ticarcillin resistant"	Mucoid			
-E-GEOD-25481	GSM625977 1	GSM625977.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient I, isolate I4NM, mutidrug resistant"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	I4NM	37		amikacin and ticarcillin resistant	Non-mucoid			
-E-GEOD-25481	GSM625978 1	GSM625978.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient M, isolate M1M"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	M1M	37			Mucoid			
-E-GEOD-25481	GSM625979 1	GSM625979.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient M, isolate M2NM"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	M2NM	37			Non-mucoid			
-E-GEOD-25481	GSM625980 1	GSM625980.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient M, isolate M3M1"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	M3M2	37			Mucoid			
-E-GEOD-25481	GSM625981 1	GSM625981.CEL	"Pseudomonas aeruginosa clinical isolate from the CF lung, Patient M, isolate M3M1"	DNA	Pseudomonas F broth 	Clinical isolate	Stationary	Planktonic	Shaking	M3M1	37			Mucoid			
-E-GEOD-25481	GSM625982 1	GSM625982.CEL	PA01 control strain	DNA	Pseudomonas F broth 	WT	Stationary	Planktonic	Shaking	PAO1	37						
-E-GEOD-29789	GSM738261 1	GSM738261.CEL	PAO1 WT; pH 6.0; replicate 1	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37	pH 6.0	20 hours				
-E-GEOD-29789	GSM738262 1	GSM738262.CEL	PAO1 WT; pH 6.0; replicate 2	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37	pH 6.0	20 hours				
-E-GEOD-29789	GSM738263 1	GSM738263.CEL	PAO1 WT; pH 6.0; replicate 3	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37	pH 6.0	20 hours				
-E-GEOD-29789	GSM738264 1	GSM738264.CEL	PAO1 WT; pH 7.5; replicate 1	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37	pH 7.5	20 hours				
-E-GEOD-29789	GSM738265 1	GSM738265.CEL	PAO1 WT; pH 7.5; replicate 2	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37	pH 7.5	20 hours				
-E-GEOD-29789	GSM738266 1	GSM738266.CEL	PAO1 WT; pH 7.5; replicate 3	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37	pH 7.5	20 hours				
-E-GEOD-24262	GSM596626 1	GSM596626.CEL	RNA extracted from planktonic cells of PA14_WT after 2 hr growth after adding 50uM C30 in QSM (OS minimal medium + 0.1% Adenosine as carbon source) when OD600 reaches 0.25. 	RNA	QSM medium (OS minimal medium + 0.1% Adenosine as carbon source) 	WT	0.25	Planktonic	Shaking	PA14	37	C30 50 µM_2hrs					
-E-GEOD-24262	GSM596819 1	GSM596819.CEL	RNA extracted from planktonic cells of PA14_mexR after 2 hr growth after adding 50uM C30 in QSM (OS minimal medium + 0.1% Adenosine as carbon source) when OD600 reaches 0.25. 	RNA	QSM medium (OS minimal medium + 0.1% Adenosine as carbon source) 	mexR mutant	0.25	Planktonic	Shaking	PA14	37	C30 50 µM_2hrs			gent		
-E-GEOD-23007	GSM567666 1	GSM567666.CEL	PBCLOp10. Planktonic in LB. Rep1	RNA	LB	WT	0.6	Planktonic 	Aerated 	Clinical isolate PBCLOp10	37		Grown shaking at 160rpm				
-E-GEOD-23007	GSM567667 1	GSM567667.CEL	PBCLOp10. Planktonic in LB. Rep2	RNA	LB	WT	0.6	Planktonic 	Aerated 	Clinical isolate PBCLOp10	37		Grown shaking at 160rpm				
-E-GEOD-23007	GSM567668 1	GSM567668.CEL	PBCLOp10. Sample directly from human burn wound. Rep1	RNA	Human tissue 	WT				Clinical isolate PBCLOp10			RNA was harvested from liquid exudates taken from burn wound surface prior to cleaning. 			Burn wound	Human
-E-GEOD-23007	GSM567669 1	GSM567669.CEL	PBCLOp10. Sample directly from human burn wound. Rep2	RNA	Human tissue 	WT				Clinical isolate PBCLOp10			RNA was harvested from liquid exudates taken from burn wound surface prior to cleaning. 			Burn wound	Human
-E-GEOD-23007	GSM567670 1	GSM567670.CEL	PBCLOp10. Biofilm on plastic. Rep1	RNA	10% LB	WT				Clinical isolate PBCLOp10	37		Biofilms grown on plastic. 24 h				
-E-GEOD-23007	GSM567671 1	GSM567671.CEL	PBCLOp10. Biofilm on plastic. Rep2	RNA	10% LB	WT				Clinical isolate PBCLOp10	37		Biofilms grown on plastic. 24 h				
-E-GEOD-23007	GSM567672 1	GSM567672.CEL	PBCLOp10. Mouse CT26 colon adenocarcinoma abdominal tumor. Rep1	RNA	Mouse tumor tissue	WT				Clinical isolate PBCLOp10			Mice bearing tumors of approximately 4–6 mm diameter in size were intravenously injected with 5x10e6 CFU of P. aeruginosa suspended in PBS. Three days post-infection mice were necrotized and bacterial RNA was harvested from infected tumors.			CT26 colon adenocarcinoma abdominal tumor	Mouse
-E-GEOD-23007	GSM567673 1	GSM567673.CEL	PBCLOp10. Mouse CT26 colon adenocarcinoma abdominal tumor. Rep2	RNA	Mouse tumor tissue	WT				Clinical isolate PBCLOp10			Mice bearing tumors of approximately 4–6 mm diameter in size were intravenously injected with 5x10e6 CFU of P. aeruginosa suspended in PBS. Three days post-infection mice were necrotized and bacterial RNA was harvested from infected tumors.			CT26 colon adenocarcinoma abdominal tumor	Mouse
-E-GEOD-23007	GSM567674 1	GSM567674.CEL	PBCLOp10. Lettuce mid-ribs. Rep1	RNA	Plant intracellular fluid	WT				Clinical isolate PBCLOp10			"Lettuce mid-ribs were inoculated with 10 ml of bacterial suspension at a concentration of 1x10e8 CFU/ml by injecting the end of the plastic pipette tip into the rib. 5 days post-infection, RNA was harvested from a 2x2 cm square piece excised from the original place of injection. "			Lettuce leaf mid-rib	Plant
-E-GEOD-23007	GSM567675 1	GSM567675.CEL	PBCLOp10. Lettuce mid-ribs. Rep2	RNA	Plant intracellular fluid	WT				Clinical isolate PBCLOp10			"Lettuce mid-ribs were inoculated with 10 ml of bacterial suspension at a concentration of 1x10e8 CFU/ml by injecting the end of the plastic pipette tip into the rib. 5 days post-infection, RNA was harvested from a 2x2 cm square piece excised from the original place of injection. "			Lettuce leaf mid-rib	Plant
-E-GEOD-23007	GSM567676 1	GSM567676.CEL	PBCLOp11. Planktonic in LB. Rep1	RNA	LB	WT	0.6	Planktonic 	Aerated 	Clinical isolate PBCLOp11	37		Grown shaking at 160rpm				
-E-GEOD-23007	GSM567677 1	GSM567677.CEL	PBCLOp11. Planktonic in LB. Rep2	RNA	LB	WT	0.6	Planktonic 	Aerated 	Clinical isolate PBCLOp11	37		Grown shaking at 160rpm				
-E-GEOD-23007	GSM567678 1	GSM567678.CEL	PBCLOp11. Sample directly from human burn wound. Rep1	RNA	Human tissue 	WT				Clinical isolate PBCLOp11			RNA was harvested from liquid exudates taken from burn wound surface prior to cleaning. 			Burn wound	Human
-E-GEOD-23007	GSM567679 1	GSM567679.CEL	PBCLOp11. Sample directly from human burn wound. Rep2	RNA	Human tissue 	WT				Clinical isolate PBCLOp11			RNA was harvested from liquid exudates taken from burn wound surface prior to cleaning. 			Burn wound	Human
-E-GEOD-23007	GSM567680 1	GSM567680.CEL	PBCLOp11. Biofilm on plastic. Rep1	RNA	10% LB	WT				Clinical isolate PBCLOp11	37		Biofilms grown on plastic. 24 h				
-E-GEOD-23007	GSM567681 1	GSM567681.CEL	PBCLOp11. Biofilm on plastic. Rep2	RNA	10% LB	WT				Clinical isolate PBCLOp11	37		Biofilms grown on plastic. 24 h				
-E-GEOD-23007	GSM567682 1	GSM567682.CEL	PBCLOp11. Mouse CT26 colon adenocarcinoma abdominal tumor. Rep1	RNA	Mouse tumor tissue	WT				Clinical isolate PBCLOp11			Mice bearing tumors of approximately 4–6 mm diameter in size were intravenously injected with 5x10e6 CFU of P. aeruginosa suspended in PBS. Three days post-infection mice were necrotized and bacterial RNA was harvested from infected tumors.			CT26 colon adenocarcinoma abdominal tumor	Mouse
-E-GEOD-23007	GSM567683 1	GSM567683.CEL	PBCLOp11. Mouse CT26 colon adenocarcinoma abdominal tumor. Rep2	RNA	Mouse tumor tissue	WT				Clinical isolate PBCLOp11			Mice bearing tumors of approximately 4–6 mm diameter in size were intravenously injected with 5x10e6 CFU of P. aeruginosa suspended in PBS. Three days post-infection mice were necrotized and bacterial RNA was harvested from infected tumors.			CT26 colon adenocarcinoma abdominal tumor	Mouse
-E-GEOD-23007	GSM567684 1	GSM567684.CEL	PBCLOp11. Lettuce mid-ribs. Rep1	RNA	Plant intracellular fluid	WT				Clinical isolate PBCLOp11			"Lettuce mid-ribs were inoculated with 10 ml of bacterial suspension at a concentration of 1x10e8 CFU/ml by injecting the end of the plastic pipette tip into the rib. 5 days post-infection, RNA was harvested from a 2x2 cm square piece excised from the original place of injection. "			Lettuce leaf mid-rib	Plant
-E-GEOD-23007	GSM567685 1	GSM567685.CEL	PBCLOp11. Lettuce mid-ribs. Rep2	RNA	Plant intracellular fluid	WT				Clinical isolate PBCLOp11			"Lettuce mid-ribs were inoculated with 10 ml of bacterial suspension at a concentration of 1x10e8 CFU/ml by injecting the end of the plastic pipette tip into the rib. 5 days post-infection, RNA was harvested from a 2x2 cm square piece excised from the original place of injection. "			Lettuce leaf mid-rib	Plant
-E-GEOD-23007	GSM567686 1	GSM567686.CEL	PBCLOp17. Planktonic in LB. Rep1	RNA	LB	WT	0.6	Planktonic 	Aerated 	Clinical isolate PBCLOp17	37		Grown shaking at 160rpm				
-E-GEOD-23007	GSM567687 1	GSM567687.CEL	PBCLOp17. Planktonic in LB. Rep2	RNA	LB	WT	0.6	Planktonic 	Aerated 	Clinical isolate PBCLOp17	37		Grown shaking at 160rpm				
-E-GEOD-23007	GSM567688 1	GSM567688.CEL	PBCLOp17. Sample directly from human burn wound. Rep1	RNA	Human tissue 	WT				Clinical isolate PBCLOp17			RNA was harvested from liquid exudates taken from burn wound surface prior to cleaning. 			Burn wound	Human
-E-GEOD-23007	GSM567689 1	GSM567689.CEL	PBCLOp17. Sample directly from human burn wound. Rep2	RNA	Human tissue 	WT				Clinical isolate PBCLOp17			RNA was harvested from liquid exudates taken from burn wound surface prior to cleaning. 			Burn wound	Human
-E-GEOD-23007	GSM567690 1	GSM567690.CEL	PBCLOp17. Biofilm on plastic. Rep1	RNA	10% LB	WT				Clinical isolate PBCLOp17	37		Biofilms grown on plastic. 24 h				
-E-GEOD-23007	GSM567691 1	GSM567691.CEL	PBCLOp17. Biofilm on plastic. Rep2	RNA	10% LB	WT				Clinical isolate PBCLOp17	37		Biofilms grown on plastic. 24 h				
-E-GEOD-23007	GSM567692 1	GSM567692.CEL	PBCLOp17. Mouse CT26 colon adenocarcinoma abdominal tumor. Rep1	RNA	Mouse tumor tissue	WT				Clinical isolate PBCLOp17			Mice bearing tumors of approximately 4–6 mm diameter in size were intravenously injected with 5x10e6 CFU of P. aeruginosa suspended in PBS. Three days post-infection mice were necrotized and bacterial RNA was harvested from infected tumors.			CT26 colon adenocarcinoma abdominal tumor	Mouse
-E-GEOD-23007	GSM567693 1	GSM567693.CEL	PBCLOp17. Mouse CT26 colon adenocarcinoma abdominal tumor. Rep2	RNA	Mouse tumor tissue	WT				Clinical isolate PBCLOp17			Mice bearing tumors of approximately 4–6 mm diameter in size were intravenously injected with 5x10e6 CFU of P. aeruginosa suspended in PBS. Three days post-infection mice were necrotized and bacterial RNA was harvested from infected tumors.			CT26 colon adenocarcinoma abdominal tumor	Mouse
-E-GEOD-23007	GSM567694 1	GSM567694.CEL	PBCLOp17. Lettuce mid-ribs. Rep1	RNA	Plant intracellular fluid	WT				Clinical isolate PBCLOp17			"Lettuce mid-ribs were inoculated with 10 ml of bacterial suspension at a concentration of 1x10e8 CFU/ml by injecting the end of the plastic pipette tip into the rib. 5 days post-infection, RNA was harvested from a 2x2 cm square piece excised from the original place of injection. "			Lettuce leaf mid-rib	Plant
-E-GEOD-23007	GSM567695 1	GSM567695.CEL	PBCLOp17. Lettuce mid-ribs. Rep2	RNA	Plant intracellular fluid	WT				Clinical isolate PBCLOp17			"Lettuce mid-ribs were inoculated with 10 ml of bacterial suspension at a concentration of 1x10e8 CFU/ml by injecting the end of the plastic pipette tip into the rib. 5 days post-infection, RNA was harvested from a 2x2 cm square piece excised from the original place of injection. "			Lettuce leaf mid-rib	Plant
-E-GEOD-31227	GSM774085 1	GSM774085_CF30-1979a.CEL	CF30 patient 1979 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF30-1979	37						
-E-GEOD-31227	GSM774086 1	GSM774086_CF30-1979b.CEL	CF30 patient 1979 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF30-1979	37						
-E-GEOD-31227	GSM774087 1	GSM774087_CF30-1979c.CEL	CF30 patient 1979 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF30-1979	37						
-E-GEOD-31227	GSM774088 1	GSM774088_CF43-1973a.CEL	CF43 patient 1973 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF43-1973	37						
-E-GEOD-31227	GSM774089 1	GSM774089_CF43-1973b.CEL	CF43 patient 1973 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF43-1973	37						
-E-GEOD-31227	GSM774090 1	GSM774090_CF43-1973c.CEL	CF43 patient 1973 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF43-1973	37						
-E-GEOD-31227	GSM774091 1	GSM774091_CF66-1973a.CEL	CF66 patient 1973 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-1973	37						
-E-GEOD-31227	GSM774092 1	GSM774092_CF66-1973b.CEL	CF66 patient 1973 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-1973	37						
-E-GEOD-31227	GSM774093 1	GSM774093_CF66-1973c.CEL	CF66 patient 1973 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-1973	37						
-E-GEOD-31227	GSM774094 1	GSM774094_CF66-1992a.CEL	CF66 patient 1992 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-1992	37						
-E-GEOD-31227	GSM774095 1	GSM774095_CF66-1992b.CEL	CF66 patient 1992 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-1992	37						
-E-GEOD-31227	GSM774096 1	GSM774096_CF66-1992c.CEL	CF66 patient 1992 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-1992	37						
-E-GEOD-31227	GSM774097 1	GSM774097_CF66-2008a.CEL	CF66 patient 2008 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-2008	37						
-E-GEOD-31227	GSM774098 1	GSM774098_CF66-2008b.CEL	CF66 patient 2008 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-2008	37						
-E-GEOD-31227	GSM774099 1	GSM774099_CF66-2008c.CEL	CF66 patient 2008 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF66-2008	37						
-E-GEOD-31227	GSM774100 1	GSM774100_CF105-1973a.CEL	CF105 patient 1973 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF105-1973	37						
-E-GEOD-31227	GSM774101 1	GSM774101_CF105-1973b.CEL	CF105 patient 1973 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF105-1973	37						
-E-GEOD-31227	GSM774102 1	GSM774102_CF105-1973c.CEL	CF105 patient 1973 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF105-1973	37						
-E-GEOD-31227	GSM774103 1	GSM774103_CF114-1973a.CEL	CF114 patient 1973 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF114-1973	37						
-E-GEOD-31227	GSM774104 1	GSM774104_CF114-1973b.CEL	CF114 patient 1973 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF114-1973	37						
-E-GEOD-31227	GSM774105 1	GSM774105_CF114-1973c.CEL	CF114 patient 1973 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF114-1973	37						
-E-GEOD-31227	GSM774106 1	GSM774106_CF173-1984a.CEL	CF173 patient 1984 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-1984	37						
-E-GEOD-31227	GSM774107 1	GSM774107_CF173-1984b.CEL	CF173 patient 1984 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-1984	37						
-E-GEOD-31227	GSM774108 1	GSM774108_CF173-1984c.CEL	CF173 patient 1984 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-1984	37						
-E-GEOD-31227	GSM774109 1	GSM774109_CF173-2002a.CEL	CF173 patient 2002 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-2002	37						
-E-GEOD-31227	GSM774110 1	GSM774110_CF173-2002b.CEL	CF173 patient 2002 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-2002	37						
-E-GEOD-31227	GSM774111 1	GSM774111_CF173-2002c.CEL	CF173 patient 2002 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-2002	37						
-E-GEOD-31227	GSM774112 1	GSM774112_CF173-2005a.CEL	CF173 patient 2005 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-2005	37						
-E-GEOD-31227	GSM774113 1	GSM774113_CF173-2005b.CEL	CF173 patient 2005 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-2005	37						
-E-GEOD-31227	GSM774114 1	GSM774114_CF173-2005c.CEL	CF173 patient 2005 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF173-2005	37						
-E-GEOD-31227	GSM774115 1	GSM774115_CF243-2002a.CEL	CF243 patient 2002 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF243-2002	37						
-E-GEOD-31227	GSM774116 1	GSM774116_CF243-2002b.CEL	CF243 patient 2002 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF243-2002	37						
-E-GEOD-31227	GSM774117 1	GSM774117_CF243-2002c.CEL	CF243 patient 2002 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF243-2002	37						
-E-GEOD-31227	GSM774118 1	GSM774118_CF333-1991a.CEL	CF333 patient 1991 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-1991	37						
-E-GEOD-31227	GSM774119 1	GSM774119_CF333-1991b.CEL	CF333 patient 1991 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-1991	37						
-E-GEOD-31227	GSM774120 1	GSM774120_CF333-1991c.CEL	CF333 patient 1991 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-1991	37						
-E-GEOD-31227	GSM774121 1	GSM774121_CF333-1997a.CEL	CF333 patient 1997 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-1997	37						
-E-GEOD-31227	GSM774122 1	GSM774122_CF333-1997b.CEL	CF333 patient 1997 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-1997	37						
-E-GEOD-31227	GSM774123 1	GSM774123_CF333-1997c.CEL	CF333 patient 1997 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-1997	37						
-E-GEOD-31227	GSM774124 1	GSM774124_CF333-2003a.CEL	CF333 patient 2003 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2003	37						
-E-GEOD-31227	GSM774125 1	GSM774125_CF333-2003b.CEL	CF333 patient 2003 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2003	37						
-E-GEOD-31227	GSM774126 1	GSM774126_CF333-2003c.CEL	CF333 patient 2003 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2003	37						
-E-GEOD-31227	GSM774127 1	GSM774127_CF333-2005a.CEL	CF333 patient 2005 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2005	37						
-E-GEOD-31227	GSM774128 1	GSM774128_CF333-2005b.CEL	CF333 patient 2005 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2005	37						
-E-GEOD-31227	GSM774129 1	GSM774129_CF333-2005c.CEL	CF333 patient 2005 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2005	37						
-E-GEOD-31227	GSM774130 1	GSM774130_CF333-2007a.CEL	CF333 patient 2007 isolate. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2007	37						
-E-GEOD-31227	GSM774131 1	GSM774131_CF333-2007b.CEL	CF333 patient 2007 isolate. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2007	37						
-E-GEOD-31227	GSM774132 1	GSM774132_CF333-2007c.CEL	CF333 patient 2007 isolate. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	CF333-2007	37						
-E-GEOD-31227	GSM774133 1	GSM774133_B6-0_1.CEL	B6 patient isolate at month 0. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B6-0	37						
-E-GEOD-31227	GSM774134 1	GSM774134_B6-0_2.CEL	B6 patient isolate at month 0. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B6-0	37						
-E-GEOD-31227	GSM774135 1	GSM774135_B6-0_3.CEL	B6 patient isolate at month 0. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B6-0	37						
-E-GEOD-31227	GSM774136 1	GSM774136_B6-4_1.CEL	B6 patient isolate at month 4. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B6-4	37						
-E-GEOD-31227	GSM774137 1	GSM774137_B6-4_2.CEL	B6 patient isolate at month 4. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B6-4	37						
-E-GEOD-31227	GSM774138 1	GSM774138_B6-4_3.CEL	B6 patient isolate at month 4. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B6-4	37						
-E-GEOD-31227	GSM774139 1	GSM774139_B12-0_1.CEL	B12 patient isolate at month 0. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-0	37						
-E-GEOD-31227	GSM774140 1	GSM774140_B12-0_2.CEL	B12 patient isolate at month 0. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-0	37						
-E-GEOD-31227	GSM774141 1	GSM774141_B12-0_3.CEL	B12 patient isolate at month 0. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-0	37						
-E-GEOD-31227	GSM774142 1	GSM774142_B12-4_1.CEL	B12 patient isolate at month 4. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-4	37						
-E-GEOD-31227	GSM774143 1	GSM774143_B12-4_2.CEL	B12 patient isolate at month 4. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-4	37						
-E-GEOD-31227	GSM774144 1	GSM774144_B12-4_3.CEL	B12 patient isolate at month 4. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-4	37						
-E-GEOD-31227	GSM774145 1	GSM774145_B12-7_1.CEL	B12 patient isolate at month 7. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-7	37						
-E-GEOD-31227	GSM774146 1	GSM774146_B12-7_2.CEL	B12 patient isolate at month 7. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-7	37						
-E-GEOD-31227	GSM774147 1	GSM774147_B12-7_3.CEL	B12 patient isolate at month 7. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B12-7	37						
-E-GEOD-31227	GSM774148 1	GSM774148_B38-1_1.CEL	B38 patient isolate at month 1. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-1	37						
-E-GEOD-31227	GSM774149 1	GSM774149_B38-1_2.CEL	B38 patient isolate at month 1. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-1	37						
-E-GEOD-31227	GSM774150 1	GSM774150_B38-1_3.CEL	B38 patient isolate at month 1. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-1	37						
-E-GEOD-31227	GSM774151 1	GSM774151_B38-2M_1.CEL	B38 patient mucoid isolate at month 2. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-2M	37			Mucoid			
-E-GEOD-31227	GSM774152 1	GSM774152_B38-2M_2.CEL	B38 patient mucoid isolate at month 2. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-2M	37			Mucoid			
-E-GEOD-31227	GSM774153 1	GSM774153_B38-2M_3.CEL	B38 patient mucoid isolate at month 2. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-2M	37			Mucoid			
-E-GEOD-31227	GSM774154 1	GSM774154_B38-2NM_1.CEL	Non-mucoid mutant derived from B38 patient mucoid isolate at month 2. Planktonic. Rep1	RNA	LB	#NAME?	0.5	Planktonic 	Aerated	B38-2NM	37		Allelic replacement of mucA in the B38-2M background.				
-E-GEOD-31227	GSM774155 1	GSM774155_B38-2NM_2.CEL	Non-mucoid mutant derived from B38 patient mucoid isolate at month 2. Planktonic. Rep1	RNA	LB	#NAME?	0.5	Planktonic 	Aerated	B38-2NM	37		Allelic replacement of mucA in the B38-2M background.				
-E-GEOD-31227	GSM774156 1	GSM774156_B38-2NM_3.CEL	Non-mucoid mutant derived from B38 patient mucoid isolate at month 2. Planktonic. Rep1	RNA	LB	#NAME?	0.5	Planktonic 	Aerated	B38-2NM	37		Allelic replacement of mucA in the B38-2M background.				
-E-GEOD-31227	GSM774157 1	GSM774157_B38-6M_1.CEL	B38 patient mucoid isolate at month 6. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-6M	37			Mucoid			
-E-GEOD-31227	GSM774158 1	GSM774158_B38-6M_2.CEL	B38 patient mucoid isolate at month 6. Planktonic. Rep2	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-6M	37			Mucoid			
-E-GEOD-31227	GSM774159 1	GSM774159_B38-6M_3.CEL	B38 patient mucoid isolate at month 6. Planktonic. Rep3	RNA	LB	WT	0.5	Planktonic 	Aerated	B38-6M	37			Mucoid			
-E-GEOD-31227	GSM774160 1	GSM774160_PAO1a.CEL	Lab wild-type strain. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	PAO1	37						
-E-GEOD-31227	GSM774161 1	GSM774161_PAO1b.CEL	Lab wild-type strain. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	PAO1	37						
-E-GEOD-31227	GSM774162 1	GSM774162_PAO1c.CEL	Lab wild-type strain. Planktonic. Rep1	RNA	LB	WT	0.5	Planktonic 	Aerated	PAO1	37						
-E-GEOD-30967	GSM767700 1	GSM767700.CEL	PAO1 WT; 25 mM phosphate; replicate 1	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37		20 hours				
-E-GEOD-30967	GSM767701 1	GSM767701.CEL	PAO1 WT; 25 mM phosphate; replicate 2	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37		20 hours				
-E-GEOD-30967	GSM767702 1	GSM767702.CEL	PAO1 WT; 25 mM phosphate; replicate 3	RNA	NGM + 25 mM phosphate	WT		Colony	Lawn	PAO1	37		20 hours				
-E-GEOD-30967	GSM767703 1	GSM767703.CEL	PAO1 WT; <0.1 mM phosphate; replicate 1	RNA	NGM + <0.1  mM phosphate	WT		Colony	Lawn	PAO1	37		20 hours				
-E-GEOD-30967	GSM767704 1	GSM767704.CEL	PAO1 WT; <0.1 mM phosphate; replicate 2	RNA	NGM + <0.1  mM phosphate	WT		Colony	Lawn	PAO1	37		20 hours				
-E-GEOD-30967	GSM767705 1	GSM767705.CEL	PAO1 WT; <0.1 mM phosphate; replicate 3	RNA	NGM + <0.1  mM phosphate	WT		Colony	Lawn	PAO1	37		20 hours				
-E-GEOD-5887	GSE5887GSM136932		"P. aeruginosa PAO1 on poplar root, 1 x hrp + 0.25 % sucrose 48 hr biofilm cells_rep1"	RNA	1× HRP minimal medium with 0.25% sucrose	WT		Biofilm	shaking - on poplar root	PAO1	25					Poplar	Plant
-E-GEOD-5887	GSE5887GSM136934		"P. aeruginosa  PAO1 on glasswool, 1 x hrp + 0.25 % sucrose 48 hr biofilm cells_rep1"	RNA	1× HRP minimal medium with 0.25% sucrose	WT		Biofilm	shaking - glass wool	PAO1	25						
-E-GEOD-5887	GSE5887GSM136936		Poplar in 1 x hrp + 0.25 % sucrose no bacteria_rep12	RNA	1× HRP minimal medium with 0.25% sucrose			Biofilm	shaking	Poplar	25		analyzed with Affymetrix Poplar Genome Array				
-E-GEOD-5887	GSE5887GSM136938		Poplar contacted with P. aeruginosa PAO1 in 1 x hrp + 0.25 % sucrose_rep1	RNA	1× HRP minimal medium with 0.25% sucrose			Biofilm	shaking	Poplar	25		analyzed with Affymetrix Poplar Genome Array			Pseudomonas aeruginosa	Bacteria
-E-MEXP-2812	H_1	H_1.CEL	7NR. Biofilms on plant roots. 330 mM phosphate. Rep1	RNA	Plant root exudate 			Biofilm 		7NR	21	330 uM phosphate 	"Inoculated seedlings (five per microcosm) were then planted into the sand and were incubated for 28 days under controlled conditions (14 h light / 10 h dark, 21 C, PAR 590-640 umol.m-2.s-1 at plant level)."		Rifampicin	Lolium perenne roots	Plant
-E-MEXP-2812	H_3	H_3.CEL	7NR. Biofilms on plant roots. 330 mM phosphate. Rep2	RNA	Plant root exudate 			Biofilm 		7NR	21	330 uM phosphate	"Inoculated seedlings (five per microcosm) were then planted into the sand and were incubated for 28 days under controlled conditions (14 h light / 10 h dark, 21 C, PAR 590-640 umol.m-2.s-1 at plant level)."		Rifampicin	Lolium perenne roots	Plant
-E-MEXP-2812	H_5	H_5.CEL	7NR. Biofilms on plant roots. 330 mM phosphate. Rep3	RNA	Plant root exudate 			Biofilm 		7NR	21	330 uM phosphate	"Inoculated seedlings (five per microcosm) were then planted into the sand and were incubated for 28 days under controlled conditions (14 h light / 10 h dark, 21 C, PAR 590-640 umol.m-2.s-1 at plant level)."		Rifampicin	Lolium perenne roots	Plant
-E-MEXP-2812	L_12	L_12.CEL	7NR. Biofilms on plant roots. 3-6 mM phosphate. Rep1	RNA	Plant root exudate 			Biofilm 		7NR	21	3-6 uM phosphate	"Inoculated seedlings (five per microcosm) were then planted into the sand and were incubated for 28 days under controlled conditions (14 h light / 10 h dark, 21 C, PAR 590-640 umol.m-2.s-1 at plant level)."		Rifampicin	Lolium perenne roots	Plant
-E-MEXP-2812	L_13	L_13.CEL	7NR. Biofilms on plant roots. 3-6 mM phosphate. Rep2	RNA	Plant root exudate 			Biofilm 		7NR	21	3-6 uM phosphate	"Inoculated seedlings (five per microcosm) were then planted into the sand and were incubated for 28 days under controlled conditions (14 h light / 10 h dark, 21 C, PAR 590-640 umol.m-2.s-1 at plant level)."		Rifampicin	Lolium perenne roots	Plant
-E-MEXP-2812	L_14	L_14.CEL	7NR. Biofilms on plant roots. 3-6 mM phosphate. Rep3	RNA	Plant root exudate 			Biofilm 		7NR	21	3-6 uM phosphate	"Inoculated seedlings (five per microcosm) were then planted into the sand and were incubated for 28 days under controlled conditions (14 h light / 10 h dark, 21 C, PAR 590-640 umol.m-2.s-1 at plant level)."		Rifampicin	Lolium perenne roots	Plant
-E-GEOD-22684	GSM424832 1	GSM424832.CEL	PAO1 RWV vertical; replicate 1	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	Aerated	PAO1	28	Normal gravity (25 rpm) 	24 hours; 25 rpm (control); bioreactor		GentR		
-E-GEOD-22684	GSM424833 1	GSM424833.CEL	PAO1 RWV vertical; replicate 2	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	Aerated	PAO1	28	Normal gravity (25 rpm) 	24 hours; 25 rpm (control); bioreactor		GentR		
-E-GEOD-22684	GSM560754 1	GSM560754.CEL	"Pseudomonas aeruginosa, PAO1, Ground Sample - biological replicate 1"	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	fluid-processing apparatus (FPA)	PAO1	23				GentR		
-E-GEOD-22684	GSM560755 1	GSM560755.CEL	"Pseudomonas aeruginosa, PAO1, Ground Sample - biological replicate 2"	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	fluid-processing apparatus (FPA)	PAO1	23				GentR		
-E-GEOD-22684	GSM560756 1	GSM560756.CEL	"Pseudomonas aeruginosa, PAO1, Ground Sample - biological replicate 3"	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	fluid-processing apparatus (FPA)	PAO1	23				GentR		
-E-GEOD-22684	GSM560757 1	GSM560757.CEL	"Pseudomonas aeruginosa, PAO1, Space flight Sample - biological replicate 1"	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	fluid-processing apparatus (FPA)	PAO1	23	"Zero gravity, experiment started and performed in space"			GentR		
-E-GEOD-22684	GSM560758 1	GSM560758.CEL	"Pseudomonas aeruginosa, PAO1, Space flight Sample - biological replicate 2"	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	fluid-processing apparatus (FPA)	PAO1	23	"Zero gravity, experiment started and performed in space"			GentR		
-E-GEOD-22684	GSM560759 1	GSM560759.CEL	"Pseudomonas aeruginosa, PAO1, Space flight Sample - biological replicate 3"	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	fluid-processing apparatus (FPA)	PAO1	23	"Zero gravity, experiment started and performed in space"			GentR		
-E-GEOD-25595	GSM629176 1	GSM629176.CEL	"Pseudomonas aeruginosa phrS deletion strain with parental vector  pJT19, was grown to OD 1 then treated with 2mM toluate for 30 min, replicate B"	RNA	LB	ΔphrS+PJT19 (parental vector)	1	Planktonic	Shaking	PAO1	37	2 mM toluate			Kanamycin		
-E-GEOD-25595	GSM629177 1	GSM629177.CEL	"Pseudomonas aeruginosa phrS deletion strain with parental vector  pJT19, was grown to OD 1 then treated with 2mM toluate for 30 min, replicate A"	RNA	LB	ΔphrS+PJT19 (parental vector)	1	Planktonic	Shaking	PAO1	37	2 mM toluate			Kanamycin		
-E-GEOD-25595	GSM629178 1	GSM629178.CEL	"Pseudomonas aeruginosa phrS deletion strain with inducible phrS overexpression plasmid, pJTphrS, was grown to OD 1 then induced with 2mM toluate for 30 min, replicate B"	RNA	LB	ΔphrS+PJTphrS	1	Planktonic	Shaking	PAO1	37	2 mM toluate			Kanamycin		
-E-GEOD-25595	GSM629179 1	GSM629179.CEL	"Pseudomonas aeruginosa phrS deletion strain with inducible phrS overexpression plasmid, pJTphrS, was grown to OD 1 then induced with 2mM toluate for 30 min, replicate A"	RNA	LB	ΔphrS+PJTphrS	1	Planktonic	Shaking	PAO1	37	2 mM toluate			Kanamycin		
-E-GEOD-30021	GSM743001 1	GSM743001_PAO1-PC4-a.CEL	Planktonic P. aeruginosa PAO1 cells cultivated for 4 hours_rep1	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37	no treatment_4 hours planktonic					
-E-GEOD-30021	GSM743002 1	GSM743002_PAO1-PC4-b.CEL	Planktonic P. aeruginosa PAO1 cells cultivated for 4 hours_rep2	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37	no treatment_4 hours planktonic					
-E-GEOD-30021	GSM743003 1	GSM743003_PAO1-PC4-c.CEL	Planktonic P. aeruginosa PAO1 cells cultivated for 4 hours_rep3	RNA	LB	WT	0.3	Planktonic	shaking	PAO1	37	no treatment_4 hours planktonic					
-E-GEOD-30021	GSM743004 1	GSM743004_PAO1-PC24-a.CEL	Planktonic P. aeruginosa PAO1 cells cultivated for 24 hours_rep1	RNA	LB	WT	5	Planktonic	shaking	PAO1	37	no treatment_24 hours planktonic					
-E-GEOD-30021	GSM743005 1	GSM743005_PAO1-PC24-b.CEL	Planktonic P. aeruginosa PAO1 cells cultivated for 24 hours_rep2	RNA	LB	WT	5	Planktonic	shaking	PAO1	37	no treatment_24 hours planktonic					
-E-GEOD-30021	GSM743006 1	GSM743006_PAO1-PC24-c.CEL	Planktonic P. aeruginosa PAO1 cells cultivated for 24 hours_rep3	RNA	LB	WT	5	Planktonic	shaking	PAO1	37	no treatment_24 hours planktonic					
-E-GEOD-30021	GSM743007 1	GSM743007_PAO1-SC24-a.CEL	Biofilm (sessile) P. aeruginosa PAO1 cells cultivated for 24 hours_rep1	RNA	LB	WT		Biofilm (sessile)	shaking - glass wool	PAO1	37	no treatment_24 hours biofilm					
-E-GEOD-30021	GSM743008 1	GSM743008_PAO1-SC24-b.CEL	Biofilm (sessile) P. aeruginosa PAO1 cells cultivated for 24 hours_rep2	RNA	LB	WT		Biofilm (sessile)	shaking - glass wool	PAO1	37	no treatment_24 hours biofilm					
-E-GEOD-30021	GSM743009 1	GSM743009_PAO1-SC24-c.CEL	Biofilm (sessile) P. aeruginosa PAO1 cells cultivated for 24 hours_rep3	RNA	LB	WT		Biofilm (sessile)	shaking - glass wool	PAO1	37	no treatment_24 hours biofilm					
-E-GEOD-28953	GSM717419 1	GSM717419.CEL	"P. aeruginosa PAO1 grown in a flow-through bioflm, rep1"	RNA	1/20 diluted LB and/or VBMM w/citrate	WT	6 days	Biofilm	flow-through system	PAO1	22						
-E-GEOD-28953	GSM717420 1	GSM717420.CEL	"P. aeruginosa PAO1 grown in a flow-through bioflm, rep2"	RNA	1/20 diluted LB and/or VBMM w/citrate	WT	6 days	Biofilm	flow-through system	PAO1	22						
-E-GEOD-28953	GSM717421 1	GSM717421.CEL	"P. aeruginosa PAO1 grown in a flow-through bioflm, rep3"	RNA	1/20 diluted LB and/or VBMM w/citrate	WT	6 days	Biofilm	flow-through system	PAO1	22						
-E-GEOD-28953	GSM717422 1	GSM717422.CEL	"P. aeruginosa delta PA4101 (∆bfmR) grown in a flow-through bioflm, rep1"	RNA	1/20 diluted LB and/or VBMM w/citrate	∆bfmR		Biofilm	flow-through system	PAO1	22						
-E-GEOD-28953	GSM717423 1	GSM717423.CEL	"P. aeruginosa delta PA4101 (∆bfmR) grown in a flow-through bioflm, rep2"	RNA	1/20 diluted LB and/or VBMM w/citrate	∆bfmR		Biofilm	flow-through system	PAO1	22						
-E-GEOD-28953	GSM717424 1	GSM717424.CEL	"P. aeruginosa delta PA4101 (∆bfmR) grown in a flow-through bioflm, rep3"	RNA	1/20 diluted LB and/or VBMM w/citrate	∆bfmR		Biofilm	flow-through system	PAO1	22						
-E-GEOD-24784	GSE17296GSM432866	GSM432866.CEL	WT. Planktonic. Early stationary phase. Rep1	RNA	LB	WT	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-24784	GSE17296GSM432867	GSM432867.CEL	WT. Planktonic. Early stationary phase. Rep2	RNA	LB	WT	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-24784	GSM610406 1	GSM610406.CEL	"Pseudomonas aeruginosa, PAO1, pellicle #1"	RNA	LB	WT	1.4	Planktonic	static	PAO1	37		24 hours; static; pellicle cells				
-E-GEOD-24784	GSM610407 1	GSM610407.CEL	"Pseudomonas aeruginosa, PAO1, pellicle #2"	RNA	LB	WT	1.4	Planktonic	static	PAO1	37		24 hours; static; pellicle cells				
-E-GEOD-21508	GSM537320 1	GSM537320.CEL	P. aeruginosa PAO1 wild type after 5 h of growth in LB and 0.1 % DMSO at 37oC	RNA	LB	WT		Planktonic	Shaking	PAO1	37	5 h  at 0.1% DMSO					
-E-GEOD-21508	GSM537321 1	GSM537321.CEL	P. aeruginosa PAO1 wild type after 5 h of growth in LB and 0.1mg/ml of IAN (plant auxin 3-indolylacetonitrile) at 37oC	RNA	LB	WT		Planktonic	Shaking	PAO1	37	5 h  at 0.1 mg/ml IAN					
-E-GEOD-22999	GSM567541 1	GSM567541.CEL	"PAO1 grown 6 hours in LB with 1mM Lyngbyoic acid extract at 37 degrees with shaking, replicate 1"	RNA	LB	WT	6 hrs	Planktonic	aerated	PAO1	37	1 mM Lyngbyoic acid extract	"Cultures (1 mL) of PAO1 were grown either in the presence or absence of 1 mM Lyngbyoic acid (added as 10 mL of a 100 mM stock solution in EtOH), for 6 h at 37 1C with shaking in 15 mm diameter glass tubes."				
