B/B3_Combine_Concat_Scanorama.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1af9461-8a1f-4ac3-a072-758649a233ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Combine concat and scanorama versions of data integration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b217e2-eaf0-4a43-8fbc-6587715b1458",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13d2a75e-c729-423b-8104-098b97dc6d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import anndata as an\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scanorama\n",
    "import os\n",
    "import multiprocessing\n",
    "import random\n",
    "import time\n",
    "import git\n",
    "import sys\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "044bd36f-b973-4ed3-b155-c77d96537500",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe714b6-81f0-4fab-8fdd-f3f04a028f89",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Configurations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb80741e-5365-4ec2-99dc-9abb4ec5af94",
   "metadata": {},
   "source": [
    "### Technical configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a962c22-9ec1-45df-ac94-6ee5b8283730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiprocessing.cpu_count()  ### total amount of cpu on the used core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f55f45e-2103-465d-90a5-c76eccb7b71c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.sched_getaffinity(0)) ### amount of available cpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd4915c2-dc1b-42d3-9e6d-bad7430da165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.settings.n_jobs   # default number of CPUs to use for parallel computing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99ea59e6-5409-46b4-a551-8047031498f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.settings.max_memory  # maximum memory to use in GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a016758d-046e-4ee2-82ec-81c581afc26d",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57af568b-62cb-4424-8cce-e3985f368c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "ncore = '24'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b732a939-0919-469e-b09d-2cc1d47357ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state_var = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46877310-bb62-475e-866b-ef94dced669a",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_jobs_var = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77c60118-b406-4de3-9960-249f9b319f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"OMP_NUM_THREADS\"] = ncore\n",
    "os.environ[\"OPENBLAS_NUM_THREADS\"] = ncore\n",
    "os.environ[\"MKL_NUM_THREADS\"] = ncore\n",
    "os.environ[\"VECLIB_MAXIMUM_THREADS\"] = ncore\n",
    "os.environ[\"NUMEXPR_NUM_THREADS\"] = ncore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "001e1e28-50e5-4590-a851-17bdb51018e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The `sinfo` package has changed name and is now called `session_info` to become more discoverable and self-explanatory. The `sinfo` PyPI package will be kept around to avoid breaking old installs and you can downgrade to 0.3.2 if you want to use it without seeing this message. For the latest features and bug fixes, please install `session_info` instead. The usage and defaults also changed slightly, so please review the latest README at https://gitlab.com/joelostblom/session_info.\n",
      "-----\n",
      "anndata     0.7.6\n",
      "scanpy      1.8.1\n",
      "sinfo       0.3.4\n",
      "-----\n",
      "PIL                 8.3.1\n",
      "annoy               NA\n",
      "anyio               NA\n",
      "attr                21.2.0\n",
      "babel               2.9.1\n",
      "backcall            0.2.0\n",
      "beta_ufunc          NA\n",
      "binom_ufunc         NA\n",
      "brotli              NA\n",
      "certifi             2021.05.30\n",
      "cffi                1.14.6\n",
      "chardet             4.0.0\n",
      "charset_normalizer  2.0.0\n",
      "cycler              0.10.0\n",
      "cython_runtime      NA\n",
      "dateutil            2.8.2\n",
      "decorator           5.0.9\n",
      "defusedxml          0.7.1\n",
      "entrypoints         0.3\n",
      "fbpca               NA\n",
      "git                 3.1.23\n",
      "gitdb               4.0.7\n",
      "google              NA\n",
      "h5py                3.3.0\n",
      "idna                3.1\n",
      "igraph              0.9.6\n",
      "intervaltree        NA\n",
      "ipykernel           6.2.0\n",
      "ipython_genutils    0.2.0\n",
      "jedi                0.18.0\n",
      "jinja2              3.0.1\n",
      "joblib              1.0.1\n",
      "json5               NA\n",
      "jsonschema          3.2.0\n",
      "jupyter_server      1.10.2\n",
      "jupyterlab_server   2.7.1\n",
      "kiwisolver          1.3.1\n",
      "leidenalg           0.8.7\n",
      "llvmlite            0.36.0\n",
      "markupsafe          2.0.1\n",
      "matplotlib          3.4.3\n",
      "matplotlib_inline   NA\n",
      "mpl_toolkits        NA\n",
      "natsort             7.1.1\n",
      "nbclassic           NA\n",
      "nbformat            5.1.3\n",
      "nbinom_ufunc        NA\n",
      "numba               0.53.1\n",
      "numexpr             2.7.3\n",
      "numpy               1.21.2\n",
      "packaging           21.0\n",
      "pandas              1.3.2\n",
      "parso               0.8.2\n",
      "pexpect             4.8.0\n",
      "pickleshare         0.7.5\n",
      "pkg_resources       NA\n",
      "prometheus_client   NA\n",
      "prompt_toolkit      3.0.19\n",
      "ptyprocess          0.7.0\n",
      "pvectorc            NA\n",
      "pyexpat             NA\n",
      "pygments            2.10.0\n",
      "pyparsing           2.4.7\n",
      "pyrsistent          NA\n",
      "pytz                2021.1\n",
      "requests            2.26.0\n",
      "scanorama           1.7.1\n",
      "scipy               1.7.1\n",
      "seaborn             0.11.2\n",
      "send2trash          NA\n",
      "six                 1.16.0\n",
      "sklearn             0.24.2\n",
      "smmap               3.0.5\n",
      "sniffio             1.2.0\n",
      "socks               1.7.1\n",
      "sortedcontainers    2.4.0\n",
      "statsmodels         0.12.2\n",
      "storemagic          NA\n",
      "tables              3.6.1\n",
      "terminado           0.10.1\n",
      "texttable           1.6.4\n",
      "tornado             6.1\n",
      "traitlets           5.0.5\n",
      "typing_extensions   NA\n",
      "urllib3             1.26.6\n",
      "wcwidth             0.2.5\n",
      "websocket           0.57.0\n",
      "zmq                 22.2.1\n",
      "-----\n",
      "IPython             7.26.0\n",
      "jupyter_client      6.1.12\n",
      "jupyter_core        4.7.1\n",
      "jupyterlab          3.1.7\n",
      "notebook            6.4.3\n",
      "-----\n",
      "Python 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:39:48) [GCC 9.3.0]\n",
      "Linux-4.18.0-513.11.1.el8_9.x86_64-x86_64-with-glibc2.28\n",
      "64 logical CPU cores, x86_64\n",
      "-----\n",
      "Session information updated at 2024-03-04 15:12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sc.logging.print_versions()\n",
    "sc.set_figure_params(facecolor=\"white\", figsize=(8, 8))\n",