-E-GEOD-22999	GSM567542 1	GSM567542.CEL	"PAO1 grown 6 hours in LB with 1% EtOH as control at 37 degrees with shaking, replicate 2"	RNA	LB	WT	6 hrs	Planktonic	aerated	PAO1	37	1% EtOH control	"Cultures (1 mL) of PAO1 were grown either in the presence or absence of 1 mM Lyngbyoic acid (added as 10 mL of a 100 mM stock solution in EtOH), for 6 h at 37 1C with shaking in 15 mm diameter glass tubes."				
-E-GEOD-22999	GSM567543 1	GSM567543.CEL	"PAO1 grown 6 hours in LB with 1mM Lyngbyoic acid extract at 37 degrees with shaking, replicate 1"	RNA	LB	WT	6 hrs	Planktonic	aerated	PAO1	37	1 mM Lyngbyoic acid extract	"Cultures (1 mL) of PAO1 were grown either in the presence or absence of 1 mM Lyngbyoic acid (added as 10 mL of a 100 mM stock solution in EtOH), for 6 h at 37 1C with shaking in 15 mm diameter glass tubes."				
-E-GEOD-22999	GSM567544 1	GSM567544.CEL	"PAO1 grown 6 hours in LB with 1% EtOH as control at 37 degrees with shaking, replicate 2"	RNA	LB	WT	6 hrs	Planktonic	aerated	PAO1	37	1% EtOH control	"Cultures (1 mL) of PAO1 were grown either in the presence or absence of 1 mM Lyngbyoic acid (added as 10 mL of a 100 mM stock solution in EtOH), for 6 h at 37 1C with shaking in 15 mm diameter glass tubes."				
-E-MEXP-2867	Glucose and vitamins- Rep 2	glu_plus_nt_2_8-25-09_s2.CEL	PA14 WT grown in MOPS with 20 mM glucose at 37C to an OD of 0.5; replicate 1	RNA	MOPS	WT	0.5	planktonic	aerated	PA14	37	20 mM glucose					
-E-MEXP-2867	Glucose and vitamins-Rep 1	AKGlu_plus_nt_7-8-09_s1.CEL	PA14 WT grown in MOPS with 20 mM glucose at 37C to an OD of 0.5; replicate 2	RNA	MOPS	WT	0.5	planktonic	aerated	PA14	37	20 mM glucose					
-E-MEXP-2867	N-acetylglucosamine and vitamins- Rep 1	Nag_cDNA_1-30-09_S2.CEL	PA14 WT grown in MOPS with 20 mM N-acetylglucosamine at 37C to an OD of 0.5; replicate 1	RNA	MOPS	WT	0.5	planktonic	aerated	PA14	37	20 mM N-acetylglucosamine					
-E-MEXP-2867	N-acetylglucosamine and vitamins- Rep 2	Nag_cDNA_2-26-09_s1.CEL	PA14 WT grown in MOPS with 20 mM N-acetylglucosamine at 37C to an OD of 0.5; replicate 2	RNA	MOPS	WT	0.5	planktonic	aerated	PA14	37	20 mM N-acetylglucosamine					
-E-GEOD-22164	GSM551147 1	GSM551147.CEL	"Pseudomonas aeruginosa, PAO1, 0hr ctrl, baseline, rep1"	RNA	PBM w/ 0.2 g/l glucose	WT		Biofilm	Drip-flow reactor	PAO1	37	baseline control at 72hrs					
-E-GEOD-22164	GSM551148 1	GSM551148.CEL	"Pseudomonas aeruginosa, PAO1, 0hr ctrl, baseline, rep2"	RNA	PBM w/ 0.2 g/l glucose	WT		Biofilm	Drip-flow reactor	PAO1	37	baseline control at 72hrs					
-E-GEOD-22164	GSM551149 1	GSM551149.CEL	"Pseudomonas aeruginosa, PAO1, 0hr ctrl, baseline, rep3"	RNA	PBM w/ 0.2 g/l glucose	WT		Biofilm	Drip-flow reactor	PAO1	37	baseline control at 72hrs					
-E-GEOD-22164	GSM551150 1	GSM551150.CEL	"Pseudomonas aeruginosa, PAO1, 12hr ctrl, rep1"	RNA	PBM w/ 0.2 g/l glucose	WT		Biofilm	Drip-flow reactor	PAO1	37	no treatment for 12 hours past baseline					
-E-GEOD-22164	GSM551151 1	GSM551151.CEL	"Pseudomonas aeruginosa, PAO1, 12hr ctrl, rep2"	RNA	PBM w/ 0.2 g/l glucose	WT		Biofilm	Drip-flow reactor	PAO1	37	no treatment for 12 hours past baseline					
-E-GEOD-22164	GSM551152 1	GSM551152.CEL	"Pseudomonas aeruginosa, PAO1, 12hr ctrl, rep3"	RNA	PBM w/ 0.2 g/l glucose	WT		Biofilm	Drip-flow reactor	PAO1	37	no treatment for 12 hours past baseline					
-E-GEOD-25130	GSM617286 1	GSM617286.CEL	A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 1	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA85	22			QS-			
-E-GEOD-25130	GSM617287 1	GSM617287.CEL	A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 2	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA85	22			QS-			
-E-GEOD-25130	GSM617288 1	GSM617288.CEL	A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 3	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA85	22			QS-			
-E-GEOD-25130	GSM617289 1	GSM617289.CEL	A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 1	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA38	22			QS+			
-E-GEOD-25130	GSM617290 1	GSM617290.CEL	A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 2	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA38	22			QS+			
-E-GEOD-25130	GSM617291 1	GSM617291.CEL	A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 3	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA38	22			QS+			
-E-GEOD-25130	GSM617292 1	GSM617292.CEL	"A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB to stationary phase, replicate 1"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA85	37			QS-			
-E-GEOD-25130	GSM617293 1	GSM617293.CEL	"A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB to stationary phase, replicate 2"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA85	37			QS-			
-E-GEOD-25130	GSM617294 1	GSM617294.CEL	"A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB to stationary phase, replicate 3"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA85	37			QS-			
-E-GEOD-25130	GSM617295 1	GSM617295.CEL	"A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB to stationary phase, replicate 1"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA38	37			QS+			
-E-GEOD-25130	GSM617296 1	GSM617296.CEL	"A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB to stationary phase, replicate 2"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA38	37			QS+			
-E-GEOD-25130	GSM617297 1	GSM617297.CEL	"A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB to stationary phase, replicate 3"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA38	37			QS+			
-E-GEOD-25130	GSM617298 1	GSM617298.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA85, replicate 1"	DNA	LB	Clinical isolate				UUPA85				QS-			
-E-GEOD-25130	GSM617299 1	GSM617299.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA85, replicate 2"	DNA	LB	Clinical isolate				UUPA85				QS-			
-E-GEOD-25130	GSM617300 1	GSM617300.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA38, replicate 1"	DNA	LB	Clinical isolate				UUPA38				QS+			
-E-GEOD-25130	GSM617301 1	GSM617301.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA38, replicate 2"	DNA	LB	Clinical isolate				UUPA38				QS+			
-E-GEOD-25129	GSM617298 1	GSM617298.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA85, replicate 1"	DNA	LB	Clinical isolate				UUPA85				QS-			
-E-GEOD-25129	GSM617299 1	GSM617299.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA85, replicate 2"	DNA	LB	Clinical isolate				UUPA85				QS-			
-E-GEOD-25129	GSM617300 1	GSM617300.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA38, replicate 1"	DNA	LB	Clinical isolate				UUPA38				QS+			
-E-GEOD-25129	GSM617301 1	GSM617301.CEL	"Comparative genomic hybridisation data from P. aeruginosa isolate UUPA38, replicate 2"	DNA	LB	Clinical isolate				UUPA38				QS+			
-E-GEOD-25128	GSM617286 1	GSM617286.CEL	A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 1	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA85	22			QS-			
-E-GEOD-25128	GSM617287 1	GSM617287.CEL	A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 2	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA85	22			QS-			
-E-GEOD-25128	GSM617288 1	GSM617288.CEL	A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 3	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA85	22			QS-			
-E-GEOD-25128	GSM617289 1	GSM617289.CEL	A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 1	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA38	22			QS+			
-E-GEOD-25128	GSM617290 1	GSM617290.CEL	A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 2	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA38	22			QS+			
-E-GEOD-25128	GSM617291 1	GSM617291.CEL	A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB as a biofilm in 1 m flow-through tube cultures (bore=5 mm; flow rate=0.5 ml/min) replicate 3	RNA	LB	Clinical isolate	5 days	Biofilm	Flow through tube	UUPA38	22			QS+			
-E-GEOD-25128	GSM617292 1	GSM617292.CEL	"A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB to stationary phase, replicate 1"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA85	37			QS-			
-E-GEOD-25128	GSM617293 1	GSM617293.CEL	"A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB to stationary phase, replicate 2"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA85	37			QS-			
-E-GEOD-25128	GSM617294 1	GSM617294.CEL	"A QS- P. aeruginosa clinical isolate UUPA85 from the CF lung was grown in LB to stationary phase, replicate 3"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA85	37			QS-			
-E-GEOD-25128	GSM617295 1	GSM617295.CEL	"A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB to stationary phase, replicate 1"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA38	37			QS+			
-E-GEOD-25128	GSM617296 1	GSM617296.CEL	"A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB to stationary phase, replicate 2"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA38	37			QS+			
-E-GEOD-25128	GSM617297 1	GSM617297.CEL	"A QS+ P. aeruginosa clinical isolate UUPA38 from the CF lung was grown in LB to stationary phase, replicate 3"	RNA	LB	Clinical isolate	stationary phase	planktonic	Shaking	UUPA38	37			QS+			
-E-GEOD-10065	GSM177216 1	GSM177216.CEL	Pseudomonas aeruginosa PAO1 biofilm + 500 uM 7-hydroxyindole	RNA	LB	WT		Biofilm on glass wool	batch with aeration	PAO1	37	500 uM 7-hydroxyindole	7 h				
-E-GEOD-10065	GSM177218 1	GSM177218.CEL	Pseudomonas aeruginosa PAO1 biofilm control	RNA	LB	WT		Biofilm on glass wool	batch with aeration	PAO1	37	DMF vehicle control	7 h				
-E-GEOD-10065	GSM254432 1	GSM254432.CEL	Pseudomonas aeruginosa PAO1 biofilm + 1 mM indole	RNA	LB	WT		Biofilm on glass wool	batch with aeration	PAO1	37	1mM  indole	7 h				
-E-GEOD-10065	GSM307134 1	GSM307134.CEL	Pseudomonas aeruginosa PAO1 biofilm control	RNA	LB	WT		Biofilm on glass wool	batch with aeration	PAO1	37	DMF vehicle control	7 h				
-E-GEOD-10065	GSM307135 1	GSM307135.CEL	Pseudomonas aeruginosa PAO1 biofilm + 1 mM IAA	RNA	LB	WT		Biofilm on glass wool	batch with aeration	PAO1	37	1 mM indole acetic acid	7 h				
-E-GEOD-22665	GSM562109 1	GSM562109_PAK_GI_colonization_2_6-9-04.CEL	"PAK from infected mice ceca, pooled from 8 mice, at 7 DPI, 3 replicates"	RNA	in vivo growth	WT		in vivo	Mouse GI	PAK	37		Pa isolated from infected C3H/HeN adult mice ceca			Cecum	Mouse
-E-GEOD-22665	GSM562110 1	GSM562110_PAK_GI_colonization_4_6-9-04.CEL	"PAK from infected mice ceca, pooled from 8 mice, at 7 DPI, 3 replicates"	RNA	in vivo growth	WT		in vivo	Mouse GI	PAK	37		Pa isolated from infected C3H/HeN adult mice ceca			Cecum	Mouse
-E-GEOD-22665	GSM562111 1	GSM562111_PAK_GI_colonization_5-12-04.CEL	"PAK from infected mice ceca, pooled from 8 mice, at 7 DPI, 3 replicates"	RNA	in vivo growth	WT		in vivo	Mouse GI	PAK	37		Pa isolated from infected C3H/HeN adult mice ceca			Cecum	Mouse
-E-GEOD-22665	GSM562112 1	GSM562112_Koh_PAK_H2O_1.CEL	"PAK isolated from mouse drinking water inoculated with PAK for the purpose of inoculating mice, 3 replicates"	RNA	Water with 1500 Units penicillin G/mL	WT		planktonic	static	PAK	24		Pa isolated from inoculated drinking water for mice				
-E-GEOD-22665	GSM562113 1	GSM562113_Koh_PAK_H2O_2.CEL	"PAK isolated from mouse drinking water inoculated with PAK for the purpose of inoculating mice, 3 replicates"	RNA	Water with 1500 Units penicillin G/mL	WT		planktonic	static	PAK	24		Pa isolated from inoculated drinking water for mice				
-E-GEOD-22665	GSM562114 1	GSM562114_Koh_PAK_H2O_3.CEL	"PAK isolated from mouse drinking water inoculated with PAK for the purpose of inoculating mice, 3 replicates"	RNA	Water with 1500 Units penicillin G/mL	WT		planktonic	static	PAK	24		Pa isolated from inoculated drinking water for mice				
-E-GEOD-16970	GSM424829 1	GSM424829.CEL	PAO1 RWV horizontal; replicate 1	RNA	LB	WT		Planktonic	Aerated	PAO1	28	Microgravity (25 rpm)	24 hours; 25 rpm; bioreactor				
-E-GEOD-16970	GSM424830 1	GSM424830.CEL	PAO1 RWV horizontal; replicate 2	RNA	LB	WT		Planktonic	Aerated	PAO1	28	Microgravity (25 rpm)	24 hours; 25 rpm; bioreactor				
-E-GEOD-16970	GSM424831 1	GSM424831.CEL	PAO1 RWV horizontal; replicate 3	RNA	LB	WT		Planktonic	Aerated	PAO1	28	Microgravity (25 rpm)	24 hours; 25 rpm; bioreactor				
-E-GEOD-16970	GSM424832 1	GSM424832.CEL	PAO1 RWV vertical; replicate 1	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	Aerated	PAO1	28	Normal gravity (25 rpm) 	24 hours; 25 rpm (control); bioreactor		GentR		
-E-GEOD-16970	GSM424833 1	GSM424833.CEL	PAO1 RWV vertical; replicate 2	RNA	LB medium containing 25 μg/ml gentamicin	WT w/ GentR at attB site		Planktonic	Aerated	PAO1	28	Normal gravity (25 rpm) 	24 hours; 25 rpm (control); bioreactor		GentR		
-E-GEOD-16970	GSM424834 1	GSM424834.CEL	PAO1 RPM; replicate 1	RNA	LB	WT		Planktonic	Aerated	PAO1	28	Randomized gravity (10 rpm)	24 hours; 10 rpm; bioreactor				
-E-GEOD-16970	GSM424835 1	GSM424835.CEL	PAO1 RPM; replicate 2	RNA	LB	WT		Planktonic	Aerated	PAO1	28	Randomized gravity (10 rpm)	24 hours; 10 rpm; bioreactor				
-E-GEOD-16970	GSM424836 1	GSM424836.CEL	PAO1 RPM; replicate 3	RNA	LB	WT		Planktonic	Aerated	PAO1	28	Randomized gravity (10 rpm)	24 hours; 10 rpm; bioreactor				
-E-GEOD-17179	GSM429472 1	GSM429472.CEL	"Pseudomonas aeruginosa, PAO1, aerobic, replicate1"	RNA	modified AB minimal medium	WT	0.3	Planktonic	shaking	PAO1	37	aerobic					
-E-GEOD-17179	GSM429475 1	GSM429475.CEL	"Pseudomonas aeruginosa, PAO1, aerobic, replicate2"	RNA	modified AB minimal medium	WT	0.3	Planktonic	shaking	PAO1	37	aerobic					
-E-GEOD-17179	GSM429476 1	GSM429476.CEL	"Pseudomonas aeruginosa, PAO1, anaerobic, replicate1"	RNA	modified AB minimal medium	WT	0.3	Planktonic	static	PAO1	37	anaerobic (130 ml of the respective aerobic culture were transferred to a 135 ml sealed serum flask)					
-E-GEOD-17179	GSM429477 1	GSM429477.CEL	"Pseudomonas aeruginosa, PAO1, anaerobic, replicate2"	RNA	modified AB minimal medium	WT	0.3	Planktonic	static	PAO1	37	anaerobic (130 ml of the respective aerobic culture were transferred to a 135 ml sealed serum flask)					
-E-GEOD-17179	GSM429478 1	GSM429478.CEL	"Pseudomonas aeruginosa strain PAO6261, anr mutant, anaerobic, replicate1"	RNA	modified AB minimal medium	∆anr	0.3	Planktonic	static	PAO6261 (PAO1)	37	anaerobic (130 ml of the respective aerobic culture were transferred to a 135 ml sealed serum flask)					
-E-GEOD-17179	GSM429479 1	GSM429479.CEL	"Pseudomonas aeruginosa strain PAO6261, anr mutant, anaerobic, replicate2"	RNA	modified AB minimal medium	∆anr	0.3	Planktonic	static	PAO6261 (PAO1)	37	anaerobic (130 ml of the respective aerobic culture were transferred to a 135 ml sealed serum flask)					
-E-GEOD-17179	GSM429480 1	GSM429480.CEL	"Pseudomonas aeruginosa strain RM536, dnr mutant, anaerobic, replicate1"	RNA	modified AB minimal medium	∆dnr	0.3	Planktonic	static	RM536 (PAO1)	37	anaerobic (130 ml of the respective aerobic culture were transferred to a 135 ml sealed serum flask)					
-E-GEOD-17179	GSM429481 1	GSM429481.CEL	"Pseudomonas aeruginosa strain RM536, dnr mutant, anaerobic, replicate2"	RNA	modified AB minimal medium	∆dnr	0.3	Planktonic	static	RM536 (PAO1)	37	anaerobic (130 ml of the respective aerobic culture were transferred to a 135 ml sealed serum flask)					
-E-GEOD-21966	GSM546244 1	GSM546244.CEL	Classical; initial isolate (A); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C2773C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546245 1	GSM546245.CEL	Classical; initial isolate (A); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C2773C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546246 1	GSM546246.CEL	Classical; 570 days post isolation (A); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C3470C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546247 1	GSM546247.CEL	Classical; 570 days post isolation (A); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C3470C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546248 1	GSM546248.CEL	Mucoid; 703 days post isolation (A); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C3639M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546249 1	GSM546249.CEL	Mucoid; 703 days post isolation (A); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C3639M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546250 1	GSM546250.CEL	SCV; 703 days post isolation (A); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C3640D	37		Exponential phase cells; 250 rpm	SCV			
-E-GEOD-21966	GSM546251 1	GSM546251.CEL	SCV; 703 days post isolation (A); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C3640D	37		Exponential phase cells; 250 rpm	SCV			
-E-GEOD-21966	GSM546252 1	GSM546252.CEL	Mucoid; 1194 days post isolation (A); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C4278M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546253 1	GSM546253.CEL	Mucoid; 1194 days post isolation (A); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C4278M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546254 1	GSM546254.CEL	Classical; initial isolate (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C1913C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546255 1	GSM546255.CEL	Classical; initial isolate (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C1913C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546256 1	GSM546256.CEL	Classical; 1669 days post isolation (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C4218C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546257 1	GSM546257.CEL	Classical; 1669 days post isolation (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C4218C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546258 1	GSM546258.CEL	SCV; 1669 days post isolation (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C4219D	37		Exponential phase cells; 250 rpm	SCV			
-E-GEOD-21966	GSM546259 1	GSM546259.CEL	SCV; 1669 days post isolation (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C4219D	37		Exponential phase cells; 250 rpm	SCV			
-E-GEOD-21966	GSM546260 1	GSM546260.CEL	Mucoid; 1669 days post isolation (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C4220M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546261 1	GSM546261.CEL	Mucoid; 1669 days post isolation (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C4220M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546262 1	GSM546262.CEL	Mucoid; 2790 days post isolation (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C5912M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546263 1	GSM546263.CEL	Mucoid; 2790 days post isolation (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C5912M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546264 1	GSM546264.CEL	Classical; 2790 days post islation (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C5913C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546265 1	GSM546265.CEL	Classical; 2790 days post islation (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C5913C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546266 1	GSM546266.CEL	Mucoid; 2790 days post isolation (B); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C5914M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546267 1	GSM546267.CEL	Mucoid; 2790 days post isolation (B); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C5914M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546268 1	GSM546268.CEL	Classical; initial isolate (Ca); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C0324C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546269 1	GSM546269.CEL	Classical; initial isolate (Ca); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C0324C	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546270 1	GSM546270.CEL	Mucoid; 91 days post isolation (Ca); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C0476M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546271 1	GSM546271.CEL	Mucoid; 91 days post isolation (Ca); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C0476M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546272 1	GSM546272.CEL	Mucoid; 1523 days post isolation (Cb); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C2159M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546273 1	GSM546273.CEL	Mucoid; 1523 days post isolation (Cb); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C2159M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546274 1	GSM546274.CEL	SCV; 2548 days post isolation (Cb); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C3488D	37		Exponential phase cells; 250 rpm	SCV			
-E-GEOD-21966	GSM546275 1	GSM546275.CEL	SCV; 2548 days post isolation (Cb); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C3488D	37		Exponential phase cells; 250 rpm	SCV			
-E-GEOD-21966	GSM546276 1	GSM546276.CEL	Mucoid; 4068 days post isolation (Cb); replicate 1	RNA	SCFM		0.5	Planktonic	Aerated	C5623M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546277 1	GSM546277.CEL	Mucoid; 4068 days post isolation (Cb); replicate 2	RNA	SCFM		0.5	Planktonic	Aerated	C5623M	37		Exponential phase cells; 250 rpm	Mucoid			
-E-GEOD-21966	GSM546278 1	GSM546278.CEL	Classical; control; replicate 1	RNA	SCFM	WT	0.5	Planktonic	Aerated	PA14	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546279 1	GSM546279.CEL	Classical; control; replicate 2	RNA	SCFM	WT	0.5	Planktonic	Aerated	PA14	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546280 1	GSM546280.CEL	Classical; control; replicate 1	RNA	SCFM	WT	0.5	Planktonic	Aerated	PAO1	37		Exponential phase cells; 250 rpm				
-E-GEOD-21966	GSM546281 1	GSM546281.CEL	Classical; control; replicate 2	RNA	SCFM	WT	0.5	Planktonic	Aerated	PAO1	37		Exponential phase cells; 250 rpm				
-E-GEOD-18594	GSM462061 1	GSM462061.CEL	WT. Biofilm in ASMDM media. Rep1	RNA	ASMDM	WT UCBPP-PA14	1.1	planktonic	aerated 	PA14	37		Grown statically in ASMDM media. Harvest at 72 h				
-E-GEOD-18594	GSM462062 1	GSM462062.CEL	WT. Biofilm in ASMDM media. Rep2	RNA	ASMDM	WT UCBPP-PA14	1.1	planktonic	aerated 	PA14	37		Grown shaking at 50rpm.				
-E-GEOD-18594	GSM462063 1	GSM462063.CEL	WT. Planktonic in LB. Stationary phase. Rep1	RNA	LB	WT UCBPP-PA14	1.1	planktonic	aerated 	PA14	37		Grown shaking at 50rpm				
-E-GEOD-18594	GSM462064 1	GSM462064.CEL	WT. Planktonic in LB. Stationary phase. Rep2	RNA	LB	WT UCBPP-PA14	1.1	planktonic	aerated 	PA14	37		Grown shaking at 250rpm. 				
-E-GEOD-18594	GSM462352 1	GSM462352.CEL	WT. Planktonic in MOPS-glucose. Exponential phase. Rep1	RNA	MOPS-Glucose	WT UCBPP-PA14	0.15	planktonic	aerated 	PA14	37		Grown shaking at 250rpm. 				
-E-GEOD-18594	GSM462353 1	GSM462353.CEL	WT. Planktonic in MOPS-glucose. Exponential phase. Rep2	RNA	MOPS-Glucose	WT UCBPP-PA14	0.15	planktonic	aerated 	PA14	37		Grown shaking at 250rpm. 				
-E-GEOD-18594	GSM462354 1	GSM462354.CEL	WT. Planktonic in ASMDM. Exponential phase. Rep1	RNA	ASMDM	WT UCBPP-PA14	0.15	planktonic	aerated 	PA14	37		Grown shaking at 250rpm. 				
-E-GEOD-18594	GSM462355 1	GSM462355.CEL	WT. Planktonic in ASMDM. Exponential phase. Rep2	RNA	ASMDM	WT UCBPP-PA14	0.15	planktonic	aerated 	PA14	37		Grown shaking at 250rpm. 				
-E-GEOD-21704	GSM541640 1	GSM541640.CEL	WT. Planktonic in LB plus H2O2. Rep1	RNA	LB	WT		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	H2O2	"Stationary phase-grown cultures (3x10e10 cells) were resuspended in LB and kept in a dialysis tube (14-kDa cutoff, 25 mm) with an effective length of 6 cm for the exchange of fluids. The dialysis tube was resuspended in a 1-liter Erlenmeyer flask containing 600 ml of LB with 10 mM hydrogen peroxide. The flasks were incubated at 37°C and 200 rpm on a rotary shaker for 2 h."				
-E-GEOD-21704	GSM541641 1	GSM541641.CEL	WT. Planktonic in LB plus H2O2. Rep2	RNA	LB	WT		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	H2O2	"Stationary phase-grown cultures (3x10e10 cells) were resuspended in LB and kept in a dialysis tube (14-kDa cutoff, 25 mm) with an effective length of 6 cm for the exchange of fluids. The dialysis tube was resuspended in a 1-liter Erlenmeyer flask containing 600 ml of LB with 10 mM hydrogen peroxide. The flasks were incubated at 37°C and 200 rpm on a rotary shaker for 2 h."				
-E-GEOD-21704	GSM541642 1	GSM541642.CEL	"WT. Planktonic in RPMI 1640, exposed to PMN exoproducts. Rep1"	RNA	RPMI 1640 	WT		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	PMN exoproducts 	"Stationary phase-grown bacterial cultures (1x10e10 cells) were pelleted and resuspended in 2 ml of RPMI 1640 medium, placed into a dialysis bag (300 kDa) and incubated in presence of 1x10e7 PMNs in 15 ml RPMI 1640 medium containing 10 % (v/v) human AB blood serum, at 200 rpm on a rotary shaker for 2 h (37°C)."			Polymorphonuclear neutrophils	Human
-E-GEOD-21704	GSM541643 1	GSM541643.CEL	"WT. Planktonic in RPMI 1640, exposed to PMN exoproducts. Rep2"	RNA	RPMI 1640 	WT		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	PMN exoproducts 	"Stationary phase-grown bacterial cultures (1x10e10 cells) were pelleted and resuspended in 2 ml of RPMI 1640 medium, placed into a dialysis bag (300 kDa) and incubated in presence of 1x10e7 PMNs in 15 ml RPMI 1640 medium containing 10 % (v/v) human AB blood serum, at 200 rpm on a rotary shaker for 2 h (37°C)."			Polymorphonuclear neutrophils	Human
-E-GEOD-21704	GSM541644 1	GSM541644.CEL	nadK1::tn5. Planktonic in LB plus H2O2. Rep1	RNA	LB	nadK1::tn5		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	H2O2	"Stationary phase-grown cultures (3x10e10 cells) were resuspended in LB and kept in a dialysis tube (14-kDa cutoff, 25 mm) with an effective length of 6 cm for the exchange of fluids. The dialysis tube was resuspended in a 1-liter Erlenmeyer flask containing 600 ml of LB with 10 mM hydrogen peroxide. The flasks were incubated at 37°C and 200 rpm on a rotary shaker for 2 h."		KanR		
-E-GEOD-21704	GSM541645 1	GSM541645.CEL	nadK1::tn5. Planktonic in LB plus H2O2. Rep2	RNA	LB	nadK1::tn5		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	H2O2	"Stationary phase-grown cultures (3x10e10 cells) were resuspended in LB and kept in a dialysis tube (14-kDa cutoff, 25 mm) with an effective length of 6 cm for the exchange of fluids. The dialysis tube was resuspended in a 1-liter Erlenmeyer flask containing 600 ml of LB with 10 mM hydrogen peroxide. The flasks were incubated at 37°C and 200 rpm on a rotary shaker for 2 h."		KanR		
-E-GEOD-21704	GSM541646 1	GSM541646.CEL	"nadK1::tn5. Planktonic in RPMI 1640, exposed to PMN exoproducts. Rep1"	RNA	RPMI 1640 	nadK1::tn5		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	PMN exoproducts 	"Stationary phase-grown bacterial cultures (1x10e10 cells) were pelleted and resuspended in 2 ml of RPMI 1640 medium, placed into a dialysis bag (300 kDa) and incubated in presence of 1x10e7 PMNs in 15 ml RPMI 1640 medium containing 10 % (v/v) human AB blood serum, at 200 rpm on a rotary shaker for 2 h (37°C)."		KanR	Polymorphonuclear neutrophils	Human
-E-GEOD-21704	GSM541647 1	GSM541647.CEL	"nadK1::tn5. Planktonic in RPMI 1640, exposed to PMN exoproducts. Rep2"	RNA	RPMI 1640 	nadK1::tn5		Planktonic 	Aerated	Clinical isolate TBCF10839 	37	PMN exoproducts 	"Stationary phase-grown bacterial cultures (1x10e10 cells) were pelleted and resuspended in 2 ml of RPMI 1640 medium, placed into a dialysis bag (300 kDa) and incubated in presence of 1x10e7 PMNs in 15 ml RPMI 1640 medium containing 10 % (v/v) human AB blood serum, at 200 rpm on a rotary shaker for 2 h (37°C)."		KanR	Polymorphonuclear neutrophils	Human
-E-GEOD-21704	GSM541648 1	GSM541648.CEL	WT. Planktonic in LB. Rep1	RNA	LB	WT		Planktonic 	Aerated	Clinical isolate TBCF10839 	37		"Stationary phase-grown cultures (3x10e10 cells) were resuspended in LB and kept in a dialysis tube (14-kDa cutoff, 25 mm) with an effective length of 6 cm for the exchange of fluids. The dialysis tube was resuspended in a 1-liter Erlenmeyer flask containing 600 ml of LB. The flasks were incubated at 37°C and 200 rpm on a rotary shaker for 2 h."				
-E-GEOD-21704	GSM541649 1	GSM541649.CEL	WT. Planktonic in LB. Rep2	RNA	LB	WT		Planktonic 	Aerated	Clinical isolate TBCF10839 	37		"Stationary phase-grown cultures (3x10e10 cells) were resuspended in LB and kept in a dialysis tube (14-kDa cutoff, 25 mm) with an effective length of 6 cm for the exchange of fluids. The dialysis tube was resuspended in a 1-liter Erlenmeyer flask containing 600 ml of LB. The flasks were incubated at 37°C and 200 rpm on a rotary shaker for 2 h."				
-E-MEXP-2606	bphO-1	TUBS0091.CEL	PAO1 ΔbphO; replicate 1	RNA	LB	ΔbphO	3.5	Planktonic		PAO1	37		OD578; stationary phase cells		GentR		
-E-MEXP-2606	bphO-2	TUBS0092.CEL	PAO1 ΔbphO; replicate 2	RNA	LB	ΔbphO	3.5	Planktonic		PAO1	37		OD578; stationary phase cells		GentR		
-E-MEXP-2606	bphO-3	TUBS0093.CEL	PAO1 ΔbphO; replicate 3	RNA	LB	ΔbphO	3.5	Planktonic		PAO1	37		OD578; stationary phase cells		GentR		
-E-MEXP-2606	bphP-1	TUBS0094.CEL	PAO1 ΔbphP; replicate 1	RNA	LB	ΔbphP	3.5	Planktonic		PAO1	37		OD578; stationary phase cells		GentR		
-E-MEXP-2606	bphP-2	TUBS0095.CEL	PAO1 ΔbphP; replicate 2	RNA	LB	ΔbphP	3.5	Planktonic		PAO1	37		OD578; stationary phase cells		GentR		
-E-MEXP-2606	bphP-3	TUBS0096.CEL	PAO1 ΔbphP; replicate 3	RNA	LB	ΔbphP	3.5	Planktonic		PAO1	37		OD578; stationary phase cells		GentR		
-E-MEXP-2606	WT-1	TUBS0088.CEL	PAO1 WT; control; replicate 1	RNA	LB	WT	3.5	Planktonic		PAO1	37		OD578; stationary phase cells				
-E-MEXP-2606	WT-2	TUBS0089.CEL	PAO1 WT; control; replicate 2	RNA	LB	WT	3.5	Planktonic		PAO1	37		OD578; stationary phase cells				
-E-MEXP-2606	WT-3	TUBS0090.CEL	PAO1 WT; control; replicate 3	RNA	LB	WT	3.5	Planktonic		PAO1	37		OD578; stationary phase cells				
-E-MEXP-2593	PA14 SCFM 1	PA14_3-23-07_s2.CEL	PA14 WT grown planktonically in synthetic CF sputum medium with aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 1	RNA	Synthetic CF sputum	WT	0.35-0.45	Planktonic	aerated	PA14	37	with aromatic amino acids	Grown shaking at 250rpm				
-E-MEXP-2593	PA14 SCFM 2	05_PA14000-4-2_5-10-07_S2.CEL	PA14 WT grown planktonically in synthetic CF sputum medium with aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 2	RNA	Synthetic CF sputum	WT	0.35-0.45	Planktonic	aerated	PA14	37	with aromatic amino acids	Grown shaking at 250rpm				
-E-MEXP-2593	PA14 SCFM 3	PA14_cDNA_labeled_4-09-08_S2.CEL	PA14 WT grown planktonically in synthetic CF sputum medium with aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 3	RNA	Synthetic CF sputum	WT	0.35-0.45	Planktonic	aerated	PA14	37	with aromatic amino acids	Grown shaking at 250rpm				
-E-MEXP-2593	PA14 SCFM 4	HH-PA14_7-6-09_7-8-09_s1.CEL	PA14 WT grown planktonically in synthetic CF sputum medium with aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 4	RNA	Synthetic CF sputum	WT	0.35-0.45	Planktonic	aerated	PA14	37	with aromatic amino acids	Grown shaking at 250rpm				
-E-MEXP-2593	PA14 SCFM no aromatic amino acids 1	PA14-Aro_10-5-07_s2.CEL	PA14 WT cells grown planktonically in synthetic CF sputum medium without aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 1	RNA	Synthetic CF sputum	WT	0.35-0.45	Planktonic	aerated	PA14	37	No aromatic amino acids	Grown shaking at 250rpm				
-E-MEXP-2593	PA14 SCFM no aromatic amino acids 2	KP_PA14_5-28_5-30-08_s2.CEL	PA14 WT cells grown planktonically in synthetic CF sputum medium without aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 2	RNA	Synthetic CF sputum	WT	0.35-0.45	Planktonic	aerated	PA14	37	No aromatic amino acids	Grown shaking at 250rpm				
-E-MEXP-2593	phhR mutant SCFM 1	GPphhRminus_11-4-09_s2.CEL	PA14 phhR mutant cells grown planktonically in synthetic CF sputum medium with aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 1	RNA	Synthetic CF sputum	phhR::Mariner	0.35-0.45	Planktonic	aerated	PA14	37	with aromatic amino acids	Grown shaking at 250rpm		Gentamicin		
-E-MEXP-2593	phhR mutant SCFM 2	GPphhRminus_11-30-09_12-3-09_s1.CEL	PA14 phhR mutant cells grown planktonically in synthetic CF sputum medium with aromatic amino acids at 37C with aeration to an OD of 0.35 to 0.45; replicate 2	RNA	Synthetic CF sputum	phhR::Mariner	0.35-0.45	Planktonic	aerated	PA14	37	with aromatic amino acids	Grown shaking at 250rpm		Gentamicin		
-E-GEOD-13252	GSE13252GSM334310	GSM334310.CEL	Pseudomonas aeruginosa PAO1_untreated control_rep1	RNA	MH	WT	0.8	Planktonic	shaking	PAO1	37		Shaking at 180 rpm				
-E-GEOD-13252	GSE13252GSM334311	GSM334311.CEL	Pseudomonas aeruginosa PAO1 0.15µg/ml colistin treated rep1	RNA	MH	WT	0.8	Planktonic	shaking	PAO1	37	0.15 µg/ml colistin	Shaking at 180 rpm				
-E-GEOD-13252	GSE13252GSM334312	GSM334312.CEL	Pseudomonas aeruginosa PAO1_untreated control_rep2	RNA	MH	WT	0.8	Planktonic	shaking	PAO1	37		Shaking at 180 rpm				
-E-GEOD-13252	GSE13252GSM334313	GSM334313.CEL	Pseudomonas aeruginosa PAO1 0.15µg/ml colistin treated rep2	RNA	MH	WT	0.8	Planktonic	shaking	PAO1	37	0.15 µg/ml colistin	Shaking at 180 rpm				
-E-GEOD-13252	GSE13252GSM334314	GSM334314.CEL	Pseudomonas aeruginosa PAO1 0.15µg/ml colistin treated rep3	RNA	MH	WT	0.8	Planktonic	shaking	PAO1	37	0.15 µg/ml colistin	Shaking at 180 rpm				
-E-GEOD-13252	GSE13252GSM334315	GSM334315.CEL	Pseudomonas aeruginosa PAO1_untreated control_rep3	RNA	MH	WT	0.8	Planktonic	shaking	PAO1	37		Shaking at 180 rpm				
-E-GEOD-13326	GSM335182 1	GSM335182.CEL	RNA extracted from suspension cells of Pseudomonas aeruginosa PA14 WT untreated (control for 500uM NE expt) for 7 hours in serum-RPMI medium at 37ºC	RNA	serum-RPMI		0.5	planktonic	aeration	PA14	37	control	7 h				
-E-GEOD-13326	GSM335183 1	GSM335183.CEL	RNA extracted from suspension cells of Pseudomonas aeruginosa PA14 WT treated with 500 uM norepinephrine for 7 hours in serum-RPMI medium at 37ºC	RNA	serum-RPMI		1.5	planktonic	aeration	PA14	37	500 uM norepinephrine	7 h				
-E-GEOD-13326	GSM366976 1	GSM366976.CEL	RNA extracted from suspension cells of Pseudomonas aeruginosa PA14 WT untreated (control 50 uM NE expt) for 7 hours in serum-RPMI medium at 37ºC	RNA	serum-RPMI		1.1	planktonic	aeration	PA14	37	50 uM norepinephrine	7 h				
-E-GEOD-13326	GSM366977 1	GSM366977.CEL	RNA extracted from suspension cells of Pseudomonas aeruginosa PA14 WT treated with 50 uM norepinephrine for 7 hour in serum-RPMI medium at 37ºC	RNA	serum-RPMI		0.5	planktonic	aeration	PA14	37	control	7 h				
-E-GEOD-17296	GSE17296GSM432860	GSM432860.CEL	WT. Planktonic. Exponential phase. Rep1	RNA	LB	WT	0.3	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432861	GSM432861.CEL	WT. Planktonic. Exponential phase. Rep2	RNA	LB	WT	0.3	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432862	GSM432862.CEL	∆roxSR. Planktonic. Exponential phase. Rep1	RNA	LB	∆roxSR	0.3	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432863	GSM432863.CEL	∆roxSR. Planktonic. Exponential phase. Rep2	RNA	LB	∆roxSR	0.3	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432864	GSM432864.CEL	∆anr. Planktonic. Exponential phase. Rep1	RNA	LB	∆anr	0.3	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432865	GSM432865.CEL	∆anr. Planktonic. Exponential phase. Rep2	RNA	LB	∆anr	0.3	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432866	GSM432866.CEL	WT. Planktonic. Early stationary phase. Rep1	RNA	LB	WT	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432867	GSM432867.CEL	WT. Planktonic. Early stationary phase. Rep2	RNA	LB	WT	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432868	GSM432868.CEL	∆roxSR. Planktonic. Early stationary phase. Rep1	RNA	LB	∆roxSR	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432869	GSM432869.CEL	∆roxSR. Planktonic. Early stationary phase. Rep2	RNA	LB	∆roxSR	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432870	GSM432870.CEL	∆anr. Planktonic. Early stationary phase. Rep1	RNA	LB	∆anr	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-17296	GSE17296GSM432871	GSM432871.CEL	∆anr. Planktonic. Early stationary phase. Rep2	RNA	LB	∆anr	1.4	Planktonic 	Aerated 	PAO1	37		Grown shaking. 				
-E-GEOD-6769	GSM156153 1	GSM156153.CEL	PAO1 WT. Flow cell biofilm. Rep1	RNA	ABT media plus 0.5% glucose	WT		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days. 				
-E-GEOD-6769	GSM156154 1	GSM156154.CEL	PAO1 WT. Flow cell biofilm plus PMNs. Rep1	RNA	ABT media plus 0.5% glucose	WT		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days before challenge with PMNs.			Polymorphonuclear neutrophils	Human
-E-GEOD-6769	GSM156155 1	GSM156155.CEL	PAO1 WT. Flow cell biofilm. Rep2	RNA	ABT media plus 0.5% glucose	WT		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days. 				
-E-GEOD-6769	GSM156156 1	GSM156156.CEL	PAO1 WT. Flow cell biofilm plus PMNs. Rep2	RNA	ABT media plus 0.5% glucose	WT		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days before challenge with PMNs.			Polymorphonuclear neutrophils	Human
-E-GEOD-6769	GSM156157 1	GSM156157.CEL	PAO1 WT. Flow cell biofilm plus PMNs. Rep3	RNA	ABT media plus 0.5% glucose	WT		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days before challenge with PMNs.			Polymorphonuclear neutrophils	Human
-E-GEOD-6769	GSM156158 1	GSM156158.CEL	PAO1 WT. Flow cell biofilm. Rep3	RNA	ABT media plus 0.5% glucose	WT		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days. 				
-E-GEOD-6769	GSM156172 1	GSM156172.CEL	PAO1 ∆lasR ∆rhlR. Flow cell biofilm plus PMNs. Rep1	RNA	ABT media plus 0.5% glucose	∆lasR ∆rhlR		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days before challenge with PMNs.			Polymorphonuclear neutrophils	Human
-E-GEOD-6769	GSM156173 1	GSM156173.CEL	PAO1 ∆lasR ∆rhlR. Flow cell biofilm plus PMNs. Rep1	RNA	ABT media plus 0.5% glucose	∆lasR ∆rhlR		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days. 				
-E-GEOD-6769	GSM156174 1	GSM156174.CEL	PAO1 ∆lasR ∆rhlR. Flow cell biofilm plus PMNs. Rep2	RNA	ABT media plus 0.5% glucose	∆lasR ∆rhlR		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days before challenge with PMNs.			Polymorphonuclear neutrophils	Human
-E-GEOD-6769	GSM156175 1	GSM156175.CEL	PAO1 ∆lasR ∆rhlR. Flow cell biofilm plus PMNs. Rep2	RNA	ABT media plus 0.5% glucose	∆lasR ∆rhlR		Biofilm	Flow cell	PAO1	37		Biofilms were allowed to grow and develop in the biofilm flow chambers for 3 days before challenge with PMNs.				
-E-GEOD-10362	GSE10362GSM261915	GSM261915.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261917 and GSM261916"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M1-1	37						
-E-GEOD-10362	GSE10362GSM261916	GSM261916.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261917 and GSM261915"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M1-2	37						
-E-GEOD-10362	GSE10362GSM261917	GSM261917.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261916 and GSM261915"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M1-3	37						
-E-GEOD-10362	GSE10362GSM261918	GSM261918.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261920 and GSM261919"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M9-1	37		ceftazidime resistant	Mucoid			
-E-GEOD-10362	GSE10362GSM261919	GSM261919.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261920 and GSM261918"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M9-2	37		ceftazidime resistant	Mucoid			
-E-GEOD-10362	GSE10362GSM261920	GSM261920.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261918 and GSM261919"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M9-3	37		ceftazidime resistant	Mucoid			
-E-GEOD-10362	GSE10362GSM261921	GSM261921.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator, mucoid strain, replicate of GSM261922 and GSM261923"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M11-1	37		ceftazidime and ciprofloxacin resistant	Mucoid			
-E-GEOD-10362	GSE10362GSM261922	GSM261922.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator, mucoid strain, replicate of GSM261923 and GSM261921"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M11-2	37		ceftazidime and ciprofloxacin resistant	Mucoid			
-E-GEOD-10362	GSE10362GSM261923	GSM261923.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator, mucoid strain, replicate of GSM261922 and GSM261921"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M11-3	37		ceftazidime and ciprofloxacin resistant	Mucoid			
-E-GEOD-10362	GSE10362GSM261924	GSM261924.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain, replicate of GSM261926 and GSM261925"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M13-1	37			Mutator			
-E-GEOD-10362	GSE10362GSM261925	GSM261925.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain, replicate of GSM261924 and GSM261926"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M13-2	37			Mutator			
-E-GEOD-10362	GSE10362GSM261926	GSM261926.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain, replicate of GSM261924 and GSM261925"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M13-3	37			Mutator			
-E-GEOD-10362	GSE10362GSM261927	GSM261927.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain replicate of GSM261928 and GSM261929"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M22-1	37		"ceftazidime, fosfomycin, meropenem resistant"	Mutator	Trp		
-E-GEOD-10362	GSE10362GSM261928	GSM261928.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain replicate of GSM261929 and GSM261927"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M22-2	37		"ceftazidime, fosfomycin, meropenem resistant"	Mutator	Trp		
-E-GEOD-10362	GSE10362GSM261929	GSM261929.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain replicate of GSM261928 and GSM261927"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M22-3	37		"ceftazidime, fosfomycin, meropenem resistant"	Mutator	Trp		
-E-GEOD-10362	GSE10362GSM261930	GSM261930.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain replicate of GSM261932 and GSM261931"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M23-1	37		ceftazidime resistant				
-E-GEOD-10362	GSE10362GSM261931	GSM261931.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain, replicate of GSM261932 and GSM261930"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M23-2	37		ceftazidime resistant				
-E-GEOD-10362	GSE10362GSM261932	GSM261932.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, non-mutator strain, replicate of GSM261931 and GSM261930"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M23-3	37		ceftazidime resistant				
-E-GEOD-10362	GSE10362GSM261933	GSM261933.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain, replicate of GSM261934"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M25-1	37			Mutator	Trp		
-E-GEOD-10362	GSE10362GSM261934	GSM261934.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain, replicate of GSM261933"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M25-2	37			Mutator	Trp		
-E-GEOD-10362	GSE10362GSM261935	GSM261935.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain, replicate of GSM261934 and GSM261933"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M25-3	37			Mutator	Trp		
-E-GEOD-10362	GSE10362GSM261936	GSM261936.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain replicate of GSM261938 and GSM261937"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M26-1	37		"ceftazidime, fosfomycin, meropenem resistant"	Mutator	"Met, Trp"		
-E-GEOD-10362	GSE10362GSM261937	GSM261937.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain replicate of GSM261938 and GSM261936"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M26-2	37		"ceftazidime, fosfomycin, meropenem resistant"	Mutator	"Met, Trp"		
-E-GEOD-10362	GSE10362GSM261938	GSM261938.CEL	"Pseudomonas aeruginosa, clinical isolate from patient M, mutator strain replicate of GSM261938 and GSM261937"	RNA	LB	Clinical isolate	3.5	Planktonic	Shaking	M26-3	37		"ceftazidime, fosfomycin, meropenem resistant"	Mutator	"Met, Trp"		
-E-GEOD-13871	GSM349042 1	GSM349042.CEL	"RNA extracted from biofilm cells of Pseudomonas aeruginosa PA14 wild type after 7 hours of growth at 37C in LB with glasswool, replicate 1"	RNA	LB	WT		Biofilm	shaking with submerged glass wool	PA14	37	no treatment 7 hours biofilm					
-E-GEOD-13871	GSM349045 1	GSM349045.CEL	RNA extracted from biofilm cells of Pseudomonas aeruginosa PA14_tpbA mutant after 7 hours of growth at 37C in LB with glasswool replicate 1	RNA	LB	ΔtpbA		Biofilm	shaking with submerged glass wool	PA14	37	no treatment 7 hours biofilm					
-E-GEOD-13871	GSM349047 1	GSM349047.CEL	RNA extracted from biofilm cells of Pseudomonas aeruginosa PA14 wild type after 7 hours of growth at 37C in LB with glasswool replicate 2	RNA	LB	WT		Biofilm	shaking with submerged glass wool	PA14	37	no treatment 7 hours biofilm					
-E-GEOD-13871	GSM349048 1	GSM349048.CEL	"RNA extracted from biofilm cells of Pseudomonas aeruginosa PA14_tpbA mutant after 7 hours of growth at 37C in LB with glasswool, replicate 2"	RNA	LB	ΔtpbA		Biofilm	shaking with submerged glass wool	PA14	37	no treatment 7 hours biofilm					
-E-GEOD-13871	GSM375702 1	GSM375702.CEL	RNA extracted from biofilm cells of Pseudomonas aeruginosa PA14 WT after 4 hours of growth at 37C in LB with glasswool	RNA	LB	WT		Biofilm	shaking with submerged glass wool	PA14	37	no treatment 4 hours biofilm					
-E-GEOD-13871	GSM375703 1	GSM375703.CEL	RNA extracted from biofilm cells of Pseudomonas aeruginosa PA14_tpA mutant after 4 hours of growth at 37C in LB with glasswool	RNA	LB	ΔtpbA		Biofilm	shaking with submerged glass wool	PA14	37	no treatment 4 hours biofilm					
-E-GEOD-15697	GSE9657GSM244065	GSM244065.CEL	"Pseudomonas aeruginosa PAO25 containing the plasmid pMMB67EH (Empty vector), grown with 25 µg/ml piperacillin, replicate 1"	RNA	LB	WT + pMMB67EH (empty plasmid)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-15697	GSE9657GSM244066	GSM244066.CEL	"Pseudomonas aeruginosa PAO25 containing the plasmid pMMB67EH (Empty vector), grown with 25 µg/ml piperacillin, replicate 2"	RNA	LB	WT + pMMB67EH (empty plasmid)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-15697	GSM393044 1	GSM393044.CEL	"Pseudomonas aeruginosa PAO25 containing the plasmid pMUM3 (overexpressing the PA0675 ECF sigma factor), grown with 25 µg/ml piperacillin, replicate 1"	RNA	LB	WT + pMUM3 (PA0675 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	IPTG induced overexpression of PA0675 (vreI)		AmpR		
-E-GEOD-15697	GSM393045 1	GSM393045.CEL	"Pseudomonas aeruginosa PAO25 containing the plasmid pMUM3 (overexpressing the PA0675 ECF sigma factor), grown with 25 µg/ml piperacillin, replicate 2"	RNA	LB	WT + pMUM3 (PA0675 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	IPTG induced overexpression of PA0675 (vreI)		AmpR		
-E-GEOD-14253	GSE14253GSM356953	GSM356953.CEL	Pseudomonas aeruginosa PAO1 WT Control	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	control					
-E-GEOD-14253	GSE14253GSM356954	GSM356954.CEL	Pseudomonas aeruginosa PAO1 WT Control	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	control					
-E-GEOD-14253	GSE14253GSM356955	GSM356955.CEL	Pseudomonas aeruginosa PAO1 WT Control	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	control					
-E-GEOD-14253	GSE14253GSM356956	GSM356956.CEL	Pseudomonas aeruginosa PAO1 Chlorhexidine diacetate_10min_replicate1	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	8 µM clorhexidine	10 min exposure				
-E-GEOD-14253	GSE14253GSM356957	GSM356957.CEL	Pseudomonas aeruginosa PAO1 Chlorhexidine diacetate_10min_replicate2	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	8 µM clorhexidine	10 min exposure				
-E-GEOD-14253	GSE14253GSM356958	GSM356958.CEL	Pseudomonas aeruginosa PAO1 Chlorhexidine diacetate_10min_replicate3	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	8 µM clorhexidine	10 min exposure				
-E-GEOD-14253	GSE14253GSM356959	GSM356959.CEL	Pseudomonas aeruginosa PAO1 Chlorhexidine diacetate_60min_replicate1	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	8 µM clorhexidine	60 min exposure				
-E-GEOD-14253	GSE14253GSM356960	GSM356960.CEL	Pseudomonas aeruginosa PAO1 Chlorhexidine diacetate_60min_replicate2	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	8 µM clorhexidine	60 min exposure				
-E-GEOD-14253	GSE14253GSM356961	GSM356961.CEL	Pseudomonas aeruginosa PAO1 Chlorhexidine diacetate_60min_replicate3	RNA	LB	WT	0.8	planktonic	aeration	PAO1	37	8 µM clorhexidine	60 min exposure				
-E-GEOD-9592	GSE9592GSM242671	GSM242671.CEL	PA14 WT. Biofilm on glass wool. 	RNA	LB	WT		Biofilm 	Glass wool 	PA14	37		Biofilms grown on glass wool in shaking (25rpm) culture for 7 h. 				
-E-GEOD-9592	GSE9592GSM242676	GSM242676.CEL	PA14 ∆pyrF. Biofilms on glass wool. 	RNA	LB	pyrF::Mariner		Biofilm 	Glass wool 	PA14	37		Biofilms grown on glass wool in shaking (25rpm) culture for 7 h. 		Gentamicin		
-E-GEOD-9592	GSE9592GSM242677	GSM242677.CEL	PA14 ∆pyrF. Biofilms on glass wool. Plus 1 mM uricil. 	RNA	LB	pyrF::Mariner		Biofilm 	Glass wool 	PA14	37	1 mM uracil 	Biofilms grown on glass wool in shaking (25rpm) culture for 7 h. 		Gentamicin		
-E-GEOD-9592	GSE9592GSM318716	GSM318716.CEL	PA14 WT. Biofilm on glass wool. Plus 10 mM uracil. 	RNA	LB	WT		Biofilm 	Glass wool 	PA14	37	10 mM uracil 	Biofilms grown on glass wool in shaking (25rpm) culture for 7 h. 				
-E-GEOD-12207	GSE12207GSM307112	GSM307112.CEL	Planktonic. Late exponential phase. Rep1	RNA	AGSY	WT		Planktonic 	Aerated 	PAO1	37		Vigorous shaking. Harvest at 3 hrs. Late exponential phase.				
-E-GEOD-12207	GSE12207GSM307113	GSM307113.CEL	Planktonic. Late exponential phase. Rep2	RNA	AGSY	WT		Planktonic 	Aerated 	PAO1	37		Vigorous shaking. Harvest at 3 hrs. Late exponential phase.				
-E-GEOD-12207	GSE12207GSM307114	GSM307114.CEL	Planktonic. Late exponential phase. Rep3 	RNA	AGSY	WT		Planktonic 	Aerated 	PAO1	37		Vigorous shaking. Harvest at 3 hrs. Late exponential phase.				
-E-GEOD-12207	GSE12207GSM307115	GSM307115.CEL	Planktonic. Early stationary phase. Rep1 	RNA	AGSY	WT		Planktonic 	Aerated 	PAO1	37		Vigorous shaking. Harvest at 9 hrs. Early stationary phase. 				
-E-GEOD-12207	GSE12207GSM307116	GSM307116.CEL	Planktonic. Early stationary phase. Rep2	RNA	AGSY	WT		Planktonic 	Aerated 	PAO1	37		Vigorous shaking. Harvest at 9 hrs. Early stationary phase.				
-E-GEOD-12207	GSE12207GSM307117	GSM307117.CEL	Planktonic. Early stationary phase. Rep3 	RNA	AGSY	WT		Planktonic 	Aerated 	PAO1	37		Vigorous shaking. Harvest at 9 hrs. Early stationary phase.				
-E-GEOD-12207	GSE12207GSM307118	GSM307118.CEL	Colony biofilm. 15 hrs. Rep1 	RNA	AGSY	WT		Biofilm	Colony biofilm 	PAO1	37		Agar plate. 15 hrs.				
-E-GEOD-12207	GSE12207GSM307119	GSM307119.CEL	Colony biofilm. 15 hrs. Rep2 	RNA	AGSY	WT		Biofilm	Colony biofilm 	PAO1	37		Agar plate. 15 hrs.				
-E-GEOD-12207	GSE12207GSM307120	GSM307120.CEL	Colony biofilm. 15 hrs. Rep3 	RNA	AGSY	WT		Biofilm	Colony biofilm 	PAO1	37		Agar plate. 15 hrs.				
-E-GEOD-12207	GSE12207GSM307121	GSM307121.CEL	Colony biofilm. 40 hrs. Rep1 	RNA	AGSY	WT		Biofilm	Colony biofilm 	PAO1	37		Agar plate. 40 hrs.				
-E-GEOD-12207	GSE12207GSM307122	GSM307122.CEL	Colony biofilm. 40 hrs. Rep2	RNA	AGSY	WT		Biofilm	Colony biofilm 	PAO1	37		Agar plate. 40 hrs.				
-E-GEOD-12207	GSE12207GSM307123	GSM307123.CEL	Colony biofilm. 40 hrs. Rep3 	RNA	AGSY	WT		Biofilm	Colony biofilm 	PAO1	37		Agar plate. 40 hrs.				
-E-GEOD-12207	GSE12207GSM307124	GSM307124.CEL	Flow cell biofilm. 3 days. Rep1 	RNA	AGSY	WT		Biofilm	Flow cell 	PAO1	37		Continuous flow system. 3 days. 				
-E-GEOD-12207	GSE12207GSM307125	GSM307125.CEL	Flow cell biofilm. 3 days. Rep2 	RNA	AGSY	WT		Biofilm	Flow cell 	PAO1	37		Continuous flow system. 3 days. 				
-E-GEOD-12207	GSE12207GSM307126	GSM307126.CEL	Flow cell biofilm. 3 days. Rep3 	RNA	AGSY	WT		Biofilm	Flow cell 	PAO1	37		Continuous flow system. 3 days. 				
-E-GEOD-3836	GSE3836GSM87345		"P. aeruginosa PAO1 WT grown in BHI to early log phase (5hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37		early log phase cells; 180 rpm; 5 hours				
-E-GEOD-3836	GSE3836GSM87346		"P. aeruginosa PAO1 WT grown in BHI to early log phase (5hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37		early log phase cells; 180 rpm; 5 hours				
-E-GEOD-3836	GSE3836GSM87347		"P. aeruginosa PAO1 WT grown in BHI + 40uM PQS to early log phase (5hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37	40uM PQS	early log phase cells; 180 rpm; 5 hours				
-E-GEOD-3836	GSE3836GSM87348		"P. aeruginosa PAO1 WT grown in BHI + 40uM PQS to early log phase (5hr), replicate 2"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37	40uM PQS	early log phase cells; 180 rpm; 5 hours				
-E-GEOD-3836	GSE3836GSM87349		"P. aeruginosa PAO1 WT grown in BHI to mid log phase (11hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37		late log phase cells; 180 rpm; 11 hours				
-E-GEOD-3836	GSE3836GSM87350		"P. aeruginosa PAO1 WT grown in BHI to mid log phase (11hr), replicate 2"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37		late log phase cells; 180 rpm; 11 hours				
-E-GEOD-3836	GSE3836GSM87352		"P. aeruginosa PAO1 WT grown in BHI + 40uM PQS to mid log phase (11hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37	40uM PQS	late log phase cells; 180 rpm; 11 hours				
-E-GEOD-3836	GSE3836GSM87353		"P. aeruginosa PAO1 WT grown in BHI + 40uM PQS to mid log phase (11hr), replicate 2"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37	40uM PQS	late log phase cells; 180 rpm; 11 hours				
-E-GEOD-3836	GSE3836GSM87354		"P. aeruginosa PAO1 WT grown in BHI to late stationary phase (20hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37		stationary phase cells; 180 rpm; 20 hours				
-E-GEOD-3836	GSE3836GSM87355		"P. aeruginosa PAO1 WT grown in BHI to late stationary phase (20hr), replicate 2"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37		stationary phase cells; 180 rpm; 20 hours				
-E-GEOD-3836	GSE3836GSM87356		"P. aeruginosa PAO1 WT grown in BHI + 40uM PQS to late stationary phase (20hr), replicate 1"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37	40uM PQS	stationary phase cells; 180 rpm; 20 hours				
-E-GEOD-3836	GSE3836GSM87357		"P. aeruginosa PAO1 WT grown in BHI + 40uM PQS to late stationary phase (20hr), replicate 2"	RNA	BHI	WT		Planktonic	Shaking	PAO1	37	40uM PQS	stationary phase cells; 180 rpm; 20 hours				
-E-GEOD-11544	GSE11544GSM290747	GSM290747.CEL	"Pseudomonas aeruginosa PAO1 24 hour growth of Pa in resistant cultivar tobacco plant leaves, replicate 1"	RNA	Plant intracellular fluid 	WT	24 hours	in vivo		PAO1	30		24 h; Nicotiana tabacum cultivars Xanthi nc [NN]		Rifampin	Leaf	Plant (Nicotiana tabacum cv. Xanthi)
-E-GEOD-11544	GSE11544GSM290748	GSM290748.CEL	"Pseudomonas aeruginosa PAO1 24 hour growth of Pa in resistant cultivar tobacco plant leaves, replicate 2"	RNA	Plant intracellular fluid 	WT	24 hours	in vivo		PAO1	30		24 h; Nicotiana tabacum cultivars Xanthi nc [NN]		Rifampin	Leaf	Plant (Nicotiana tabacum cv. Xanthi)
-E-GEOD-11544	GSE11544GSM290749	GSM290749.CEL	"Pseudomonas aeruginosa PAO1 24 hour growth of Pa in resistant cultivar tobacco plant leaves, replicate 3"	RNA	Plant intracellular fluid 	WT	24 hours	in vivo		PAO1	30		24 h; Nicotiana tabacum cultivars Xanthi nc [NN]		Rifampin	Leaf	Plant (Nicotiana tabacum cv. Xanthi)
-E-GEOD-11544	GSE11544GSM290750	GSM290750.CEL	"Pseudomonas aeruginosa PAO1 24 hour growth of Pa in susceptible cultivar tobacco plant leaves, replicate 1"	RNA	Plant intracellular fluid 	WT	24 hours	in vivo		PAO1	30		24 h; Nicotiana tabacum cultivars Samsun nn		Rifampin	Leaf	Plant (Nicotiana tabacum cv. Samsung)
-E-GEOD-11544	GSE11544GSM290751	GSM290751.CEL	"Pseudomonas aeruginosa PAO1 24 hour growth of Pa in susceptible cultivar tobacco plant leaves, replicate 2"	RNA	Plant intracellular fluid 	WT	24 hours	in vivo		PAO1	30		24 h; Nicotiana tabacum cultivars Samsun nn		Rifampin	Leaf	Plant (Nicotiana tabacum cv. Samsung)
-E-GEOD-11544	GSE11544GSM290752	GSM290752.CEL	"Pseudomonas aeruginosa PAO1 24 hour growth of Pa in susceptible cultivar tobacco plant leaves, replicate 3"	RNA	Plant intracellular fluid 	WT	24 hours	in vivo		PAO1	30		24 h; Nicotiana tabacum cultivars Samsun nn		Rifampin	Leaf	Plant (Nicotiana tabacum cv. Samsung)
-E-GEOD-9991	GSE9991GSM252559	GSM252559.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells, replicate 1"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37						
-E-GEOD-9991	GSE9991GSM252560	GSM252560.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells, replicate 2"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37						
-E-GEOD-9991	GSE9991GSM252561	GSM252561.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells treated with Tobramycin, replicate 1"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37	5 μg/mL Tobramycin					
-E-GEOD-9991	GSE9991GSM252562	GSM252562.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells treated with Tobramycin, replicate 2"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37	5 μg/mL Tobramycin					
-E-GEOD-7968	GSE7968GSM197237	GSM197237.CEL	PAO1 WT grown as static biofilm on glass wool at 30C for 7 hours in LB; 1 replicate	RNA	LB	WT	7 hours	Biofilm	Static	PAO1	30		Biofilm grown on glass wool				
-E-GEOD-7968	GSE7968GSM197238	GSM197238.CEL	PA2663 mutant grown as static biofilm on glass wool at 30C for 7 hours in LB; 1 replicate	RNA	LB	PA2663 mutant	7 hours	Biofilm	Static	PAO1	30		Biofilm grown on glass wool				
-E-GEOD-10030	GSE9989GSM252496	GSM252496.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs, replicate 1"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37		9.5 h biofilms			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-10030	GSE9989GSM252501	GSM252501.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs, replicate 2"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37		9.5 h biofilms			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-10030	GSE9989GSM252505	GSM252505.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs, replicate 3"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37		9.5 h biofilms			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-10030	GSE9989GSM252506	GSM252506.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs treated with tobramycin, replicate 1"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37	500 μg/mL Tobramycin	"9.5 h biofilms, 30 min  exposure"			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-10030	GSE9989GSM252507	GSM252507.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs treated with tobramycin, replicate 2"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37	500 μg/mL Tobramycin	"9.5 h biofilms, 30 min  exposure"			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-10030	GSE9989GSM252508	GSM252508.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs treated with tobramycin, replicate 3"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37	500 μg/mL Tobramycin	"9.5 h biofilms, 30 min  exposure"			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-10030	GSE9991GSM252559	GSM252559.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells, replicate 1"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37						
-E-GEOD-10030	GSE9991GSM252560	GSM252560.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells, replicate 2"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37						
-E-GEOD-10030	GSE9991GSM252561	GSM252561.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells treated with Tobramycin, replicate 1"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37	5 μg/mL Tobramycin					
-E-GEOD-10030	GSE9991GSM252562	GSM252562.CEL	"Pseudomonas aeruginosa PA14 WT planktonic cells treated with Tobramycin, replicate 2"	RNA	"MEM, 2% LB"	WT	mid-exponential	Planktonic		PA14	37	5 μg/mL Tobramycin					
-E-GEOD-9989	GSE9989GSM252496	GSM252496.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs, replicate 1"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37		9.5 h biofilms			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-9989	GSE9989GSM252501	GSM252501.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs, replicate 2"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37		9.5 h biofilms			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-9989	GSE9989GSM252505	GSM252505.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs, replicate 3"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37		9.5 h biofilms			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-9989	GSE9989GSM252506	GSM252506.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs treated with tobramycin, replicate 1"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37	500 μg/mL Tobramycin	"9.5 h biofilms, 30 min  exposure"			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-9989	GSE9989GSM252507	GSM252507.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs treated with tobramycin, replicate 2"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37	500 μg/mL Tobramycin	"9.5 h biofilms, 30 min  exposure"			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-9989	GSE9989GSM252508	GSM252508.CEL	"Pseudomonas aeruginosa 9.5 hour static coculture with human CFBEs treated with tobramycin, replicate 3"	RNA	"MEM, 0.4% arginine"	WT	9.5 hours	Biofilm	Static	PA14	37	500 μg/mL Tobramycin	"9.5 h biofilms, 30 min  exposure"			Lung epithelial cells (CFBE41o- cells)	Human
-E-GEOD-9926	GSE9926GSM251201	GSM251201.CEL	PAO1 WT grown planktonically in minimal medium P (MMP) with 20mM glutamate at 37C with shaking to an OD of 0.5-0.6; replicate 1	RNA	MMP	WT	0.5-0.6	Planktonic	aerated	PAO1	37	20 mM glutamate					
-E-GEOD-9926	GSE9926GSM251202	GSM251202.CEL	PAO1 WT grown planktonically in minimal medium P (MMP) with 20mM glutamate and 20mM putrescine at 37C with shaking to an OD of 0.5-0.6; replicate 1	RNA	MMP	WT	0.5-0.6	Planktonic	aerated	PAO1	37	20mM glutamate + 20mM putrescine					
-E-GEOD-9926	GSE9926GSM251203	GSM251203.CEL	PAO1 WT grown planktonically in minimal medium P (MMP) with 20mM glutamate and 20mM agmatate at 37C with shaking to an OD of 0.5-0.6; replicate 2	RNA	MMP	WT	0.5-0.6	Planktonic	aerated	PAO1	37	20mM glutamate + 20mM agmatine					
-E-GEOD-9926	GSE9926GSM251204	GSM251204.CEL	PAO1 WT grown planktonically in minimal medium P (MMP) with 20mM glutamate and 20mM agmatate at 37C with shaking to an OD of 0.5-0.6; replicate 1	RNA	MMP	WT	0.5-0.6	Planktonic	aerated	PAO1	37	20mM glutamate + 20mM agmatine					
-E-GEOD-9926	GSE9926GSM251205	GSM251205.CEL	PAO1 WT grown planktonically in minimal medium P (MMP) with 20mM glutamate and 20mM GABA at 37C with shaking to an OD of 0.5-0.6; 1 replicate	RNA	MMP	WT	0.5-0.6	Planktonic	aerated	PAO1	37	20mM glutamate + 20mM GABA					
-E-GEOD-9926	GSE9926GSM251206	GSM251206.CEL	PAO1 WT grown planktonically in minimal medium P (MMP) with 20mM glutamate and 20mM putrescine at 37C with shaking to an OD of 0.5-0.6; replicate 2	RNA	MMP	WT	0.5-0.6	Planktonic	aerated	PAO1	37	20mM glutamate + 20mM putrescine					
-E-GEOD-9657	GSE9657GSM244065	GSM244065.CEL	"Pseudomonas aeruginosa PAO25 containing the plasmid pMMB67EH (Empty vector), grown with 25 µg/ml piperacillin, replicate 1"	RNA	LB	WT + pMMB67EH (empty plasmid)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244066	GSM244066.CEL	"Pseudomonas aeruginosa PAO25 containing the plasmid pMMB67EH (Empty vector), grown with 25 µg/ml piperacillin, replicate 2"	RNA	LB	WT + pMMB67EH (empty plasmid)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244067	GSM244067.CEL	"Pseudomonas aeruginosa PAO25 containing the pMMB67EH carrying the 1.5 kb SacI-XhoI insert from pUCMA1 containing the PA4896 gene, grown with 25 µg/ml piperacillin, replicate 1"	RNA	LB	pMMB-PA4896 (PA4896 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244068	GSM244068.CEL	"Pseudomonas aeruginosa PAO25 containing the pMMB67EH carrying the 1.5 kb SacI-XhoI insert from pUCMA1 containing the PA4896 gene, grown with 25 µg/ml piperacillin, replicate 2"	RNA	LB	pMMB-PA4896 (PA4896 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244069	GSM244069.CEL	"Pseudomonas aeruginosa PAO25 containing the pMMB67EH carrying the 958 bp BamHI-SphI insert from pUCMA2 containing the PA0149 gene, grown with 25 µg/ml piperacillin, replicate 1"	RNA	LB	pMMB-PA0149 (PA0149 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244070	GSM244070.CEL	"Pseudomonas aeruginosa PAO25 containing the pMMB67EH carrying the 958 bp BamHI-SphI insert from pUCMA2 containing the PA0149 gene, grown with 25 µg/ml piperacillin, replicate 2"	RNA	LB	pMMB-PA0149 (PA0149 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244071	GSM244071.CEL	"Pseudomonas aeruginosa PAO25 containing the, pMMB67EH carrying the 964 bp SacI-XhoI insert from pUCMA4 containing the PA2050 gene, grown with 25 µg/ml piperacillin."	RNA	LB	pMMB-PA2050 (PA2050 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9657	GSE9657GSM244072	GSM244072.CEL	"Pseudomonas aeruginosa PAO25 containing the, pMMB67EH carrying a 936 bp EcoRI-XbaI PCR fragment containing the PA2093 gene grown with 25 µg/ml piperacillin."	RNA	LB	pMMB-PA2093 (PA2093 overexpression)	0.7-0.8	Planktonic	Shaking	PAO25	37	25 µg/ml piperacillin	Induced with 1mM IPTG for 45 minutes before harvest		AmpR		
-E-GEOD-9621	GSE9621GSM243221	GSM243221.CEL	"Pseudomonas aeruginosa mucoid clinical isolate from an individual CF patient, grown to mid-log phase, replicate 1"	RNA	LB	Clinical isolate	0.5	Planktonic 	Aerated 	Cystic fibrosis C.I. 2192	37		Grown shaking at 200rpm	Mucoid 			
-E-GEOD-9621	GSE9621GSM243222	GSM243222.CEL	"Pseudomonas aeruginosa mucoid clinical isolate from an individual CF patient, grown to mid-log phase, replicate 2"	RNA	LB	Clinical isolate	0.5	Planktonic 	Aerated 	Cystic fibrosis C.I. 2192	37		Grown shaking at 200rpm	Mucoid 			
-E-GEOD-9621	GSE9621GSM243223	GSM243223.CEL	"Pseudomonas aeruginosa non-mucoid clinical isolate from an individual CF patient, grown to mid-log phase, replicate 1"	RNA	LB	Clinical isolate	0.5	Planktonic 	Aerated 	Cystic fibrosis C.I. 383	37		Grown shaking at 200rpm				
-E-GEOD-9621	GSE9621GSM243224	GSM243224.CEL	"Pseudomonas aeruginosa non-mucoid clinical isolate from an individual CF patient, grown to mid-log phase, replicate 2"	RNA	LB	Clinical isolate	0.5	Planktonic 	Aerated 	Cystic fibrosis C.I. 383	37		Grown shaking at 200rpm				
-E-GEOD-8408	GSE8408GSM208602	GSM208602.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. These cells were then used to inoculate the starter culture needed for RNA preparation. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 1"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM Sulfate 	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208603	GSM208603.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. These cells were then used to inoculate the starter culture needed for RNA preparation. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM Sulfate 	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208604	GSM208604.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. These cells were then used to inoculate the starter culture needed for RNA preparation. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 3"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM Sulfate 	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208605	GSM208605.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 500 uM cyclamate (sodium cyclohexylsulfamate) as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 1"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208606	GSM208606.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 500 uM cyclamate (sodium cyclohexylsulfamate) as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208607	GSM208607.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 500 uM cyclamate (sodium cyclohexylsulfamate) as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 3"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208608	GSM208608.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 500 uM cyclamate (sodium cyclohexylsulfamate) as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208609	GSM208609.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 25 ug/ml human colon cell line mucin LS174T as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 25 ug/ml LS174T mucin as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 1"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	25 ug/ml human colon cell line mucin LS174T	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208610	GSM208610.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 25 ug/ml human colon cell line mucin LS174T as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 25 ug/ml LS174T mucin as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	25 ug/ml human colon cell line mucin LS174T	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208611	GSM208611.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with 25 ug/ml human colon cell line mucin LS174T as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 25 ug/ml LS174T mucin as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 3"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	25 ug/ml human colon cell line mucin LS174T	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208612	GSM208612.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with a combination of 500 uM cyclamate (sodium cyclohexylsulfamate) and 25 ug/ml human colon cell line mucin LS174T as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with the same mixture of cyclamate and mucin as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 1"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate) and 25 ug/ml human colon cell line mucin LS174T	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208613	GSM208613.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with a combination of 500 uM cyclamate (sodium cyclohexylsulfamate) and 25 ug/ml human colon cell line mucin LS174T as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with the same mixture of cyclamate and mucin as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate) and 25 ug/ml human colon cell line mucin LS174T	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208614	GSM208614.CEL	"Pseudomonas aeruginosa E601 clinical isolate 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. The cells were then grown for at least 12 generations with a combination of 500 uM cyclamate (sodium cyclohexylsulfamate) and 25 ug/ml human colon cell line mucin LS174T as sulfur source.These cells were then used to inoculate the starter culture needed for RNA preparation, grown with the same mixture of cyclamate and mucin as S source). For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 3"	RNA	succinate-based minimal medium	Clinical isolate	0.2	Planktonic	Shaking	Cystic fibrosis C.I. E601	37	500 uM cyclamate (sodium cyclohexylsulfamate) +25 ug/ml human colon cell line mucin LS174T	5 ml culture grown in 50 ml flasks shaking at 220rpm	Mucin sulfatase activity 			
-E-GEOD-8408	GSE8408GSM208615	GSM208615.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations,  For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2,  replicate 1"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM Sulfate 	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-8408	GSE8408GSM208616	GSM208616.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations,  For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2,  replicate 2"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM Sulfate 	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-8408	GSE8408GSM208617	GSM208617.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations,  For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2,  replicate 3"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM Sulfate 	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-8408	GSE8408GSM208618	GSM208618.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. They were then grown for at least 12 generations with 500 uM cyclamate (sodium cyclohexylsulfamate) as S source. These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate as S source. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 1"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-8408	GSE8408GSM208619	GSM208619.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. They were then grown for at least 12 generations with 500 uM cyclamate (sodium cyclohexylsulfamate) as S source. These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate as S source. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-8408	GSE8408GSM208620	GSM208620.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. They wer then grown for at least 12 generations with 500 uM sulfate and 500 uM cyclamate (sodium cyclohexylsulfamate) as S source. These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate/500 uM sulfate as S source. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 1"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM Sulfate + 500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-8408	GSE8408GSM208621	GSM208621.CEL	"Pseudomonas aeruginosa PAO1 5 ml cultures of cells were grown aerobically in succinate-based minimal medium in 50 ml flasks shaken with 220 rpm at 37 C. Cells were grown with 500 uM Sulfate in repeated batch cultures for at least 20 generations. They wer then grown for at least 12 generations with 500 uM sulfate and 500 uM cyclamate (sodium cyclohexylsulfamate) as S source. These cells were then used to inoculate the starter culture needed for RNA preparation, grown with 500 uM cyclamate/500 uM sulfate as S source. For the preparation of RNA a 5 ml culture was started with 100 ul of a freshly grown culture. and grown to an OD600 of 0.2, replicate 2"	RNA	succinate-based minimal medium	WT	0.2	Planktonic	Shaking	PAO1	37	500 uM Sulfate + 500 uM cyclamate (sodium cyclohexylsulfamate)	5 ml culture grown in 50 ml flasks shaking at 220rpm				
-E-GEOD-7704	GSE7704GSM187265	GSM187265.CEL	PAO1 WT; citrate; replicate 1	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	20 mM citrate	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM187266	GSM187266.CEL	PAO1 WT; citrate; replicate 2	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	20 mM citrate	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM187267	GSM187267.CEL	PAO1 WT; citrate; replicate 3	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	20 mM citrate	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM187270	GSM187270.CEL	CF sputum pooled isolate; citrate; replicate 1	RNA	M9 minimal medium + 0.2% Brij 58	Clinical	0.6	Planktonic	Aerated	CF sputum pool isolate	37	20 mM citrate	OD540; 225 rpm; in vitro				
-E-GEOD-7704	GSE7704GSM187281	GSM187281.CEL	CF sputum pooled isolate; citrate; replicate 2	RNA	M9 minimal medium + 0.2% Brij 58	Clinical	0.6	Planktonic	Aerated	CF sputum pool isolate	37	20 mM citrate	OD540; 225 rpm; in vitro				
-E-GEOD-7704	GSE7704GSM187282	GSM187282.CEL	CF sputum pooled isolate; citrate; replicate 3	RNA	M9 minimal medium + 0.2% Brij 58	Clinical	0.6	Planktonic	Aerated	CF sputum pool isolate	37	20 mM citrate	OD540; 225 rpm; in vitro				
-E-GEOD-7704	GSE7704GSM187283	GSM187283.CEL	CF sputum isolate; control; replicate 1	RNA	CF sputum	Clinical				CF sputum isolate	37		Direct RNA extraction from CF sputum; in vivo			Lung	Human
-E-GEOD-7704	GSE7704GSM187284	GSM187284.CEL	CF sputum isolate; control; replicate 2	RNA	CF sputum	Clinical				CF sputum isolate	37		Direct RNA extraction from CF sputum; in vivo			Lung	Human
-E-GEOD-7704	GSE7704GSM198186	GSM198186.CEL	PAO1 WT; palmitic acid; replicate 1	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	0.4% palmitic acid	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM198187	GSM198187.CEL	PAO1 WT; palmitic acid; replicate 2	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	0.4% palmitic acid	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM198188	GSM198188.CEL	PAO1 WT; palmitic acid; replicate 3	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	0.4% palmitic acid	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM198189	GSM198189.CEL	PAO1 WT; phosphatidylcholine; replicate 1	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	0.4% phosphatidylcholine	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM198190	GSM198190.CEL	PAO1 WT; phosphatidylcholine; replicate 2	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	0.4% phosphatidylcholine	OD540; 225 rpm				
-E-GEOD-7704	GSE7704GSM198191	GSM198191.CEL	PAO1 WT; phosphatidylcholine; replicate 3	RNA	M9 minimal medium + 0.2% Brij 58	WT	0.6	Planktonic	Aerated	PAO1	37	0.4% phosphatidylcholine	OD540; 225 rpm				
-E-GEOD-7402	GSE7402GSM178564		PAO1 WT grown planktonically in LB with 4.4 mM sodium hypochlorite at 37C with shaking; replicate 1	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37	4.4 mM sodium hypochlorite					
-E-GEOD-7402	GSE7402GSM178565		PAO1 WT grown planktonically in LB with 4.4 mM sodium hypochlorite at 37C with shaking; replicate 2	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37	4.4 mM sodium hypochlorite					
-E-GEOD-7402	GSE7402GSM178566		PAO1 WT grown planktonically in LB with 4.4 mM sodium hypochlorite at 37C with shaking; replicate 3	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37	4.4 mM sodium hypochlorite					
-E-GEOD-7402	GSE7402GSM178567		PAO1 WT grown planktonically in LB with 4.4 mM sodium hypochlorite at 37C with shaking; replicate 4	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37	4.4 mM sodium hypochlorite					
-E-GEOD-7402	GSE7402GSM178568		PAO1 WT grown planktonically in LB with 4.4 mM sodium hypochlorite at 37C with shaking; replicate 5	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37	4.4 mM sodium hypochlorite	20 m exposure				
-E-GEOD-7402	GSE7402GSM68692		PAO1 WT grown planktonically in LB at 37C with shaking; replicate 1	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37		20 m exposure				
-E-GEOD-7402	GSE7402GSM68693		PAO1 WT grown planktonically in LB at 37C with shaking; replicate 2	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37		20 m exposure				
-E-GEOD-7402	GSE7402GSM68694		PAO1 WT grown planktonically in LB at 37C with shaking; replicate 3	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37		20 m exposure				
-E-GEOD-7402	GSE7402GSM68695		PAO1 WT grown planktonically in LB at 37C with shaking; replicate 4	RNA	LB	WT	0.8	Planktonic	Aerated	PAO1	37		20 m exposure				
-E-GEOD-7266	GSE7266GSM175247	GSM175247.CEL	"Pseudomonas aeruginosa PAO1 suspended cells were collected from a reverse osmosis unit after 20 hours of growth and biofouling of the membrane. A defined media was used, resembeling secondary wastewaters."	RNA	"Synthetic wastewater (1.16 mm sodium citrate, 0.94 mm NH4Cl, 0.45 mm KH2PO4, 0.5 mm CaCl2·2H2O, 0.5 mm NaHCO3, 2.0 mm NaCl and 0.6 mm MgSO4·7H2O. In addition, 1 ml of LB broth was added per 1 L of solution)"	WT		Planktonic	Flow cell 	PAO1	25		Planktonic cells not attached to reverse osmosis membrane. RNA collected at 20 h				
-E-GEOD-7266	GSE7266GSM175248	GSM175248.CEL	"Pseudomonas aeruginosa PAO1 were collected from a reverse osmosis membrane after 20 hours of growth and biofouling of the membrane. A defined media was used, resembeling secondary wastewaters."	RNA	"Synthetic wastewater (1.16 mm sodium citrate, 0.94 mm NH4Cl, 0.45 mm KH2PO4, 0.5 mm CaCl2·2H2O, 0.5 mm NaHCO3, 2.0 mm NaCl and 0.6 mm MgSO4·7H2O. In addition, 1 ml of LB broth was added per 1 L of solution)"	WT		Biofilm	Flow cell 	PAO1	25		Biofilms grown on reverse osmosis membrane. RNA collected at 20 h. 				