    "sc.settings.verbosity = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75f37728-660c-4720-bfca-3dde55639e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'B3_Combine_Concat_Scanorama.ipynb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4975a0a1-e9e0-4975-b154-c142a6a3a3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#repo = git.Repo('stark-stemi')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e4c4a7-3d6b-428e-b254-62eb3e2e66cc",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9fe54da1-e4e1-4da3-9ae9-2dbcbcace869",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../data/current'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c2e5515d-8a1c-44ff-98e2-364c650ce6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/current'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1dd3be2f-b0f8-4cf5-9795-2c87eaafed52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "variants = ['V1_Concat_Singlet', \n",
    "    'V2_Scanorama_Singlet' #,'V3_Concat_All_incl_Doublets'\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "37256511-adde-4c95-8872-960a483b9062",
   "metadata": {},
   "outputs": [],
   "source": [
    "variants_dictionary = {'V1_Concat_Singlet':  '/analysis/B/B2_Integrated_Concat_Singlet_annotated_rna.h5ad', \n",
    "                       'V2_Scanorama_Singlet': '/analysis/B/B2_Integrated_Scanorama_Singlet_annotated_rna.h5ad'\n",
    "     }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938cffd2-2c2f-45f3-9f3d-e85039b7b1ec",
   "metadata": {},
   "source": [
    "## Meta-Data Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbdef1f-2ce8-45cb-8926-7868e5593a93",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Mapping_Hashtag_Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7e386b0-a86a-4ed7-9745-f56d239df5d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last modifiedThu Sep  2 13:17:03 2021\n"
     ]
    }
   ],
   "source": [
    "dataset_path =  data_path + \"/preprocessed-data/meta-data/Mapping_Hashtag_Sample.csv\"\n",
    "library_hashtag_mapping = pd.read_csv(dataset_path) \n",
    "print('Last modified' + time.ctime(os.path.getmtime(dataset_path)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30691cc3-209d-477e-b0aa-c84d4a5a6551",
   "metadata": {},
   "source": [
    "### Mapping_Sample_Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c925a8cd-a146-43e6-b518-2457463ff9af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last modifiedFri Jan 14 09:35:27 2022\n"
     ]
    }
   ],
   "source": [
    "dataset_path = data_path + \"/preprocessed-data/meta-data/Mapping_Sample_Groups.csv\"\n",
    "library_sample_group_mapping = pd.read_csv(dataset_path, encoding = \"utf-8\") \n",
    "print('Last modified' + time.ctime(os.path.getmtime(dataset_path)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f774b327-2f0c-4b2b-b66a-59b8c1766dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# library_sample_group_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd41a0a2-0a44-45a0-93b7-03e885065115",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Adapt format and add library identifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "33ae13f0-c8de-45b0-aa7c-d9a7ea1948e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_group_mapping['library.hashtag'] = library_sample_group_mapping['library.hashtag'].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8d0c138d-66fe-4128-942e-1201da865459",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_group_mapping['library'] = 'L' + library_sample_group_mapping['library.hashtag'].str.extract('(.*)\\.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bbbf8ae3-29fc-423d-ac73-b37b0a41a76d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<NA>, 'L1', 'L3', 'L2', 'L4', 'L5', 'L6', 'L7', 'L9', 'L8', 'L10',\n",
       "       'L14', 'L11', 'L12', 'L13'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(library_sample_group_mapping['library']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f1e6efcb-0b76-4eca-ab2b-505d8a6e0d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_group_mapping['library'] = library_sample_group_mapping['library'].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "80c64d88-50f1-41fe-a3d8-c6690f65366b",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_group_mapping['sample'] = library_sample_group_mapping['sample'].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "530695ef-c8c3-4fac-a124-bf0b00e7de5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2537879/3491674588.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  library_sample_group_mapping['sample'] = library_sample_group_mapping['sample'].str.replace('(\\.0)', '')\n"
     ]
    }
   ],
   "source": [
    "library_sample_group_mapping['sample'] = library_sample_group_mapping['sample'].str.replace('(\\.0)', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41338a4-1d48-48ea-98bd-8c52d2bb7358",
   "metadata": {},
   "source": [
    "### Mapping_Sample_EF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "61f87b86-91bb-4a93-874a-942d31ec0851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last modifiedFri Oct  1 11:15:19 2021\n"
     ]
    }
   ],
   "source": [
    "dataset_path = data_path + \"/preprocessed-data/meta-data/Mapping_Sample_EF.csv\"\n",
    "library_sample_ef_mapping = pd.read_csv(dataset_path, encoding = \"utf-8\")\n",
    "print('Last modified' + time.ctime(os.path.getmtime(dataset_path)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "454a89b5-445c-43eb-8a8d-614286b96f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Adapt format and add library identifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8fde3559-0a48-4e2b-9c4d-730e9ba6b863",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_ef_mapping['library.hashtag'] =library_sample_ef_mapping['library.hashtag'].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "09f455f9-41f0-4f92-9717-9d3c9c776619",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_ef_mapping['library'] = 'L' + library_sample_ef_mapping['library.hashtag'].str.extract('(.*)\\.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "239f9f56-2181-4d8e-91c2-fcfe74db6700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['L1', 'L2', 'L3', <NA>, 'L4', 'L5', 'L6', 'L7', 'L8', 'L9', 'L10',\n",
       "       'L14'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(library_sample_ef_mapping['library']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ab0e0043-390e-4730-9c78-d33f450fb19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_sample_ef_mapping['sample'] = library_sample_ef_mapping['sample'].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b8f3391f-4c81-4f00-96a2-7b1ec5a85c5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2537879/2411877743.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  library_sample_ef_mapping['sample'] = library_sample_ef_mapping['sample'].str.replace('(\\.0)', '')\n"
     ]
    }
   ],
   "source": [
    "library_sample_ef_mapping['sample'] = library_sample_ef_mapping['sample'].str.replace('(\\.0)', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa436518-a9cf-4c0b-94d9-faff14d17599",
   "metadata": {},
   "source": [
    "## Data from B2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "969a45e5-5e89-4b87-8f9d-6394a2c588fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata_dict = dict.fromkeys(variants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0c9359e9-576a-4fe3-8016-3bf564479923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/current/analysis/B/B2_Integrated_Concat_Singlet_annotated_rna.h5ad\n",
      "Last modifiedMon Mar  4 14:51:47 2024\n",
      "../data/current/analysis/B/B2_Integrated_Scanorama_Singlet_annotated_rna.h5ad\n",
      "Last modifiedMon Mar  4 14:24:49 2024\n"
     ]
    }