-E-GEOD-6741	GSE6741GSM155231	GSM155231.CEL	PAO1 WT. Planktonic. Anaerobic plus 100 mM nitrate. Rep1	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Anaerobic 	PAO1	37	0% oxygen + 100mM nitrate	Grown in a stirred sealed flask connected to a gas mixer. Anaerobic plus nitrate. 				
-E-GEOD-6741	GSE6741GSM155239	GSM155239.CEL	PAO1 WT. Planktonic. Anaerobic plus 100 mM nitrate. Rep2	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Anaerobic 	PAO1	37	0% oxygen + 100mM nitrate	Grown in a stirred sealed flask connected to a gas mixer. Anaerobic plus nitrate. 				
-E-GEOD-6741	GSE6741GSM155240	GSM155240.CEL	PAO1 WT. Planktonic. 0.4% oxygen. Rep1	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Aerated 	PAO1	37	0.4% oxygen	Grown in a stirred sealed flask connected to a gas mixer. 0.4% oxygen. 				
-E-GEOD-6741	GSE6741GSM155241	GSM155241.CEL	PAO1 WT. Planktonic. 0.4% oxygen. Rep2	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Aerated 	PAO1	37	0.4% oxygen	Grown in a stirred sealed flask connected to a gas mixer. 0.4% oxygen. 				
-E-GEOD-6741	GSE6741GSM155242	GSM155242.CEL	PAO1 WT. Planktonic. 2% oxygen. Rep1	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Aerated 	PAO1	37	2% oxygen	Grown in a stirred sealed flask connected to a gas mixer. 2% oxygen. 				
-E-GEOD-6741	GSE6741GSM155243	GSM155243.CEL	PAO1 WT. Planktonic. 2% oxygen. Rep2	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Aerated 	PAO1	37	2% oxygen	Grown in a stirred sealed flask connected to a gas mixer. 2% oxygen. 				
-E-GEOD-6741	GSE6741GSM155244	GSM155244.CEL	PAO1 WT. Planktonic. 20% oxygen. Rep1	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Aerated 	PAO1	37	20% oxygen	Grown in a stirred sealed flask connected to a gas mixer. 20% oxygen. 				
-E-GEOD-6741	GSE6741GSM155245	GSM155245.CEL	PAO1 WT. Planktonic. 20% oxygen. Rep2	RNA	CF synthetic sputum 	WT	0.8	Planktonic 	Aerated 	PAO1	37	20% oxygen	Grown in a stirred sealed flask connected to a gas mixer. 20% oxygen. 				
-E-GEOD-5604	GSE5604GSM130787		"P. aeruginosa PA01 rpoS mutant, grown in chemostat without Pentachlorophenol (PCP) (0 hours), replicate 1"	RNA	Minimal medium with acetate as carbon source	rpoS-	12 h doubling time	Planktonic	chemostat	PAO1	20						
-E-GEOD-5604	GSE5604GSM130788		"P. aeruginosa PA01 rpoS mutant, grown in chemostat in the presence of Pentachlorophenol (PCP) for 6.5 h, replicate 1"	RNA	Minimal medium with acetate as carbon source	rpoS-	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	6.5 h exposure				
-E-GEOD-5604	GSE5604GSM130789		"P. aeruginosa PA01 rpoS mutant, grown in chemostat in the presence of Pentachlorophenol (PCP) for 13 h, replicate 1"	RNA	Minimal medium with acetate as carbon source	rpoS-	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	13 h exposure				
-E-GEOD-5604	GSE5604GSM130790		"P. aeruginosa PA01 rpoS mutant, grown in chemostat in the presence of Pentachlorophenol (PCP) for 26 h, replicate 1"	RNA	Minimal medium with acetate as carbon source	rpoS-	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	25 h exposure				
-E-GEOD-5604	GSE5604GSM130791		"P. aeruginosa PA01 WT, grown in chemostat without Pentachlorophenol (PCP) (0 hours), replicate 1"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20		12 h doubling time				
-E-GEOD-5604	GSE5604GSM130792		"P. aeruginosa PA01 WT, grown in chemostat in the presence of Pentachlorophenol (PCP) for 6.5 h, replicate 1"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	6.5 h exposure				
-E-GEOD-5604	GSE5604GSM130793		"P. aeruginosa PA01 WT, grown in chemostat in the presence of Pentachlorophenol (PCP) for 13 h, replicate 1"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	13 h exposure				
-E-GEOD-5604	GSE5604GSM130794		"P. aeruginosa PA01 WT, grown in chemostat in the presence of Pentachlorophenol (PCP) for 26 h, replicate 1"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	25 h exposure				
-E-GEOD-5604	GSE5604GSM130795		"P. aeruginosa PA01 WT, grown in chemostat without Pentachlorophenol (PCP) (0 hours), replicate 2"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20		12 h doubling time				
-E-GEOD-5604	GSE5604GSM130796		"P. aeruginosa PA01 WT, grown in chemostat in the presence of Pentachlorophenol (PCP) for 6.5 h, replicate 2"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	6.5 h exposure				
-E-GEOD-5604	GSE5604GSM130797		"P. aeruginosa PA01 WT, grown in chemostat in the presence of Pentachlorophenol (PCP) for 13 h, replicate 2"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	13 h exposure				
-E-GEOD-5604	GSE5604GSM130798		"P. aeruginosa PA01 WT, grown in chemostat in the presence of Pentachlorophenol (PCP) for 26 h, replicate 2"	RNA	Minimal medium with acetate as carbon source	WT	12 h doubling time	Planktonic	chemostat	PAO1	20	25 mg/L pentachlorophenol	25 h exposure				
-E-GEOD-4026	GSE4026GSM92164	GSM92164.CEL	YL113 (PAO1)	RNA	LB + 50 mM MOPS	qscR OE	3.5	Planktonic	Aerated	YL113 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92165	GSM92165.CEL	YL113 (PAO1)	RNA	LB + 50 mM MOPS	qscR OE	2	Planktonic	Aerated	YL113 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92166	GSM92166.CEL	YL113 (PAO1)	RNA	LB + 50 mM MOPS	qscR OE	1.4	Planktonic	Aerated	YL113 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92167	GSM92167.CEL	YL113 (PAO1)	RNA	LB + 50 mM MOPS	qscR OE	0.8	Planktonic	Aerated	YL113 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92168	GSM92168.CEL	YL113 (PAO1)	RNA	LB + 50 mM MOPS	qscR OE	0.5	Planktonic	Aerated	YL113 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92169	GSM92169.CEL	YL117 (PAO1)	RNA	LB + 50 mM MOPS	ΔqscR 3'	3.5	Planktonic	Aerated	YL117 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92170	GSM92170.CEL	YL117 (PAO1)	RNA	LB + 50 mM MOPS	ΔqscR 3'	2	Planktonic	Aerated	YL117 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92171	GSM92171.CEL	YL117 (PAO1)	RNA	LB + 50 mM MOPS	ΔqscR 3'	1.4	Planktonic	Aerated	YL117 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92172	GSM92172.CEL	YL117 (PAO1)	RNA	LB + 50 mM MOPS	ΔqscR 3'	0.8	Planktonic	Aerated	YL117 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92173	GSM92173.CEL	YL117 (PAO1)	RNA	LB + 50 mM MOPS	ΔqscR 3'	0.5	Planktonic	Aerated	YL117 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92174	GSM92174.CEL	PAO1 WT; OD600= 3.5; replicate 1	RNA	LB + 50 mM MOPS	WT	3.5	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92175	GSM92175.CEL	PAO1 WT; OD600= 3.5; replicate 2	RNA	LB + 50 mM MOPS	WT	3.5	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92176	GSM92176.CEL	PAO-R3; OD600= 3.5; replicate 1	RNA	LB + 50 mM MOPS	ΔqscR	3.5	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92177	GSM92177.CEL	PAO-R3; OD600= 3.5; replicate 2	RNA	LB + 50 mM MOPS	ΔqscR	3.5	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92178	GSM92178.CEL	PAO1 WT; OD600= 2.0; replicate 1	RNA	LB + 50 mM MOPS	WT	2	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92179	GSM92179.CEL	PAO1 WT; OD600= 2.0; replicate 2	RNA	LB + 50 mM MOPS	WT	2	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92180	GSM92180.CEL	PAO-R3; OD600= 2.0; replicate 1	RNA	LB + 50 mM MOPS	ΔqscR	2	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92181	GSM92181.CEL	PAO-R3; OD600= 2.0; replicate 2	RNA	LB + 50 mM MOPS	ΔqscR	2	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92182	GSM92182.CEL	PAO1 WT; OD600= 1.4; replicate 1	RNA	LB + 50 mM MOPS	WT	1.4	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92183	GSM92183.CEL	PAO1 WT; OD600= 1.4; replicate 2	RNA	LB + 50 mM MOPS	WT	1.4	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92184	GSM92184.CEL	PAO-R3; OD600= 1.4; replicate 1	RNA	LB + 50 mM MOPS	ΔqscR	1.4	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92185	GSM92185.CEL	PAO-R3; OD600= 1.4; replicate 2	RNA	LB + 50 mM MOPS	ΔqscR	1.4	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92186	GSM92186.CEL	PAO1 WT; OD600= 0.8; replicate 1	RNA	LB + 50 mM MOPS	WT	0.8	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92187	  	PAO1 WT; OD600= 0.8; replicate 2	RNA	LB + 50 mM MOPS	WT	0.8	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92188	GSM92188.CEL	PAO-R3; OD600= 0.8; replicate 1	RNA	LB + 50 mM MOPS	ΔqscR	0.8	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92189	GSM92189.CEL	PAO-R3; OD600= 0.8; replicate 2	RNA	LB + 50 mM MOPS	ΔqscR	0.8	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92190	GSM92190.CEL	PAO1 WT; OD600= 0.5; replicate 1	RNA	LB + 50 mM MOPS	WT	0.5	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92191	GSM92191.CEL	PAO1 WT; OD600= 0.5; replicate 2	RNA	LB + 50 mM MOPS	WT	0.5	Planktonic	Aerated	PAO1	37		pH 7; 0.2% L-arabinose; 250 rpm				
-E-GEOD-4026	GSE4026GSM92192	GSM92192.CEL	PAO-R3; OD600= 0.5; replicate 1	RNA	LB + 50 mM MOPS	ΔqscR	0.5	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-4026	GSE4026GSM92193	GSM92193.CEL	PAO-R3; OD600= 0.5; replicate 2	RNA	LB + 50 mM MOPS	ΔqscR	0.5	Planktonic	Aerated	PAO-R3 (PAO1)	37		pH 7; 0.2% L-arabinose; 250 rpm		GentR		
-E-GEOD-2430	GSE2430GSM45588	GSM45588.CEL	"Pseudomonas aeruginosa grown in brain heart infusion grown to early stationary phase, replicate 1"	RNA	BHI	WT (DSM 1707)	2.8	Planktonic	aerated	PAO1	37		Early stationary phase cells; shaking				
-E-GEOD-2430	GSE2430GSM45589	GSM45589.CEL	"Pseudomonas aeruginosa grown in brain heart infusion grown to early stationary phase, replicate 2"	RNA	BHI	WT (DSM 1707)	2.8	Planktonic	aerated	PAO1	37		Early stationary phase cells; shaking				
-E-GEOD-2430	GSE2430GSM45590	GSM45590.CEL	"Pseudomonas aeruginosa grown in brain heart infusion grown to early stationary phase with 2 μg/mL Azithromycin, replicate 1"	RNA	BHI 	WT (DSM 1707)	2.8	Planktonic	aerated	PAO1	37	2 μg/mL Azithromycin	Early stationary phase cells; shaking				
-E-GEOD-2430	GSE2430GSM45591	GSM45591.CEL	"Pseudomonas aeruginosa grown in brain heart infusion grown to early stationary phase with 2 μg/mL Azithromycin, replicate 2"	RNA	BHI	WT (DSM 1707)	2.8	Planktonic	aerated	PAO1	37	2 μg/mL Azithromycin	Early stationary phase cells; shaking				
-E-GEOD-10604	GSE10604GSM267248	GSM267248.CEL	"Pseudomonas aeruginosa, no treatment_0min_replicate1"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	no treatment time 0	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267249	GSM267249.CEL	"Pseudomonas aeruginosa, no treatment_0min_replicate2"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	no treatment time 0	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267250	GSM267250.CEL	"Pseudomonas aeruginosa, no treatment_0min_replicate3"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	no treatment time 0	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267251	GSM267251.CEL	"Pseudomonas aeruginosa, no treatment_0min_replicate4"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	no treatment time 0	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267252	GSM267252.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_20min_replicate1"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 20min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267253	GSM267253.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_20min_replicate2"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 20min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267254	GSM267254.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_20min_replicate3"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 20min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267255	GSM267255.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_20min_replicate4"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 20min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267256	GSM267256.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_60min_replicate1"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 60min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267257	GSM267257.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_60min_replicate2"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 60min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267258	GSM267258.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_60min_replicate3"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 60min	Shaking at 250rpm 				
-E-GEOD-10604	GSE10604GSM267259	GSM267259.CEL	"Pseudomonas aeruginosa, Ortho-Phenylphenol_60min_replicate4"	RNA	LB	WT	0.8	Planktonic	Shaking	PAO1	37	Ortho-Phenylphenol 60min	Shaking at 250rpm 				
-E-MEXP-1591	pycR mutant PAO1-1	54375-4-05.CEL	PAO1 ΔpycR; replicate 1	RNA	TSB	ΔpycR	0.4	Planktonic	Aerated	PAO1	37		Shaking; early exponential phase cells				
-E-MEXP-1591	pycR mutant PAO1-2	dPA54375-18-05.CEL	PAO1 ΔpycR; replicate 2	RNA	TSB	ΔpycR	0.4	Planktonic	Aerated	PAO1	37		Shaking; early exponential phase cells				
-E-MEXP-1591	wild-type PAO1-1	WT12935-4-05.CEL	PAO1 WT; control; replicate 1	RNA	TSB	WT	0.4	Planktonic	Aerated	PAO1	37		Shaking; early exponential phase cells				
-E-MEXP-1591	wild-type PAO1-2	WT12935-18-05.CEL	PAO1 WT; control; replicate 2	RNA	TSB	WT	0.4	Planktonic	Aerated	PAO1	37		Shaking; early exponential phase cells				
-E-GEOD-9255	GSE9255GSM234928	GSM234928.CEL	PAO1 WT grown as static biofilm on glass wool at 30C for 24 hours in LB; 1 replicate	RNA	LB	WT	24 hours	Biofilm	Static	PAO1	30		Biofilm grown on glass wool				
-E-GEOD-9255	GSE9255GSM234929	GSM234929.CEL	PA2663 mutant grown as static biofilm on glass wool at 30C for 24 hours in LB; 1 replicate	RNA	LB	PA2663 mutant	24 hours	Biofilm	Static	PAO1	30		Biofilm grown on glass wool				
-E-GEOD-8083	GSE8083GSM199982	GSM199982.CEL	Pseudomonas aeruginosa PAO1 LB replicate1	RNA	LB	WT	0.6	Planktonic	shaking	PAO1	37						
-E-GEOD-8083	GSE8083GSM199983	GSM199983.CEL	Pseudomonas aeruginosa PAO1 LB replicate2	RNA	LB	WT	0.6	Planktonic	shaking	PAO1	37						
-E-GEOD-8083	GSE8083GSM199984	GSM199984.CEL	Pseudomonas aeruginosa ∆psrA::Tn (PAO1 background) LB replicate1	RNA	LB	∆psrA	0.6	Planktonic	shaking	PAO1	37						
-E-GEOD-8083	GSE8083GSM199985	GSM199985.CEL	Pseudomonas aeruginosa ∆psrA::Tn (PAO1 background) LB replicate2	RNA	LB	∆psrA	0.6	Planktonic	shaking	PAO1	37						
-E-GEOD-8083	GSE8083GSM199986	GSM199986.CEL	Pseudomonas aeruginosa ∆psrA::Tn (PAO1 background) LB replicate3	RNA	LB	∆psrA	0.6	Planktonic	shaking	PAO1	37						
-E-GEOD-4152	GSE4152GSM94619		PAO1 WT; adapted; treated; replicate 1	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37	10 mM CuSO4	6 hours; early log phase cells; pH 6.95				
-E-GEOD-4152	GSE4152GSM94620		PAO1 WT; adapted; control; replicate 1	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37		6 hours; early log phase cells; pH 7 (1 M HCl)				
-E-GEOD-4152	GSE4152GSM94621		PAO1 WT; adapted; treated; replicate 2	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37	10 mM CuSO4	6 hours; early log phase cells; pH 6.95				
-E-GEOD-4152	GSE4152GSM94622		PAO1 WT; adapted; control; replicate 2	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37		6 hours; early log phase cells;  pH 7 (1 M HCl)				
-E-GEOD-4152	GSE4152GSM94623		PAO1 WT; shock; treated; replicate 1	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37	10 mM CuSO4	grown to OD=0.2 before 45 min. treatment; pH 6.95				
-E-GEOD-4152	GSE4152GSM94624		PAO1 WT; shock; control; replicate 1	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37		grown to OD=0.2 before 45 min. treatment;  pH 7 (1 M HCl)				
-E-GEOD-4152	GSE4152GSM94625		PAO1 WT; shock; treated; replicate 2	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37	10 mM CuSO4	grown to OD=0.2 before 45 min. treatment; pH 6.95				
-E-GEOD-4152	GSE4152GSM94626		PAO1 WT; shock; control; replicate 2	RNA	LB + 100 mM MOPS	WT	0.2	Planktonic		PAO1	37		grown to OD=0.2 before 45 min. treatment;  pH 7 (1 M HCl)				
-E-GEOD-5443	GSE5443GSM124823		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 30 minutes following ciprofloxacin treatment; replicate 1	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 30 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124814		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 0 minutes following ciprofloxacin treatment; replicate 1	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 0 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124815		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 30 minutes following ciprofloxacin treatment; replicate 1	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 30 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124816		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 120 minutes following ciprofloxacin treatment; replicate 1	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 120 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124817		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 0 minutes following ciprofloxacin treatment; replicate 2	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 0 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124818		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 30 minutes following ciprofloxacin treatment; replicate 2	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 30 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124819		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 120 minutes following ciprofloxacin treatment; replicate 2	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 120 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124820		PAO1 lexA mutant grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 120 minutes following ciprofloxacin treatment; replicate 3	RNA	LB	LexA mutant	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 120 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124821		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 0 minutes following ciprofloxacin treatment; replicate 1	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 0 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124822		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 0 minutes following ciprofloxacin treatment; replicate 2	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 0 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124824		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 30 minutes following ciprofloxacin treatment; replicate 2	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 30 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124825		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 120 minutes following ciprofloxacin treatment; replicate 1	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 120 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124826		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 120 minutes following ciprofloxacin treatment; replicate 2	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 120 minutes following drug treatment				
-E-GEOD-5443	GSE5443GSM124827		PAO1 WT grown planktonically with shaking at 37C to mid-log phase in LB and treatment with 1ug/mL ciprofloxacin; RNA harvested 120 minutes following ciprofloxacin treatment; replicate 3	RNA	LB	WT	0.5	planktonic	aerated	PAO1	37	ciprofloxacin (1 ug/ml;~8x MIC)	Collected 120 minutes following drug treatment				
-E-GEOD-2885	GSE2885GSM63125		PAO1 ΔPA2384; replicate 1	RNA	Iron limited minimal medium C	ΔPA2384	0.2	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63145		PAO1 WT; replicate 1	RNA	Iron limited minimal medium C	WT	0.2	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63147		PAO1 ΔPA2384; replicate 1	RNA	Iron limited minimal medium C	ΔPA2384	1.3	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63149		PAO1 WT; replicate 1	RNA	Iron limited minimal medium C	WT	1.3	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63150		PAO1 ΔPA2384; replicate 1	RNA	Iron limited minimal medium C	ΔPA2384	2.1	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63152		PAO1 WT; replicate 1	RNA	Iron limited minimal medium C	WT	2.1	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63153		PAO1 ΔPA2384; replicate 2	RNA	Iron limited minimal medium C	ΔPA2384	0.2	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63154		PAO1 WT; replicate 2	RNA	Iron limited minimal medium C	WT	0.2	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63155		PAO1 ΔPA2384; replicate 2	RNA	Iron limited minimal medium C	ΔPA2384	1.3	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63157		PAO1 WT; replicate 2	RNA	Iron limited minimal medium C	WT	1.3	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63158		PAO1 ΔPA2384; replicate 2	RNA	Iron limited minimal medium C	ΔPA2384	2.1	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-GEOD-2885	GSE2885GSM63162		PAO1 WT; replicate 2	RNA	Iron limited minimal medium C	WT	2.1	Planktonic	Aerated	PAO1	37		Bioreactor system; 500 rpm; pH 7.0				
-E-MEXP-1183	MSC_01	MSC_01.CEL	"Pseudomonas aeruginosa PAO1 WT, no acyl-HSL, rep1"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid 	WT		Planktonic	shaking	PAO1	37						
-E-MEXP-1183	MSC_02	MSC_02.CEL	"Pseudomonas aeruginosa PAO1 WT, no acyl-HSL, rep2"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid 	WT		Planktonic	shaking	PAO1	37						
-E-MEXP-1183	MSC_03	MSC_03.CEL	"Pseudomonas aeruginosa PAO1 WT, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, rep1"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	WT		Planktonic	shaking	PAO1	37						
-E-MEXP-1183	MSC_04	MSC_04.CEL	"Pseudomonas aeruginosa PAO1 WT, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, rep2"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	WT		Planktonic	shaking	PAO1	37						
-E-MEXP-1183	MSC_05	MSC_05.CEL	"Pseudomonas aeruginosa PAO1 ∆lasR ∆rhlR, chromosomal pBAD lasR, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, plus unknown L-arabinose induction, rep1"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	∆lasR ∆rhlR 		Planktonic	shaking	PAO1	37	unknown L- arabinose treatment 					
-E-MEXP-1183	MSC_06	MSC_06.CEL	"Pseudomonas aeruginosa PAO1 ∆lasR ∆rhlR, chromosomal pBAD lasR, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, plus unknown L-arabinose induction, rep2"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	∆lasR ∆rhlR 		Planktonic	shaking	PAO1	37	unknown L- arabinose treatment 					
-E-MEXP-1183	MSC_07	MSC_07.CEL	"Pseudomonas aeruginosa PAO1 ∆lasR ∆rhlR  chromosomal pBAD rhlR, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, plus unknown L-arabinose induction, rep1"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	∆lasR ∆rhlR 		Planktonic	shaking	PAO1	37	unknown L- arabinose treatment 					
-E-MEXP-1183	MSC_08	MSC_08.CEL	"Pseudomonas aeruginosa PAO1 ∆lasR ∆rhlR  chromosomal pBAD rhlR, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, plus unknown L-arabinose induction, rep2"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	∆lasR ∆rhlR 		Planktonic	shaking	PAO1	37	unknown L- arabinose treatment 					
-E-MEXP-1183	MSC_09	MSC_09.CEL	"Pseudomonas aeruginosa PAO1 ∆rpoS chromosomal pBAD rpoS, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, plus unknown L-arabinose induction, rep1"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	∆rpoS		Planktonic	shaking	PAO1	37	unknown L- arabinose treatment 					
-E-MEXP-1183	MSC_10	MSC_10.CEL	"Pseudomonas aeruginosa PAO1 ∆rpoS chromosomal pBAD rpoS, plus 2 μM 3OC12-HSL and 10 μM C4-HSL, plus unknown L-arabinose induction, rep2"	RNA	LB buffered with 50 mM 3-(N-morpholino) propanesulphonic acid plus 2 μM 3OC12-HSL and 10 μM C4-HSL	∆rpoS		Planktonic	shaking	PAO1	37	unknown L- arabinose treatment 					
-E-GEOD-3090	GSE3090GSM68687		"PAO1 with 10 mM H2O2 for 20 min, Rep1"	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37	1 mM hydrogen peroxide	Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68688		"PAO1 with 10 mM H2O2 for 20 min, Rep2"	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37	1 mM hydrogen peroxide	Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68689		"PAO1 with 10 mM H2O2 for 20 min, Rep3"	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37	1 mM hydrogen peroxide	Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68690		"PAO1 with 10 mM H2O2 for 20 min, Rep4"	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37	1 mM hydrogen peroxide	Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68691		"PAO1 with 10 mM H2O2 for 20 min, Rep5"	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37	1 mM hydrogen peroxide	Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68692		PAO1 WT. Planktonic. Rep1	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37		Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68693		PAO1 WT. Planktonic. Rep2	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37		Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68694		PAO1 WT. Planktonic. Rep3	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37		Grown shaking at 250rpm. 20 m exposure				
-E-GEOD-3090	GSE3090GSM68695		PAO1 WT. Planktonic. Rep4	RNA	LB	WT	0.8	Planktonic	Aeration	PAO1	37		Grown shaking at 250rpm. 20 m exposure				
-E-MEXP-1051	aerobic_NO3_1	aerobic_NO3_1.CEL	"PA14 WT grown planktonically with aeration in MOPS with 10% lyophilized CF sputum and 100mM KNO3 to an OD of 0.1, replicate 1"	RNA	MOPS with 10% CF sputum and 100mM KNO3	WT	0.1	Planktonic	aerated	PA14	37						
-E-MEXP-1051	aerobic_NO3_2	aerobic_NO3_2.CEL	"PA14 WT grown planktonically with aeration in MOPS with 10% lyophilized CF sputum and 100mM KNO3 to an OD of 0.1, replicate 2"	RNA	MOPS with 10% CF sputum and 100mM KNO3	WT	0.1	Planktonic	aerated	PA14	37						
-E-MEXP-1051	anaerobic_NO3_1	anaerobic_NO3_1.CEL	"PA14 WT grown planktonically under anaerobic conditions in MOPS with 10% lyophilized CF sputum and 100mM KNO3 to an OD of 0.1, replicate 1"	RNA	MOPS with 10% CF sputum and 100mM KNO3	WT	0.1	Planktonic	anaerobic	PA14	37	anaerobic growth					
-E-MEXP-1051	anaerobic_NO3_2	anaerobic_NO3_2.CEL	"PA14 WT grown planktonically under anaerobic conditions in MOPS with 10% lyophilized CF sputum and 100mM KNO3 to an OD of 0.1, replicate 2"	RNA	MOPS with 10% CF sputum and 100mM KNO3	WT	0.1	Planktonic	anaerobic	PA14	37	anaerobic growth					
-E-MEXP-87	Pseudomonas aeruginosa	DC2.CEL	PAO1 biofilm in minimal medium with glutamate	RNA	Minimal medium with 1.8 mM glutamate			Planktonic	Dispersed from biofilm	PAO1	22						
-E-MEXP-87	Pseudomonas aeruginosa__S:BioSource:MEXP:168319	DB1.CEL	PAO1 planktonic dispersed from biofilm (high glutamate)	RNA	Minimal medium with 19.8 mM glutamate			Biofilm	Flow in tubing	PAO1	22						
-E-MEXP-87	Pseudomonas aeruginosa__S:BioSource:MEXP:168323	DC3.CEL	PAO1 planktonic dispersed from biofilm (high glutamate)	RNA	Minimal medium with 1.8 mM glutamate			Planktonic	Dispersed from biofilm	PAO1	22						
-E-MEXP-87	Pseudomonas aeruginosa__S:BioSource:MEXP:168320	DB3.CEL	PAO1 biofilm in minimal medium with glutamate	RNA	Minimal medium with 19.8 mM glutamate			Biofilm	Flow in tubing	PAO1	22						
-E-MEXP-87	Pseudomonas aeruginosa__S:BioSource:MEXP:168321	DC1.CEL	PAO1 planktonic dispersed from biofilm (high glutamate)	RNA	Minimal medium with 19.8 mM glutamate			Planktonic	Dispersed from biofilm	PAO1	22						
diff --git a/README.md b/README.md
index 7a6e333..4bc1522 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,31 @@ pip install ponyo
 ```
 
 ## How to use
-This method has been used in [simulate-expression-compendia repo](https://github.com/greenelab/simulate-expression-compendia)
-
-Example analysis notebooks using ponyo can be found in [Human_tests](Human_tests/) and [Pseudomonas_tests](Pseudomonas_tests/). 
+Example notebooks using ponyo on test data can be found in [human_tests](https://github.com/greenelab/ponyo/tree/master/human_tests)
+
+Additionally, this method has been used in [simulate-expression-compendia](https://github.com/greenelab/simulate-expression-compendia) repository.
+
+## Configuration file
+
+The tables lists the core parameters required to generate simulated data using modules from [ponyo](https://github.com/greenelab/ponyo). Those marked with * indicate those parameters that will vary depending on the type of approach .
+
+| Name | Description |
+| :--- | :---------- |
+| local_dir| str: Parent directory on local machine to store intermediate results|
+| scaler_transform_file| str: File to store mapping from normalized to raw gene expression range|
+| dataset_name| str: Name for analysis directory containing notebooks using ponyo|
+| simulation_type | str: Name of simulation approach directory to store results locally|
+| NN_architecture | str: Name of neural network architecture to use. Format 'NN_<intermediate layer>_<latent layer>'|
+| learning_rate| float: Step size used for gradient descent. In other words, it's how quickly the  methods is learning|
+| batch_size | str: Training is performed in batches. So this determines the number of samples to consider at a given time|
+| epochs | int: Number of times to train over the entire input dataset|
+| kappa | float: How fast to linearly ramp up KL loss|
+| intermediate_dim| int: Size of the hidden layer|
+| latent_dim | int: Size of the bottleneck layer|
+| epsilon_std | float: Standard deviation of Normal distribution to sample latent space|
+| validation_frac | float: Fraction of input samples to use to validate for VAE training|
+| num_simulated_samples* | int: If using random sampling approach, simulate a compendia with these many samples|
+| num_simulated_experiments*| int: If using latent-transformation approach, simulate a compendia with these many experiments|
+| num_simulated*| int: If using template-based approach, simulate these many experiments|
+| project_id*| int: If using template-based approach, experiment id to use as template experiment|
+| metadata_colname | str: Column header that contains sample id that maps expression data and metadata|
diff --git a/configs/config_test_Pa_experiment_limma.tsv b/configs/config_test_Pa_experiment_limma.tsv
deleted file mode 100644
index 42cc6b4..0000000
--- a/configs/config_test_Pa_experiment_limma.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-local_dir	"../Batch_effects_test/"
-dataset_name	"Pseudomonas_tests"
-simulation_type	"experiment_lvl_sim"
-NN_architecture	"NN_2500_30"
-learning_rate	0.001
-batch_size	10
-epochs	5
-kappa	0.01
-intermediate_dim	2500
-latent_dim	30
-epsilon_std	1.0
-num_simulated_experiments	50
-lst_num_partitions	[1, 5, 50]
-use_pca	True
-num_PCs	10
-correction_method	'limma'
-metadata_colname	'ml_data_source'
-iterations	range(5)
-num_cores	5
-validation_frac	0.1
\ No newline at end of file
diff --git a/configs/config_test_Pa_sample_combat.tsv b/configs/config_test_Pa_sample_combat.tsv
deleted file mode 100644
index ed5d607..0000000
--- a/configs/config_test_Pa_sample_combat.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-local_dir	"../Batch_effects_test/"
-dataset_name	"Pseudomonas_tests"
-simulation_type	"sample_lvl_sim"
-NN_architecture	"NN_2500_30"
-learning_rate	0.001
-batch_size	10
-epochs	5
-kappa	0.01
-intermediate_dim	2500
-latent_dim	30
-epsilon_std	1.0
-num_simulated_samples	50
-lst_num_experiments	[1, 5, 40]
-use_pca	True
-num_PCs	10
-correction_method	'combat'
-metadata_colname	'ml_data_source'
-iterations	range(5)
-num_cores	5
-validation_frac	0.1
\ No newline at end of file
diff --git a/configs/config_test_Pa_sample_limma.tsv b/configs/config_test_Pa_sample_limma.tsv
deleted file mode 100644
index 42cdcef..0000000
--- a/configs/config_test_Pa_sample_limma.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-local_dir	"../Batch_effects_test/"
-dataset_name	"Pseudomonas_tests"
-simulation_type	"sample_lvl_sim"
-NN_architecture	"NN_2500_30"
-learning_rate	0.001
-batch_size	10
-epochs	5
-kappa	0.01
-intermediate_dim	2500
-latent_dim	30
-epsilon_std	1.0
-num_simulated_samples	50
-lst_num_experiments	[1, 5, 50]
-use_pca	True
-num_PCs	10
-correction_method	'limma'
-metadata_colname	'ml_data_source'
-iterations	range(5)
-num_cores	5
-validation_frac	0.1
\ No newline at end of file
diff --git a/configs/config_test_human_experiment_limma.tsv b/configs/config_test_human_experiment_limma.tsv
deleted file mode 100644
index c387999..0000000
--- a/configs/config_test_human_experiment_limma.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-local_dir	"../Batch_effects_test/"
-dataset_name	"Human_tests"
-simulation_type	"experiment_lvl_sim"
-NN_architecture	"NN_2500_30"
-learning_rate	0.001
-batch_size	5
-epochs	5
-kappa	0.01
-intermediate_dim	2500
-latent_dim	30
-epsilon_std	1.0
-num_simulated_experiments	6
-lst_num_partitions	[1, 2, 6]
-use_pca	True
-num_PCs	10
-correction_method	'limma'
-metadata_colname	'run'
-iterations	range(5)
-num_cores	5
-validation_frac	0.1
\ No newline at end of file
diff --git a/configs/config_test_human_sample_limma.tsv b/configs/config_test_human_sample_limma.tsv
deleted file mode 100644
index 7930c2f..0000000
--- a/configs/config_test_human_sample_limma.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-local_dir	"../Batch_effects_test/"
-dataset_name	"Human_tests"
-simulation_type	"sample_lvl_sim"
-NN_architecture	"NN_2500_30"
-learning_rate	0.001
-batch_size	5
-epochs	10
-kappa	0.01
-intermediate_dim	2500
-latent_dim	30
-epsilon_std	1.0
-num_simulated_samples	50
-lst_num_experiments	[1, 5, 50]
-use_pca	True
-num_PCs	10
-correction_method	'limma'
-metadata_colname	'run'
-iterations	range(5)
-num_cores	5
-validation_frac	0.1
\ No newline at end of file
diff --git a/environment.yml b/environment.yml
index fe5867e..a8b54e7 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,12 +1,7 @@
-name: simulate_expression_compendia
+name: test_ponyo
 channels:
 - conda-forge
-- r
-- bioconda
 dependencies:
-- r::r-base=3.6.0
-- bioconda::bioconductor-limma
-- bioconda::bioconductor-sva
 - conda-forge::python=3.7
 - conda-forge::keras
 - conda-forge::tensorflow=1
@@ -21,6 +16,7 @@ dependencies:
 - conda-forge::ipykernel=5.1.1
 - conda-forge::nb_conda_kernels
 - conda-forge::gxx_linux-64
+- conda-forge::rpy2
 - conda-forge::libiconv=1.15
 - conda-forge::joblib=0.13.2
 - anaconda::protobuf
@@ -29,5 +25,4 @@ dependencies:
   - torch>=0.4.0
   - matplotlib==3.0.0
   - pillow
-  - requests
-  - rpy2
\ No newline at end of file
+  - requests
\ No newline at end of file
diff --git a/human_tests/Human_latent_transform_simulation.ipynb b/human_tests/Human_latent_transform_simulation.ipynb
new file mode 100644
index 0000000..252fb92
--- /dev/null
+++ b/human_tests/Human_latent_transform_simulation.ipynb
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test simulating compendia by latent transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import umap\n",
+    "import glob\n",
+    "from keras.models import load_model\n",
+    "from sklearn.decomposition import PCA\n",
+    "from plotnine import (ggplot,\n",
+    "                      labs,  \n",
+    "                      geom_point,\n",
+    "                      aes, \n",
+    "                      ggsave, \n",
+    "                      theme_bw,\n",
+    "                      theme,\n",
+    "                      facet_wrap,\n",
+    "                      scale_color_manual,\n",
+    "                      guides, \n",
+    "                      guide_legend,\n",
+    "                      element_blank,\n",
+    "                      element_text,\n",
+    "                      element_rect,\n",
+    "                      element_line,\n",
+    "                      coords)\n",
+    "\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore')\n",
+    "\n",
+    "from ponyo import utils, train_vae_modules, simulate_expression_data\n",
+    "\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in config variables\n",
+    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
+    "config_file = os.path.abspath(os.path.join(base_dir,\n",
+    "                                           \"human_tests\", \n",
+    "                                           \"config_test_human.tsv\"))\n",
+    "params = utils.read_config(config_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load parameters\n",
+    "local_dir = params[\"local_dir\"]\n",
+    "dataset_name = params['dataset_name']\n",
+    "analysis_name = params[\"simulation_type\"]\n",
+    "NN_architecture = params['NN_architecture']\n",
+    "sample_id_colname = params['metadata_colname']\n",
+    "num_simulated_experiments = params['num_simulated_experiments']\n",
+    "\n",
+    "NN_dir = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name, \n",
+    "    \"models\", \n",
+    "    NN_architecture)\n",
+    "\n",
+    "metadata_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"metadata\",\n",
+    "    \"recount2_metadata.tsv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input files\n",
+    "rpkm_data_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"input\",\n",
+    "    \"recount2_gene_RPKM_data_test.tsv\")\n",
+    "assert os.path.exists(rpkm_data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup directories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.setup_dir(config_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output file\n",
+    "normalized_data_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"input\",\n",
+    "    \"recount2_gene_normalized_data_test.tsv.xz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input: dataset contains 50 samples and 5000 genes\n",
+      "Output: normalized dataset contains 50 samples and 5000 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_vae_modules.normalize_expression_data(base_dir,\n",
+    "                                            config_file,\n",
+    "                                            rpkm_data_file,\n",
+    "                                            normalized_data_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output file\n",
+    "experiment_id_file = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"metadata\", \n",
+    "    \"experiment_ids.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 3219 experiments in the compendium\n",
+      "There are 6 experiments with gene expression data\n",
+      "6 experiment ids saved to file\n"
+     ]
+    }
+   ],
+   "source": [
+    "utils.create_experiment_id_file(metadata_file,\n",
+    "                                normalized_data_file,\n",
+    "                                experiment_id_file,\n",
+    "                                config_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train VAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Directory containing log information from VAE training\n",
+    "vae_log_dir = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name,\n",
+    "    \"logs\",\n",
+    "    NN_architecture)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input dataset contains 50 samples and 5000 genes\n",
+      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n",
+      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
+      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "Train on 45 samples, validate on 5 samples\n",
+      "Epoch 1/10\n",
+      "45/45 [==============================] - 4s 88ms/step - loss: 2466.1158 - val_loss: 2732.4883\n",
+      "Epoch 2/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1671.1998 - val_loss: 2180.0183\n",
+      "Epoch 3/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1566.8178 - val_loss: 1799.2496\n",
+      "Epoch 4/10\n",
+      "45/45 [==============================] - 4s 78ms/step - loss: 1481.5816 - val_loss: 1730.1412\n",
+      "Epoch 5/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1577.6224 - val_loss: 1477.2603\n",
+      "Epoch 6/10\n",
+      "45/45 [==============================] - 4s 78ms/step - loss: 1444.6422 - val_loss: 1510.6864\n",
+      "Epoch 7/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1475.2912 - val_loss: 1676.2584\n",
+      "Epoch 8/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1398.2218 - val_loss: 1403.8243\n",
+      "Epoch 9/10\n",
+      "45/45 [==============================] - 4s 78ms/step - loss: 1352.8320 - val_loss: 1309.4790\n",
+      "Epoch 10/10\n",
+      "45/45 [==============================] - 4s 78ms/step - loss: 1411.7664 - val_loss: 1252.2395\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Train VAE\n",
+    "train_vae_modules.train_vae(config_file,\n",
+    "                            normalized_data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulate data by latent transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized gene expression data contains 50 samples and 5000 genes\n",
+      "Return: simulated gene expression data containing 426 samples and 5001 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run simulation\n",
+    "simulated_data = simulate_expression_data.simulate_by_latent_transformation(\n",
+    "    num_simulated_experiments,\n",
+    "    normalized_data_file,\n",
+    "    NN_architecture,\n",
+    "    dataset_name,\n",
+    "    analysis_name,\n",
+    "    experiment_id_file,\n",
+    "    sample_id_colname,\n",
+    "    local_dir,\n",
+    "    base_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize latent transform compendium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load VAE models\n",
+    "model_encoder_file = glob.glob(os.path.join(\n",
+    "        NN_dir,\n",
+    "        \"*_encoder_model.h5\"))[0]\n",
+    "\n",
+    "weights_encoder_file = glob.glob(os.path.join(\n",
+    "    NN_dir,\n",
+    "    \"*_encoder_weights.h5\"))[0]\n",
+    "\n",
+    "model_decoder_file = glob.glob(os.path.join(\n",
+    "    NN_dir,\n",
+    "    \"*_decoder_model.h5\"))[0]\n",
+    "\n",
+    "weights_decoder_file = glob.glob(os.path.join(\n",
+    "    NN_dir,\n",
+    "    \"*_decoder_weights.h5\"))[0]\n",
+    "\n",
+    "# Load saved models\n",
+    "loaded_model = load_model(model_encoder_file)\n",
+    "loaded_decode_model = load_model(model_decoder_file)\n",
+    "\n",
+    "loaded_model.load_weights(weights_encoder_file)\n",
+    "loaded_decode_model.load_weights(weights_decoder_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca = PCA(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read data\n",
+    "normalized_compendium = pd.read_csv(normalized_data_file, header=0, sep=\"\\t\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Encode normalized compendium into latent space\n",
+    "compendium_encoded = loaded_model.predict_on_batch(normalized_compendium)\n",
+    "\n",
+    "compendium_encoded_df = pd.DataFrame(data=compendium_encoded, \n",
+    "                                     index=normalized_compendium.index)\n",
+    "\n",
+    "# Get and save PCA model\n",
+    "model = pca.fit(compendium_encoded_df)\n",
+    "\n",
+    "compendium_PCAencoded = model.transform(compendium_encoded_df)\n",
+    "\n",
+    "compendium_PCAencoded_df = pd.DataFrame(data=compendium_PCAencoded,\n",
+    "                                         index=compendium_encoded_df.index,\n",
+    "                                         columns=['1','2'])\n",
+    "\n",
+    "# Add label\n",
+    "compendium_PCAencoded_df['experiment_id'] = 'background'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Embedding of real template experiment (encoded)\n",
+    "before_encoded_file = os.path.join(local_dir, \"simulated_before_encoded.txt\")\n",
+    "\n",
+    "template_encoded_df = pd.read_csv(before_encoded_file, header=0, sep='\\t', index_col=0)\n",
+    "\n",
+    "template_PCAencoded = model.transform(template_encoded_df)\n",
+    "\n",
+    "template_PCAencoded_df = pd.DataFrame(data=template_PCAencoded,\n",
+    "                                         index=template_encoded_df.index,\n",
+    "                                         columns=['1','2'])\n",
+    "\n",
+    "# Add back label column\n",
+    "template_PCAencoded_df['experiment_id'] = 'before_experiment'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Embedding of simulated experiment (encoded)\n",
+    "after_encoded_file = os.path.join(local_dir, \"simulated_after_encoded.txt\")\n",
+    "\n",
+    "simulated_encoded_df = pd.read_csv(after_encoded_file,header=0, sep='\\t', index_col=0)\n",
+    "\n",
+    "simulated_PCAencoded = model.transform(simulated_encoded_df)\n",
+    "\n",
+    "simulated_PCAencoded_df = pd.DataFrame(data=simulated_PCAencoded,\n",
+    "                                         index=simulated_encoded_df.index,\n",
+    "                                         columns=['1','2'])\n",
+    "\n",
+    "# Add back label column\n",
+    "simulated_PCAencoded_df['experiment_id'] = 'after_experiment'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(58, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>experiment_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SRR592745</th>\n",
+       "      <td>-1.557785</td>\n",
+       "      <td>6.548028</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592746</th>\n",
+       "      <td>-1.335538</td>\n",
+       "      <td>6.123819</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592747</th>\n",
+       "      <td>-1.234933</td>\n",
+       "      <td>6.325553</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592748</th>\n",
+       "      <td>0.633263</td>\n",
+       "      <td>9.497215</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592749</th>\n",
+       "      <td>1.389616</td>\n",
+       "      <td>8.344598</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  1         2 experiment_id\n",
+       "SRR592745 -1.557785  6.548028    background\n",
+       "SRR592746 -1.335538  6.123819    background\n",
+       "SRR592747 -1.234933  6.325553    background\n",
+       "SRR592748  0.633263  9.497215    background\n",
+       "SRR592749  1.389616  8.344598    background"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Concatenate dataframes\n",
+    "combined_PCAencoded_df = pd.concat([compendium_PCAencoded_df, \n",
+    "                                    template_PCAencoded_df,\n",
+    "                                    simulated_PCAencoded_df])\n",
+    "\n",
+    "print(combined_PCAencoded_df.shape)\n",
+    "combined_PCAencoded_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ggplot: (-9223363271418897685)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "fig = ggplot(combined_PCAencoded_df, aes(x='1', y='2'))\n",
+    "fig += geom_point(aes(color='experiment_id'), alpha=0.5)\n",
+    "fig += labs(x ='PCA 1',\n",
+    "            y = 'PCA 2',\n",
+    "            title = 'PCA original data with experiments (latent space)')\n",
+    "fig += theme_bw()\n",
+    "fig += theme(\n",
+    "    legend_title_align = \"center\",\n",
+    "    plot_background=element_rect(fill='white'),\n",
+    "    legend_key=element_rect(fill='white', colour='white'), \n",
+    "    legend_title=element_text(family='sans-serif', size=15),\n",
+    "    legend_text=element_text(family='sans-serif', size=12),\n",
+    "    plot_title=element_text(family='sans-serif', size=15),\n",
+    "    axis_text=element_text(family='sans-serif', size=12),\n",
+    "    axis_title=element_text(family='sans-serif', size=15)\n",
+    "    )\n",
+    "fig += guides(colour=guide_legend(override_aes={'alpha': 1}))\n",
+    "fig += scale_color_manual(['red', '#bdbdbd', 'blue'])\n",
+    "fig += geom_point(data=combined_PCAencoded_df[combined_PCAencoded_df['experiment_id'] == 'after_experiment'],\n",
+    "                  alpha=0.2, \n",
+    "                  color='red')\n",
+    "fig += geom_point(data=combined_PCAencoded_df[combined_PCAencoded_df['experiment_id'] == 'before_experiment'],\n",
+    "                  alpha=0.2, \n",
+    "                  color='blue')\n",
+    "\n",
+    "print(fig)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:simulate_expression_compendia] *",
+   "language": "python",
+   "name": "conda-env-simulate_expression_compendia-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/human_tests/Human_random_sampling_simulation.ipynb b/human_tests/Human_random_sampling_simulation.ipynb
new file mode 100644
index 0000000..d276e4a
--- /dev/null
+++ b/human_tests/Human_random_sampling_simulation.ipynb
@@ -0,0 +1,870 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test simulating compendia by random sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import umap\n",
+    "from plotnine import (ggplot,\n",
+    "                      labs,  \n",
+    "                      geom_point,\n",
+    "                      aes, \n",
+    "                      ggsave, \n",
+    "                      theme_bw,\n",
+    "                      theme,\n",
+    "                      facet_wrap,\n",
+    "                      scale_color_manual,\n",
+    "                      guides, \n",
+    "                      guide_legend,\n",
+    "                      element_blank,\n",
+    "                      element_text,\n",
+    "                      element_rect,\n",
+    "                      element_line,\n",
+    "                      coords)\n",
+    "\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore')\n",
+    "\n",
+    "from ponyo import utils, train_vae_modules, simulate_expression_data\n",
+    "\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in config variables\n",
+    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
+    "config_file = os.path.abspath(os.path.join(base_dir,\n",
+    "                                           \"human_tests\", \n",
+    "                                           \"config_test_human.tsv\"))\n",
+    "params = utils.read_config(config_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load parameters\n",
+    "local_dir = params[\"local_dir\"]\n",
+    "dataset_name = params['dataset_name']\n",
+    "analysis_name = params[\"simulation_type\"]\n",
+    "train_architecture = params['NN_architecture']\n",
+    "num_simulated_samples = params['num_simulated_samples']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input files\n",
+    "rpkm_data_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"input\",\n",
+    "    \"recount2_gene_RPKM_data_test.tsv\")\n",
+    "assert os.path.exists(rpkm_data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup directories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.setup_dir(config_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output file\n",
+    "normalized_data_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"input\",\n",
+    "    \"recount2_gene_normalized_data_test.tsv.xz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input: dataset contains 50 samples and 5000 genes\n",
+      "Output: normalized dataset contains 50 samples and 5000 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_vae_modules.normalize_expression_data(base_dir,\n",
+    "                                            config_file,\n",
+    "                                            rpkm_data_file,\n",
+    "                                            normalized_data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train VAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Directory containing log information from VAE training\n",
+    "vae_log_dir = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name,\n",
+    "    \"logs\",\n",
+    "    train_architecture)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input dataset contains 50 samples and 5000 genes\n",
+      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n",
+      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
+      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "Train on 45 samples, validate on 5 samples\n",
+      "Epoch 1/10\n",
+      "45/45 [==============================] - 4s 88ms/step - loss: 2466.1158 - val_loss: 2732.4883\n",
+      "Epoch 2/10\n",
+      "45/45 [==============================] - 4s 80ms/step - loss: 1671.1998 - val_loss: 2180.0183\n",
+      "Epoch 3/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1566.8178 - val_loss: 1799.2496\n",
+      "Epoch 4/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1481.5816 - val_loss: 1730.1412\n",
+      "Epoch 5/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1577.6224 - val_loss: 1477.2603\n",
+      "Epoch 6/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1444.6422 - val_loss: 1510.6864\n",
+      "Epoch 7/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1475.2912 - val_loss: 1676.2584\n",
+      "Epoch 8/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1398.2218 - val_loss: 1403.8243\n",
+      "Epoch 9/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1352.8320 - val_loss: 1309.4790\n",
+      "Epoch 10/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1411.7664 - val_loss: 1252.2395\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Train VAE\n",
+    "train_vae_modules.train_vae(config_file,\n",
+    "                            normalized_data_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check reproducibility of VAE training\n",
+    "template_path = \"data/test_vae_log.tsv\"\n",
+    "output_path = \"logs/NN_2500_30/tybalt_2layer_30latent_stats.tsv\"\n",
+    "assert np.all(np.isclose(\n",
+    "    pd.read_csv(output_path, sep=\"\\t\").values,\n",
+    "    pd.read_csv(template_path, sep=\"\\t\").values\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulate data by random sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized gene expression data contains 50 samples and 5000 genes\n",
+      "Return: simulated gene expression data containing 100 samples and 5000 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run simulation\n",
+    "simulated_data = simulate_expression_data.simulate_by_random_sampling(\n",
+    "    normalized_data_file,\n",
+    "    train_architecture,\n",
+    "    dataset_name,\n",
+    "    analysis_name,\n",
+    "    num_simulated_samples,\n",
+    "    local_dir,\n",
+    "    base_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>4990</th>\n",
+       "      <th>4991</th>\n",
+       "      <th>4992</th>\n",
+       "      <th>4993</th>\n",
+       "      <th>4994</th>\n",
+       "      <th>4995</th>\n",
+       "      <th>4996</th>\n",
+       "      <th>4997</th>\n",
+       "      <th>4998</th>\n",
+       "      <th>4999</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.107708</td>\n",
+       "      <td>0.254322</td>\n",
+       "      <td>0.091486</td>\n",
+       "      <td>0.098933</td>\n",
+       "      <td>0.169752</td>\n",
+       "      <td>0.185692</td>\n",
+       "      <td>0.296073</td>\n",
+       "      <td>0.283656</td>\n",
+       "      <td>0.027364</td>\n",
+       "      <td>0.024563</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.302836</td>\n",
+       "      <td>0.346906</td>\n",
+       "      <td>0.042573</td>\n",
+       "      <td>0.086233</td>\n",
+       "      <td>0.054200</td>\n",
+       "      <td>0.093757</td>\n",
+       "      <td>0.034767</td>\n",
+       "      <td>0.182133</td>\n",
+       "      <td>0.133820</td>\n",
+       "      <td>0.025069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.521881</td>\n",
+       "      <td>0.163992</td>\n",
+       "      <td>0.077538</td>\n",
+       "      <td>0.016292</td>\n",
+       "      <td>0.102869</td>\n",
+       "      <td>0.162192</td>\n",
+       "      <td>0.552984</td>\n",
+       "      <td>0.766563</td>\n",
+       "      <td>0.014100</td>\n",
+       "      <td>0.015000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.299863</td>\n",
+       "      <td>0.418889</td>\n",
+       "      <td>0.007024</td>\n",
+       "      <td>0.115648</td>\n",
+       "      <td>0.006958</td>\n",
+       "      <td>0.079686</td>\n",
+       "      <td>0.044777</td>\n",
+       "      <td>0.334944</td>\n",
+       "      <td>0.426920</td>\n",
+       "      <td>0.020790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.551535</td>\n",
+       "      <td>0.202164</td>\n",
+       "      <td>0.101121</td>\n",
+       "      <td>0.051845</td>\n",
+       "      <td>0.126703</td>\n",
+       "      <td>0.259566</td>\n",
+       "      <td>0.534539</td>\n",
+       "      <td>0.639785</td>\n",
+       "      <td>0.035570</td>\n",
+       "      <td>0.029662</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.292004</td>\n",
+       "      <td>0.347224</td>\n",
+       "      <td>0.023687</td>\n",
+       "      <td>0.147598</td>\n",
+       "      <td>0.026355</td>\n",
+       "      <td>0.103412</td>\n",
+       "      <td>0.082812</td>\n",
+       "      <td>0.206011</td>\n",
+       "      <td>0.389102</td>\n",
+       "      <td>0.031277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.174245</td>\n",
+       "      <td>0.167301</td>\n",
+       "      <td>0.208217</td>\n",
+       "      <td>0.171929</td>\n",
+       "      <td>0.106976</td>\n",
+       "      <td>0.168417</td>\n",
+       "      <td>0.220404</td>\n",
+       "      <td>0.174601</td>\n",
+       "      <td>0.037360</td>\n",
+       "      <td>0.068209</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.201284</td>\n",
+       "      <td>0.190060</td>\n",
+       "      <td>0.133151</td>\n",
+       "      <td>0.140554</td>\n",
+       "      <td>0.167005</td>\n",
+       "      <td>0.227725</td>\n",
+       "      <td>0.055806</td>\n",
+       "      <td>0.177414</td>\n",
+       "      <td>0.165894</td>\n",
+       "      <td>0.073089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.310609</td>\n",
+       "      <td>0.189269</td>\n",
+       "      <td>0.192279</td>\n",
+       "      <td>0.174314</td>\n",
+       "      <td>0.137617</td>\n",
+       "      <td>0.242249</td>\n",
+       "      <td>0.331464</td>\n",
+       "      <td>0.416984</td>\n",
+       "      <td>0.044677</td>\n",
+       "      <td>0.060262</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.229771</td>\n",
+       "      <td>0.272926</td>\n",
+       "      <td>0.075518</td>\n",
+       "      <td>0.156485</td>\n",
+       "      <td>0.110277</td>\n",
+       "      <td>0.174624</td>\n",
+       "      <td>0.074767</td>\n",
+       "      <td>0.206038</td>\n",
+       "      <td>0.271069</td>\n",
+       "      <td>0.068040</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 5000 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0         1         2         3         4         5         6     \\\n",
+       "0  0.107708  0.254322  0.091486  0.098933  0.169752  0.185692  0.296073   \n",
+       "1  0.521881  0.163992  0.077538  0.016292  0.102869  0.162192  0.552984   \n",
+       "2  0.551535  0.202164  0.101121  0.051845  0.126703  0.259566  0.534539   \n",
+       "3  0.174245  0.167301  0.208217  0.171929  0.106976  0.168417  0.220404   \n",
+       "4  0.310609  0.189269  0.192279  0.174314  0.137617  0.242249  0.331464   \n",
+       "\n",
+       "       7         8         9     ...      4990      4991      4992      4993  \\\n",
+       "0  0.283656  0.027364  0.024563  ...  0.302836  0.346906  0.042573  0.086233   \n",
+       "1  0.766563  0.014100  0.015000  ...  0.299863  0.418889  0.007024  0.115648   \n",
+       "2  0.639785  0.035570  0.029662  ...  0.292004  0.347224  0.023687  0.147598   \n",
+       "3  0.174601  0.037360  0.068209  ...  0.201284  0.190060  0.133151  0.140554   \n",
+       "4  0.416984  0.044677  0.060262  ...  0.229771  0.272926  0.075518  0.156485   \n",
+       "\n",
+       "       4994      4995      4996      4997      4998      4999  \n",
+       "0  0.054200  0.093757  0.034767  0.182133  0.133820  0.025069  \n",
+       "1  0.006958  0.079686  0.044777  0.334944  0.426920  0.020790  \n",
+       "2  0.026355  0.103412  0.082812  0.206011  0.389102  0.031277  \n",
+       "3  0.167005  0.227725  0.055806  0.177414  0.165894  0.073089  \n",
+       "4  0.110277  0.174624  0.074767  0.206038  0.271069  0.068040  \n",
+       "\n",
+       "[5 rows x 5000 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulated_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize random sampled compendium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read data\n",
+    "normalized_data = pd.read_csv(normalized_data_file, header=0, sep=\"\\t\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ENSG00000086200.16</th>\n",
+       "      <th>ENSG00000279019.1</th>\n",
+       "      <th>ENSG00000206772.1</th>\n",
+       "      <th>ENSG00000252996.1</th>\n",
+       "      <th>ENSG00000277635.1</th>\n",
+       "      <th>ENSG00000224790.2</th>\n",
+       "      <th>ENSG00000106636.7</th>\n",
+       "      <th>ENSG00000152443.12</th>\n",
+       "      <th>ENSG00000225819.1</th>\n",
+       "      <th>ENSG00000237377.2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENSG00000281392.1</th>\n",
+       "      <th>ENSG00000241627.3</th>\n",
+       "      <th>ENSG00000239216.1</th>\n",
+       "      <th>ENSG00000256797.1</th>\n",
+       "      <th>ENSG00000221263.1</th>\n",
+       "      <th>ENSG00000249478.1</th>\n",
+       "      <th>ENSG00000283340.1</th>\n",
+       "      <th>ENSG00000242052.1</th>\n",
+       "      <th>ENSG00000264443.1</th>\n",
+       "      <th>ENSG00000236858.1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SRR592745</th>\n",
+       "      <td>0.080978</td>\n",
+       "      <td>0.886399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000654</td>\n",
+       "      <td>0.676113</td>\n",
+       "      <td>0.092019</td>\n",
+       "      <td>0.563159</td>\n",
+       "      <td>0.626964</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.237537</td>\n",
+       "      <td>0.241719</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.308343</td>\n",
+       "      <td>0.111808</td>\n",
+       "      <td>0.025641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592746</th>\n",
+       "      <td>0.087339</td>\n",
+       "      <td>0.161515</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.053521</td>\n",
+       "      <td>0.386222</td>\n",
+       "      <td>0.333747</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.114370</td>\n",
+       "      <td>0.334752</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.124547</td>\n",
+       "      <td>0.166928</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592747</th>\n",
+       "      <td>0.131139</td>\n",
+       "      <td>0.411128</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.138028</td>\n",
+       "      <td>0.514365</td>\n",
+       "      <td>0.629170</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.064516</td>\n",
+       "      <td>0.514277</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.326923</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.267231</td>\n",
+       "      <td>0.149227</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592748</th>\n",
+       "      <td>0.144581</td>\n",
+       "      <td>0.241113</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044672</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.135211</td>\n",
+       "      <td>0.597027</td>\n",
+       "      <td>0.866832</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.237624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.547814</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.009901</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.637243</td>\n",
+       "      <td>0.255882</td>\n",
+       "      <td>0.041026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592749</th>\n",
+       "      <td>0.202282</td>\n",
+       "      <td>0.047913</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.090141</td>\n",
+       "      <td>0.667133</td>\n",
+       "      <td>0.773918</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.850440</td>\n",
+       "      <td>0.486658</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.014851</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.712213</td>\n",
+       "      <td>0.230114</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 5000 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           ENSG00000086200.16  ENSG00000279019.1  ENSG00000206772.1  \\\n",
+       "SRR592745            0.080978           0.886399                0.0   \n",
+       "SRR592746            0.087339           0.161515                0.0   \n",
+       "SRR592747            0.131139           0.411128                0.0   \n",
+       "SRR592748            0.144581           0.241113                0.0   \n",
+       "SRR592749            0.202282           0.047913                0.0   \n",
+       "\n",
+       "           ENSG00000252996.1  ENSG00000277635.1  ENSG00000224790.2  \\\n",
+       "SRR592745           0.000654           0.676113           0.092019   \n",
+       "SRR592746           0.000000           1.000000           0.053521   \n",
+       "SRR592747           0.000000           0.000000           0.138028   \n",
+       "SRR592748           0.044672           0.000000           0.135211   \n",
+       "SRR592749           0.000000           0.000000           0.090141   \n",
+       "\n",
+       "           ENSG00000106636.7  ENSG00000152443.12  ENSG00000225819.1  \\\n",
+       "SRR592745           0.563159            0.626964                0.0   \n",
+       "SRR592746           0.386222            0.333747                0.0   \n",
+       "SRR592747           0.514365            0.629170                0.0   \n",
+       "SRR592748           0.597027            0.866832                0.0   \n",
+       "SRR592749           0.667133            0.773918                0.0   \n",
+       "\n",
+       "           ENSG00000237377.2  ...  ENSG00000281392.1  ENSG00000241627.3  \\\n",
+       "SRR592745           0.000000  ...           0.237537           0.241719   \n",
+       "SRR592746           0.000000  ...           0.114370           0.334752   \n",
+       "SRR592747           0.000000  ...           0.064516           0.514277   \n",
+       "SRR592748           0.237624  ...           1.000000           0.547814   \n",
+       "SRR592749           0.000000  ...           0.850440           0.486658   \n",
+       "\n",
+       "           ENSG00000239216.1  ENSG00000256797.1  ENSG00000221263.1  \\\n",
+       "SRR592745                0.0           0.000000                0.0   \n",
+       "SRR592746                0.0           0.000000                0.0   \n",
+       "SRR592747                0.0           0.326923                0.0   \n",
+       "SRR592748                0.0           0.000000                0.0   \n",
+       "SRR592749                0.0           0.000000                0.0   \n",
+       "\n",
+       "           ENSG00000249478.1  ENSG00000283340.1  ENSG00000242052.1  \\\n",
+       "SRR592745           0.000000                0.0           0.308343   \n",
+       "SRR592746           0.000000                0.0           0.124547   \n",
+       "SRR592747           0.000000                0.0           0.267231   \n",
+       "SRR592748           0.009901                0.0           0.637243   \n",
+       "SRR592749           0.014851                0.0           0.712213   \n",
+       "\n",
+       "           ENSG00000264443.1  ENSG00000236858.1  \n",
+       "SRR592745           0.111808           0.025641  \n",
+       "SRR592746           0.166928           0.000000  \n",
+       "SRR592747           0.149227           0.000000  \n",
+       "SRR592748           0.255882           0.041026  \n",
+       "SRR592749           0.230114           0.000000  \n",
+       "\n",
+       "[5 rows x 5000 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = umap.UMAP(random_state=123).fit(normalized_data)\n",
+    "\n",
+    "input_data_UMAPencoded = model.transform(normalized_data)\n",
+    "input_data_UMAPencoded_df = pd.DataFrame(data=input_data_UMAPencoded,\n",
+    "                                         index=normalized_data.index,\n",
+    "                                         columns=['1','2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# UMAP embedding of simulated data\n",
+    "\n",
+    "simulated_data_UMAPencoded = model.transform(simulated_data)\n",
+    "simulated_data_UMAPencoded_df = pd.DataFrame(data=simulated_data_UMAPencoded,\n",
+    "                                         index=simulated_data.index,\n",
+    "                                         columns=['1','2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ggplot: (-9223363269550871338)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Overlay original input vs simulated data\n",
+    "\n",
+    "# Add label for input or simulated dataset\n",
+    "input_data_UMAPencoded_df['dataset'] = 'original'\n",
+    "simulated_data_UMAPencoded_df['dataset'] = 'simulated'\n",
+    "\n",
+    "# Concatenate input and simulated dataframes together\n",
+    "combined_data_df = pd.concat([input_data_UMAPencoded_df, simulated_data_UMAPencoded_df])\n",
+    "\n",
+    "# Plot\n",
+    "g_input_sim = ggplot(combined_data_df, aes(x='1', y='2'))\n",
+    "g_input_sim += geom_point(aes(color='dataset'),alpha=0.3)\n",
+    "g_input_sim += labs(x = \"UMAP 1\",\n",
+    "                    y = \"UMAP 2\", \n",
+    "                    title = \"Original and simulated data\")\n",
+    "g_input_sim += theme_bw()\n",
+    "g_input_sim += theme(\n",
+    "    legend_title_align = \"center\",\n",
+    "    plot_background=element_rect(fill='white'),\n",
+    "    legend_key=element_rect(fill='white', colour='white'), \n",
+    "    plot_title=element_text(family='sans-serif', size=15),\n",
+    "    axis_text=element_text(family='sans-serif', size=12),\n",
+    "    axis_title=element_text(family='sans-serif', size=15)\n",
+    "\n",
+    ")\n",
+    "\n",
+    "print(g_input_sim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:simulate_expression_compendia] *",
+   "language": "python",
+   "name": "conda-env-simulate_expression_compendia-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/human_tests/Human_template_simulation.ipynb b/human_tests/Human_template_simulation.ipynb
new file mode 100644
index 0000000..ae8e624
--- /dev/null
+++ b/human_tests/Human_template_simulation.ipynb
@@ -0,0 +1,649 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test shifting template experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import umap\n",
+    "import glob\n",
+    "import pickle\n",
+    "from keras.models import load_model\n",
+    "from sklearn.decomposition import PCA\n",
+    "from plotnine import (ggplot,\n",
+    "                      labs,  \n",
+    "                      geom_point,\n",
+    "                      aes, \n",
+    "                      ggsave, \n",
+    "                      theme_bw,\n",
+    "                      theme,\n",
+    "                      facet_wrap,\n",
+    "                      scale_color_manual,\n",
+    "                      guides, \n",
+    "                      guide_legend,\n",
+    "                      element_blank,\n",
+    "                      element_text,\n",
+    "                      element_rect,\n",
+    "                      element_line,\n",
+    "                      coords)\n",
+    "\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore')\n",
+    "\n",
+    "from ponyo import utils, train_vae_modules, simulate_expression_data\n",
+    "\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in config variables\n",
+    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
+    "config_file = os.path.abspath(os.path.join(base_dir,\n",
+    "                                           \"human_tests\", \n",
+    "                                           \"config_test_human.tsv\"))\n",
+    "params = utils.read_config(config_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load parameters\n",
+    "local_dir = params[\"local_dir\"]\n",
+    "dataset_name = params['dataset_name']\n",
+    "analysis_name = params[\"simulation_type\"]\n",
+    "project_id = params['project_id']\n",
+    "NN_architecture = params['NN_architecture']\n",
+    "sample_id_colname = params['metadata_colname']\n",
+    "scaler_file = params['scaler_transform_file']\n",
+    "num_runs = params['num_simulated']\n",
+    "\n",
+    "NN_dir = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name, \n",
+    "    \"models\", \n",
+    "    NN_architecture)\n",
+    "\n",
+    "metadata_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"metadata\",\n",
+    "    \"recount2_metadata.tsv\")\n",
+    "\n",
+    "# Load pickled file\n",
+    "scaler = pickle.load(open(scaler_file, \"rb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input files\n",
+    "rpkm_data_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"input\",\n",
+    "    \"recount2_gene_RPKM_data_test.tsv\")\n",
+    "assert os.path.exists(rpkm_data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup directories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "utils.setup_dir(config_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output file\n",
+    "normalized_data_file = os.path.join(\n",
+    "    base_dir,\n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"input\",\n",
+    "    \"recount2_gene_normalized_data_test.tsv.xz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input: dataset contains 50 samples and 5000 genes\n",
+      "Output: normalized dataset contains 50 samples and 5000 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_vae_modules.normalize_expression_data(base_dir,\n",
+    "                                            config_file,\n",
+    "                                            rpkm_data_file,\n",
+    "                                            normalized_data_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output file\n",
+    "experiment_id_file = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name,\n",
+    "    \"data\",\n",
+    "    \"metadata\", \n",
+    "    \"experiment_ids.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 3219 experiments in the compendium\n",
+      "There are 6 experiments with gene expression data\n",
+      "6 experiment ids saved to file\n"
+     ]
+    }
+   ],
+   "source": [
+    "utils.create_experiment_id_file(metadata_file,\n",
+    "                                normalized_data_file,\n",
+    "                                experiment_id_file,\n",
+    "                                config_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train VAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Directory containing log information from VAE training\n",
+    "vae_log_dir = os.path.join(\n",
+    "    base_dir, \n",
+    "    dataset_name,\n",
+    "    \"logs\",\n",
+    "    NN_architecture)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input dataset contains 50 samples and 5000 genes\n",
+      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n",
+      "tracking <tf.Variable 'Variable:0' shape=() dtype=float32> beta\n",
+      "WARNING:tensorflow:From /home/alexandra/anaconda3/envs/simulate_expression_compendia/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "Train on 45 samples, validate on 5 samples\n",
+      "Epoch 1/10\n",
+      "45/45 [==============================] - 4s 88ms/step - loss: 2466.1158 - val_loss: 2732.4883\n",
+      "Epoch 2/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1671.1998 - val_loss: 2180.0183\n",
+      "Epoch 3/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1566.8178 - val_loss: 1799.2496\n",
+      "Epoch 4/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1481.5816 - val_loss: 1730.1412\n",
+      "Epoch 5/10\n",
+      "45/45 [==============================] - 4s 78ms/step - loss: 1577.6224 - val_loss: 1477.2603\n",
+      "Epoch 6/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1444.6422 - val_loss: 1510.6864\n",
+      "Epoch 7/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1475.2912 - val_loss: 1676.2584\n",
+      "Epoch 8/10\n",
+      "45/45 [==============================] - 4s 78ms/step - loss: 1398.2218 - val_loss: 1403.8243\n",
+      "Epoch 9/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1352.8320 - val_loss: 1309.4790\n",
+      "Epoch 10/10\n",
+      "45/45 [==============================] - 4s 79ms/step - loss: 1411.7664 - val_loss: 1252.2395\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Train VAE\n",
+    "train_vae_modules.train_vae(config_file,\n",
+    "                            normalized_data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shift template experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tmp result dir\n",
+    "tmp = os.path.join(local_dir, \"pseudo_experiment\")\n",
+    "os.makedirs(tmp, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Run simulation\n",
+    "for run in range(num_runs):\n",
+    "    simulate_expression_data.shift_template_experiment(\n",
+    "        normalized_data_file,\n",
+    "        project_id,\n",
+    "        sample_id_colname,\n",
+    "        NN_architecture,\n",
+    "        dataset_name,\n",
+    "        scaler,\n",
+    "        local_dir,\n",
+    "        base_dir,\n",
+    "        run)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize latent transform compendium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load VAE models\n",
+    "model_encoder_file = glob.glob(os.path.join(\n",
+    "        NN_dir,\n",
+    "        \"*_encoder_model.h5\"))[0]\n",
+    "\n",
+    "weights_encoder_file = glob.glob(os.path.join(\n",
+    "    NN_dir,\n",
+    "    \"*_encoder_weights.h5\"))[0]\n",
+    "\n",
+    "model_decoder_file = glob.glob(os.path.join(\n",
+    "    NN_dir,\n",
+    "    \"*_decoder_model.h5\"))[0]\n",
+    "\n",
+    "weights_decoder_file = glob.glob(os.path.join(\n",
+    "    NN_dir,\n",
+    "    \"*_decoder_weights.h5\"))[0]\n",
+    "\n",
+    "# Load saved models\n",
+    "loaded_model = load_model(model_encoder_file)\n",
+    "loaded_decode_model = load_model(model_decoder_file)\n",
+    "\n",
+    "loaded_model.load_weights(weights_encoder_file)\n",
+    "loaded_decode_model.load_weights(weights_decoder_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca = PCA(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read data\n",
+    "normalized_compendium = pd.read_csv(normalized_data_file, header=0, sep=\"\\t\", index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Encode normalized compendium into latent space\n",
+    "compendium_encoded = loaded_model.predict_on_batch(normalized_compendium)\n",
+    "\n",
+    "compendium_encoded_df = pd.DataFrame(data=compendium_encoded, \n",
+    "                                     index=normalized_compendium.index)\n",
+    "\n",
+    "# Get and save PCA model\n",
+    "model = pca.fit(compendium_encoded_df)\n",
+    "\n",
+    "compendium_PCAencoded = model.transform(compendium_encoded_df)\n",
+    "\n",
+    "compendium_PCAencoded_df = pd.DataFrame(data=compendium_PCAencoded,\n",
+    "                                         index=compendium_encoded_df.index,\n",
+    "                                         columns=['1','2'])\n",
+    "\n",
+    "# Add label\n",
+    "compendium_PCAencoded_df['experiment_id'] = 'background'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Embedding of real template experiment (encoded)\n",
+    "template_file = os.path.join(local_dir,\n",
+    "    \"pseudo_experiment\",\n",
+    "    \"template_normalized_data_\"+project_id+\"_test.txt\")\n",
+    "\n",
+    "template_data = pd.read_csv(template_file, header=0, sep='\\t', index_col=0)\n",
+    "\n",
+    "# Encode template experiment into latent space\n",
+    "template_encoded = loaded_model.predict_on_batch(template_data)\n",
+    "template_encoded_df = pd.DataFrame(data=template_encoded,\n",
+    "                                   index=template_data.index)\n",
+    "\n",
+    "template_PCAencoded = model.transform(template_encoded_df)\n",
+    "\n",
+    "template_PCAencoded_df = pd.DataFrame(data=template_PCAencoded,\n",
+    "                                         index=template_encoded_df.index,\n",
+    "                                         columns=['1','2'])\n",
+    "\n",
+    "# Add back label column\n",
+    "template_PCAencoded_df['experiment_id'] = 'template_experiment'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Embedding of simulated experiment (encoded)\n",
+    "encoded_simulated_file = os.path.join(local_dir,\n",
+    "    \"pseudo_experiment\",\n",
+    "    \"selected_simulated_encoded_data_\"+project_id+\"_1.txt\")\n",
+    "\n",
+    "simulated_encoded_df = pd.read_csv(encoded_simulated_file,header=0, sep='\\t', index_col=0)\n",
+    "\n",
+    "simulated_PCAencoded = model.transform(simulated_encoded_df)\n",
+    "\n",
+    "simulated_PCAencoded_df = pd.DataFrame(data=simulated_PCAencoded,\n",
+    "                                         index=simulated_encoded_df.index,\n",
+    "                                         columns=['1','2'])\n",
+    "\n",
+    "# Add back label column\n",
+    "simulated_PCAencoded_df['experiment_id'] = 'simulated_experiment'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>experiment_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SRR592745</th>\n",
+       "      <td>-1.557785</td>\n",
+       "      <td>6.548028</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592746</th>\n",
+       "      <td>-1.335538</td>\n",
+       "      <td>6.123819</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592747</th>\n",
+       "      <td>-1.234933</td>\n",
+       "      <td>6.325553</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592748</th>\n",
+       "      <td>0.633263</td>\n",
+       "      <td>9.497215</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SRR592749</th>\n",
+       "      <td>1.389616</td>\n",
+       "      <td>8.344598</td>\n",
+       "      <td>background</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  1         2 experiment_id\n",
+       "SRR592745 -1.557785  6.548028    background\n",
+       "SRR592746 -1.335538  6.123819    background\n",
+       "SRR592747 -1.234933  6.325553    background\n",
+       "SRR592748  0.633263  9.497215    background\n",
+       "SRR592749  1.389616  8.344598    background"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Concatenate dataframes\n",
+    "combined_PCAencoded_df = pd.concat([compendium_PCAencoded_df, \n",
+    "                                    template_PCAencoded_df,\n",
+    "                                    simulated_PCAencoded_df])\n",
+    "\n",
+    "print(combined_PCAencoded_df.shape)\n",
+    "combined_PCAencoded_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<ggplot: (-9223363306623618528)>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "fig = ggplot(combined_PCAencoded_df, aes(x='1', y='2'))\n",
+    "fig += geom_point(aes(color='experiment_id'), alpha=0.2)\n",
+    "fig += labs(x ='PCA 1',\n",
+    "            y = 'PCA 2',\n",
+    "            title = 'PCA original data with experiments (latent space)')\n",
+    "fig += theme_bw()\n",
+    "fig += theme(\n",
+    "    legend_title_align = \"center\",\n",
+    "    plot_background=element_rect(fill='white'),\n",
+    "    legend_key=element_rect(fill='white', colour='white'), \n",
+    "    legend_title=element_text(family='sans-serif', size=15),\n",
+    "    legend_text=element_text(family='sans-serif', size=12),\n",
+    "    plot_title=element_text(family='sans-serif', size=15),\n",
+    "    axis_text=element_text(family='sans-serif', size=12),\n",
+    "    axis_title=element_text(family='sans-serif', size=15)\n",
+    "    )\n",
+    "fig += guides(colour=guide_legend(override_aes={'alpha': 1}))\n",
+    "fig += scale_color_manual(['#bdbdbd', 'red', 'blue'])\n",
+    "fig += geom_point(data=combined_PCAencoded_df[combined_PCAencoded_df['experiment_id'] == 'template_experiment'],\n",
+    "                  alpha=0.2, \n",
+    "                  color='blue')\n",
+    "fig += geom_point(data=combined_PCAencoded_df[combined_PCAencoded_df['experiment_id'] == 'simulated_experiment'],\n",
+    "                  alpha=0.1, \n",
+    "                  color='red')\n",
+    "\n",
+    "print(fig)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:simulate_expression_compendia] *",
+   "language": "python",
+   "name": "conda-env-simulate_expression_compendia-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/human_tests/config_test_human.tsv b/human_tests/config_test_human.tsv
new file mode 100644
index 0000000..f908577
--- /dev/null
+++ b/human_tests/config_test_human.tsv
@@ -0,0 +1,18 @@
+local_dir	"../test_results/"
+scaler_transform_file	"../test_results/scaler_transform.pickle"
+dataset_name	"human_tests"
+simulation_type	"None"
+NN_architecture	"NN_2500_30"
+learning_rate	0.001
+batch_size	5
+epochs	10
+kappa	0.01
+intermediate_dim	2500
+latent_dim	30
+epsilon_std	1.0
+validation_frac	0.1
+num_simulated_samples	100
+num_simulated_experiments	50
+num_simulated	2
+project_id	"SRP016140"
+metadata_colname	'run'
diff --git a/Human_tests/data/input/recount2_gene_RPKM_data_test.tsv b/human_tests/data/input/recount2_gene_RPKM_data_test.tsv
similarity index 100%
rename from Human_tests/data/input/recount2_gene_RPKM_data_test.tsv
rename to human_tests/data/input/recount2_gene_RPKM_data_test.tsv
diff --git a/human_tests/data/input/recount2_gene_normalized_data_test.tsv.xz b/human_tests/data/input/recount2_gene_normalized_data_test.tsv.xz
new file mode 100644
index 0000000..fb670d3
Binary files /dev/null and b/human_tests/data/input/recount2_gene_normalized_data_test.tsv.xz differ
diff --git a/Human_tests/data/metadata/experiment_ids.txt b/human_tests/data/metadata/experiment_ids.txt
similarity index 100%
rename from Human_tests/data/metadata/experiment_ids.txt
rename to human_tests/data/metadata/experiment_ids.txt
diff --git a/Human_tests/data/metadata/recount2_metadata.tsv b/human_tests/data/metadata/recount2_metadata.tsv
similarity index 100%
rename from Human_tests/data/metadata/recount2_metadata.tsv
rename to human_tests/data/metadata/recount2_metadata.tsv
diff --git a/human_tests/data/test_vae_log.tsv b/human_tests/data/test_vae_log.tsv
new file mode 100644
index 0000000..609ea3a
--- /dev/null
+++ b/human_tests/data/test_vae_log.tsv
@@ -0,0 +1,11 @@
+val_loss	loss	learning_rate	batch_size	epochs	kappa
+2732.48828125	2466.1158040364585	0.001	5	10	0.01
+2180.018310546875	1671.1998291015625	0.001	5	10	0.01
+1799.2496337890625	1566.8177761501736	0.001	5	10	0.01
+1730.1412353515625	1481.5816379123264	0.001	5	10	0.01
+1477.26025390625	1577.6224093967014	0.001	5	10	0.01
+1510.6864013671875	1444.6422390407986	0.001	5	10	0.01
+1676.2584228515625	1475.2911783854167	0.001	5	10	0.01
+1403.8243408203125	1398.2217678493923	0.001	5	10	0.01
+1309.47900390625	1352.832010904948	0.001	5	10	0.01
+1252.239501953125	1411.7664116753472	0.001	5	10	0.01
diff --git a/ponyo/cca_core.py b/ponyo/cca_core.py
deleted file mode 100644
index f7f8ff8..0000000
--- a/ponyo/cca_core.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# Copyright 2016 Google Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-The core code for applying Canonical Correlation Analysis to deep networks.