   ],
   "source": [
    "for key in anndata_dict:\n",
    "    dataset_path = data_path + variants_dictionary[key]\n",
    "    \n",
    "    print(dataset_path)\n",
    "    print('Last modified' + time.ctime(os.path.getmtime(dataset_path)))\n",
    "    \n",
    "    adata_orig = sc.read_h5ad(dataset_path)\n",
    "    \n",
    "    anndata_dict[key]= adata_orig # save anndata in dictionary\n",
    "    anndata_dict[key].uns['data_load_time'] = time.ctime(os.path.getmtime(dataset_path))  # save the last modified timestamp of the data loaded\n",
    "    anndata_dict[key].uns['data_load_name'] = dataset_path # save the data path of the data loaded \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3554ff7a-52c9-4253-a1d6-911cbc337218",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Short data check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6387eea6-6ca7-4be8-bb1e-a8163010b02c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# anndata_dict # cells have been filtered based on QC metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b7972f-f827-4119-a78b-a02c8befe170",
   "metadata": {},
   "source": [
    "## Add new metadata to anndata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2a1dc928-02f2-4915-a1e3-b8b37f14b2a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:794: UserWarning: \n",
      "AnnData expects .obs.index to contain strings, but got values like:\n",
      "    [0, 1, 2, 3, 4]\n",
      "\n",
      "    Inferred to be: integer\n",
      "\n",
      "  value_idx = self._prep_dim_index(value.index, attr)\n"
     ]
    }
   ],
   "source": [
    "for key in anndata_dict:\n",
    "    anndata_dict_index = anndata_dict[key].obs.index   # Save index for adding afterwards\n",
    "    \n",
    "    #### Merge library_sample_group_mapping\n",
    "    anndata_dict[key].obs = pd.merge(anndata_dict[key].obs, library_sample_group_mapping ,how=\"left\", left_on=['library', 'display_name'], right_on=['library', 'sample']) \n",
    "    \n",
    "    #### Merge library_sample_ef_mapping\n",
    "    anndata_dict[key].obs = pd.merge(anndata_dict[key].obs, library_sample_ef_mapping ,how=\"left\", left_on=['library', 'display_name'], right_on=['library', 'sample'])\n",
    "    \n",
    "    #### Restore index\n",
    "    anndata_dict[key].obs.index = anndata_dict_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "303eb42a-562d-4488-8497-3f19414ee096",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Check out added columsn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ca7f3a39-8d25-4b12-af0a-e8ccf7c899df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['nCount_HTO', 'nFeature_HTO', 'nCount_RNA', 'nFeature_RNA',\n",
       "       'percent_mt', 'HTO_maxID', 'HTO_secondID', 'HTO_margin',\n",
       "       'HTO_classification', 'HTO_classification.global', 'hash.ID',\n",
       "       'HTO_classification_final', 'name', 'library', 'id', 'read', 'pattern',\n",
       "       'sequence', 'feature_type', 'Unnamed: 7', 'in_sample', 'display_name',\n",
       "       'group_x', 'HTO_Doublet_Classification', 'doublet_score',\n",
       "       'predicted_doublet', 'A5_scrublet_predicted_doublet_lib0.1',\n",
       "       'A5_scrublet_doublet_score_lib_0.1',\n",
       "       'A5_scrublet_predicted_doublet_lib0.2',\n",
       "       'A5_scrublet_doublet_score_lib_0.2',\n",
       "       'A5_scrublet_predicted_doublet_lib', 'A5_scrublet_doublet_score_lib',\n",
       "       'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt',\n",
       "       'pct_counts_mt', 'valid_cell_filter_dying', 'valid_cell_filter_doublet',\n",
       "       'A7_PCA_50_10_neighbors_cluster', 'A7_PCA_100_10_neighbors_cluster',\n",
       "       'A7_PCA_100_50_neighbors_cluster', 'A7_PCA_500_50_neighbors_cluster',\n",
       "       'A7_Singlet_PCA_50_10_neighbors_cluster',\n",
       "       'A7_Singlet_PCA_100_10_neighbors_cluster',\n",
       "       'A7_Singlet_RB_MT_PCA_50_10_neighbors_cluster',\n",
       "       'A7_Singlet_RB_MT_PCA_100_10_neighbors_cluster', 'library_id',\n",
       "       'B2_Scanorama_Singlet_cluster', 'B2_Scanorama_Singlet_rb_mt_cluster',\n",
       "       'leiden', 'cell_type', 'cluster_cell_type', 'library.hashtag_x', 'age',\n",
       "       'sex', 'm_x', 'classification', 'group_y', 'measurement_x', 'sample_x',\n",
       "       'Unnamed: 0', 'library.hashtag_y', 'm_y', 'measurement_y',\n",
       "       'delta_ef_value_group', 'delta_ef_value', 'sample_y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata_dict[key].obs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "57e722fd-2d29-4d16-a4c8-a8564c4e8371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['acs_w_o_infection', 'acs_subacute', 'acs_w_infection',\n",
       "       'koronarsklerose', 'vollstaendiger_ausschluss', 'ccs'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(anndata_dict[key].obs['classification'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "10a3e3bc-f5a5-4ac4-95f3-5837e8ee3670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['acs', 'no_ccs', 'ccs'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(anndata_dict[key].obs['group_y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df89630-46f9-465a-b2ce-7e6b0848e73d",
   "metadata": {},
   "source": [
    "# Data Adaptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2f91558c-b4cb-4e95-af30-e4e2bb5d477e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### convert scrublet doublet predictions to string for visualization\n",
    "\n",
    "for key in anndata_dict:\n",
    "    \n",
    "    ## convert to inspect in string\n",
    "    anndata_dict[key].obs['A5_scrublet_predicted_doublet_lib'] =anndata_dict[key].obs['A5_scrublet_predicted_doublet_lib'].apply(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06554c21-2e22-418b-900e-0a25cf990ad6",
   "metadata": {},
   "source": [
    "# Combine different variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "27ca2183-0f4e-4ff0-8881-0bf2739a0598",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1 = anndata_dict['V1_Concat_Singlet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "82dc0e77-53ab-4129-ba62-43044b80482d",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata2 = anndata_dict['V2_Scanorama_Singlet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d96a245-4e7f-4312-a69d-9451849fd630",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9e02f266-f8ca-474e-ad3f-b2b937fd3afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 148275 × 1393\n",
       "    obs: 'nCount_HTO', 'nFeature_HTO', 'nCount_RNA', 'nFeature_RNA', 'percent_mt', 'HTO_maxID', 'HTO_secondID', 'HTO_margin', 'HTO_classification', 'HTO_classification.global', 'hash.ID', 'HTO_classification_final', 'name', 'library', 'id', 'read', 'pattern', 'sequence', 'feature_type', 'Unnamed: 7', 'in_sample', 'display_name', 'group_x', 'HTO_Doublet_Classification', 'doublet_score', 'predicted_doublet', 'A5_scrublet_predicted_doublet_lib0.1', 'A5_scrublet_doublet_score_lib_0.1', 'A5_scrublet_predicted_doublet_lib0.2', 'A5_scrublet_doublet_score_lib_0.2', 'A5_scrublet_predicted_doublet_lib', 'A5_scrublet_doublet_score_lib', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'valid_cell_filter_dying', 'valid_cell_filter_doublet', 'A7_PCA_50_10_neighbors_cluster', 'A7_PCA_100_10_neighbors_cluster', 'A7_PCA_100_50_neighbors_cluster', 'A7_PCA_500_50_neighbors_cluster', 'A7_Singlet_PCA_50_10_neighbors_cluster', 'A7_Singlet_PCA_100_10_neighbors_cluster', 'A7_Singlet_RB_MT_PCA_50_10_neighbors_cluster', 'A7_Singlet_RB_MT_PCA_100_10_neighbors_cluster', 'library_id', 'B2_Concat_Singlet_PCA_50_10_neighbors_cluster', 'B2_Concat_Singlet_PCA_100_10_neighbors_cluster', 'B2_Concat_Singlet_RB_MT_PCA_50_10_neighbors_cluster', 'B2_Concat_Singlet_RB_MT_PCA_100_10_neighbors_cluster', 'leiden', 'cell_type', 'cluster_cell_type', 'library.hashtag_x', 'age', 'sex', 'm_x', 'classification', 'group_y', 'measurement_x', 'sample_x', 'Unnamed: 0', 'library.hashtag_y', 'm_y', 'measurement_y', 'delta_ef_value_group', 'delta_ef_value', 'sample_y'\n",