-This module contains the core functions to apply canonical correlation analysis
-to deep neural networks. The main function is get_cca_similarity, which takes in
-two sets of activations, typically the neurons in two layers and their outputs
-on all of the datapoints D = [d_1,...,d_m] that have been passed through.
-Inputs have shape (num_neurons1, m), (num_neurons2, m). This can be directly
-applied used on fully connected networks. For convolutional layers, the 3d block
-of neurons can either be flattened entirely, along channels, or alternatively,
-the dft_ccas (Discrete Fourier Transform) module can be used.
-See https://arxiv.org/abs/1706.05806 for full details.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import numpy as np
-
-num_cca_trials = 5
-epsilon = 1e-6
-
-
-def positivedef_matrix_sqrt(array):
-    """Stable method for computing matrix square roots, supports complex matrices.
-  Args:
-            array: A numpy 2d array, can be complex valued that is a positive
-                   definite symmetric (or hermitian) matrix
-  Returns:
-            sqrtarray: The matrix square root of array
-  """
-    w, v = np.linalg.eigh(array)
-    #  A - np.dot(v, np.dot(np.diag(w), v.T))
-    wsqrt = np.sqrt(w)
-    sqrtarray = np.dot(v, np.dot(np.diag(wsqrt), np.conj(v).T))
-    return sqrtarray
-
-
-def remove_small(sigma_xx, sigma_xy, sigma_yx, sigma_yy, threshold=1e-6):
-    """Takes covariance between X, Y, and removes values of small magnitude.
-  Args:
-            sigma_xx: 2d numpy array, variance matrix for x
-            sigma_xy: 2d numpy array, crossvariance matrix for x,y
-            sigma_yx: 2d numpy array, crossvariance matrixy for x,y,
-                      (conjugate) transpose of sigma_xy
-            sigma_yy: 2d numpy array, variance matrix for y
-            threshold: cutoff value for norm below which directions are thrown
-                       away
-  Returns:
-            sigma_xx_crop: 2d array with low x norm directions removed
-            sigma_xy_crop: 2d array with low x and y norm directions removed
-            sigma_yx_crop: 2d array with low x and y norm directiosn removed
-            sigma_yy_crop: 2d array with low y norm directions removed
-            x_idxs: indexes of sigma_xx that were removed
-            y_idxs: indexes of sigma_yy that were removed
-  """
-
-    x_diag = np.abs(np.diagonal(sigma_xx))
-    y_diag = np.abs(np.diagonal(sigma_yy))
-    x_idxs = x_diag >= threshold
-    y_idxs = y_diag >= threshold
-
-    sigma_xx_crop = sigma_xx[x_idxs][:, x_idxs]
-    sigma_xy_crop = sigma_xy[x_idxs][:, y_idxs]
-    sigma_yx_crop = sigma_yx[y_idxs][:, x_idxs]
-    sigma_yy_crop = sigma_yy[y_idxs][:, y_idxs]
-
-    return (sigma_xx_crop, sigma_xy_crop, sigma_yx_crop, sigma_yy_crop, x_idxs, y_idxs)
-
-
-def compute_ccas(sigma_xx, sigma_xy, sigma_yx, sigma_yy, verbose=True):
-    """Main cca computation function, takes in variances and crossvariances.
-  This function takes in the covariances and cross covariances of X, Y,
-  preprocesses them (removing small magnitudes) and outputs the raw results of
-  the cca computation, including cca directions in a rotated space, and the
-  cca correlation coefficient values.
-  Args:
-            sigma_xx: 2d numpy array, (num_neurons_x, num_neurons_x)
-                      variance matrix for x
-            sigma_xy: 2d numpy array, (num_neurons_x, num_neurons_y)
-                      crossvariance matrix for x,y
-            sigma_yx: 2d numpy array, (num_neurons_y, num_neurons_x)
-                      crossvariance matrix for x,y (conj) transpose of sigma_xy
-            sigma_yy: 2d numpy array, (num_neurons_y, num_neurons_y)
-                      variance matrix for y
-            verbose:  boolean on whether to print intermediate outputs
-  Returns:
-            [ux, sx, vx]: [numpy 2d array, numpy 1d array, numpy 2d array]
-                          ux and vx are (conj) transposes of each other, being
-                          the canonical directions in the X subspace.
-                          sx is the set of canonical correlation coefficients-
-                          how well corresponding directions in vx, Vy correlate
-                          with each other.
-            [uy, sy, vy]: Same as above, but for Y space
-            invsqrt_xx:   Inverse square root of sigma_xx to transform canonical
-                          directions back to original space
-            invsqrt_yy:   Same as above but for sigma_yy
-            x_idxs:       The indexes of the input sigma_xx that were pruned
-                          by remove_small
-            y_idxs:       Same as above but for sigma_yy
-  """
-
-    (sigma_xx, sigma_xy, sigma_yx, sigma_yy, x_idxs, y_idxs) = remove_small(
-        sigma_xx, sigma_xy, sigma_yx, sigma_yy
-    )
-
-    numx = sigma_xx.shape[0]
-    numy = sigma_yy.shape[0]
-
-    if numx == 0 or numy == 0:
-        return (
-            [0, 0, 0],
-            [0, 0, 0],
-            np.zeros_like(sigma_xx),
-            np.zeros_like(sigma_yy),
-            x_idxs,
-            y_idxs,
-        )
-
-    if verbose:
-        print("adding eps to diagonal and taking inverse")
-    sigma_xx += epsilon * np.eye(numx)
-    sigma_yy += epsilon * np.eye(numy)
-    inv_xx = np.linalg.pinv(sigma_xx)
-    inv_yy = np.linalg.pinv(sigma_yy)
-
-    if verbose:
-        print("taking square root")
-    invsqrt_xx = positivedef_matrix_sqrt(inv_xx)
-    invsqrt_yy = positivedef_matrix_sqrt(inv_yy)
-
-    if verbose:
-        print("dot products...")
-    arr_x = np.dot(sigma_yx, invsqrt_xx)
-    arr_x = np.dot(inv_yy, arr_x)
-    arr_x = np.dot(invsqrt_xx, np.dot(sigma_xy, arr_x))
-    arr_y = np.dot(sigma_xy, invsqrt_yy)
-    arr_y = np.dot(inv_xx, arr_y)
-    arr_y = np.dot(invsqrt_yy, np.dot(sigma_yx, arr_y))
-
-    if verbose:
-        print("trying to take final svd")
-    arr_x_stable = arr_x + epsilon * np.eye(arr_x.shape[0])
-    arr_y_stable = arr_y + epsilon * np.eye(arr_y.shape[0])
-    try:
-        ux, sx, vx = np.linalg.svd(arr_x_stable)
-        uy, sy, vy = np.linalg.svd(arr_y_stable)
-    except:
-        return [0, 0, 0], [0, 0, 0], 0, 0, 0, 0
-    sx = np.sqrt(np.abs(sx))
-    sy = np.sqrt(np.abs(sy))
-    if verbose:
-        print("computed everything!")
-
-    return [ux, sx, vx], [uy, sy, vy], invsqrt_xx, invsqrt_yy, x_idxs, y_idxs
-
-
-def sum_threshold(array, threshold):
-    """Computes threshold index of decreasing nonnegative array by summing.
-  This function takes in a decreasing array nonnegative floats, and a
-  threshold between 0 and 1. It returns the index i at which the sum of the
-  array up to i is threshold*total mass of the array.
-  Args:
-            array: a 1d numpy array of decreasing, nonnegative floats
-            threshold: a number between 0 and 1
-  Returns:
-            i: index at which np.sum(array[:i]) >= threshold
-  """
-    assert (threshold >= 0) and (threshold <= 1), "print incorrect threshold"
-
-    for i in range(len(array)):
-        if np.sum(array[:i]) / np.sum(array) >= threshold:
-            return i
-
-
-def create_zero_dict(compute_dirns, dimension):
-    """Outputs a zero dict when neuron activation norms too small.
-  This function creates a return_dict with appropriately shaped zero entries
-  when all neuron activations are very small.
-  Args:
-            compute_dirns: boolean, whether to have zero vectors for directions
-            dimension: int, defines shape of directions
-  Returns:
-            return_dict: a dict of appropriately shaped zero entries
-  """
-    return_dict = {}
-    return_dict["mean"] = (np.asarray(0), np.asarray(0))
-    return_dict["sum"] = (np.asarray(0), np.asarray(0))
-    return_dict["cca_coef1"] = np.asarray(0)
-    return_dict["cca_coef2"] = np.asarray(0)
-    return_dict["idx1"] = 0
-    return_dict["idx2"] = 0
-
-    if compute_dirns:
-        return_dict["cca_dirns1"] = np.zeros((1, dimension))
-        return_dict["cca_dirns2"] = np.zeros((1, dimension))
-
-    return return_dict
-
-
-def get_cca_similarity(acts1, acts2, threshold=0.98, compute_dirns=True, verbose=True):
-    """The main function for computing cca similarities.
-  This function computes the cca similarity between two sets of activations,
-  returning a dict with the cca coefficients, a few statistics of the cca
-  coefficients, and (optionally) the actual directions.
-  Args:
-            acts1: (num_neurons1, data_points) a 2d numpy array of neurons by
-                   datapoints where entry (i,j) is the output of neuron i on
-                   datapoint j.
-            acts2: (num_neurons2, data_points) same as above, but (potentially)
-                   for a different set of neurons. Note that acts1 and acts2
-                   can have different numbers of neurons, but must agree on the
-                   number of datapoints
-            threshold: float between 0, 1 used to get rid of trailing zeros in
-                       the cca correlation coefficients to output more accurate
-                       summary statistics of correlations.
-            compute_dirns: boolean value determining whether actual cca
-                           directions are computed. (For very large neurons and
-                           datasets, may be better to compute these on the fly
-                           instead of store in memory.)
-            verbose: Boolean, whether info about intermediate outputs printed
-  Returns:
-            return_dict: A dictionary with outputs from the cca computations.
-                         Contains neuron coefficients (combinations of neurons
-                         that correspond to cca directions), the cca correlation
-                         coefficients (how well aligned directions correlate),
-                         x and y idxs (for computing cca directions on the fly
-                         if compute_dirns=False), and summary statistics. If
-                         compute_dirns=True, the cca directions are also
-                         computed.
-  """
-
-    # assert dimensionality equal
-    assert acts1.shape[1] == acts2.shape[1], "dimensions don't match"
-    # check that acts1, acts2 are transposition
-    assert acts1.shape[0] < acts1.shape[1], (
-        "input must be number of neurons" "by datapoints"
-    )
-    return_dict = {}
-
-    # compute covariance with numpy function for extra stability
-    numx = acts1.shape[0]
-
-    covariance = np.cov(acts1, acts2)
-    sigmaxx = covariance[:numx, :numx]
-    sigmaxy = covariance[:numx, numx:]
-    sigmayx = covariance[numx:, :numx]
-    sigmayy = covariance[numx:, numx:]
-
-    # rescale covariance to make cca computation more stable
-    xmax = np.max(np.abs(sigmaxx))
-    ymax = np.max(np.abs(sigmayy))
-    sigmaxx /= xmax
-    sigmayy /= ymax
-    sigmaxy /= np.sqrt(xmax * ymax)
-    sigmayx /= np.sqrt(xmax * ymax)
-
-    ([_, sx, vx], [_, sy, vy], invsqrt_xx, invsqrt_yy, x_idxs, y_idxs) = compute_ccas(
-        sigmaxx, sigmaxy, sigmayx, sigmayy, verbose
-    )
-
-    # if x_idxs or y_idxs is all false, return_dict has zero entries
-    if (not np.any(x_idxs)) or (not np.any(y_idxs)):
-        return create_zero_dict(compute_dirns, acts1.shape[1])
-
-    if compute_dirns:
-        # orthonormal directions that are CCA directions
-        cca_dirns1 = np.dot(vx, np.dot(invsqrt_xx, acts1[x_idxs]))
-        cca_dirns2 = np.dot(vy, np.dot(invsqrt_yy, acts2[y_idxs]))
-
-    # get rid of trailing zeros in the cca coefficients
-    idx1 = sum_threshold(sx, threshold)
-    idx2 = sum_threshold(sy, threshold)
-
-    return_dict["neuron_coeffs1"] = np.dot(vx, invsqrt_xx)
-    return_dict["neuron_coeffs2"] = np.dot(vy, invsqrt_yy)
-    return_dict["cca_coef1"] = sx
-    return_dict["cca_coef2"] = sy
-    return_dict["x_idxs"] = x_idxs
-    return_dict["y_idxs"] = y_idxs
-    # summary statistics
-    return_dict["mean"] = (np.mean(sx[:idx1]), np.mean(sy[:idx2]))
-    return_dict["sum"] = (np.sum(sx), np.sum(sy))
-
-    if compute_dirns:
-        return_dict["cca_dirns1"] = cca_dirns1
-        return_dict["cca_dirns2"] = cca_dirns2
-
-    return return_dict
-
-
-def robust_cca_similarity(
-    acts1, acts2, threshold=0.98, compute_dirns=True, verbose=False
-):
-    """Calls get_cca_similarity multiple times while adding noise.
-  This function is very similar to get_cca_similarity, and can be used if
-  get_cca_similarity doesn't converge for some pair of inputs. This function
-  adds some noise to the activations to help convergence.
-  Args:
-            acts1: (num_neurons1, data_points) a 2d numpy array of neurons by
-                   datapoints where entry (i,j) is the output of neuron i on
-                   datapoint j.
-            acts2: (num_neurons2, data_points) same as above, but (potentially)
-                   for a different set of neurons. Note that acts1 and acts2
-                   can have different numbers of neurons, but must agree on the
-                   number of datapoints
-            threshold: float between 0, 1 used to get rid of trailing zeros in
-                       the cca correlation coefficients to output more accurate
-                       summary statistics of correlations.
-            compute_dirns: boolean value determining whether actual cca
-                           directions are computed. (For very large neurons and
-                           datasets, may be better to compute these on the fly
-                           instead of store in memory.)
-  Returns:
-            return_dict: A dictionary with outputs from the cca computations.
-                         Contains neuron coefficients (combinations of neurons
-                         that correspond to cca directions), the cca correlation
-                         coefficients (how well aligned directions correlate),
-                         x and y idxs (for computing cca directions on the fly
-                         if compute_dirns=False), and summary statistics. If
-                         compute_dirns=True, the cca directions are also
-                         computed.
-  """
-
-    for trial in range(num_cca_trials):
-        try:
-            return_dict = get_cca_similarity(
-                acts1, acts2, threshold, compute_dirns, verbose=verbose
-            )
-        except np.LinAlgError:
-            acts1 = acts1 * 1e-1 + np.random.normal(size=acts1.shape) * epsilon
-            acts2 = acts2 * 1e-1 + np.random.normal(size=acts1.shape) * epsilon
-            if trial + 1 == num_cca_trials:
-                raise
-
-    return return_dict
diff --git a/ponyo/generate_data_parallel.py b/ponyo/generate_data_parallel.py
deleted file mode 100644
index 348b3cb..0000000
--- a/ponyo/generate_data_parallel.py
+++ /dev/null
@@ -1,903 +0,0 @@
-"""
-Author: Alexandra Lee
-Date Created: 30 August 2019
-
-These scripts are the components used to run each simulation experiment,
-found in `simulations.py`.
-These scripts generate simulated compendia, add noise to simulated data,
-apply noise correction to simulated data, permute simulated data.
-"""
-
-import os
-import pandas as pd
-import numpy as np
-import random
-import glob
-import warnings
-from keras.models import load_model
-from sklearn import preprocessing
-
-from rpy2.robjects.packages import importr
-from rpy2.robjects import pandas2ri
-
-limma = importr("limma")
-sva = importr("sva")
-pandas2ri.activate()
-
-
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-random.seed(123)
-
-
-def get_sample_ids(experiment_id, dataset_name, sample_id_colname):
-    """
-    Returns sample ids (found in gene expression df) associated with
-    a given list of experiment ids (found in the metadata)
-
-    Arguments
-    ----------
-    experiment_ids_file: str
-        File containing all cleaned experiment ids
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    sample_id_colname: str
-        Column header that contains sample id that maps expression data
-        and metadata
-
-    """
-    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
-
-    if "pseudomonas" in dataset_name.lower():
-        # metadata file
-        mapping_file = os.path.join(
-            base_dir, dataset_name, "data", "metadata", "sample_annotations.tsv"
-        )
-
-        # Read in metadata
-        metadata = pd.read_csv(mapping_file, header=0, sep="\t", index_col=0)
-
-        selected_metadata = metadata.loc[experiment_id]
-        sample_ids = list(selected_metadata[sample_id_colname])
-
-    else:
-        # metadata file
-        mapping_file = os.path.join(
-            base_dir, dataset_name, "data", "metadata", "recount2_metadata.tsv"
-        )
-
-        # Read in metadata
-        metadata = pd.read_csv(mapping_file, header=0, sep="\t", index_col=0)
-
-        selected_metadata = metadata.loc[experiment_id]
-        sample_ids = list(selected_metadata[sample_id_colname])
-
-    return sample_ids
-
-
-def simulate_compendium(
-    num_simulated_experiments,
-    normalized_data_file,
-    NN_architecture,
-    dataset_name,
-    analysis_name,
-    experiment_ids_file,
-    sample_id_colname,
-    local_dir,
-    base_dir,
-):
-    """
-    Generate simulated data by randomly sampling some number of experiments
-    and linearly shifting the gene expression in the VAE latent space,
-    preserving the relationship between samples within an experiment.
-
-    Workflow:
-    1. Randomly select 1 experiment and get the gene expression data for that
-    experiment (here we are assuming that there is only biological variation
-    within this experiment)
-    2. Encode this experiment into a latent space using the trained VAE model
-    3. Encode the entire dataset from the <normalized_data_file>
-        3a. Select a random point in the encoded space. For each encoded feature, sample
-        from a distribution using the mean and standard deviation for that feature
-    4. Calculate the shift_vec_df = centroid(encoded experiment) - random encoded experiment
-    5. Shift all the samples from the experiment by the shift_vec_df
-    6. Decode the samples
-    7. Repeat steps 1-6 for <num_simulated_experiments>
-
-    This will generate a simulated compendium of different gene expression experiments that
-    are of a similar type to the original data but with different perturbations
-
-    Arguments
-    ----------
-    number_simulated_experiments: int
-        Number of experiments to simulate
-
-    normalized_data_file: str
-        File containing normalized gene expression data
-
-        ------------------------------| PA0001 | PA0002 |...
-        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
-        54375-4-05.CEL                | 0.7789 | 0.7678 |...
-        ...                           | ...    | ...    |...
-
-    NN_architecture: str
-        Name of neural network architecture to use.
-        Format 'NN_<intermediate layer>_<latent layer>'
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings will be stored.
-        Format of the directory name is <dataset_name>_<sample/experiment>_lvl_sim
-
-    experiment_ids_file: str
-        File containing all cleaned experiment ids
-
-    sample_id_colname: str
-        Column header that contains sample id that maps expression data and metadata
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    Returns
-    --------
-    simulated dataframe
-
-    """
-
-    # Files
-    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
-    latent_dim = NN_architecture.split("_")[-1]
-
-    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
-
-    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
-
-    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
-
-    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
-
-    # Load saved models
-    loaded_model = load_model(model_encoder_file)
-    loaded_decode_model = load_model(model_decoder_file)
-
-    loaded_model.load_weights(weights_encoder_file)
-    loaded_decode_model.load_weights(weights_decoder_file)
-
-    # Read data
-    experiment_ids = pd.read_csv(experiment_ids_file, header=0, sep="\t", index_col=0)
-
-    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
-
-    print(
-        "Normalized gene expression data contains {} samples and {} genes".format(
-            normalized_data.shape[0], normalized_data.shape[1]
-        )
-    )
-
-    # Simulate data
-
-    simulated_data_df = pd.DataFrame()
-
-    for i in range(num_simulated_experiments):
-
-        selected_experiment_id = np.random.choice(
-            experiment_ids["experiment_id"], size=1
-        )[0]
-
-        # Get corresponding sample ids
-        sample_ids = get_sample_ids(
-            selected_experiment_id, dataset_name, sample_id_colname
-        )
-
-        # Remove any missing sample ids
-        sample_ids = list(filter(str.strip, sample_ids))
-
-        # Remove any sample_ids that are not found in gene expression data
-        # There are some experiments where most samples have gene expression but a
-        # few do not
-        sample_ids = [
-            sample for sample in sample_ids if sample in normalized_data.index
-        ]
-
-        # Gene expression data for selected samples
-        selected_data_df = normalized_data.loc[sample_ids]
-
-        # Encode selected experiment into latent space
-        data_encoded = loaded_model.predict_on_batch(selected_data_df)
-        data_encoded_df = pd.DataFrame(data_encoded, index=selected_data_df.index)
-
-        # Get centroid of original data
-        centroid = data_encoded_df.mean(axis=0)
-
-        # Add individual vectors(centroid, sample point) to new_centroid
-
-        # Encode original gene expression data into latent space
-        data_encoded_all = loaded_model.predict_on_batch(normalized_data)
-        data_encoded_all_df = pd.DataFrame(
-            data_encoded_all, index=normalized_data.index
-        )
-
-        data_encoded_all_df.head()
-
-        # Find a new location in the latent space by sampling from the latent space
-        encoded_means = data_encoded_all_df.mean(axis=0)
-        encoded_stds = data_encoded_all_df.std(axis=0)
-
-        latent_dim = int(latent_dim)
-        new_centroid = np.zeros(latent_dim)
-
-        for j in range(latent_dim):
-            new_centroid[j] = np.random.normal(encoded_means[j], encoded_stds[j])
-
-        shift_vec_df = new_centroid - centroid
-
-        simulated_data_encoded_df = data_encoded_df.apply(
-            lambda x: x + shift_vec_df, axis=1
-        )
-
-        # Decode simulated data into raw gene space
-        simulated_data_decoded = loaded_decode_model.predict_on_batch(
-            simulated_data_encoded_df
-        )
-
-        simulated_data_decoded_df = pd.DataFrame(
-            simulated_data_decoded,
-            index=simulated_data_encoded_df.index,
-            columns=selected_data_df.columns,
-        )
-
-        # Add experiment label
-        simulated_data_decoded_df["experiment_id"] = (
-            selected_experiment_id + "_" + str(i)
-        )
-
-        # Concatenate dataframe per experiment together
-        simulated_data_df = pd.concat([simulated_data_df, simulated_data_decoded_df])
-
-    # re-normalize per gene 0-1
-    simulated_data_numeric_df = simulated_data_df.drop(
-        columns=["experiment_id"], inplace=False
-    )
-
-    simulated_data_scaled = preprocessing.MinMaxScaler().fit_transform(
-        simulated_data_numeric_df
-    )
-
-    simulated_data_scaled_df = pd.DataFrame(
-        simulated_data_scaled,
-        columns=simulated_data_numeric_df.columns,
-        index=simulated_data_numeric_df.index,
-    )
-
-    simulated_data_scaled_df["experiment_id"] = simulated_data_df["experiment_id"]
-
-    # If sampling with replacement, then there will be multiple sample ids that are the same
-    # therefore we want to reset the index.
-    simulated_data_scaled_df.reset_index(drop=True, inplace=True)
-
-    print(
-        "Return: simulated gene expression data containing {} samples and {} genes".format(
-            simulated_data_scaled_df.shape[0], simulated_data_scaled_df.shape[1]
-        )
-    )
-
-    return simulated_data_scaled_df
-
-
-def simulate_data(
-    normalized_data_file,
-    NN_architecture,
-    dataset_name,
-    analysis_name,
-    num_simulated_samples,
-    local_dir,
-    base_dir,
-):
-    """
-    Generate simulated data by randomly sampling from VAE latent space.
-
-    Workflow:
-    1. Input gene expression data the entire compendium from the <normalized_data_file>
-    2. Encode this input into a latent space using the trained VAE model
-    3. Randomly sample <num_simulated_samples> samples from the latent space.
-        For each encoded feature, sample from a distribution using the
-        the mean and standard deviation for that feature
-    4. Decode the samples
-
-    This compendium is generated by randomly sampling samples from the
-    latent space distribution of the compendium.  All samples are treated equal, where
-    association with a specific experiment is ignored.
-
-
-    Arguments
-    ----------
-    normalized_data_file: str
-        File containing normalized gene expression data
-
-        ------------------------------| PA0001 | PA0002 |...
-        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
-        54375-4-05.CEL                | 0.7789 | 0.7678 |...
-        ...                           | ...    | ...    |...
-
-    NN_architecture: str
-        Name of neural network architecture to use.
-        Format 'NN_<intermediate layer>_<latent layer>'
-
-    dataset_name: str
-        Name of analysis directory, Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings will be stored.
-        Format of the directory name is <dataset_name>_<sample/experiment>_lvl_sim
-
-    number_simulated_samples: int
-        Number of samples to simulate
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    Returns
-    --------
-    simulated dataframe
-
-    """
-
-    # Files
-    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
-    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
-
-    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
-
-    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
-
-    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
-
-    # Load saved models
-    loaded_model = load_model(model_encoder_file)
-    loaded_decode_model = load_model(model_decoder_file)
-
-    loaded_model.load_weights(weights_encoder_file)
-    loaded_decode_model.load_weights(weights_decoder_file)
-
-    # Read data
-    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
-
-    print(
-        "Normalized gene expression data contains {} samples and {} genes".format(
-            normalized_data.shape[0], normalized_data.shape[1]
-        )
-    )
-
-    # Simulate data
-
-    # Encode into latent space
-    data_encoded = loaded_model.predict_on_batch(normalized_data)
-    data_encoded_df = pd.DataFrame(data_encoded, index=normalized_data.index)
-
-    latent_dim = data_encoded_df.shape[1]
-
-    # Get mean and standard deviation per encoded feature
-    encoded_means = data_encoded_df.mean(axis=0)
-    encoded_stds = data_encoded_df.std(axis=0)
-
-    # Generate samples
-    new_data = np.zeros([num_simulated_samples, latent_dim])
-    for j in range(latent_dim):
-        # Use mean and std for feature
-        new_data[:, j] = np.random.normal(
-            encoded_means[j], encoded_stds[j], num_simulated_samples
-        )
-
-        # Use standard normal
-        # new_data[:,j] = np.random.normal(0, 1, num_simulated_samples)
-
-    new_data_df = pd.DataFrame(data=new_data)
-
-    # Decode samples
-    new_data_decoded = loaded_decode_model.predict_on_batch(new_data_df)
-    simulated_data = pd.DataFrame(data=new_data_decoded)
-
-    print(
-        "Return: simulated gene expression data containing {} samples and {} genes".format(
-            simulated_data.shape[0], simulated_data.shape[1]
-        )
-    )
-
-    return simulated_data
-
-
-def permute_data(simulated_data):
-    """
-    Permute the simulated data
-
-    Arguments
-    ----------
-    simulated_data: df
-        Dataframe containing simulated gene expression data
-
-    Returns
-    --------
-    permuted simulated dataframe. This data will be used as a
-    negative control in similarity analysis.
-    """
-
-    if "experiment_id" in list(simulated_data.columns):
-        simulated_data_tmp = simulated_data.drop(columns="experiment_id", inplace=False)
-    else:
-        simulated_data_tmp = simulated_data.copy()
-
-    # Shuffle values within each sample (row)
-    # Each sample treated independently
-    shuffled_simulated_arr = []
-    num_samples = simulated_data.shape[0]
-
-    for i in range(num_samples):
-        row = list(simulated_data_tmp.values[i])
-        shuffled_simulated_row = random.sample(row, len(row))
-        shuffled_simulated_arr.append(shuffled_simulated_row)
-
-    shuffled_simulated_data = pd.DataFrame(
-        shuffled_simulated_arr,
-        index=simulated_data_tmp.index,
-        columns=simulated_data_tmp.columns,
-    )
-
-    return shuffled_simulated_data
-
-
-def add_experiments_io(
-    simulated_data, num_experiments, run, local_dir, dataset_name, analysis_name
-):
-    """
-    Say we are interested in identifying genes that differentiate between
-    disease vs normal states. However our dataset includes samples from
-    different labs or protocols and there are variations
-    in gene expression that are due to these other conditions
-    that do not have to do with disease state.
-
-    These non-relevant variations in the data are called technical variations
-    that we want to model.  To model technical variation in our simulated data
-    we will do the following:
-
-    1. Partition our simulated data into <num_experiments>
-    2. For each partition we will shift all genes using a vector of values
-    sampled from a gaussian distribution centered around 0. This noise represents
-    noise shared acoss the samples in the partition.
-    3. Repeat this for each partition
-    4. Append all shifted partitions together
-
-    Arguments
-    ----------
-    simulated_data: df
-        Dataframe containing simulated gene expression data
-
-    num_experiments: list
-        List of different numbers of experiments to add to
-        simulated data
-
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    dataset_name: str
-        Name of analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings are be stored.
-        Format of the directory name is <dataset>_<sample/experiment>_lvl_sim
-
-    Output
-    --------
-    Files of simulated data with different numbers of experiments added are save to file.
-    Each file is named as "Experiment_<number of experiments added>"
-    """
-    analysis_dir = os.path.join(
-        local_dir, "experiment_simulated", dataset_name + "_" + analysis_name
-    )
-
-    if not os.path.exists(analysis_dir):
-        print("Creating new directory: \n {}".format(analysis_dir))
-        os.makedirs(analysis_dir, exist_ok=True)
-
-    # Add batch effects
-    num_genes = simulated_data.shape[1]
-
-    # Create an array of the simulated data indices
-    simulated_ind = np.array(simulated_data.index)
-
-    for i in num_experiments:
-        print("Creating simulated data with {} experiments..".format(i))
-
-        experiment_file = os.path.join(
-            analysis_dir, "Experiment_" + str(i) + "_" + str(run) + ".txt.xz"
-        )
-
-        experiment_map_file = os.path.join(
-            analysis_dir, "Experiment_map_" + str(i) + "_" + str(run) + ".txt.xz"
-        )
-
-        # Create dataframe with grouping
-        experiment_data_map = simulated_data.copy()
-
-        if i == 1:
-            simulated_data.to_csv(experiment_file, sep="\t", compression="xz")
-
-            # Add experiment id to map dataframe
-            experiment_data_map["experiment"] = str(i)
-            experiment_data_map_df = pd.DataFrame(
-                data=experiment_data_map["experiment"], index=simulated_ind.sort()
-            )
-
-            experiment_data_map_df.to_csv(
-                experiment_map_file, sep="\t", compression="xz"
-            )
-
-        else:
-            experiment_data = simulated_data.copy()
-
-            # Shuffle indices
-            np.random.shuffle(simulated_ind)
-
-            # Partition indices to batch
-            # Note: 'array_split' will chunk data into almost equal sized chunks.
-            # Returns arrays of size N % i and one array with the remainder
-            partition = np.array_split(simulated_ind, i)
-
-            for j in range(i):
-                # Scalar to shift gene expressiond data
-                stretch_factor = np.random.normal(0.0, 0.2, [1, num_genes])
-
-                # Tile stretch_factor to be able to add to batches
-                num_samples_per_experiment = len(partition[j])
-                stretch_factor_tile = pd.DataFrame(
-                    pd.np.tile(stretch_factor, (num_samples_per_experiment, 1)),
-                    index=experiment_data.loc[partition[j].tolist()].index,
-                    columns=experiment_data.loc[partition[j].tolist()].columns,
-                )
-
-                # Add experiments
-                experiment_data.loc[partition[j].tolist()] = (
-                    experiment_data.loc[partition[j].tolist()] + stretch_factor_tile
-                )
-
-                # Add experiment id to map dataframe
-                experiment_data_map.loc[partition[j], "experiment"] = str(j)
-
-            experiment_data_map_df = pd.DataFrame(
-                data=experiment_data_map["experiment"], index=simulated_ind.sort()
-            )
-
-            # Save
-            experiment_data.to_csv(
-                experiment_file, float_format="%.3f", sep="\t", compression="xz"
-            )
-
-            experiment_data_map_df.to_csv(
-                experiment_map_file, sep="\t", compression="xz"
-            )
-
-
-def add_experiments_grped_io(
-    simulated_data, num_partitions, run, local_dir, dataset_name, analysis_name
-):
-    """
-    Similar to `add_experiments_io` we will model technical variation in our
-    simulated data. In this case, we will keep track of which samples
-    are associated with an experiment.
-
-    To do this we will:
-    1. Partition our simulated data into <num_partitions>
-        Here we are keeping track of experiment id and partitioning
-        such that all samples from an experiment are in the same
-        partition.
-
-        Note: Partition sizes will be different since experiment
-        sizes are different per experiment.
-    2. For each partition we will shift all genes using a vector of values
-    sampled from a gaussian distribution centered around 0.
-    3. Repeat this for each partition
-    4. Append all partitions together
-
-    This function will return the files with compendia with different numbers
-    of technical variation added with one file per compendia.
-
-    Arguments
-    ----------
-    simulated_data_file: str
-        File containing simulated gene expression data
-
-    num_partitions: list
-        List of different numbers of partitions to add
-        technical variations to
-
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    dataset_name: str
-        Name of analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings are be stored.
-        Format of the directory name is <dataset>_<sample/experiment>_lvl_sim
-
-
-    Output
-    --------
-    Files of simulated data with different numbers of experiments added are saved to file.