       "    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'rb', 'n_cells-L1', 'n_cells_by_counts-L1', 'mean_counts-L1', 'pct_dropout_by_counts-L1', 'total_counts-L1', 'highly_variable-L1', 'means-L1', 'dispersions-L1', 'dispersions_norm-L1', 'n_cells-L10', 'n_cells_by_counts-L10', 'mean_counts-L10', 'pct_dropout_by_counts-L10', 'total_counts-L10', 'highly_variable-L10', 'means-L10', 'dispersions-L10', 'dispersions_norm-L10', 'n_cells-L11', 'n_cells_by_counts-L11', 'mean_counts-L11', 'pct_dropout_by_counts-L11', 'total_counts-L11', 'highly_variable-L11', 'means-L11', 'dispersions-L11', 'dispersions_norm-L11', 'n_cells-L12', 'n_cells_by_counts-L12', 'mean_counts-L12', 'pct_dropout_by_counts-L12', 'total_counts-L12', 'highly_variable-L12', 'means-L12', 'dispersions-L12', 'dispersions_norm-L12', 'n_cells-L13', 'n_cells_by_counts-L13', 'mean_counts-L13', 'pct_dropout_by_counts-L13', 'total_counts-L13', 'highly_variable-L13', 'means-L13', 'dispersions-L13', 'dispersions_norm-L13', 'n_cells-L14', 'n_cells_by_counts-L14', 'mean_counts-L14', 'pct_dropout_by_counts-L14', 'total_counts-L14', 'highly_variable-L14', 'means-L14', 'dispersions-L14', 'dispersions_norm-L14', 'n_cells-L2', 'n_cells_by_counts-L2', 'mean_counts-L2', 'pct_dropout_by_counts-L2', 'total_counts-L2', 'highly_variable-L2', 'means-L2', 'dispersions-L2', 'dispersions_norm-L2', 'n_cells-L3', 'n_cells_by_counts-L3', 'mean_counts-L3', 'pct_dropout_by_counts-L3', 'total_counts-L3', 'highly_variable-L3', 'means-L3', 'dispersions-L3', 'dispersions_norm-L3', 'n_cells-L4', 'n_cells_by_counts-L4', 'mean_counts-L4', 'pct_dropout_by_counts-L4', 'total_counts-L4', 'highly_variable-L4', 'means-L4', 'dispersions-L4', 'dispersions_norm-L4', 'n_cells-L5', 'n_cells_by_counts-L5', 'mean_counts-L5', 'pct_dropout_by_counts-L5', 'total_counts-L5', 'highly_variable-L5', 'means-L5', 'dispersions-L5', 'dispersions_norm-L5', 'n_cells-L6', 'n_cells_by_counts-L6', 'mean_counts-L6', 'pct_dropout_by_counts-L6', 'total_counts-L6', 'highly_variable-L6', 'means-L6', 'dispersions-L6', 'dispersions_norm-L6', 'n_cells-L7', 'n_cells_by_counts-L7', 'mean_counts-L7', 'pct_dropout_by_counts-L7', 'total_counts-L7', 'highly_variable-L7', 'means-L7', 'dispersions-L7', 'dispersions_norm-L7', 'n_cells-L8', 'n_cells_by_counts-L8', 'mean_counts-L8', 'pct_dropout_by_counts-L8', 'total_counts-L8', 'highly_variable-L8', 'means-L8', 'dispersions-L8', 'dispersions_norm-L8', 'n_cells-L9', 'n_cells_by_counts-L9', 'mean_counts-L9', 'pct_dropout_by_counts-L9', 'total_counts-L9', 'highly_variable-L9', 'means-L9', 'dispersions-L9', 'dispersions_norm-L9', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'\n",
       "    uns: 'A5_scrublet_predicted_doublet_lib_colors', 'B2_Concat_Singlet_PCA_100_10_neighbors', 'B2_Concat_Singlet_PCA_50_10_neighbors', 'B2_Concat_Singlet_RB_MT_PCA_100_10_neighbors_cluster_colors', 'HTO_classification.global_colors', 'cell_type_colors', 'data_load_name', 'data_load_time', 'data_save_name', 'data_save_time', 'dendrogram_cell_type', 'dendrogram_cluster_cell_type', 'dendrogram_leiden', 'group_colors', 'hvg', 'leiden', 'leiden_colors', 'library_id_colors', 'pca', 'scrublet', 'umap'\n",
       "    obsm: 'A6_only_highly_variable', 'B2_Concat_Singlet_RB_MT_X_pca', 'B2_Concat_Singlet_X_pca', 'Lib_X_pca', 'X_pca', 'X_umap', 'X_umapA7_PCA_100_10_neighbors', 'X_umapA7_PCA_100_50_neighbors', 'X_umapA7_PCA_500_50_neighbors', 'X_umapA7_PCA_50_10_neighbors', 'X_umapB2_Concat_Singlet_PCA_100_10_neighbors', 'X_umapB2_Concat_Singlet_PCA_50_10_neighbors', 'X_umapB2_Concat_Singlet_RB_MT_PCA_100_10_neighbors', 'X_umapB2_Concat_Singlet_RB_MT_PCA_50_10_neighbors'\n",
       "    varm: 'PCs'\n",
       "    layers: 'A6_normalized', 'A6_normalized_log'\n",
       "    obsp: 'B2_Concat_Singlet_PCA_100_10_neighbors_connectivities', 'B2_Concat_Singlet_PCA_100_10_neighbors_distances', 'B2_Concat_Singlet_PCA_50_10_neighbors_connectivities', 'B2_Concat_Singlet_PCA_50_10_neighbors_distances', 'B2_Concat_Singlet_RB_MT_PCA_100_10_neighbors_connectivities', 'B2_Concat_Singlet_RB_MT_PCA_100_10_neighbors_distances', 'B2_Concat_Singlet_RB_MT_PCA_50_10_neighbors_connectivities', 'B2_Concat_Singlet_RB_MT_PCA_50_10_neighbors_distances'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d611d27b-4f86-4cdb-b2fd-df9658b87ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 148275 × 2000\n",
       "    obs: 'nCount_HTO', 'nFeature_HTO', 'nCount_RNA', 'nFeature_RNA', 'percent_mt', 'HTO_maxID', 'HTO_secondID', 'HTO_margin', 'HTO_classification', 'HTO_classification.global', 'hash.ID', 'HTO_classification_final', 'name', 'library', 'id', 'read', 'pattern', 'sequence', 'feature_type', 'Unnamed: 7', 'in_sample', 'display_name', 'group_x', 'HTO_Doublet_Classification', 'doublet_score', 'predicted_doublet', 'A5_scrublet_predicted_doublet_lib0.1', 'A5_scrublet_doublet_score_lib_0.1', 'A5_scrublet_predicted_doublet_lib0.2', 'A5_scrublet_doublet_score_lib_0.2', 'A5_scrublet_predicted_doublet_lib', 'A5_scrublet_doublet_score_lib', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'valid_cell_filter_dying', 'valid_cell_filter_doublet', 'A7_PCA_50_10_neighbors_cluster', 'A7_PCA_100_10_neighbors_cluster', 'A7_PCA_100_50_neighbors_cluster', 'A7_PCA_500_50_neighbors_cluster', 'A7_Singlet_PCA_50_10_neighbors_cluster', 'A7_Singlet_PCA_100_10_neighbors_cluster', 'A7_Singlet_RB_MT_PCA_50_10_neighbors_cluster', 'A7_Singlet_RB_MT_PCA_100_10_neighbors_cluster', 'library_id', 'B2_Scanorama_Singlet_cluster', 'B2_Scanorama_Singlet_rb_mt_cluster', 'leiden', 'cell_type', 'cluster_cell_type', 'library.hashtag_x', 'age', 'sex', 'm_x', 'classification', 'group_y', 'measurement_x', 'sample_x', 'Unnamed: 0', 'library.hashtag_y', 'm_y', 'measurement_y', 'delta_ef_value_group', 'delta_ef_value', 'sample_y'\n",
       "    var: 'gene_ids', 'feature_types', 'genome', 'mt', 'rb', 'n_cells-L1', 'n_cells_by_counts-L1', 'mean_counts-L1', 'pct_dropout_by_counts-L1', 'total_counts-L1', 'highly_variable-L1', 'means-L1', 'dispersions-L1', 'dispersions_norm-L1', 'n_cells-L10', 'n_cells_by_counts-L10', 'mean_counts-L10', 'pct_dropout_by_counts-L10', 'total_counts-L10', 'highly_variable-L10', 'means-L10', 'dispersions-L10', 'dispersions_norm-L10', 'n_cells-L11', 'n_cells_by_counts-L11', 'mean_counts-L11', 'pct_dropout_by_counts-L11', 'total_counts-L11', 'highly_variable-L11', 'means-L11', 'dispersions-L11', 'dispersions_norm-L11', 'n_cells-L12', 'n_cells_by_counts-L12', 'mean_counts-L12', 'pct_dropout_by_counts-L12', 'total_counts-L12', 'highly_variable-L12', 'means-L12', 'dispersions-L12', 'dispersions_norm-L12', 'n_cells-L13', 'n_cells_by_counts-L13', 'mean_counts-L13', 'pct_dropout_by_counts-L13', 'total_counts-L13', 'highly_variable-L13', 'means-L13', 'dispersions-L13', 'dispersions_norm-L13', 'n_cells-L14', 'n_cells_by_counts-L14', 'mean_counts-L14', 'pct_dropout_by_counts-L14', 'total_counts-L14', 'highly_variable-L14', 'means-L14', 'dispersions-L14', 'dispersions_norm-L14', 'n_cells-L2', 'n_cells_by_counts-L2', 'mean_counts-L2', 'pct_dropout_by_counts-L2', 'total_counts-L2', 'highly_variable-L2', 'means-L2', 'dispersions-L2', 'dispersions_norm-L2', 'n_cells-L3', 'n_cells_by_counts-L3', 'mean_counts-L3', 'pct_dropout_by_counts-L3', 'total_counts-L3', 'highly_variable-L3', 'means-L3', 'dispersions-L3', 'dispersions_norm-L3', 'n_cells-L4', 'n_cells_by_counts-L4', 'mean_counts-L4', 'pct_dropout_by_counts-L4', 'total_counts-L4', 'highly_variable-L4', 'means-L4', 'dispersions-L4', 'dispersions_norm-L4', 'n_cells-L5', 'n_cells_by_counts-L5', 'mean_counts-L5', 'pct_dropout_by_counts-L5', 'total_counts-L5', 'highly_variable-L5', 'means-L5', 'dispersions-L5', 'dispersions_norm-L5', 'n_cells-L6', 'n_cells_by_counts-L6', 'mean_counts-L6', 'pct_dropout_by_counts-L6', 'total_counts-L6', 'highly_variable-L6', 'means-L6', 'dispersions-L6', 'dispersions_norm-L6', 'n_cells-L7', 'n_cells_by_counts-L7', 'mean_counts-L7', 'pct_dropout_by_counts-L7', 'total_counts-L7', 'highly_variable-L7', 'means-L7', 'dispersions-L7', 'dispersions_norm-L7', 'n_cells-L8', 'n_cells_by_counts-L8', 'mean_counts-L8', 'pct_dropout_by_counts-L8', 'total_counts-L8', 'highly_variable-L8', 'means-L8', 'dispersions-L8', 'dispersions_norm-L8', 'n_cells-L9', 'n_cells_by_counts-L9', 'mean_counts-L9', 'pct_dropout_by_counts-L9', 'total_counts-L9', 'highly_variable-L9', 'means-L9', 'dispersions-L9', 'dispersions_norm-L9'\n",
       "    uns: 'A5_scrublet_predicted_doublet_lib_colors', 'B2_Scanorama_Singlet', 'B2_Scanorama_Singlet_rb_mt_cluster_colors', 'HTO_classification.global_colors', 'cell_type_colors', 'data_load_name', 'data_load_time', 'data_save_name', 'data_save_time', 'dendrogram_cell_type', 'dendrogram_cluster_cell_type', 'dendrogram_leiden', 'group_colors', 'hvg', 'leiden', 'leiden_colors', 'library_id_colors', 'scrublet', 'umap'\n",
       "    obsm: 'A6_only_highly_variable', 'Lib_X_pca', 'X_pca', 'X_scanorama', 'X_scanorama_rb_mt', 'X_umap', 'X_umapA7_PCA_100_10_neighbors', 'X_umapA7_PCA_100_50_neighbors', 'X_umapA7_PCA_500_50_neighbors', 'X_umapA7_PCA_50_10_neighbors', 'X_umapB2_Scanorama', 'X_umapB2_Scanorama_Singlet', 'X_umapB2_Scanorama_Singlet_rb_mt'\n",
       "    obsp: 'B2_Scanorama_Singlet_connectivities', 'B2_Scanorama_Singlet_distances', 'B2_Scanorama_Singlet_rb_mt_connectivities', 'B2_Scanorama_Singlet_rb_mt_distances', 'B2_Scanorama_connectivities', 'B2_Scanorama_distances'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0e1cf2bb-8576-495c-b1af-17d53fa2cd62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.59824202e-02, -4.79277383e-02,  1.69348604e-01, ...,\n",
       "        -1.47481223e-02, -1.97042009e-04, -5.70852163e-03],\n",
       "       [-2.19678971e-01, -4.07962940e-03,  7.25166582e-02, ...,\n",
       "         1.44941795e-02,  6.51330260e-03, -4.06548398e-03],\n",
       "       [ 1.64831526e-01, -8.47016785e-02,  7.73093443e-02, ...,\n",
       "        -1.88633590e-02,  5.06839290e-03, -6.11221426e-03],\n",
       "       ...,\n",
       "       [-3.18065025e-01, -9.08959502e-03,  4.91448181e-02, ...,\n",
       "        -1.17716495e-02, -2.54481867e-03,  3.53122972e-02],\n",
       "       [-2.09100558e-01, -8.57580566e-02, -6.83430672e-02, ...,\n",
       "         1.03889310e-02, -9.53097295e-03, -8.07326599e-03],\n",
       "       [ 6.25325174e-02,  1.39028679e-01, -5.17241270e-02, ...,\n",
       "         1.48272639e-02,  3.53074105e-02,  7.87302821e-03]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata2.obsm['X_scanorama_rb_mt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1ce757a0-908c-44c8-a209-63144d109244",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A2M', 'A2M-AS1', 'AAK1', 'AARD', 'ABCA6', 'ABCB1', 'ABCF1', 'ABHD17A',\n",
       "       'ABHD5', 'ABLIM1',\n",
       "       ...\n",
       "       'ZNF148', 'ZNF207', 'ZNF292', 'ZNF331', 'ZNF595', 'ZNF600', 'ZNF644',\n",
       "       'ZNF683', 'ZNF706', 'ZRANB2'],\n",
       "      dtype='object', length=2000)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata2.var_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5c9100cd-2a72-46b1-b7b2-c2e95a2f6c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Rename columsn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "02d857fa-d99e-404d-84c9-77e77f27f780",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obs['cell_type_concat = '] = anndata1.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "46a5904b-9038-428f-a950-08e2d22b459b",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obs['cluster_cell_type_concat = '] = anndata1.obs['cluster_cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e660ef4b-d6de-46ae-8673-9731dbc07e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata2.obs['cell_type_Scanorama'] = anndata2.obs['cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6521defa-bdf2-49ca-a174-6b4cf735ab3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata2.obs['cluster_cell_type_Scanorama'] = anndata2.obs['cluster_cell_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a0f575d5-025a-41d5-aadd-5fdd71342dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Add Scanorama Information to Concat data (anndata1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "219f82d5-eeef-455e-a5a4-ff4c89007031",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Obs matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4a9a225b-6057-467c-9c3b-13ab70dc45d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obs = pd.merge(anndata1.obs ,anndata2.obs[['B2_Scanorama_Singlet_cluster', 'B2_Scanorama_Singlet_rb_mt_cluster', 'cell_type_Scanorama','cluster_cell_type_Scanorama' ]], how='left', left_index = True, right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "40a6aaff-c329-46fe-9739-656cf5a28fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Obsm matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c030bdc4-a26b-4d31-a483-c0326be237a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obsm['X_scanorama'] = anndata2.obsm['X_scanorama']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "24839af6-de74-41fd-8580-9f92b07286b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obsm['X_scanorama_rb_mt'] = anndata2.obsm['X_scanorama_rb_mt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1b67db21-2583-4d64-8654-49f16e3f5c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obsm['X_umapB2_Scanorama'] = anndata2.obsm['X_umapB2_Scanorama']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7d5a6545-fcba-49de-8fb5-f83531690690",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obsm['X_umapB2_Scanorama_Singlet'] = anndata2.obsm['X_umapB2_Scanorama_Singlet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "63196940-e288-4aa4-97e8-305943362174",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obsm['X_umapB2_Scanorama_Singlet_rb_mt'] = anndata2.obsm['X_umapB2_Scanorama_Singlet_rb_mt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b9771c1d-1e28-475c-bb2c-be30b4cd7591",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Scanorama adjustes X matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c83df031-3c6b-48ae-8801-e106bf6b603d",
   "metadata": {},
   "outputs": [],
   "source": [
    "anndata1.obsm['X_corected_Scanorama']=  anndata2.X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3bb31e63-ed62-4280-ad43-83b03f2ff136",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get obs matrix to save as csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1b82d952-f08e-41c5-9065-350482d87768",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_analysis = anndata1.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2d13dc63-edb1-445d-99ab-75277ffa03c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148275, 2000)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(anndata1.obsm['X_corected_Scanorama'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e8d0b3e8-cf3e-4f6e-9848-9be09fe54423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[21.065617],\n",