-    Each file is named as "Experiment_<number of experiments added>"
-    """
-
-    analysis_dir = os.path.join(
-        local_dir, "partition_simulated", dataset_name + "_" + analysis_name
-    )
-
-    if not os.path.exists(analysis_dir):
-        print("Creating new directory: \n {}".format(analysis_dir))
-        os.makedirs(analysis_dir, exist_ok=True)
-
-    # Add batch effects
-    num_genes = simulated_data.shape[1] - 1
-
-    # Create an array of the simulated data indices
-    simulated_ind = np.array(simulated_data.index)
-
-    for i in num_partitions:
-        print("Creating simulated data with {} partitions..".format(i))
-
-        partition_file = os.path.join(
-            analysis_dir, "Partition_" + str(i) + "_" + str(run) + ".txt.xz"
-        )
-
-        partition_map_file = os.path.join(
-            analysis_dir, "Partition_map_" + str(i) + "_" + str(run) + ".txt.xz"
-        )
-
-        # Create dataframe with grouping
-        partition_data_map = simulated_data.copy()
-
-        if i == 1:
-            simulated_data_out = simulated_data.drop(columns="experiment_id")
-            simulated_data_out.to_csv(partition_file, sep="\t", compression="xz")
-
-            # Add experiment id to map dataframe
-            partition_data_map["partition"] = str(i)
-
-            partition_data_map_df = pd.DataFrame(
-                data=partition_data_map["partition"], index=simulated_ind.sort()
-            )
-
-            partition_data_map_df.to_csv(partition_map_file, sep="\t", compression="xz")
-
-        else:
-            partition_data = simulated_data.copy()
-
-            # Shuffle experiment ids
-            experiment_ids = simulated_data["experiment_id"].unique()
-            np.random.shuffle(experiment_ids)
-
-            # Partition experiment ids
-            # Note: 'array_split' will chunk data into almost equal sized chunks.
-            # Returns arrays of size N % i and one array with the remainder
-            partition = np.array_split(experiment_ids, i)
-
-            for j in range(i):
-                # Randomly select experiment ids
-                # selected_experiment_ids = partition[j]
-
-                # Get sample ids associated with experiment ids
-                sample_ids = list(
-                    simulated_data[
-                        simulated_data["experiment_id"].isin(partition[j])
-                    ].index
-                )
-
-                # Scalar to shift gene expressiond data
-                stretch_factor = np.random.normal(0.0, 0.2, [1, num_genes])
-
-                # Tile stretch_factor to be able to add to batches
-                num_samples_per_partition = len(sample_ids)
-
-                if j == 0:
-                    # Drop experiment_id label to do calculation
-                    partition_data.drop(columns="experiment_id", inplace=True)
-
-                stretch_factor_tile = pd.DataFrame(
-                    pd.np.tile(stretch_factor, (num_samples_per_partition, 1)),
-                    index=partition_data.loc[sample_ids].index,
-                    columns=partition_data.loc[sample_ids].columns,
-                )
-
-                # Add noise to partition
-                partition_data.loc[sample_ids] = (
-                    partition_data.loc[sample_ids] + stretch_factor_tile
-                )
-
-                # Add partition id to map dataframe
-                partition_data_map.loc[sample_ids, "partition"] = str(j)
-
-            partition_data_map_df = pd.DataFrame(
-                data=partition_data_map["partition"], index=simulated_ind.sort()
-            )
-
-            # Save
-            partition_data.to_csv(
-                partition_file, float_format="%.3f", sep="\t", compression="xz"
-            )
-
-            partition_data_map_df.to_csv(partition_map_file, sep="\t", compression="xz")
-
-
-def apply_correction_io(
-    local_dir, run, dataset_name, analysis_name, num_experiments, correction_method
-):
-    """
-    This function uses the limma or sva R package to correct for the technical variation
-    we added using <add_experiments_io> or <add_experiments_grped_io>
-
-    This function will return the corrected gene expression files
-
-    Arguments
-    ----------
-    local_dir: str
-        Root directory where simulated data with experiments/partitionings are be stored
-
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    dataset_name:
-        Name of analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings are be stored.
-        Format of the directory name is <dataset_name>_<sample/experiment>_lvl_sim
-
-    num_experiments: list
-        List of different numbers of experiments/partitions to add
-        technical variations to
-
-    correction_method: str
-        Noise correction method. Either "limma" or "combat"
-
-
-    Returns
-    --------
-    Files of simulated data with different numbers of experiments added and corrected are saved to file.
-    Each file is named as "Experiment_<number of experiments added>".
-    Note: After the data is corrected, the dimensions are now gene x sample
-    """
-
-    for i in range(len(num_experiments)):
-
-        if "sample" in analysis_name:
-            print("Correcting for {} experiments..".format(num_experiments[i]))
-
-            experiment_file = os.path.join(
-                local_dir,
-                "experiment_simulated",
-                dataset_name + "_" + analysis_name,
-                "Experiment_" + str(num_experiments[i]) + "_" + str(run) + ".txt.xz",
-            )
-
-            experiment_map_file = os.path.join(
-                local_dir,
-                "experiment_simulated",
-                dataset_name + "_" + analysis_name,
-                f"Experiment_map_{num_experiments[i]}_{run}.txt.xz",
-            )
-
-            # Read in data
-            # data transposed to form gene x sample for R package
-            experiment_data = pd.read_csv(
-                experiment_file, header=0, index_col=0, sep="\t"
-            ).T
-
-            experiment_map = pd.read_csv(
-                experiment_map_file, header=0, index_col=0, sep="\t"
-            )["experiment"]
-        else:
-            print("Correcting for {} Partition..".format(num_experiments[i]))
-
-            experiment_file = os.path.join(
-                local_dir,
-                "partition_simulated",
-                dataset_name + "_" + analysis_name,
-                "Partition_" + str(num_experiments[i]) + "_" + str(run) + ".txt.xz",
-            )
-
-            experiment_map_file = os.path.join(
-                local_dir,
-                "partition_simulated",
-                dataset_name + "_" + analysis_name,
-                "Partition_map_" + str(num_experiments[i]) + "_" + str(run) + ".txt.xz",
-            )
-
-            # Read in data
-            # data transposed to form gene x sample for R package
-            experiment_data = pd.read_csv(
-                experiment_file, header=0, index_col=0, sep="\t"
-            ).T
-
-            experiment_map = pd.read_csv(
-                experiment_map_file, header=0, index_col=0, sep="\t"
-            )["partition"]
-
-        if i == 0:
-            corrected_experiment_data_df = experiment_data.copy()
-
-        else:
-            # Correct for technical variation
-            if correction_method == "limma":
-                corrected_experiment_data = limma.removeBatchEffect(
-                    experiment_data, batch=experiment_map
-                )
-
-                # Convert R object to pandas df
-                # corrected_experiment_data_df = pandas2ri.ri2py_dataframe(
-                #    corrected_experiment_data)
-                corrected_experiment_data_df = pd.DataFrame(corrected_experiment_data)
-
-            if correction_method == "combat":
-                corrected_experiment_data = sva.ComBat(
-                    np.array(experiment_data), batch=experiment_map
-                )
-
-                # Convert R object to pandas df
-                # corrected_experiment_data_df = pandas2ri.ri2py_dataframe(
-                #    corrected_experiment_data
-                # )
-                corrected_experiment_data_df = pd.DataFrame(corrected_experiment_data)
-
-                corrected_experiment_data_df.columns = experiment_data.columns
-
-        if "sample" in analysis_name:
-            # Write out corrected files
-            experiment_corrected_file = os.path.join(
-                local_dir,
-                "experiment_simulated",
-                dataset_name + "_" + analysis_name,
-                f"Experiment_corrected_{num_experiments[i]}_{run}.txt.xz",
-            )
-
-            corrected_experiment_data_df.to_csv(
-                experiment_corrected_file,
-                float_format="%.3f",
-                sep="\t",
-                compression="xz",
-            )
-
-        else:
-            # Write out corrected files
-            experiment_corrected_file = os.path.join(
-                local_dir,
-                "partition_simulated",
-                dataset_name + "_" + analysis_name,
-                f"Partition_corrected_{num_experiments[i]}_{run}.txt.xz",
-            )
-
-            corrected_experiment_data_df.to_csv(
-                experiment_corrected_file,
-                float_format="%.3f",
-                sep="\t",
-                compression="xz",
-            )
diff --git a/ponyo/generate_labeled_data.py b/ponyo/generate_labeled_data.py
deleted file mode 100644
index cec65b7..0000000
--- a/ponyo/generate_labeled_data.py
+++ /dev/null
@@ -1,271 +0,0 @@
-"""
-Author: Alexandra Lee
-Date Created: 30 August 2019
-
-Scripts to generate gene expression compendia using experiment-preserving
-approach with added experiment id labels for visualizations.
-These scripts are used to generate visualizations that validate this
-experiment-preserving simulation approach.
-"""
-
-from numpy.random import seed
-import os
-import pandas as pd
-import numpy as np
-import glob
-from keras.models import load_model
-from sklearn import preprocessing
-import warnings
-
-
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-
-random_state = 123
-
-
-def get_sample_ids(experiment_id, dataset_name):
-    """
-    Return sample ids for a given experiment id
-
-    """
-    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
-
-    if "pseudomonas" in dataset_name.lower():
-        # metadata file
-        mapping_file = os.path.join(
-            base_dir, dataset_name, "data", "metadata", "sample_annotations.tsv"
-        )
-
-        # Read in metadata
-        metadata = pd.read_csv(mapping_file, header=0, sep="\t", index_col=0)
-
-        selected_metadata = metadata.loc[experiment_id]
-        sample_ids = list(selected_metadata["ml_data_source"])
-
-    else:
-        # metadata file
-        mapping_file = os.path.join(
-            base_dir, dataset_name, "data", "metadata", "recount2_metadata.tsv"
-        )
-
-        # Read in metadata
-        metadata = pd.read_csv(mapping_file, header=0, sep="\t", index_col=0)
-
-        selected_metadata = metadata.loc[experiment_id]
-        sample_ids = list(selected_metadata["run"])
-
-    return sample_ids
-
-
-def simulate_compendium_labeled(
-    experiment_ids_file,
-    num_simulated_experiments,
-    normalized_data_file,
-    NN_architecture,
-    dataset_name,
-    local_dir,
-    base_dir,
-):
-    """
-    Generate simulated data using a list of experiment_ids found in
-    experiment_ids_file as templates. The compendia will contain the
-    shifted experiments using the experiment_ids as templates and following
-    the same workflow as simulate_compendia in generate_data_parallel.py
-
-    This function adds a column to label each sample with the experiment_id that
-    they originated from. This will be used for plotting.
-
-    Arguments
-    ----------
-    experiment_ids_file: str
-        File containing all cleaned experiment ids
-
-    number_simulated_experiments: int
-        Number of experiments to simulate
-
-    normalized_data_file: str
-        File containing normalized gene expression data
-
-        ------------------------------| PA0001 | PA0002 |...
-        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
-        54375-4-05.CEL                | 0.7789 | 0.7678 |...
-        ...                           | ...    | ...    |...
-
-    NN_architecture: str
-        Name of neural network architecture to use.
-        Format 'NN_<intermediate layer>_<latent layer>'
-
-    dataset_name:
-        Name of analysis directory. Either "Human" or "Pseudomonas"
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    Returns
-    --------
-    simulated_data_file: str
-        File containing simulated gene expression data
-
-    """
-    seed(random_state)
-
-    # Files
-    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
-    latent_dim = NN_architecture.split("_")[-1]
-
-    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
-
-    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
-
-    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
-
-    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
-
-    # Load saved models
-    loaded_model = load_model(model_encoder_file)
-    loaded_decode_model = load_model(model_decoder_file)
-
-    loaded_model.load_weights(weights_encoder_file)
-    loaded_decode_model.load_weights(weights_decoder_file)
-
-    # Read data
-    experiment_ids = pd.read_csv(experiment_ids_file, header=0, sep="\t", index_col=0)
-
-    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
-
-    print(
-        "Normalized gene expression data contains {} samples and {} genes".format(
-            normalized_data.shape[0], normalized_data.shape[1]
-        )
-    )
-
-    # Simulate data
-
-    simulated_data_df = pd.DataFrame()
-
-    for i in range(num_simulated_experiments):
-
-        selected_experiment_id = np.random.choice(
-            experiment_ids["experiment_id"], size=1
-        )[0]
-
-        # Get corresponding sample ids
-        sample_ids = get_sample_ids(selected_experiment_id, dataset_name)
-
-        # Remove any missing sample ids
-        sample_ids = list(filter(str.strip, sample_ids))
-
-        # Remove any sample_ids that are not found in gene expression data
-        # There are some experiments where most samples have gene expression but a
-        # few do not
-        sample_ids = [
-            sample for sample in sample_ids if sample in normalized_data.index
-        ]
-
-        # Gene expression data for selected samples
-        selected_data_df = normalized_data.loc[sample_ids]
-
-        # Encode selected experiment into latent space
-        data_encoded = loaded_model.predict_on_batch(selected_data_df)
-        data_encoded_df = pd.DataFrame(data_encoded, index=selected_data_df.index)
-
-        # Get centroid of original data
-        centroid = data_encoded_df.mean(axis=0)
-
-        # Add individual vectors(centroid, sample point) to new_centroid
-
-        # Encode original gene expression data into latent space
-        data_encoded_all = loaded_model.predict_on_batch(normalized_data)
-        data_encoded_all_df = pd.DataFrame(
-            data_encoded_all, index=normalized_data.index
-        )
-
-        data_encoded_all_df.head()
-
-        # Find a new location in the latent space by sampling from the latent space
-        encoded_means = data_encoded_all_df.mean(axis=0)
-        encoded_stds = data_encoded_all_df.std(axis=0)
-
-        latent_dim = int(latent_dim)
-        new_centroid = np.zeros(latent_dim)
-
-        for j in range(latent_dim):
-            new_centroid[j] = np.random.normal(encoded_means[j], encoded_stds[j])
-
-        shift_vec_df = new_centroid - centroid
-
-        simulated_data_encoded_df = data_encoded_df.apply(
-            lambda x: x + shift_vec_df, axis=1
-        )
-
-        # Decode simulated data into raw gene space
-        simulated_data_decoded = loaded_decode_model.predict_on_batch(
-            simulated_data_encoded_df
-        )
-
-        simulated_data_decoded_df = pd.DataFrame(
-            simulated_data_decoded,
-            index=simulated_data_encoded_df.index,
-            columns=selected_data_df.columns,
-        )
-
-        # Add experiment label
-        simulated_data_decoded_df["experiment_id"] = (
-            selected_experiment_id + "_" + str(i)
-        )
-
-        # Concatenate dataframe per experiment together
-        simulated_data_df = pd.concat([simulated_data_df, simulated_data_decoded_df])
-
-    # re-normalize per gene 0-1
-    simulated_data_numeric_df = simulated_data_df.drop(
-        columns=["experiment_id"], inplace=False
-    )
-
-    simulated_data_scaled = preprocessing.MinMaxScaler().fit_transform(
-        simulated_data_numeric_df
-    )
-
-    simulated_data_scaled_df = pd.DataFrame(
-        simulated_data_scaled,
-        columns=simulated_data_numeric_df.columns,
-        index=simulated_data_numeric_df.index,
-    )
-
-    simulated_data_scaled_df["experiment_id"] = simulated_data_df["experiment_id"]
-
-    # If sampling with replacement, then there will be multiple sample ids that are the same
-    # therefore we want to reset the index.
-    simulated_data_scaled_df.reset_index(drop=True, inplace=True)
-
-    # Remove expression data for samples that have duplicate sample id across
-    # different experiment ids
-    # We remove these because we are not sure which experiment the sample should
-    # belong to
-    # simulated_data_scaled_df = simulated_data_scaled_df.loc[~simulated_data_scaled_df.index.duplicated(
-    #    keep=False)]
-
-    print(
-        "Return: simulated gene expression data containing {} samples and {} genes".format(
-            simulated_data_scaled_df.shape[0], simulated_data_scaled_df.shape[1]
-        )
-    )
-
-    # Save
-    simulated_data_file = os.path.join(
-        local_dir, "pseudo_experiment", "simulated_data_labeled.txt.xz"
-    )
-
-    simulated_data_scaled_df.to_csv(
-        simulated_data_file, float_format="%.3f", sep="\t", compression="xz"
-    )
diff --git a/ponyo/generate_template_data.py b/ponyo/generate_template_data.py
deleted file mode 100644
index 5cc201d..0000000
--- a/ponyo/generate_template_data.py
+++ /dev/null
@@ -1,188 +0,0 @@
-"""
-Author: Alexandra Lee
-Date Created: 15 June 2020
-
-Script to generate gene expression compendia using experiment-preserving
-approach using a specific template experiment.
-This script is used to generate a null set of gene expression data with
-some context, defined by the template experiment.
-"""
-
-import os
-import pandas as pd
-import numpy as np
-import glob
-import random
-from keras.models import load_model
-
-from ponyo import generate_labeled_data
-
-import warnings
-
-
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-
-random.seed(123)
-
-
-def shift_template_experiment(
-    normalized_data_file,
-    selected_experiment_id,
-    NN_architecture,
-    dataset_name,
-    scaler,
-    local_dir,
-    base_dir,
-    run,
-):
-    """
-    Generate simulated data using the selected_experiment_id as a template
-    experiment using the same workflow as simulate_compendia in generate_data_parallel.py
-
-    This will return a file with a single simulated experiment following the workflow mentioned.
-    This function can be run multiple times to generate multiple simulated experiments from a
-    single selected_experiment_id.
-
-    Arguments
-    ----------
-    normalized_data_file: str
-        File containing normalized gene expression data
-
-        ------------------------------| PA0001 | PA0002 |...
-        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
-        54375-4-05.CEL                | 0.7789 | 0.7678 |...
-        ...                           | ...    | ...    |...
-
-    selected_experiment_id: str
-        Experiment id selected as template
-
-    NN_architecture: str
-        Name of neural network architecture to use.
-        Format 'NN_<intermediate layer>_<latent layer>'
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    scaler: minmax model
-        Model used to transform data into a different range
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    run: int
-        Simulation run
-
-    Returns
-    --------
-    simulated_data_file: str
-        File containing simulated gene expression data
-
-    """
-
-    # Files
-    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
-    latent_dim = NN_architecture.split("_")[-1]
-
-    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
-
-    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
-
-    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
-
-    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
-
-    # Load saved models
-    loaded_model = load_model(model_encoder_file, compile=False)
-    loaded_decode_model = load_model(model_decoder_file, compile=False)
-
-    loaded_model.load_weights(weights_encoder_file)
-    loaded_decode_model.load_weights(weights_decoder_file)
-
-    # Read data
-    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
-
-    # Get corresponding sample ids
-    sample_ids = generate_labeled_data.get_sample_ids(
-        selected_experiment_id, dataset_name
-    )
-
-    # Gene expression data for selected samples
-    selected_data_df = normalized_data.loc[sample_ids]
-
-    # Encode selected experiment into latent space
-    data_encoded = loaded_model.predict_on_batch(selected_data_df)
-    data_encoded_df = pd.DataFrame(data_encoded, index=selected_data_df.index)
-
-    # Get centroid of original data
-    centroid = data_encoded_df.mean(axis=0)
-
-    # Add individual vectors(centroid, sample point) to new_centroid
-
-    # Encode original gene expression data into latent space
-    data_encoded_all = loaded_model.predict_on_batch(normalized_data)
-    data_encoded_all_df = pd.DataFrame(data_encoded_all, index=normalized_data.index)
-
-    data_encoded_all_df.head()
-
-    # Find a new location in the latent space by sampling from the latent space
-    encoded_means = data_encoded_all_df.mean(axis=0)
-    encoded_stds = data_encoded_all_df.std(axis=0)
-
-    latent_dim = int(latent_dim)
-    new_centroid = np.zeros(latent_dim)
-
-    for j in range(latent_dim):
-        new_centroid[j] = np.random.normal(encoded_means[j], encoded_stds[j])
-
-    shift_vec_df = new_centroid - centroid
-    # print(shift_vec_df)
-
-    simulated_data_encoded_df = data_encoded_df.apply(
-        lambda x: x + shift_vec_df, axis=1
-    )
-
-    # Decode simulated data into raw gene space
-    simulated_data_decoded = loaded_decode_model.predict_on_batch(
-        simulated_data_encoded_df
-    )
-
-    simulated_data_decoded_df = pd.DataFrame(
-        simulated_data_decoded,
-        index=simulated_data_encoded_df.index,
-        columns=selected_data_df.columns,
-    )
-
-    simulated_data_scaled = scaler.inverse_transform(simulated_data_decoded_df)
-
-    simulated_data_scaled_df = pd.DataFrame(
-        simulated_data_scaled,
-        columns=simulated_data_decoded_df.columns,
-        index=simulated_data_decoded_df.index,
-    )
-
-    # Save
-    out_file = os.path.join(
-        local_dir,
-        "pseudo_experiment",
-        "selected_simulated_data_" + selected_experiment_id + "_" + str(run) + ".txt",
-    )
-
-    simulated_data_scaled_df.to_csv(out_file, float_format="%.3f", sep="\t")
-
-    out_encoded_file = os.path.join(
-        local_dir,
-        "pseudo_experiment",
-        f"selected_simulated_encoded_data_{selected_experiment_id}_{run}.txt",
-    )
-
-    simulated_data_encoded_df.to_csv(out_encoded_file, float_format="%.3f", sep="\t")
diff --git a/ponyo/helper_vae.py b/ponyo/helper_vae.py
index 5175b17..9e2ecd0 100644
--- a/ponyo/helper_vae.py
+++ b/ponyo/helper_vae.py
@@ -12,7 +12,6 @@
 import os
 import tensorflow as tf
 import numpy as np
-import random as rn
 
 # The below is necessary in Python 3.2.3 onwards to
 # have reproducible behavior for certain hash-based operations.
@@ -32,7 +31,7 @@
 # The below is necessary for starting core Python generated random numbers
 # in a well-defined state.
 
-rn.seed(12345)
+np.random.seed(12345)
 
 # Force TensorFlow to use single thread.
 # Multiple threads are a potential source of
diff --git a/ponyo/pipeline.py b/ponyo/pipeline.py
deleted file mode 100644
index 908f333..0000000
--- a/ponyo/pipeline.py
+++ /dev/null
@@ -1,483 +0,0 @@
-"""
-Author: Alexandra Lee
-Date Created: 11 March 2020
-
-Scripts called by analysis notebooks to run entire the entire analysis pipeline:
-1. setup directories
-2. Process data
-3. Train VAE
-4. Run simulation experiment, described in `simulations.py`
-"""
-
-from ponyo import vae, utils, simulations
-import os
-import pandas as pd
-import numpy as np
-import random
-import math
-from sklearn import preprocessing
-
-from joblib import Parallel, delayed
-
-# import multiprocessing
-
-import warnings
-
-
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-
-random.seed(123)
-
-
-def setup_dir(config_file):
-    """
-    Create directories to store files created by VAE training and
-    simulation analysis
-
-    Arguments
-    ----------
-    config_file: str
-        File containing user defined parameters
-    """
-
-    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
-
-    # Read in config variables
-    params = utils.read_config(config_file)
-
-    # Load parameters
-    local_dir = params["local_dir"]
-    dataset_name = params["dataset_name"]
-    train_architecture = params["NN_architecture"]
-
-    # Create VAE directories
-    output_dirs = [
-        os.path.join(base_dir, dataset_name, "models"),
-        os.path.join(base_dir, dataset_name, "logs"),
-    ]
-
-    # Check if analysis output directory exist otherwise create
-    for each_dir in output_dirs:
-        if not os.path.exists(each_dir):
-            print("creating new directory: {}".format(each_dir))
-            os.makedirs(each_dir, exist_ok=True)
-
-        # Check if NN architecture directory exist otherwise create
-    for each_dir in output_dirs:
-        new_dir = os.path.join(each_dir, train_architecture)
-        if not os.path.exists(new_dir):
-            print("creating new directory: {}".format(new_dir))
-            os.makedirs(new_dir, exist_ok=True)
-
-    # Create results directories
-    output_dirs = [os.path.join(base_dir, dataset_name, "results")]
-
-    # Check if analysis output directory exist otherwise create
-    for each_dir in output_dirs:
-        if not os.path.exists(each_dir):
-            print("creating new directory: {}".format(each_dir))
-            os.makedirs(each_dir, exist_ok=True)
-
-    # Check if 'saved_variables' directory exist otherwise create
-    for each_dir in output_dirs:
-        new_dir = os.path.join(each_dir, "saved_variables")
-
-        if not os.path.exists(new_dir):
-            print("creating new directory: {}".format(new_dir))
-            os.makedirs(new_dir, exist_ok=True)
-
-    # Create local directories to store intermediate files
-    output_dirs = [
-        os.path.join(local_dir, "experiment_simulated"),
-        os.path.join(local_dir, "partition_simulated"),
-    ]
-
-    # Check if analysis output directory exist otherwise create
-    for each_dir in output_dirs:
-        if not os.path.exists(each_dir):
-            print("creating new directory: {}".format(each_dir))
-            os.makedirs(each_dir, exist_ok=True)
-
-
-def transpose_data(data_file, out_file):
-    """
-    Transpose and save expression data so that it is of the form sample x gene
-
-    Arguments
-    ----------
-    data_file: str
-        File containing gene expression
-
-    out_file: str
-        File containing transposed gene expression
-    """
-    # Read data
-    data = pd.read_csv(data_file, header=0, sep="\t", index_col=0)
-
-    data.T.to_csv(out_file, sep="\t", compression="xz")
-
-
-def normalize_expression_data(
-    base_dir, config_file, raw_input_data_file, normalized_data_file
-):
-    """
-    0-1 normalize the expression data.
-
-    Arguments
-    ----------
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    config_file: str
-        File containing user defined parameters
-
-    raw_input_data_file: str
-        File containing raw expression data
-
-    normalize_data_file:
-        Output file containing normalized expression data
-    """
-
-    # Read data
-    data = pd.read_csv(raw_input_data_file, header=0, sep="\t", index_col=0)
-    print(
-        "input: dataset contains {} samples and {} genes".format(
-            data.shape[0], data.shape[1]
-        )
-    )
-
-    # 0-1 normalize per gene
-    data_scaled_df = preprocessing.MinMaxScaler().fit_transform(data)
-    data_scaled_df = pd.DataFrame(
-        data_scaled_df, columns=data.columns, index=data.index
-    )
-
-    print(
-        "Output: normalized dataset contains {} samples and {} genes".format(
-            data_scaled_df.shape[0], data_scaled_df.shape[1]
-        )
-    )
-
-    # Save scaled data
-    data_scaled_df.to_csv(normalized_data_file, sep="\t", compression="xz")
-
-
-def create_experiment_id_file(metadata_file, input_data_file, output_file, config_file):
-    """
-    Create file with experiment ids that are associated with expression data
-
-    Arguments
-    ----------
-    metadata_file: str
-        File containing metadata annotations per sample
-
-    input_data_file: str
-        File containing normalized expression data
-
-    output_file: str
-        File containing experiment ids with expression data and sample annotations
-
-    config_file: str
-        File containing user defined parameters
-
-    """
-    # Read in metadata
-    metadata = pd.read_csv(metadata_file, header=0, sep="\t", index_col=0)
-
-    # Read in expression data
-    normalized_data = pd.read_csv(input_data_file, header=0, sep="\t", index_col=0)
-
-    # Read in config variables
-    params = utils.read_config(config_file)
-
-    # Load parameters
-    sample_id_colname = params["metadata_colname"]
-    dataset_name = params["dataset_name"]
-
-    # Get sample id that maps between metadata and expression files
-    map_experiment_sample = metadata[[sample_id_colname]]
-
-    # Get all unique experiment ids
-    experiment_ids = np.unique(np.array(map_experiment_sample.index)).tolist()
-    print("There are {} experiments in the compendium".format(len(experiment_ids)))
-
-    # Get only sample ids that have expression data available
-    sample_ids_with_gene_expression = list(normalized_data.index)
-
-    # Get associated experiment ids with expression data
-    experiment_ids_with_gene_expression = []
-
-    for experiment_id in experiment_ids:
-
-        if "human" in dataset_name.lower():
-            # Some project id values are descriptions
-            # We will skip these
-            if len(experiment_id) == 9:
-                selected_metadata = metadata.loc[experiment_id]
-                sample_ids = list(selected_metadata[sample_id_colname])
-
-                if any(x in sample_ids_with_gene_expression for x in sample_ids):
-                    experiment_ids_with_gene_expression.append(experiment_id)
-        else:
-            selected_metadata = metadata.loc[experiment_id]
-            sample_ids = list(selected_metadata[sample_id_colname])
-
-            if any(x in sample_ids_with_gene_expression for x in sample_ids):
-                experiment_ids_with_gene_expression.append(experiment_id)
-
-    print(
-        "There are {} experiments with gene expression data".format(
-            len(experiment_ids_with_gene_expression)
-        )
-    )
-
-    # Save file with experiment ids
-    experiment_ids_with_gene_expression_df = pd.DataFrame(
-        experiment_ids_with_gene_expression, columns=["experiment_id"]
-    )
-    experiment_ids_with_gene_expression_df.to_csv(output_file, sep="\t")
-    print(
-        "{} experiment ids saved to file".format(
-            len(experiment_ids_with_gene_expression)
-        )
-    )
-
-
-def train_vae(config_file, input_data_file):
-    """
-    Trains VAE model using parameters set in config file
-
-    Arguments
-    ----------
-    config_file: str
-        File containing user defined parameters
-
-    input_data_file: str
-        File path corresponding to input dataset to use
-
-    """
-
-    # Read in config variables
-    params = utils.read_config(config_file)
-
-    # Load parameters
-    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
-    dataset_name = params["dataset_name"]
-    learning_rate = params["learning_rate"]
-    batch_size = params["batch_size"]
-    epochs = params["epochs"]
-    kappa = params["kappa"]
-    intermediate_dim = params["intermediate_dim"]
-    latent_dim = params["latent_dim"]
-    epsilon_std = params["epsilon_std"]
-    train_architecture = params["NN_architecture"]
-    validation_frac = params["validation_frac"]
-
-    # Read data
-    normalized_data = pd.read_csv(input_data_file, header=0, sep="\t", index_col=0)
-
-    print(
-        "input dataset contains {} samples and {} genes".format(
-            normalized_data.shape[0], normalized_data.shape[1]
-        )
-    )
-
-    # Train (VAE)
-    vae.tybalt_2layer_model(
-        learning_rate,
-        batch_size,
-        epochs,
-        kappa,
-        intermediate_dim,
-        latent_dim,
-        epsilon_std,
-        normalized_data,
-        base_dir,
-        dataset_name,
-        train_architecture,
-        validation_frac,
-    )
-
-
-def run_simulation(config_file, input_data_file, corrected, experiment_ids_file=None):
-    """
-    Runs simulation experiment without applying correction method
-
-    Arguments
-    ----------
-    config_file: str
-        File containing user defined parameters
-
-    input_data_file: str
-        File path corresponding to input dataset to use
-
-    corrected: bool
-        True if simulation is applying noise correction
-
-    experiment_ids_file: str
-        File containing experiment ids with expression data associated generated from ```create_experiment_id_file```
-
-    """
-
-    # Read in config variables
-    params = utils.read_config(config_file)
-
-    # Load parameters
-    dataset_name = params["dataset_name"]
-    simulation_type = params["simulation_type"]
-    NN_architecture = params["NN_architecture"]
-    use_pca = params["use_pca"]
-    num_PCs = params["num_PCs"]
-    local_dir = params["local_dir"]
-    correction_method = params["correction_method"]
-    sample_id_colname = params["metadata_colname"]
-    iterations = params["iterations"]
-    num_cores = params["num_cores"]
-
-    if "sample" in simulation_type:
-        num_simulated_samples = params["num_simulated_samples"]
-        lst_num_experiments = params["lst_num_experiments"]
-    else:
-        num_simulated_experiments = params["num_simulated_experiments"]
-        lst_num_partitions = params["lst_num_partitions"]
-
-    # Output files
-    # base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
-    base_dir = os.path.abspath(os.pardir)
-    if corrected:
-        similarity_uncorrected_file = os.path.join(
-            base_dir,
-            dataset_name,
-            "results",
-            "saved_variables",
-            f"{dataset_name}_{simulation_type}_svcca_corrected_{correction_method}.pickle",
-        )
-
-        ci_uncorrected_file = os.path.join(
-            base_dir,
-            dataset_name,
-            "results",
-            "saved_variables",
-            f"{dataset_name}_{simulation_type}_ci_corrected_{correction_method}.pickle",
-        )
-
-    else:
-        similarity_uncorrected_file = os.path.join(
-            base_dir,
-            dataset_name,
-            "results",
-            "saved_variables",
-            f"{dataset_name}_{simulation_type}_svcca_uncorrected_{correction_method}.pickle",
-        )
-
-        ci_uncorrected_file = os.path.join(
-            base_dir,
-            dataset_name,
-            "results",
-            "saved_variables",
-            f"{dataset_name}_{simulation_type}_ci_uncorrected_{correction_method}.pickle",
-        )
-
-    similarity_permuted_file = os.path.join(
-        base_dir,
-        dataset_name,
-        "results",
-        "saved_variables",
-        dataset_name + "_" + simulation_type + "_permuted",
-    )
-
-    # Run multiple simulations
-    if "sample" in simulation_type:
-        if corrected:
-            file_prefix = "Experiment_corrected"
-        else:
-            file_prefix = "Experiment"
-        results = Parallel(n_jobs=num_cores, verbose=100)(
-            delayed(simulations.sample_level_simulation)(
-                i,
-                NN_architecture,
-                dataset_name,
-                simulation_type,
-                num_simulated_samples,
-                lst_num_experiments,
-                corrected,
-                correction_method,
-                use_pca,
-                num_PCs,
-                file_prefix,
-                input_data_file,
-                local_dir,
-                base_dir,
-            )
-            for i in iterations
-        )
-
-    else:
-        if corrected:
-            file_prefix = "Partition_corrected"
-        else:
-            file_prefix = "Partition"
-        results = Parallel(n_jobs=num_cores, verbose=100)(
-            delayed(simulations.experiment_level_simulation)(
-                i,
-                NN_architecture,
-                dataset_name,
-                simulation_type,
-                num_simulated_experiments,
-                lst_num_partitions,
-                corrected,
-                correction_method,
-                use_pca,
-                num_PCs,
-                file_prefix,
-                input_data_file,
-                experiment_ids_file,
-                sample_id_colname,
-                local_dir,
-                base_dir,
-            )
-            for i in iterations
-        )
-
-    # permuted score
-    permuted_score = results[0][0]
-
-    # Concatenate output dataframes
-    all_svcca_scores = pd.DataFrame()
-
-    for i in iterations:
-        all_svcca_scores = pd.concat([all_svcca_scores, results[i][1]], axis=1)
-
-    # Get mean svcca score for each row (number of experiments)
-    mean_scores = all_svcca_scores.mean(axis=1).to_frame()
-    mean_scores.columns = ["score"]
-    print(mean_scores)
-
-    # Get standard dev for each row (number of experiments)
-    std_scores = (all_svcca_scores.std(axis=1) / math.sqrt(10)).to_frame()
-    std_scores.columns = ["score"]
-    print(std_scores)
-
-    # Get confidence interval for each row (number of experiments)
-    # z-score for 95% confidence interval
-    err = std_scores * 1.96
-
-    # Get boundaries of confidence interval
-    ymax = mean_scores + err
-    ymin = mean_scores - err
-
-    ci = pd.concat([ymin, ymax], axis=1)
-    ci.columns = ["ymin", "ymax"]
-    print(ci)
-
-    # Pickle dataframe of mean scores scores for first run, interval
-    mean_scores.to_pickle(similarity_uncorrected_file)
-    ci.to_pickle(ci_uncorrected_file)
-    np.save(similarity_permuted_file, permuted_score)
diff --git a/ponyo/similarity_metric_parallel.py b/ponyo/similarity_metric_parallel.py
deleted file mode 100644
index 1aa5c25..0000000
--- a/ponyo/similarity_metric_parallel.py
+++ /dev/null
@@ -1,264 +0,0 @@
-"""
-Author: Alexandra Lee
-Date Created: 30 August 2019
-
-Scripts to compare simulated compendium with simulated compendia with noise added.
-"""
-
-from sklearn.decomposition import PCA
-from ponyo import cca_core
-import os
-import pandas as pd
-import numpy as np
-import random
-import warnings
-
-
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-random.seed(123)
-
-
-def read_data(simulated_data, file_prefix, run, local_dir, dataset_name, analysis_name):
-    """
-    Script used by all similarity metrics to:
-
-    1. Read in simulated data into data
-    2. Generate directory where simulated experiment data is already stored
-    3. Read in simulated data with a single experiment/partitioning
-
-    Returns
-    --------
-    simulated_data: dataframe
-        Dataframe containing simulated gene expression data
-
-    file_prefix: str
-        File prefix to determine whether to use data before correction ("Experiment" or "Partition")
-        or after correction ("Experiment_corrected" or "Parition_corrected")
-
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    local_dir: str
-        Root directory where simulated data with experiments/partitionings are be stored
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings are be stored.
-        Format of the directory name is <dataset>_<sample/experiment>_lvl_sim
-
-    """
-
-    if "experiment_id" in list(simulated_data.columns):
-        simulated_data.drop(columns="experiment_id", inplace=True)
-
-        # Compendium directory
-        compendium_dir = os.path.join(
-            local_dir, "partition_simulated", dataset_name + "_" + analysis_name
-        )
-    else:
-        # Compendium directory
-        compendium_dir = os.path.join(
-            local_dir, "experiment_simulated", dataset_name + "_" + analysis_name
-        )
-
-    # Get compendium with 1 experiment or partitioning
-    compendium_1_file = os.path.join(
-        compendium_dir, file_prefix + "_1" + "_" + str(run) + ".txt.xz"
-    )
-
-    compendium_1 = pd.read_csv(compendium_1_file, header=0, index_col=0, sep="\t")
-
-    # Transpose compendium df because output format
-    # for correction method is swapped
-    if file_prefix.split("_")[-1] == "corrected":
-        compendium_1 = compendium_1.T
-
-    return [simulated_data, compendium_dir, compendium_1]
-
-
-def sim_svcca_io(
-    simulated_data,
-    permuted_simulated_data,
-    corrected,
-    file_prefix,
-    run,
-    num_experiments,
-    use_pca,
-    num_PCs,
-    local_dir,
-    dataset_name,
-    analysis_name,
-):
-    """
-    We want to determine if adding multiple simulated experiments is able to capture the
-    biological signal that is present in the original data:
-    How much of the simulated data with a single experiment is captured in the simulated data with multiple experiments?
-
-    In other words, we want to compare the representation of the single simulated experiment and multiple simulated experiments.
-
-    Note: For the representation of the simulated data, users can choose to use:
-    1. All genes
-    2. PCA representation with <num_PCs> dimensions
-
-    We will use **SVCCA** to compare these two representations.
-
-    How does it work?
-    Singular Vector Canonical Correlation Analysis
-    [Raghu et al. 2017](https://arxiv.org/pdf/1706.05806.pdf) [(github)](https://github.com/google/svcca)
-    to the UMAP and PCA representations of our batch 1 simulated dataset vs batch n simulated datasets.
-    The output of the SVCCA analysis is the SVCCA mean similarity score. This single number can be interpreted
-    as a measure of similarity between our original data vs batched dataset.
-
-    Briefly, SVCCA uses Singular Value Decomposition (SVD) to extract the components explaining 99% of the variation.
-    This is done to remove potential dimensions described by noise. Next, SVCCA performs a Canonical Correlation Analysis (CCA)
-     on the SVD matrices to identify maximum correlations of linear combinations of both input matrices.
-     The algorithm will identify the canonical correlations of highest magnitude across and within algorithms of the same dimensionality.
-
-    Arguments
-    ----------
-    simulated_data: df
-        Dataframe containing simulated gene expression data
-
-    permuted_simulated_data: df
-        Dataframe containing permuted simulated gene expression data
-
-    corrected: bool
-        True if correction was applied
-
-    file_prefix: str
-        File prefix to determine whether to use data before correction ("Experiment" or "Partition")
-        or after correction ("Experiment_corrected" or "Parition_corrected")
-
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    num_experiments: list
-        List of different numbers of experiments/partitions that were added to
-        simulated data
-
-    use_pca: bool
-        True if want to represent expression data in top PCs before
-        calculating similarity
-
-    num_PCs: int
-        Number of top PCs to use to represent expression data
-
-    local_dir: str
-        Root directory where simulated data with experiments/partitionings are be stored
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings are be stored.