       "        [25.584118],\n",
       "        [21.224699],\n",
       "        ...,\n",
       "        [22.754984],\n",
       "        [29.788113],\n",
       "        [23.377083]], dtype=float32)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata1.obsm['X_corected_Scanorama'].sum(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074e3081-317a-48a0-8a3b-ab5b0f24003c",
   "metadata": {},
   "source": [
    "# Save combined data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6d307b65-5232-4104-a6e4-188a92dbe3b6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/current/analysis/B/B3_Integrated_Singlet_processed_rna.h5ad\n",
      "../data/current/analysis/B/B3_Obs_Singlet_processed_rna.csv\n",
      "Last modified03/04/2024, 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'library' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'display_name' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'library.hashtag_x' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'sex' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'm_x' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'classification' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'group_y' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'measurement_x' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'sample_x' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'library.hashtag_y' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'm_y' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'measurement_y' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'delta_ef_value_group' as categorical\n",
      "/home/icb/corinna.losert/miniconda3/envs/stark_stemi_Python/lib/python3.9/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'sample_y' as categorical\n"
     ]
    }
   ],
   "source": [
    "data_name = data_path + '/analysis/B/B3_Integrated_Singlet_processed_rna.h5ad'\n",
    "data_name_csv = data_path + '/analysis/B/B3_Obs_Singlet_processed_rna.csv'\n",
    "\n",
    "print(data_name)\n",
    "print(data_name_csv)\n",
    "print('Last modified' + date.today().strftime(\"%m/%d/%Y, %H:%M:%S\"))\n",
    "\n",
    "    \n",
    "anndata1.uns['data_save_time'] = date.today().strftime(\"%m/%d/%Y, %H:%M:%S\") # save the last modified timestamp of the data saved\n",
    "anndata1.uns['data_save_name'] = data_name # save the data path of the data loaded \n",
    "\n",
    "anndata1.write(data_name)\n",
    "adata_analysis.to_csv(data_name_csv)\n",
    "\n",
    "#working_directory = os.getcwd()\n",
    "#repo.index.add([working_directory + '/' + file_name])\n",
    "#commit = repo.index.commit('Save data '+ data_name + ' '+ date.today().strftime(\"%m/%d/%Y, %H:%M:%S\") )\n",
    "#print(commit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f60b32-3a98-4d2f-853e-dbbac45d4569",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Inspect Marker Genes on combined data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4ae80cdb-69cb-46ca-830b-4f48525dda98",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.settings.figdir = 'figures/Supplementary_Figures/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "66fb8472-4b56-4dac-bdbf-ea2b4b39071a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#anndata2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8b110cb3-8ba0-4017-8169-c13546a32c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[184.6254]], dtype=float32)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata2[:,'A2M'].X.sum(axis= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c3a44aec-6b29-49f4-bb94-0996ab1ed455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ArrayView([-19.595531], dtype=float32)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anndata1[:,'A2M'].X.sum(axis= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8d383af6-6fe4-49c4-aae6-4306e06b5027",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_key = 'B3_raw_t_cell_marker'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f84ae42d-b183-4e0c-b941-460fb6267f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_genes_t_cells = {\n",
    "    'T-Cell Marker': [ 'TCF7', 'ID3', 'CCR7','AQP3', 'SELL',\n",
    "                     'IL4R', 'STAT1', 'MAL', 'SOCS1', 'IL2',\n",
    "                      'ODC1', 'PSAT1', 'WARS', 'PYCR1', 'TNF',\n",
    "                      'MIR155HG', 'NME1', 'IFI6', 'LGALS3', 'MAF',\n",
    "                      'TNFRSF18', 'FOXP3', 'IL2RA', 'CTLA4', 'TIGIT',\n",
    "                      'PMCH', 'TNFRSF4', 'KLRB1',  'PDZD8',\n",
    "                      'CXCR6', 'RCAN2', 'PRF1', 'ANXA2', 'CRIP1',\n",
    "                      'ITGA1', 'GZMB', 'IFNG', 'HOPX', 'CCL4', 'CCL3',\n",
    "                      'NKG7', 'GZMK', 'GNLY', 'IFIT3', 'KLRD1', 'MYO1F',\n",
    "                      'CCL5'\n",
    "                     \n",
    "                     ],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6e0896a1-16ad-41a6-939c-c4a5662a566c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#anndata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0e06327b-6b17-4261-8f09-932872a7f713",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1575.2x612 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 609,
       "width": 1279
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.matrixplot(anndata1, \n",
    "                  marker_genes_t_cells, 'B2_Scanorama_Singlet_rb_mt_cluster', \n",
    "                  use_raw = True,\n",
    "                  dendrogram=True, \n",
    "                  cmap='Blues', \n",
    "                  standard_scale='var', \n",
    "                  save = save_key,\n",
    "                  colorbar_title='column scaled\\nexpression', \n",
    "                  show = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "075ce225-cbd6-41b2-8de0-8baf69525eda",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_key = 'B3_cell_type_marker'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8eb18248-cc50-48c5-a6ff-483164ed4e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "## generate marker genes dict\n",
    "\n",
    "marker_genes_dict = {\n",
    "    'B-cell': ['CD79A', 'MS4A1'],\n",
    "    'Dendritic': ['FCER1A', 'CST3'],\n",
    "    'Monocytes - CD14': ['CD14'],\n",
    "    'Monocytes - FCGR3A': ['FCGR3A'],\n",
    "    'NK': ['GNLY', 'NKG7'],\n",
    "    #'Other': ['IGLL1'], ## not in sample data\n",
    "    #'Plasma': ['IGJ'],  ## not in sample data \n",
    "    'T-cell': [#'CD4', \n",
    "        'MKI67', 'S100A8', 'S100A9', 'LYZ', 'CD74' #, 'CD34'\n",
    "              ],\n",
    "    'T-cell-CD4': ['CD3D', 'CD47', 'IL7R','CCR7' , 'CD48' ,'CD44'],\n",
    "    'T-cell-CD8': ['CD3D', 'CD8A', 'CD8B', ]\n",
    "}\n",