-        Format of the directory name is <dataset>_<sample/experiment>_lvl_sim
-
-
-    Returns
-    --------
-    output_list: array
-        Similarity scores for each number of experiment/partition added
-
-    permuted_svcca: float
-        Similarity score comparing the permuted data to the simulated data
-
-    """
-
-    [simulated_data, compendium_dir, compendium_1] = read_data(
-        simulated_data, file_prefix, run, local_dir, dataset_name, analysis_name
-    )
-
-    output_list = []
-
-    for i in range(len(num_experiments)):
-        if "sample" in analysis_name:
-            print(
-                "Calculating SVCCA score for 1 experiment vs {} experiments..".format(
-                    num_experiments[i]
-                )
-            )
-        else:
-            print(
-                "Calculating SVCCA score for 1 partition vs {} partitions..".format(
-                    num_experiments[i]
-                )
-            )
-
-        # All experiments/partitions
-        compendium_other_file = os.path.join(
-            compendium_dir,
-            file_prefix + "_" + str(num_experiments[i]) + "_" + str(run) + ".txt.xz",
-        )
-
-        compendium_other = pd.read_csv(
-            compendium_other_file, header=0, index_col=0, sep="\t"
-        )
-
-        # Transpose compendium df because output format
-        # for correction method is swapped
-        if corrected:
-            compendium_other = compendium_other.T
-
-        if use_pca:
-            # PCA projection
-            pca = PCA(n_components=num_PCs)
-
-            original_data_PCAencoded = pca.fit_transform(compendium_1)
-
-            original_data_df = pd.DataFrame(
-                original_data_PCAencoded, index=compendium_1.index
-            )
-            # Use trained model to encode expression data into SAME latent space
-            noisy_original_data_PCAencoded = pca.fit_transform(compendium_other)
-            noisy_original_data_df = pd.DataFrame(
-                noisy_original_data_PCAencoded, index=compendium_other.index
-            )
-        else:
-            # Use trained model to encode expression data into SAME latent space
-            original_data_df = compendium_1
-
-            # Use trained model to encode expression data into SAME latent space
-            noisy_original_data_df = compendium_other
-
-        # SVCCA
-        svcca_results = cca_core.get_cca_similarity(
-            original_data_df.T, noisy_original_data_df.T, verbose=False
-        )
-
-        output_list.append(np.mean(svcca_results["cca_coef1"]))
-
-    # SVCCA of permuted data
-    if use_pca:
-        simulated_data_PCAencoded = pca.fit_transform(simulated_data)
-        simulated_data_PCAencoded_df = pd.DataFrame(
-            simulated_data_PCAencoded, index=simulated_data.index
-        )
-
-        shuffled_data_PCAencoded = pca.fit_transform(permuted_simulated_data)
-        shuffled_data_PCAencoded_df = pd.DataFrame(
-            shuffled_data_PCAencoded, index=permuted_simulated_data.index
-        )
-
-        svcca_results = cca_core.get_cca_similarity(
-            simulated_data_PCAencoded_df.T, shuffled_data_PCAencoded_df.T, verbose=False
-        )
-
-        permuted_svcca = np.mean(svcca_results["cca_coef1"])
-
-    else:
-        svcca_results = cca_core.get_cca_similarity(
-            simulated_data.T, permuted_simulated_data.T, verbose=False
-        )
-
-        permuted_svcca = np.mean(svcca_results["cca_coef1"])
-
-    return output_list, permuted_svcca
diff --git a/ponyo/simulate_expression_data.py b/ponyo/simulate_expression_data.py
new file mode 100644
index 0000000..892b5a3
--- /dev/null
+++ b/ponyo/simulate_expression_data.py
@@ -0,0 +1,586 @@
+"""
+Author: Alexandra Lee
+Date Created: 30 August 2019
+
+These scripts generate simulated compendia using the low-dimensional
+representation of the gene expressiond data, created by training the
+VAE on gene expression data.
+"""
+
+import os
+import pandas as pd
+import numpy as np
+import glob
+import warnings
+from keras.models import load_model
+from sklearn import preprocessing
+
+
+def fxn():
+    warnings.warn("deprecated", DeprecationWarning)
+
+
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    fxn()
+
+np.random.seed(123)
+
+
+def get_sample_ids(experiment_id, dataset_name, sample_id_colname):
+    """
+    Returns sample ids (found in gene expression df) associated with
+    a given list of experiment ids (found in the metadata)
+
+    Arguments
+    ----------
+    experiment_ids_file: str
+        File containing all cleaned experiment ids
+
+    dataset_name: str
+        Name for analysis directory. Either "Human" or "Pseudomonas"
+
+    sample_id_colname: str
+        Column header that contains sample id that maps expression data
+        and metadata
+
+    """
+    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
+
+    if "pseudomonas" in dataset_name.lower():
+        # metadata file
+        mapping_file = os.path.join(
+            base_dir, dataset_name, "data", "metadata", "sample_annotations.tsv"
+        )
+
+        # Read in metadata
+        metadata = pd.read_csv(mapping_file, header=0, sep="\t", index_col=0)
+
+        selected_metadata = metadata.loc[experiment_id]
+        sample_ids = list(selected_metadata[sample_id_colname])
+
+    else:
+        # metadata file
+        mapping_file = os.path.join(
+            base_dir, dataset_name, "data", "metadata", "recount2_metadata.tsv"
+        )
+
+        # Read in metadata
+        metadata = pd.read_csv(mapping_file, header=0, sep="\t", index_col=0)
+
+        selected_metadata = metadata.loc[experiment_id]
+        sample_ids = list(selected_metadata[sample_id_colname])
+
+    return sample_ids
+
+
+def simulate_by_random_sampling(
+    normalized_data_file,
+    NN_architecture,
+    dataset_name,
+    analysis_name,
+    num_simulated_samples,
+    local_dir,
+    base_dir,
+):
+    """
+    Generate simulated data by randomly sampling from VAE latent space.
+
+    Workflow:
+    1. Input gene expression data the entire compendium from the <normalized_data_file>
+    2. Encode this input into a latent space using the trained VAE model
+    3. Randomly sample <num_simulated_samples> samples from the latent space.
+        For each encoded feature, sample from a distribution using the
+        the mean and standard deviation for that feature
+    4. Decode the samples
+
+    This compendium is generated by randomly sampling samples from the
+    latent space distribution of the compendium.  All samples are treated equal, where
+    association with a specific experiment is ignored.
+
+
+    Arguments
+    ----------
+    normalized_data_file: str
+        File containing normalized gene expression data
+
+        ------------------------------| PA0001 | PA0002 |...
+        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
+        54375-4-05.CEL                | 0.7789 | 0.7678 |...
+        ...                           | ...    | ...    |...
+
+    NN_architecture: str
+        Name of neural network architecture to use.
+        Format 'NN_<intermediate layer>_<latent layer>'
+
+    dataset_name: str
+        Name of analysis directory, Either "Human" or "Pseudomonas"
+
+    analysis_name: str
+        Parent directory where simulated data with experiments/partitionings will be stored.
+        Format of the directory name is <dataset_name>_<sample/experiment>_lvl_sim
+
+    number_simulated_samples: int
+        Number of samples to simulate
+
+    local_dir: str
+        Parent directory on local machine to store intermediate results
+
+    base_dir: str
+        Root directory containing analysis subdirectories
+
+    Returns
+    --------
+    simulated dataframe
+
+    """
+
+    # Files
+    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
+    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
+
+    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
+
+    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
+
+    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
+
+    # Load saved models
+    loaded_model = load_model(model_encoder_file)
+    loaded_decode_model = load_model(model_decoder_file)
+
+    loaded_model.load_weights(weights_encoder_file)
+    loaded_decode_model.load_weights(weights_decoder_file)
+
+    # Read data
+    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
+
+    print(
+        "Normalized gene expression data contains {} samples and {} genes".format(
+            normalized_data.shape[0], normalized_data.shape[1]
+        )
+    )
+
+    # Simulate data
+
+    # Encode into latent space
+    data_encoded = loaded_model.predict_on_batch(normalized_data)
+    data_encoded_df = pd.DataFrame(data_encoded, index=normalized_data.index)
+
+    latent_dim = data_encoded_df.shape[1]
+
+    # Get mean and standard deviation per encoded feature
+    encoded_means = data_encoded_df.mean(axis=0)
+    encoded_stds = data_encoded_df.std(axis=0)
+
+    # Generate samples
+    new_data = np.zeros([num_simulated_samples, latent_dim])
+    for j in range(latent_dim):
+        # Use mean and std for feature
+        new_data[:, j] = np.random.normal(
+            encoded_means[j], encoded_stds[j], num_simulated_samples
+        )
+
+        # Use standard normal
+        # new_data[:,j] = np.random.normal(0, 1, num_simulated_samples)
+
+    new_data_df = pd.DataFrame(data=new_data)
+
+    # Decode samples
+    new_data_decoded = loaded_decode_model.predict_on_batch(new_data_df)
+    simulated_data = pd.DataFrame(data=new_data_decoded)
+
+    print(
+        "Return: simulated gene expression data containing {} samples and {} genes".format(
+            simulated_data.shape[0], simulated_data.shape[1]
+        )
+    )
+
+    return simulated_data
+
+
+def simulate_by_latent_transformation(
+    num_simulated_experiments,
+    normalized_data_file,
+    NN_architecture,
+    dataset_name,
+    analysis_name,
+    experiment_ids_file,
+    sample_id_colname,
+    local_dir,
+    base_dir,
+):
+    """
+    Generate simulated data by randomly sampling some number of experiments
+    and linearly shifting the gene expression in the VAE latent space,
+    preserving the relationship between samples within an experiment.
+
+    Workflow:
+    1. Randomly select 1 experiment and get the gene expression data for that
+    experiment (here we are assuming that there is only biological variation
+    within this experiment)
+    2. Encode this experiment into a latent space using the trained VAE model
+    3. Encode the entire dataset from the <normalized_data_file>
+        3a. Select a random point in the encoded space. For each encoded feature, sample
+        from a distribution using the mean and standard deviation for that feature
+    4. Calculate the shift_vec_df = centroid(encoded experiment) - random encoded experiment
+    5. Shift all the samples from the experiment by the shift_vec_df
+    6. Decode the samples
+    7. Repeat steps 1-6 for <num_simulated_experiments>
+
+    This will generate a simulated compendium of different gene expression experiments that
+    are of a similar type to the original data but with different perturbations
+
+    Arguments
+    ----------
+    number_simulated_experiments: int
+        Number of experiments to simulate
+
+    normalized_data_file: str
+        File containing normalized gene expression data
+
+        ------------------------------| PA0001 | PA0002 |...
+        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
+        54375-4-05.CEL                | 0.7789 | 0.7678 |...
+        ...                           | ...    | ...    |...
+
+    NN_architecture: str
+        Name of neural network architecture to use.
+        Format 'NN_<intermediate layer>_<latent layer>'
+
+    dataset_name: str
+        Name for analysis directory. Either "Human" or "Pseudomonas"
+
+    analysis_name: str
+        Parent directory where simulated data with experiments/partitionings will be stored.
+        Format of the directory name is <dataset_name>_<sample/experiment>_lvl_sim
+
+    experiment_ids_file: str
+        File containing all cleaned experiment ids
+
+    sample_id_colname: str
+        Column header that contains sample id that maps expression data and metadata
+
+    local_dir: str
+        Parent directory on local machine to store intermediate results
+
+    base_dir: str
+        Root directory containing analysis subdirectories
+
+    Returns
+    --------
+    simulated dataframe
+
+    """
+
+    # Files
+    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
+    latent_dim = NN_architecture.split("_")[-1]
+
+    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
+
+    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
+
+    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
+
+    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
+
+    # Load saved models
+    loaded_model = load_model(model_encoder_file)
+    loaded_decode_model = load_model(model_decoder_file)
+
+    loaded_model.load_weights(weights_encoder_file)
+    loaded_decode_model.load_weights(weights_decoder_file)
+
+    # Read data
+    experiment_ids = pd.read_csv(experiment_ids_file, header=0, sep="\t", index_col=0)
+
+    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
+
+    print(
+        "Normalized gene expression data contains {} samples and {} genes".format(
+            normalized_data.shape[0], normalized_data.shape[1]
+        )
+    )
+
+    # Simulate data
+
+    simulated_data_df = pd.DataFrame()
+
+    for i in range(num_simulated_experiments):
+
+        selected_experiment_id = np.random.choice(
+            experiment_ids["experiment_id"], size=1
+        )[0]
+
+        # Get corresponding sample ids
+        sample_ids = get_sample_ids(
+            selected_experiment_id, dataset_name, sample_id_colname
+        )
+
+        # Remove any missing sample ids
+        sample_ids = list(filter(str.strip, sample_ids))
+
+        # Remove any sample_ids that are not found in gene expression data
+        # There are some experiments where most samples have gene expression but a
+        # few do not
+        sample_ids = [
+            sample for sample in sample_ids if sample in normalized_data.index
+        ]
+
+        # Gene expression data for selected samples
+        selected_data_df = normalized_data.loc[sample_ids]
+
+        # Encode selected experiment into latent space
+        data_encoded = loaded_model.predict_on_batch(selected_data_df)
+        data_encoded_df = pd.DataFrame(data_encoded, index=selected_data_df.index)
+
+        # Get centroid of original data
+        centroid = data_encoded_df.mean(axis=0)
+
+        # Encode original gene expression data into latent space
+        data_encoded_all = loaded_model.predict_on_batch(normalized_data)
+        data_encoded_all_df = pd.DataFrame(
+            data_encoded_all, index=normalized_data.index
+        )
+
+        data_encoded_all_df.head()
+
+        # Find a new location in the latent space by sampling from the latent space
+        encoded_means = data_encoded_all_df.mean(axis=0)
+        encoded_stds = data_encoded_all_df.std(axis=0)
+
+        latent_dim = int(latent_dim)
+        new_centroid = np.zeros(latent_dim)
+
+        for j in range(latent_dim):
+            new_centroid[j] = np.random.normal(encoded_means[j], encoded_stds[j])
+
+        shift_vec_df = new_centroid - centroid
+
+        simulated_data_encoded_df = data_encoded_df.apply(
+            lambda x: x + shift_vec_df, axis=1
+        )
+
+        # Decode simulated data into raw gene space
+        simulated_data_decoded = loaded_decode_model.predict_on_batch(
+            simulated_data_encoded_df
+        )
+
+        simulated_data_decoded_df = pd.DataFrame(
+            simulated_data_decoded,
+            index=simulated_data_encoded_df.index,
+            columns=selected_data_df.columns,
+        )
+
+        # Add experiment label
+        simulated_data_decoded_df["experiment_id"] = (
+            selected_experiment_id + "_" + str(i)
+        )
+
+        # Concatenate dataframe per experiment together
+        simulated_data_df = pd.concat([simulated_data_df, simulated_data_decoded_df])
+
+    # re-normalize per gene 0-1
+    simulated_data_numeric_df = simulated_data_df.drop(
+        columns=["experiment_id"], inplace=False
+    )
+
+    simulated_data_scaled = preprocessing.MinMaxScaler().fit_transform(
+        simulated_data_numeric_df
+    )
+
+    simulated_data_scaled_df = pd.DataFrame(
+        simulated_data_scaled,
+        columns=simulated_data_numeric_df.columns,
+        index=simulated_data_numeric_df.index,
+    )
+
+    simulated_data_scaled_df["experiment_id"] = simulated_data_df["experiment_id"]
+
+    # If sampling with replacement, then there will be multiple sample ids that are the same
+    # therefore we want to reset the index.
+    simulated_data_scaled_df.reset_index(drop=True, inplace=True)
+
+    print(
+        "Return: simulated gene expression data containing {} samples and {} genes".format(
+            simulated_data_scaled_df.shape[0], simulated_data_scaled_df.shape[1]
+        )
+    )
+
+    # Save before and after experiment for visualization validation
+    before_encoded_file = os.path.join(local_dir, "simulated_before_encoded.txt")
+    after_encoded_file = os.path.join(local_dir, "simulated_after_encoded.txt")
+
+    data_encoded_df.to_csv(before_encoded_file, float_format="%.3f", sep="\t")
+    simulated_data_encoded_df.to_csv(after_encoded_file, float_format="%.3f", sep="\t")
+
+    return simulated_data_scaled_df
+
+
+def shift_template_experiment(
+    normalized_data_file,
+    selected_experiment_id,
+    sample_id_colname,
+    NN_architecture,
+    dataset_name,
+    scaler,
+    local_dir,
+    base_dir,
+    run,
+):
+    """
+    Generate new simulated experiment using the selected_experiment_id as a template
+    experiment using the same workflow as `simulate_by_latent_transform`
+
+    This will return a file with a single simulated experiment following the workflow mentioned.
+    This function can be run multiple times to generate multiple simulated experiments from a
+    single selected_experiment_id.
+
+    Arguments
+    ----------
+    normalized_data_file: str
+        File containing normalized gene expression data
+
+        ------------------------------| PA0001 | PA0002 |...
+        05_PA14000-4-2_5-10-07_S2.CEL | 0.8533 | 0.7252 |...
+        54375-4-05.CEL                | 0.7789 | 0.7678 |...
+        ...                           | ...    | ...    |...
+
+    selected_experiment_id: str
+        Experiment id selected as template
+
+    sample_id_colname: str
+        Column header that contains sample id that maps expression data and metadata
+
+    NN_architecture: str
+        Name of neural network architecture to use.
+        Format 'NN_<intermediate layer>_<latent layer>'
+
+    dataset_name: str
+        Name for analysis directory. Either "Human" or "Pseudomonas"
+
+    scaler: minmax model
+        Model used to transform data into a different range
+
+    local_dir: str
+        Parent directory on local machine to store intermediate results
+
+    base_dir: str
+        Root directory containing analysis subdirectories
+
+    run: int
+        Simulation run
+
+    Returns
+    --------
+    simulated_data_file: str
+        File containing simulated gene expression data
+
+    """
+
+    # Files
+    NN_dir = os.path.join(base_dir, dataset_name, "models", NN_architecture)
+    latent_dim = NN_architecture.split("_")[-1]
+
+    model_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_model.h5"))[0]
+
+    weights_encoder_file = glob.glob(os.path.join(NN_dir, "*_encoder_weights.h5"))[0]
+
+    model_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_model.h5"))[0]
+
+    weights_decoder_file = glob.glob(os.path.join(NN_dir, "*_decoder_weights.h5"))[0]
+
+    # Load saved models
+    loaded_model = load_model(model_encoder_file, compile=False)
+    loaded_decode_model = load_model(model_decoder_file, compile=False)
+
+    loaded_model.load_weights(weights_encoder_file)
+    loaded_decode_model.load_weights(weights_decoder_file)
+
+    # Read data
+    normalized_data = pd.read_csv(normalized_data_file, header=0, sep="\t", index_col=0)
+
+    # Get corresponding sample ids
+    sample_ids = get_sample_ids(selected_experiment_id, dataset_name, sample_id_colname)
+
+    # Gene expression data for selected samples
+    selected_data_df = normalized_data.loc[sample_ids]
+
+    # Encode selected experiment into latent space
+    data_encoded = loaded_model.predict_on_batch(selected_data_df)
+    data_encoded_df = pd.DataFrame(data_encoded, index=selected_data_df.index)
+
+    # Get centroid of original data
+    centroid = data_encoded_df.mean(axis=0)
+
+    # Add individual vectors(centroid, sample point) to new_centroid
+
+    # Encode original gene expression data into latent space
+    data_encoded_all = loaded_model.predict_on_batch(normalized_data)
+    data_encoded_all_df = pd.DataFrame(data_encoded_all, index=normalized_data.index)
+
+    data_encoded_all_df.head()
+
+    # Find a new location in the latent space by sampling from the latent space
+    encoded_means = data_encoded_all_df.mean(axis=0)
+    encoded_stds = data_encoded_all_df.std(axis=0)
+
+    latent_dim = int(latent_dim)
+    new_centroid = np.zeros(latent_dim)
+
+    for j in range(latent_dim):
+        new_centroid[j] = np.random.normal(encoded_means[j], encoded_stds[j])
+
+    shift_vec_df = new_centroid - centroid
+    # print(shift_vec_df)
+
+    simulated_data_encoded_df = data_encoded_df.apply(
+        lambda x: x + shift_vec_df, axis=1
+    )
+
+    # Decode simulated data into raw gene space
+    simulated_data_decoded = loaded_decode_model.predict_on_batch(
+        simulated_data_encoded_df
+    )
+
+    simulated_data_decoded_df = pd.DataFrame(
+        simulated_data_decoded,
+        index=simulated_data_encoded_df.index,
+        columns=selected_data_df.columns,
+    )
+
+    # Un-normalize the data in order to run DE analysis downstream
+    simulated_data_scaled = scaler.inverse_transform(simulated_data_decoded_df)
+
+    simulated_data_scaled_df = pd.DataFrame(
+        simulated_data_scaled,
+        columns=simulated_data_decoded_df.columns,
+        index=simulated_data_decoded_df.index,
+    )
+
+    # Save template data for visualization validation
+    test_file = os.path.join(
+        local_dir,
+        "pseudo_experiment",
+        "template_normalized_data_" + selected_experiment_id + "_test.txt",
+    )
+
+    selected_data_df.to_csv(test_file, float_format="%.3f", sep="\t")
+
+    # Save
+    out_file = os.path.join(
+        local_dir,
+        "pseudo_experiment",
+        "selected_simulated_data_" + selected_experiment_id + "_" + str(run) + ".txt",
+    )
+
+    simulated_data_scaled_df.to_csv(out_file, float_format="%.3f", sep="\t")
+
+    out_encoded_file = os.path.join(
+        local_dir,
+        "pseudo_experiment",
+        f"selected_simulated_encoded_data_{selected_experiment_id}_{run}.txt",
+    )
+
+    simulated_data_encoded_df.to_csv(out_encoded_file, float_format="%.3f", sep="\t")
diff --git a/ponyo/simulations.py b/ponyo/simulations.py
deleted file mode 100644
index 03a596a..0000000
--- a/ponyo/simulations.py
+++ /dev/null
@@ -1,327 +0,0 @@
-"""
-Author: Alexandra Lee
-Date Created: 11 November 2019
-
-Scripts to run simulation different types of simulations
-(sample-level or experiment-level)
-using functions in `generate_data_parallel.py`
-"""
-from ponyo import similarity_metric_parallel
-from ponyo import generate_data_parallel
-import pandas as pd
-import random
-import warnings
-
-
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-random.seed(123)
-
-
-def sample_level_simulation(
-    run,
-    NN_architecture,
-    dataset_name,
-    analysis_name,
-    num_simulated_samples,
-    lst_num_experiments,
-    corrected,
-    correction_method,
-    use_pca,
-    num_PCs,
-    file_prefix,
-    input_file,
-    local_dir,
-    base_dir,
-):
-    """
-    This function performs runs series of scripts that performs the following steps:
-    1. Simulate gene expression data, ignorning the sample-experiment relationship
-    2. Add varying numbers of technical variation
-    3. Compare the similarity of the gene expression structure between the simulated data
-        vs simulated data + technical variation.
-
-    Arguments
-    ----------
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    NN_architecture: str
-        Name of neural network architecture to use.
-        Format 'NN_<intermediate layer>_<latent layer>'
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings will be stored.
-        Format of the directory name is <dataset>_<sample/experiment>_lvl_sim
-
-    num_simulated_samples: int
-        Number of samples to simulate
-
-    lst_num_experiments: list
-        List of different numbers of experiments to add to
-        simulated data.  These are the number of sources of
-        technical variation that are added to the simulated
-        data
-
-    corrected: bool
-        True if correction was applied
-
-    correction_method: str
-        Noise correction method to use. Either 'limma' or 'combat
-
-    use_pca: bool
-        True if want to represent expression data in top PCs before
-        calculating similarity
-
-    num_PCs: int
-        Number of top PCs to use to represent expression data
-
-    file_prefix: str
-        File prefix to determine whether to use data before correction ("Experiment" or "Partition")
-        or after correction ("Experiment_corrected" or "Parition_corrected")
-
-    input_file: str
-        File name containing normalized gene expressiond data
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    Returns
-    --------
-    similarity_score_df: df
-        Similarity scores for each number of experiment/partition added per run
-
-    permuted_scre: df
-        Similarity score comparing the permuted data to the simulated data per run
-
-    """
-
-    # Generate simulated data
-    simulated_data = generate_data_parallel.simulate_data(
-        input_file,
-        NN_architecture,
-        dataset_name,
-        analysis_name,
-        num_simulated_samples,
-        local_dir,
-        base_dir,
-    )
-
-    # Permute simulated data to be used as a negative control
-    permuted_data = generate_data_parallel.permute_data(simulated_data)
-
-    if not corrected:
-        # Add technical variation
-        generate_data_parallel.add_experiments_io(
-            simulated_data,
-            lst_num_experiments,
-            run,
-            local_dir,
-            dataset_name,
-            analysis_name,
-        )
-
-    if corrected:
-        # Remove technical variation
-        generate_data_parallel.apply_correction_io(
-            local_dir,
-            run,
-            dataset_name,
-            analysis_name,
-            lst_num_experiments,
-            correction_method,
-        )
-
-    # Calculate similarity between compendium and compendium + noise
-    batch_scores, permuted_score = similarity_metric_parallel.sim_svcca_io(
-        simulated_data,
-        permuted_data,
-        corrected,
-        file_prefix,
-        run,
-        lst_num_experiments,
-        use_pca,
-        num_PCs,
-        local_dir,
-        dataset_name,
-        analysis_name,
-    )
-
-    # Convert similarity scores to pandas dataframe
-    similarity_score_df = pd.DataFrame(
-        data={"score": batch_scores}, index=lst_num_experiments, columns=["score"]
-    )
-
-    similarity_score_df.index.name = "number of experiments"
-    similarity_score_df
-
-    # Return similarity scores and permuted score
-    return permuted_score, similarity_score_df
-
-
-def experiment_level_simulation(
-    run,
-    NN_architecture,
-    dataset_name,
-    analysis_name,
-    num_simulated_experiments,
-    lst_num_partitions,
-    corrected,
-    correction_method,
-    use_pca,
-    num_PCs,
-    file_prefix,
-    input_file,
-    experiment_ids_file,
-    sample_id_colname,
-    local_dir,
-    base_dir,
-):
-    """
-    This function performs runs series of scripts that performs the following steps:
-    1. Simulate gene expression data, keeping track of which sample is associated
-        with a given experiment
-    2. Add varying numbers of technical variation
-    3. Compare the similarity of the gene expression structure between the simulated data
-        vs simulated data + technical variation.
-
-    Arguments
-    ----------
-    run: int
-        Unique core identifier that is used to create unique filenames for intermediate files
-
-    NN_architecture: str
-        Name of neural network architecture to use.
-        Format 'NN_<intermediate layer>_<latent layer>'
-
-    dataset_name: str
-        Name for analysis directory. Either "Human" or "Pseudomonas"
-
-    analysis_name: str
-        Parent directory where simulated data with experiments/partitionings will be stored.
-        Format of the directory name is <dataset>_<sample/experiment>_lvl_sim
-
-    num_simulated_samples: int
-        Number of samples to simulate
-
-    lst_num_experiments: list
-        List of different numbers of partitions to add to
-        simulated data.  These are the number of sources of
-        technical variation that are added to the simulated
-        data
-
-    corrected: bool
-        True if correction was applied
-
-    correction_method: str
-        Noise correction method to use. Either 'limma' or 'combat
-
-    use_pca: bool
-        True if want to represent expression data in top PCs before
-        calculating similarity
-
-    num_PCs: int
-        Number of top PCs to use to represent expression data
-
-    file_prefix: str
-        File prefix to determine whether to use data before correction ("Experiment" or "Partition")
-        or after correction ("Experiment_corrected" or "Parition_corrected")
-
-    input_file: str
-        File name containing normalized gene expressiond data
-
-    experiment_ids_file: str
-        File containing all cleaned experiment ids
-
-    sample_id_colname: str
-        Column header that contains sample id that maps expression data and metadata
-
-    local_dir: str
-        Parent directory on local machine to store intermediate results
-
-    base_dir: str
-        Root directory containing analysis subdirectories
-
-    Returns
-    --------
-    similarity_score_df: df
-        Similarity scores for each number of experiment/partition added per run
-
-    permuted_scre: df
-        Similarity score comparing the permuted data to the simulated data per run
-    """
-
-    # Generate simulated data
-    simulated_data = generate_data_parallel.simulate_compendium(
-        num_simulated_experiments,
-        input_file,
-        NN_architecture,
-        dataset_name,
-        analysis_name,
-        experiment_ids_file,
-        sample_id_colname,
-        local_dir,
-        base_dir,
-    )
-
-    # Permute simulated data to be used as a negative control
-    permuted_data = generate_data_parallel.permute_data(simulated_data)
-
-    if not corrected:
-        # Add technical variation
-        generate_data_parallel.add_experiments_grped_io(
-            simulated_data,
-            lst_num_partitions,
-            run,
-            local_dir,
-            dataset_name,
-            analysis_name,
-        )
-
-    if corrected:
-        # Remove technical variation
-        generate_data_parallel.apply_correction_io(
-            local_dir,
-            run,
-            dataset_name,
-            analysis_name,
-            lst_num_partitions,
-            correction_method,
-        )
-
-    # Calculate similarity between compendium and compendium + noise
-    batch_scores, permuted_score = similarity_metric_parallel.sim_svcca_io(
-        simulated_data,
-        permuted_data,
-        corrected,
-        file_prefix,
-        run,
-        lst_num_partitions,
-        use_pca,
-        num_PCs,
-        local_dir,
-        dataset_name,
-        analysis_name,
-    )
-
-    # Convert similarity scores to pandas dataframe
-    similarity_score_df = pd.DataFrame(
-        data={"score": batch_scores}, index=lst_num_partitions, columns=["score"]
-    )
-
-    similarity_score_df.index.name = "number of partitions"
-
-    # Return similarity scores and permuted score
-    return permuted_score, similarity_score_df
diff --git a/ponyo/train_vae_modules.py b/ponyo/train_vae_modules.py
new file mode 100644
index 0000000..b6471d6
--- /dev/null
+++ b/ponyo/train_vae_modules.py
@@ -0,0 +1,141 @@
+"""
+Author: Alexandra Lee
+Date Created: 11 March 2020
+
+Scripts related to training the VAE including
+1. Normalizing gene expression data
+2. Wrapper function to input training parameters and run vae
+training in `vae.tybalt_2layer_model`
+"""
+
+from ponyo import vae, utils
+import os
+import pickle
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+
+import warnings
+
+
+def fxn():
+    warnings.warn("deprecated", DeprecationWarning)
+
+
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    fxn()
+
+
+np.random.seed(123)
+
+
+def normalize_expression_data(
+    base_dir, config_file, raw_input_data_file, normalized_data_file
+):
+    """
+    0-1 normalize the expression data.
+
+    Arguments
+    ----------
+    base_dir: str
+        Root directory containing analysis subdirectories
+
+    config_file: str
+        File containing user defined parameters
+
+    raw_input_data_file: str
+        File containing raw expression data
+
+    normalize_data_file:
+        Output file containing normalized expression data
+    """
+    # Read in config variables
+    params = utils.read_config(config_file)
+
+    # Read data
+    data = pd.read_csv(raw_input_data_file, header=0, sep="\t", index_col=0)
+    print(
+        "input: dataset contains {} samples and {} genes".format(
+            data.shape[0], data.shape[1]
+        )
+    )
+
+    # 0-1 normalize per gene
+    scaler = preprocessing.MinMaxScaler()
+    data_scaled_df = scaler.fit_transform(data)
+    data_scaled_df = pd.DataFrame(
+        data_scaled_df, columns=data.columns, index=data.index
+    )
+
+    print(
+        "Output: normalized dataset contains {} samples and {} genes".format(
+            data_scaled_df.shape[0], data_scaled_df.shape[1]
+        )
+    )
+
+    # Save scaler transform
+    scaler_file = params["scaler_transform_file"]
+
+    outfile = open(scaler_file, "wb")
+    pickle.dump(scaler, outfile)
+    outfile.close()
+
+    # Save scaled data
+    data_scaled_df.to_csv(normalized_data_file, sep="\t", compression="xz")
+
+
+def train_vae(config_file, input_data_file):
+    """
+    Trains VAE model using parameters set in config file
+
+    Arguments
+    ----------
+    config_file: str
+        File containing user defined parameters
+
+    input_data_file: str
+        File path corresponding to input dataset to use
+
+    """
+
+    # Read in config variables
+    params = utils.read_config(config_file)
+
+    # Load parameters
+    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
+    dataset_name = params["dataset_name"]
+    learning_rate = params["learning_rate"]
+    batch_size = params["batch_size"]
+    epochs = params["epochs"]
+    kappa = params["kappa"]
+    intermediate_dim = params["intermediate_dim"]
+    latent_dim = params["latent_dim"]
+    epsilon_std = params["epsilon_std"]
+    train_architecture = params["NN_architecture"]
+    validation_frac = params["validation_frac"]
+
+    # Read data
+    normalized_data = pd.read_csv(input_data_file, header=0, sep="\t", index_col=0)
+
+    print(
+        "input dataset contains {} samples and {} genes".format(
+            normalized_data.shape[0], normalized_data.shape[1]
+        )
+    )
+
+    # Train (VAE)
+    vae.tybalt_2layer_model(
+        learning_rate,
+        batch_size,
+        epochs,
+        kappa,
+        intermediate_dim,
+        latent_dim,
+        epsilon_std,
+        normalized_data,
+        base_dir,
+        dataset_name,
+        train_architecture,
+        validation_frac,
+    )
diff --git a/ponyo/utils.py b/ponyo/utils.py
index 3869f60..ad9aba2 100644
--- a/ponyo/utils.py
+++ b/ponyo/utils.py
@@ -1,3 +1,8 @@
+import os
+import pandas as pd
+import numpy as np
+
+
 def read_config(filename):
     """
     Read and parse configuration file containing stored user variables.
@@ -11,3 +16,151 @@ def read_config(filename):
         items = lines.split("\t", 1)
         config_dict[items[0]] = eval(items[1])
     return config_dict
+
+
+def setup_dir(config_file):
+    """
+    Create directories to store files created by VAE training and
+    simulation analysis
+
+    Arguments
+    ----------
+    config_file: str
+        File containing user defined parameters
+    """
+
+    base_dir = os.path.abspath(os.path.join(os.getcwd(), "../"))
+
+    # Read in config variables
+    params = read_config(config_file)
+
+    # Load parameters
+    local_dir = params["local_dir"]
+    dataset_name = params["dataset_name"]
+    train_architecture = params["NN_architecture"]
+
+    # Create VAE directories
+    output_dirs = [
+        os.path.join(base_dir, dataset_name, "models"),
+        os.path.join(base_dir, dataset_name, "logs"),
+    ]
+
+    # Check if the following directories exist
+    # and if not to create them
+    for each_dir in output_dirs:
+
+        # Check if analysis output directory exist otherwise create
+        if not os.path.exists(each_dir):
+            print("creating new directory: {}".format(each_dir))
+            os.makedirs(each_dir, exist_ok=True)
+
+        # Check if NN architecture directory exist otherwise create
+        NN_dir = os.path.join(each_dir, train_architecture)
+        if not os.path.exists(NN_dir):
+            print("creating new directory: {}".format(NN_dir))
+            os.makedirs(NN_dir, exist_ok=True)
+
+    # Check if analysis output directory exist otherwise create
+    results_dir = os.path.join(base_dir, dataset_name, "results")
+    if not os.path.exists(results_dir):
+        print("creating new directory: {}".format(results_dir))
+        os.makedirs(results_dir, exist_ok=True)
+
+    # Check if 'saved_variables' directory exist otherwise create
+    var_dir = os.path.join(each_dir, "saved_variables")
+    if not os.path.exists(var_dir):
+        print("creating new directory: {}".format(var_dir))
+        os.makedirs(var_dir, exist_ok=True)
+
+    # Create local directories to store intermediate files
+    output_dirs = [
+        os.path.join(local_dir, "experiment_simulated"),
+        os.path.join(local_dir, "partition_simulated"),
+    ]
+
+    # Check if analysis output directory exist otherwise create
+    for each_dir in output_dirs:
+        if not os.path.exists(each_dir):
+            print("creating new directory: {}".format(each_dir))
+            os.makedirs(each_dir, exist_ok=True)
+
+
+def create_experiment_id_file(metadata_file, input_data_file, output_file, config_file):
+    """
+    Create file with experiment ids that are associated with expression data
+
+    Arguments
+    ----------
+    metadata_file: str
+        File containing metadata annotations per sample
+
+    input_data_file: str
+        File containing normalized expression data
+
+    output_file: str
+        File containing experiment ids with expression data and sample annotations
+
+    config_file: str
+        File containing user defined parameters
+
+    """
+    # Read in metadata
+    metadata = pd.read_csv(metadata_file, header=0, sep="\t", index_col=0)
+
+    # Read in expression data
+    normalized_data = pd.read_csv(input_data_file, header=0, sep="\t", index_col=0)
+
+    # Read in config variables
+    params = read_config(config_file)
+
+    # Load parameters
+    sample_id_colname = params["metadata_colname"]
+    dataset_name = params["dataset_name"]
+
+    # Get sample id that maps between metadata and expression files
+    map_experiment_sample = metadata[[sample_id_colname]]
+
+    # Get all unique experiment ids
+    experiment_ids = np.unique(np.array(map_experiment_sample.index)).tolist()
+    print("There are {} experiments in the compendium".format(len(experiment_ids)))
+
+    # Get only sample ids that have expression data available
+    sample_ids_with_gene_expression = list(normalized_data.index)
+
+    # Get associated experiment ids with expression data
+    experiment_ids_with_gene_expression = []
+
+    for experiment_id in experiment_ids:
+
+        if "human" in dataset_name.lower():
+            # Some project id values are descriptions
+            # We will skip these
+            if len(experiment_id) == 9:
+                selected_metadata = metadata.loc[experiment_id]
+                sample_ids = list(selected_metadata[sample_id_colname])
+
+                if any(x in sample_ids_with_gene_expression for x in sample_ids):
+                    experiment_ids_with_gene_expression.append(experiment_id)
+        else:
+            selected_metadata = metadata.loc[experiment_id]
+            sample_ids = list(selected_metadata[sample_id_colname])
+
+            if any(x in sample_ids_with_gene_expression for x in sample_ids):
+                experiment_ids_with_gene_expression.append(experiment_id)
+
+    print(
+        "There are {} experiments with gene expression data".format(
+            len(experiment_ids_with_gene_expression)
+        )
+    )
+
+    # Save file with experiment ids
+    experiment_ids_with_gene_expression_df = pd.DataFrame(
+        experiment_ids_with_gene_expression, columns=["experiment_id"]
+    )
+    experiment_ids_with_gene_expression_df.to_csv(output_file, sep="\t")
+    print(
+        "{} experiment ids saved to file".format(
+            len(experiment_ids_with_gene_expression)
+        )
+    )
diff --git a/setup.cfg b/setup.cfg
index 625f749..8039bf2 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -6,5 +6,5 @@ exclude =
     # No need to traverse our git directory
     .git,
     setup.py,
-    ponyo/cca_core.py
+    *.ipynb
 max-line-length = 88
diff --git a/setup.py b/setup.py
index 943aa29..c713869 100644
--- a/setup.py
+++ b/setup.py
@@ -1,13 +1,23 @@
+import pathlib
 from setuptools import setup
 
+# The directory containing this file
+HERE = pathlib.Path(__file__).parent
+
+# The text of the README file
+README = (HERE / "README.md").read_text()
+
 setup(
     name="ponyo",
     version="0.1",
-    description="Install functions from ponyo: simulate gene expression compendia",
-    url="#",
+    description="Install functions to simulate gene expression compendia",
+    long_description=README,
+    long_description_content_type="text/markdown",
+    url="https://github.com/greenelab/ponyo",
     author="Alexandra Lee",
     author_email="alexjlee.21@gmail.com",
     license="BSD 3-Clause",
     packages=["ponyo"],
     zip_safe=False,
+    install_requires=["pandas", "numpy", "glob2", "keras", "tensorflow", "sklearn"],
 )