    "## ! only used highly variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c42d340f-a633-4715-8dc7-141d824238d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+8N1QgC9hvQAA7ITCF36pY8eOkqSUlBS9+OKLJR43YsQIpaSkSJLuuusut2Szg4LtLIKCgixOUnJ2zAwAgDvNnz9fx48fl2EYGjFihOrWrXtF89SrV08vvPCCJOnYsWNasGCBK2MCXoH1AgCwEwpf+KVevXqpfPnykqT3339fAwcOLNyLqzhnzpzRwIED9eGHH8owDMXGxurxxx/3VFyvs2vXLklSVFSUtUGcYMfMAAC408qVKyVJgYGBGjBgwFXNNWDAAAUGnr88yIoVK646G+BtWC8AADvhom3wS2FhYfr44491//33yzRNffrpp5oyZYo6d+6s5s2bKy4uToZh6Pjx41qzZo3mzJmj9PR0maYph8OhDz/8UOHh4VZ/Gx5lmqZSU1O1dOlSTZ48WYZhOP02Nk+zY2YAADxl/fr1ks7vLxoTE3NVc8XGxqpx48b67bfftG7dOlfEA7wK6wUAYCcUvvBb9957rz755BMNGTJE2dnZSk9P11dffaWvvvqq2ONN01RwcLD+85//qFu3bh5Oe3Vee+01vf766xfcbpqmhg8fruHDhzs1n2maMgxDPXr0cFXEC9gxMwAAdrJr1y4ZhqFmzZq5ZL5mzZpp/fr1he+qAXwJ6wUAYCds6QC/1q9fP61YsaJwT1/TNIv9kKS7775bK1euVP/+/a2MfMWK+56Ku70kH5J0//33q1+/fmQGAMCm0tLSJElly5Z1yXwF8xTMC/gS1gsAwE44wxd+r2nTpvrhhx906NAhLVmyRNu2bSu8MFtsbKwaNmyo1q1bq3LlyhYnvXI1atRQ69ati9y2dOlSGYahmjVrqmrVqpedIygoSGXKlFH9+vXVoUMH3Xbbbe6KK8memQEAsJPTp09Lct3+9pGRkZIosOCbWC8AADuh8AX+v8qVK6tnz55Wx3CLXr16qVevXkVuczjOn+A/ePBgDRs2zIpYl2THzAAA2ElGRoYMw1BAQIBL5iuY59y5cy6ZD/AmrBcAgJ1Q+AJ+qlq1ajIMo/DsAjuwY2YAAAAAAABPovAF/NTevXutjuA0O2YGAAAAAADwJApfwE+tWbNGzZs3tzqGU+yYGQAAb/f1119r8+bNVz1PUlKSC9IA3o31AgCwA8MsuHw94IMSEhIKPzcMQ4sWLbrg9iv1x/nsyOFw6Nprr1WfPn30yCOPqFy5clZHuiw7ZgYAwFs5HA4ZhuHSOU3TlGEYysvLc+m8gNVYLwAAO6HwhU8reGH25xdTV/uCzRdenP3xMQgICFDHjh3Vp08f3XPPPQoM9M6T/+2YGQAAb1VwMVRXs/trJKA4rBcAgJ1Q+MKn/fmFWX5+frG3Xwm7vzi7/fbbtXz5chX8CCgoUsuWLauePXuqd+/eaty4sZURL2DHzAAAeKvXXnvNbXP//e9/d9vcgBVYLwAAO6HwBfzY3r17NWnSJH3++efas2dP4e0FRWqTJk3Ut29fPfTQQ4qJibEqZhF2zAwAAAAAAOApFL4AJEnLli3TpEmTNG3aNKWnp0v6X4kaHByszp07q3fv3urQoYPL9y+7UnbMDAAAAAAA4E4UvgCKyMjI0LfffqvJkydryZIlF2yfUKFCBT322GPq1auXGjRoYGXUQnbMDAAAAAAA4A4UvvBLkydPliQ1b95cDRs2dGrs9u3btXLlSknSY4895vJs3mT//v2F2yfs2rWr8PaCIrVly5bq27evevTooYiICKtiFmHHzAAAAAAAAK5C4Qu/5HA4ZBiGRo0apWHDhjk19oMPPtCTTz4ph8Oh3NxcNyX0Pr/88osmTZqkzz77rPBidQUlamhoqB544AENGTJEN9xwg5Uxi7BjZgAAPOngwYPq2rWr8vLyVK5cOX333XcqVapUicdnZGSoa9euSk5OVlBQkObMmaNy5cq5MTFgHdYLAMAuHFYHAOzKn/5WcvDgQS1btkzLly9Xfn6+DMOQYRgyTVOmaSozM1OTJ09Wy5Yt1blzZ+3fv9/qyLbMDACAp7388stat26dNm7cqBdeeMGp8kqSwsLCNHz4cG3YsEFr167VK6+84qakgPVYLwAAu+AMX/ilqznD9/3339dTTz3l82f4njt3TjNmzNCkSZP0888/Kz8/X9L/iu4mTZqoT58+Sk5O1ueff659+/ZJOn8GbVxcnNatW6fKlSuTGQAAL5WcnKzKlSsrLy9P3bp109dff33Fc3Xv3l3Tp09XSEiIDh8+rOjoaBcmBazHegEA2Aln+AJOSk1NlSSFh4dbnMQ9fv31V/Xv318VK1bUo48+qkWLFikvL0+maSoqKkqDBw/WunXrtH79ej355JN6/fXXtWfPHs2dO1c33HCDTNNUcnKyR89YsGNmAACs9s033xT+8fqvf/3rVc1V8G9odnb2VRVhgLdivQAA7ITCF3BCVlaWvvvuO0lSzZo1LU7jOocOHdLIkSNVv3593XbbbRo/frzS0tIKz4xt06aNvvzySx0+fFjvv/++mjZtesEcHTp00K+//qrrrrtOpmlqyZIlZAYAwIstW7ZMklSrVi3Fx8df1Vzx8fGqXbu2JGnp0qVXnQ3wNqwXAICdBFodAHC3WbNmadasWcXe9/XXX2vz5s2XnSMvL0+pqalavXq1Tpw4IcMw1KZNG1dH9ajLbX9QrVo19e7dW3369FH16tVLNGdgYKDuv/9+bdiwwS174toxMwAA3uq3336TYRhq3bq1S+Zr1aqVdu3apfXr17tkPsCbsF4AAHZC4Qufl5iYqEmTJskwjCK3m6aptWvXau3atSWeq6BYLFu2rNN7/3qbihUr6vTp05L+932FhISoS5cu6tu3r9q2bXvBY1YSBXuQFZSxrmTHzAAAeKsTJ05IOv/vqytUqlRJ0vm9TgFfw3oBANgJhS/8RnHXJ3T2moUVK1ZUhw4d9MorrxS+SLOrtLS0ws+bNGmivn37qmfPnld90YiGDRuqV69eVxuvWHbMDACAtzp79qwkKSwszCXzFcxTMC/gS1gvAAA7ofCFzxs6dKh69+5d+LVpmqpVq5YMw9BLL72kxx9//LJzBAUFqUyZMj51obbo6Gg99NBD6tevX7H7216phIQEJSQkuGy+P7JjZgAAvFV0dLSSk5MLz1y8WikpKZKkqKgol8wHeBPWCwDATih84fMiIyMVGRl5we2maSo6OrrEe736msOHDyskJMTqGE6xY2YAALxVXFyckpOTtWXLFpfMl5SUJEkqV66cS+YDvAnrBQBgJxS+8Es///yzJBVeHdcf2bE4tWNmAAC8VbNmzZSUlKTly5crMzNTpUqVuuK5MjIytGzZMhmGoeuvv96FKQHvwHoBANiJw+oAgBVat26t1q1bq0qVKlZHAQAAsETbtm0lSZmZmRozZsxVzfXee+8pMzOzyLyAL2G9AADshMIX8FNHjx5VXFycYmJi9I9//KPE49544w3FxMSoQoUKhXuPeYodMwMA4K3uvfdexcTESJJGjhypdevWXdE8a9as0T//+U9J5/c57dq1q8syAt6C9QIAsBMKX8BPTZkyRSkpKTp9+rT69etX4nF9+vRRWlqakpOTNWXKFDcmvJAdMwMA4K0iIiL03HPPyTRNnT17Vh06dNCcOXOcmmPOnDm66667lJGRIcMw9Nxzz/nURW6BAqwXAICdUPjCpwUEBBR+BAYGFnv7lX78cT47WrRokSSpRYsWqlixYonHValSRS1btpQkzZ8/3y3ZLsaOmQEA8GbPPfecbrvtNklSSkqKunTpog4dOmjGjBlKTU0tdkxqaqpmzJih9u3bq0uXLkpJSZFhGLrttts0fPhwT8YHPIr1AgCwC3s3VsBlmKbp1O3+ZNOmTTIMQy1atHB6bPPmzbVy5Upt3rzZDckuzo6ZAQDwZoGBgZoxY4YSEhK0adMmSdKCBQu0YMECSef/aBobG6uIiAilp6crJSVFBw4cKBxf8JoqPj5eM2bMUEBAgOe/CcBDWC8AALug8IVPa9WqlQzDKPHt/uT48eOSpAoVKjg9tmDMsWPHXJrpcuyYGQAAbxcbG6tVq1Zp0KBB+uyzz4rcd+DAAR08eLDIbX/+w3nv3r31wQcfqFSpUm7PCliN9QIAsAMKX/i0JUuWOHW7PwkKClJOTk7hFYKdkZWV5YZEl2fHzAAA2EFoaKgmTpyoZ555Rm+//bZ++OEHpaWlSSr+nVGRkZG6++679fzzz6tx48aejgtYivUCAPB2FL6An4qLi9O+ffu0detWp8cWjClbtqyrY12SHTMDAGAnjRs31hdffCHTNJWYmKitW7cqNTVVp0+fVkREhGJjY9WgQQM1adJEDgeXA4F/Y70AALwVhS/80uzZsyWdP2O0Y8eOFqexxg033KC9e/dq3rx5Sk1NVUxMTInGpaSk6Mcff5RhGGrSpIl7Q/6JHTMDAGBHhmGoadOmatq0qdVRAK/HegEAeBv+zAi/1KVLF3Xt2lVjx461Oopl7rnnHklSenq6Bg0aVOJxgwYNUnp6uiSpc+fObsl2MXbMDAAAAAAA4EkUvvBLYWFhkuTXZ3s+/PDDqlGjhiRp2rRpuvvuu7V79+6LHr97927dddddmj59ugzDUNWqVdWrVy8PpT3PjpkBAPAVhw4d0rJly7Rs2TKrowBej/UCALASWzrAL1WsWPGSRaE/CAwM1BdffKE2bdooOztbP/30k+rVq6cbb7xRzZs3V1xcnAzD0PHjx7VmzRqtXLlSpmnKNE2FhIRo8uTJCgoKIjMAAH5i6tSpGj58uBwOh3Jzc62OA3g11gsAwEoUvvBLt956q3bt2qUNGzZYHcVSN998s2bMmKFHHnlEJ0+elGmaWrFihVasWHHBsQVXHI6OjtbkyZPVqlUrT8eVZM/MAAD4koJ/XwFcHusFAGAFtnSAX+rXr58k6ccff9SuXbssTmOtjh07auPGjRo0aJDCwsIKz4j980fp0qU1ZMgQbdy4UXfffTeZAQAAAAAAvJBh8idH+KlnnnlG7733nho0aKA5c+aodu3aVkeyXE5OjlavXq1t27YpJSVFkhQbG6uGDRuqefPmXrkdgh0zAwBgR6NHj9bw4cNlGIby8vKsjgN4NdYLAMBKbOkAv7R//34NHTpU586d0yeffKL4+Hj16NFDHTt2VP369RUZGSmH4/InwFerVs0DaT0nKChIt9xyi2655Raro5SYHTMDAAAAAAC4C4Uv/FKNGjVkGIYkyTAMnTt3TpMnT9bkyZNLPIdhGFyAAQAAAAAAAF6Fwhd+68+7mbC7CQAAAAAAAOyOwhd+qVevXlZH8Drbtm3T559/rl9//VU7d+5UamqqDMNQdHS06tSpo5tvvlmPPfaY6tevb3XUQnbMDAAAAAAA4E5ctA3wc6mpqRo0aJCmTZtW5PaCHw0FW18UeOCBB/TBBx8oJibGYxn/zI6ZAQAAAAAAPIHCF/BjR44c0a233qq9e/desKVFYOD5NwD8eZ9iwzBUs2ZNLV++XBUrVvRY1gJ2zAwAAAAAAOApFL4WqVWrltvmNgxDu3btctv88B033nijVq9eLUkKDQ3V448/rvvvv19NmzZVmTJlJEmnT59WYmKipk2bpvHjxyszM1OS1LJlS61YsYLMAAAAAAAAXoTC1yIOh0OGYbjlQmGGYSgvL8/l88K3fPvtt+rRo4cMw1D9+vX1/fffX/YPEXv27NHdd9+tbdu2yTAMff311+rWrZuHEtszMwAAAAAAgCdR+FqkRo0aF+wz6kp79uxx29zwDd27d9f06dMVFhamTZs2qWbNmiUat2fPHjVq1Ejnzp1T165dL9hH153smBmwq759+7ptbsMwNH78eLfND+DyWONAybFeAAB2Q+ELn8aLs4urUaOGDhw4oB49emjKlClOjX344Yc1depUVa1aVfv27XNTwgvZMTNgVwXvRHEX3okCWIs1DpQc6wUAYDeBVgcA3GnSpElufXFm58L3+PHjkqRrr73W6bEFY5KTk12a6XLsmBmwM3f9TdidP5cBlBxrHCg51gsAwE4ofOHzSvLirCT7Kf/5GLu/OAsJCVFWVpYyMjKcHlswJiQkxNWxLsmOmQG7YmsgwLexxoGSY70AAOyGwhc+7VIvzkzT1H/+8x+99957Mk1T7du3V9euXRUfH6/Y2FiZpqnU1FRt2rRJM2fO1Lx58+RwOPT000/rySeftH3hW6VKFW3ZskU///yz02MLxlSpUsXVsS7JjpkBu6pevbrVEQC4EWscKDnWCwDAbih84dMu9eLsmWee0X/+8x9VrFhR33zzjW6++eZij7vpppvUv39/rVy5Ut27d9eYMWOUm5ur9957z12xPaJNmzZKSkrSqlWrNHXqVD344IMlGjd16lStXLlShmGoTZs2bk5ZlB0zAwAAAAAAeBIXbbPI5MmT3Tr/Y4895tb57W7hwoW68847FRwcrLVr16pRo0YlGrd582bdcMMNysnJ0Y8//qg777zTzUndJykpSU2aNFF+fr4CAwP1r3/9S0899ZQCAgKKPT4vL0//+c9/NGLECOXk5CggIECJiYlXtJ+uP2UGAAAAAADwJApfi7jzSq+GYSg3N9ctc/uKbt26acaMGbr//vv17bffOjW2e/fumj59urp06aIZM2a4KaFnvPDCCxo1alThczEuLk6dOnVSkyZNFBsbK0lKSUlRYmKivv/+ex0/flymacowDD333HN66623yAwAAAAAAOBF2NLBQnTt1lmzZo0Mw9B1113n9NjrrrtO06dP17p169yQzLPeeustpaWlaezYsZKk5ORkTZgw4aLHFzxnBwwYYFlxasfMgB3t37/frfNXq1bNrfMDuDTWOFByrBcAgN1Q+Fpk4sSJVkfwa8ePH5cknTt3zumxBWMK5rC7jz/+WHfddZf+8Y9/aO3atZc89oYbbtDf/vY3derUyUPpimfHzIDd1KhRg3eiAD6MNQ6UHOsFAGA3bOkAv1S5cmUdPXpULVq00IoVK5wae9NNN2nVqlWqWLGiDh065KaE1ti2bZt+/fVX7dixQ6dOnZIkRUdHq06dOrrllltUv359awMWw46ZATtwOBxum9swDOXl5bltfgCXxxoHSo71AgCwG87whV+66aabNGPGDK1evVoTJ05Unz59SjRu0qRJWrVqlQzD0E033eTmlJ7XoEEDNWjQwOoYTrFjZsAOevXqZXUEAG7EGgdKjvUCALAbzvCFX1q4cKHuvPNOGYYhwzD0/PPPa8SIESpTpkyxx58+fVpvvfWW3n77beXl5ckwDM2fP19t2rTxcHIAAAAAAADg4ih8bSI/P18nT55URkaGoqOjFR4ebnUk2xsyZIg++uijwv24QkJC1KpVK8XHxys2NlaSlJKSok2bNmnZsmXKysoqcgGwjz76yLLsAAAAAAAAQHEofL1YcnKyPvjgA33//ffauHFj4d5Oo0aN0rBhw4ocO3r0aGVlZalmzZp66KGHrIhrS88++6zGjBlTWORe7GIMf7z/6aef1ujRo9124QYAAAAAAADgSrGHr5f68ssvNXjwYKWnp0vSZQvJ33//XePGjVPp0qXVqVMnzgAuodGjR6t79+5688039eOPP170CrmBgYG666679MILL/jc3r05OTn69ttvNXPmTCUmJur48eNKT09XSf4WZNVVhe2YGQAAAAAAwBMofL3QpEmT1K9fv8Lyqn79+mrcuLG+/fbbi44ZOHCgxo0bp4yMDH3//fd68MEHPRXX9m688UbNmjVLp0+f1tq1a7Vjxw6dOnVKpmkqOjpaderUUfPmzS+6v6+dJSUlqXv37vr9998Lb/P2k/7tmBnwZUuWLNH333+v9evXKzk5WRkZGXrppZfUr1+/IsetWLFCpmkqNjZW9evXtygtAGexxoGSY70AALwFha+XOXz4sIYMGSLTNBUXF6fPP/9cd955pyRdsvC9/vrrVb16de3fv18LFy6k8L0CZcqUUUJCghISEqyO4hEnTpxQQkKCTpw4UViYlilTRnXq1FF4eLhXbllhx8yAr9q+fbseffRRrV27tvA20zRlGIbS0tIuOP7VV1/VwoULVbFiRe3fv18Oh8OTcQE4iTUOlBzrBQDgbSh8vcx///tfZWZmyuFwaNasWbrxxhtLPPbGG2/Uvn37lJiY6L6A8Blvv/22kpOTZRiGGjRooPfee09t27b16tLUjpkBX7R+/XrdcccdRbZSiYuLU3Jy8kXHDB06VAsWLNCRI0e0aNEitWvXzlNxATiJNQ6UHOsFAOCN+FOil5k3b54Mw1CbNm2cKnslqVatWpKkvXv3uiEZfM0PP/wgSYqJidGyZcvUrl07ry9O7ZgZ8DWZmZnq0qWLzpw5I8MwNGLECB05ckTHjh275Lg777xTMTExkqSffvrJE1EBXAHWOFByrBcAgLfiDF8vU1DWOlv2SircY/bMmTOujOQ3TNNUWlqa0tPTlZ+fX6Ix1apVc3Mq99m3b58Mw1D37t1VtmxZq+OUiB0zA75m7NixOnjwoAzD0Mcff6zHH3+8ROMCAgJ04403au7cuVqzZo2bUwK4UqxxoORYLwAAb0Xh62UyMzMlSaVKlXJ6bHp6+hWP9Vepqan6+OOPNXPmTG3evFlZWVklHmsYhnJzc92Yzr2CgoKUmZmp6tWrWx2lxOyYGfA1s2bNkiTFx8eX+BfbAtdcc43mzp2rnTt3uiMaABdgjQMlx3oBAHgrCl8vU7ZsWR0+fFhHjhxxeuymTZskSeXKlXN1LJ+0ZMkSPfDAA0pJSZGkwj23/EXNmjW1YcMGpaamWh2lxOyYGfA1W7ZskWEYhRcUdUZ0dLQk6eTJk66OBcBFWONAybFeAADeisLXyzRq1EiHDh3SwoULnRp34sQJLViwQIZhqGXLlm5K5zv27dune+65R5mZmYVFb6NGjVSvXj2Fh4f7xb6w3bp1U2JiotPPNSvZMTPgawp+MY2NjXV6bE5OjqTzb2UF4J1Y40DJsV4AAN6KwtfL3HPPPZo3b562bdumb7/9Vt27dy/RuCeffFKZmZkyDEOdO3d2c0r7e+utt5SRkSHDMHTbbbdp3Lhxqlu3rtWxPGrw4MH65JNPlJiYqC+//FI9e/a0OtJl2TEz4GuioqJ04sSJKzojadeuXZLEHtyAF2ONAyXHegEAeCuH1QFQVN++fVWpUqXCzwv2hbqYo0ePqnv37vrmm29kGIbq1q2rbt26eSKqrS1YsECSVLlyZf30009+V/ZK51+gzpw5U+XKldPjjz+uDz74QHl5eVbHuiQ7ZgZ8Ta1atWSaplauXOnUuOzsbM2fP1+GYahRo0ZuSgfgarHGgZJjvQAAvBVn+HqZUqVKafz48erUqZMyMjJ033336YYbblC7du0Kj1m7dq3eeust/frrr1qwYIGysrJkmqZCQ0M1efJkv9iO4GodOnRIhmHo/vvv99uL3L3++uuSzp9VPn78eD311FP65z//qYSEBFWrVk2hoaElmueVV15xZ8wi7JgZ8DXt27fXqlWr9Msvv2jDhg267rrrSjRu9OjROnbsmAzDUPv27d2cEsCVYo0DJcd6AQB4K8P0tytV2cT06dPVr18/nT59+pIFbsH/fWXKlNGXX36pu+++21MRbS0uLk6pqakaOXKkXnjhBavjWMLhcFzw3DJN0+k/GHjyDFs7ZgZ8zaFDh1SvXj2dO3dOdevW1cKFC1WlShVJ/1ujo0aN0rBhwwrHvP/++3rmmWeUn5+vqKgo7d27VxEREVZ9CwAugTUOlBzrBQDgrdjSwUvdf//9SkxMVO/evRUcHCzTNIv9CAgIUM+ePbV+/XrKXicUbOFw9OhRi5NY68/Pp+Juu9QHmQH/U7lyZb366qsyTVM7duxQo0aN9MILLxS5mOLJkye1atUqvfvuu2rSpImefvrpwj+0vPPOO/xiC3gx1jhQcqwXAIC34gxfGzh79qxWrFihTZs2KSUlRbm5uYqNjVX9+vXVunVrRUZGWh3Rdv7zn/9o6NChqlevnrZt22Z1HEssXbrUJfO0bt3aJfOUhB0zA75q2LBhGjNmjCRd9iz7gpcaL7/8st544w13RwPgAqxxoORYLwAAb0PhC7907tw5tWzZUps3b9bbb7+tZ5991upIAGA706dP14svvqidO3de8rgaNWro7bff5qKigM2wxoGSY70AALwJhS/81t69e3X33Xfr999/1zPPPKOXXnpJ0dHRVscCAFsxTVOLFi3SkiVLin0nSps2bdShQwcFBARYHRXAFWCNAyXHegEAeAsKX/ilvn37SpLS0tL03XffyTAMBQcHq1mzZqpWrZpCQ0MvO4dhGBo/fry7owIAAAAAAAAlRuHrZU6dOqV27dopLy9Pjz76qJ555pkSjfv3v/+tL774QsHBwVq0aJFKly7t5qT2VnDV3D8yTfOye279WcEFF3xNbm6uTp48KcMwFBUVpcDAQKsjXZYdMwMAAAAAALiaw+oAKGrq1Klat26dNmzYoE6dOpV43D333KPExEStWbNGX3/9tRsT+g7TNIt8FHfbpT58zbx589SzZ0/VrFlTISEhqlChgsqXL6+QkBDVrFlTPXv21Pz5862OWYQdMwMAAAAAALgTZ/h6mfvuu08zZ85UkyZNtH79eqfGNm3aVBs3btR9992nb7/91k0JfcO+fftcMk/16tVdMo+Vdu7cqUcffVSrV68uvO3PPxb+eOZzy5Yt9cUXX6hWrVoey/hndswM+Irc3Fy9+OKLysvL04033qgHHnigROO+/vprrVq1SsHBwXrzzTedfkcFAM9gjQMlx3oBAHgr3vPsZRITE2UYhm666Sanx950003asGGDEhMTXR/Mx/hCUesKW7duVatWrZSamlqkMI2KilLZsmVlmqZSUlJ06tSpwvtWrlypG2+8UcuWLVODBg3I7EPy8/PlcPDGD1za7NmzNXr0aBmGoS5dupR4XPny5TVmzBgZhqFbb71V99xzj/tCArhirHGg5FgvAABvxW/2Xubo0aOSpMqVKzs9tkqVKpKkI0eOuDQTfFNubq66du2qlJQUmaapcuXK6Z133tGePXuUmpqq7du3a8eOHUpNTdXevXv1zjvvqHz58pKkEydOqGvXrh7fw9iOmd0hLS3N5XPm5uaqR48eLp8XvmfOnDmSpKpVq6pVq1YlHnf77beratWqkqRZs2a5JRuAq8caB0qO9QIA8FYUvl6m4O08ubm5To8tKLKuZCz8z2effabt27fLMAzdcsst2rZtm4YNG1bs2c/VqlXTsGHDtHXrVt16662SpO3bt+uzzz4jswU6duyozMxMl82Xm5urbt26acaMGS6bE75rzZo1MgzDqV9sC7Ru3VqmaRbZjgWAd2GNAyXHegEAeCsKXy9TtmxZSef3KHVWwZiCOeCc06dPa9myZZo+fbo+//xzbd261epIbvXdd99JkiIjI/Xdd98pKirqsmOioqI0ffr0wmOnT5/uxoQXsmNmd1i5cqXuueceZWVlXfVcOTk5uu+++zR79mwXJIM/KNgDvU6dOk6PrV27dpE5AHgf1jhQcqwXAIC3ovD1Mtddd51M09RPP/2kjIyMEo87e/as5s6dK8MwdM0117gxoe/58ccfdfvttys2NlZ33HGHHnjgAfXu3Vs//vjjBcc+9NBDuvPOOzV06FDPB3WxDRs2yDAMde3a1ak/EsTFxalr164yTVMbNmxwY8IL2TGzuyxZskTdunW7qjP6s7Ozde+99+qHH36QJPbvRYlkZ2dLkoKCgpweGxwcLElO/fsGwLNY40DJsV4AAN6K3+69TMeOHSVJKSkpevHFF0s8bsSIEUpJSZEk3XXXXW7J5mvy8/PVv39/3XPPPVq+fLny8vJkmmaRC4H92TXXXKOFCxfq/fff18GDBz2Y1vWSk5MlXd0ZCQXPOU+xY2Z3KFeunCRp7ty5evjhhy/5nL2YrKwsderUSfPmzZNpmgoMDNSkSZNcnBS+KDY2VpJ06NAhp8cWjCnJ2fkArMEaB0qO9QIA8FYUvl6mV69ehReZev/99zVw4ECdOXPmosefOXNGAwcO1IcffijDMBQbG6vHH3/cU3Ft7amnntK4ceNkmqbKlCmjAQMG6IMPPrjkmN69e8swDJmmafu3wIeHh0uSTp065fTYgjFhYWEuTHR5dszsDgsWLFB0dLSk81tU9OnTx6nxGRkZuvvuu7Vw4cLCsnfy5Mnq2bOnO+LCx9SpU0emaWr+/PlOj50/f74Mw1DNmjXdkAyAK7DGgZJjvQAAvBWFr5cJCwvTxx9/XHjxtk8//VSVK1fWI488ovfee09TpkzRV199pffee0+PPPKIqlSpok8//VSmacowDH344YeFpRgubu3atYUlecuWLbV9+3Z99NFHGjRo0CXHVa1aVY0bN5Yk/fzzz56I6jbVq1eXaZrFbl1xOT/99JMkqUaNGi5OdWl2zOwO8fHx+umnnxQRESFJ+vzzzzVkyJASjS0oe3/++WeZpqmgoCBNmTJFDz74oDsjw4e0bdtWkrRr1y5NmDChxOPGjx9fuNd8u3bt3JINwNVjjQMlx3oBAHgtE15p3LhxZkhIiGkYhmkYhulwOC76YRiGGRISYn7yySdWx7aNJ554wjQMwyxdurR55MiRIvcVPN6jR48uduzjjz9uGoZhNmzY0BNR3WbEiBGX/V6LM3r06MJxL774ohsTXsiOmd1p2bJlZunSpQt/Fjz//POXPP706dPmrbfeWuTnxowZMzyUFr7i4MGDZmhoqOlwOMzQ0FDzq6++uuyYr776qvDftJCQEHPPnj3uDwrgirDGgZJjvQAAvBWFrxdbv369eddddxWWvsV9OBwO85577jF/++03q+PaSt26dU2Hw2E+/PDDF9x3uULx73//u2kYhhkZGenmlO61b98+s1SpUoVl4VNPPWWmpKRc9PiUlBTzqaeeKiwLS5UqZe7bt8+Die2Z2d3mz59f+IuGw+EwX3vttWKPS0tLM2+66abCxyI0NNScPXu2h9PCV7z22mtF/hjZqlUr89NPPzUTExPNQ4cOmYcPHzYTExPNTz/91GzVqlXh887hcJgvv/yy1fEBXAZrHCg51gsAwBsZpnkFV/uBRx06dEhLlizRtm3bCi84FRsbq4YNG6p169aqXLmyxQntJyIiQhkZGXr99df18ssvF7nP4XDIMAyNGjVKw4YNu2DsW2+9pRdffFGBgYGFV+a1q3fffVfPPvts4RYiQUFBuu2229SkSZPCi1CkpKQoMTFRy5cvV05OTuH2If/+97/19NNPk9kLzJ49W926dVNeXp4k6Z133tEzzzxTeP+pU6fUvn17rV27VqZpKjQ0VNOmTeMCj7gqvXv31uTJkwvX4qUUvNR45JFHNHnyZHdHA+ACrHGg5FgvAABvE2h1AFxe5cqVuZiSi13N3zkKSvfIyEhXxbHMM888o+zsbP3tb39Tbm6usrOztXjxYi1evPiCYwses8DAQL3xxhuWFad2zOxunTt31uTJk/XII48oPz9fzz33nEqXLq3+/fvr5MmTateunX777TeZpqlSpUrpu+++05133ml1bNjcpEmT1Lx5c7322ms6ceLEJY8tW7asXn31VQ0ePNhD6QBcLdY4UHKsFwCAt+EMX/il2rVra+/everVq9cFF1i43Bm+rVu31vLly9W4cWMlJiZ6KLF7rV+/XiNHjtTs2bOVm5tb7DGBgYG699579dJLL6lp06YeTnghO2Z2twkTJuiJJ56QaZpyOBx69913NXHixMLnaVhYmGbNmqU2bdpYGxQ+5dy5c5o+ffpF34ly++2367777lNoaKjFSQFcCdY4UHKsFwCAt6DwtZH09HSlpqZKkqpVq2ZxGnvr2bOnvvrqK5UvX1779+9XUFBQ4X2XKnyTkpLUuHFjSdKQIUP0n//8x6O53S09PV2rV6/Wjh07dOrUKUlSdHS06tSpoxYtWig8PNzagMWwY2Z3+u9//6unn366yFsKTdNUWFiYvv/+e91+++3WhQMAAAAAAG5H4Wsjo0eP1vPPPy/DMC56RiNK5rvvvtP9998vwzD04osv6h//+EfhfRcrfNPS0tS6dWtt3LhRhmFo+fLluvnmm62ID1zSm2++WWRv6oiICP3www+69dZbLUwFAAAAAAA8gT18bYZ+3jW6du2qZs2aad26dXrzzTd19uxZvfrqq8Xuy5ufn69Zs2bp+eef165du2QYhtq1a0fZC8ssW7bskvffcsstateunRYsWCDDMDRixAjl5+dfdpwktWrVylUxAQAAAACABTjD10ZGjx6t4cOHyzAM5eXlWR3H9vbs2aObbrpJx48fl2EYCg4OVrNmzfTrr7/KMAzdcMMNiomJ0erVqwu3CjBNU1WqVNHatWtVrlw5a78B+K2Cs9BdjXcP4GqNGzdOI0eOlGEY2rVrl9VxALgYaxwoOdYLAMBKnOELv1WzZk0tX75cPXr0UGJiorKysrRixYrCIm3t2rWSip5V3aRJE3333XeUvfAKl/t7XcFzmb/rwVPS0tK0d+9et/xBAoD1WONAybFeAABWovCFX6tbt65Wr16tKVOmaOzYsVqzZo1ycnKKHONwONS0aVMNHDhQvXr1UmCg/ZZNQkKCW+Y1DEOLFi1yy9x2zOwp1apV45cHAAAAAABQLPs1V4CLBQYG6rHHHtNjjz2mzMxMbdu2TSkpKcrNzVVsbKzq1q2rqKgoq2NelSVLlri8IDRN062lox0ze8revXutjgAAAAAAALwUhS/wB6VKlVLTpk2tjuE2dnxrvx0zAwAAAAAAWIXCF/AjAQEBuuOOO9SrVy81aNDA6jglYsfMgK/Yv3+/JCkmJkbh4eEWpwHgaqxxoORYLwAAO6HwtZH+/furW7duVsfwWenp6Tpz5ozKlCmj0qVLWx3HpSIjI5WWlqa8vDwtWrRIixYtUvPmzdWnTx/16NHDK7essGNmwNfUqFFDhmFo1KhRGjZsmNVxALgYaxwoOdYLAMBOHFYHQMlFRESoevXqql69utVRbC87O1uff/65+vTpozp16igkJES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\n",
      "text/plain": [
       "<Figure size 835.2x612 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 654,
       "width": 702
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.matrixplot(anndata1, \n",
    "                  marker_genes_dict, 'B2_Scanorama_Singlet_rb_mt_cluster', \n",
    "                  use_raw = True,\n",
    "                  dendrogram=True, \n",
    "                  cmap='Blues', \n",
    "                  standard_scale='var',\n",
    "                  save = save_key,\n",
    "                  colorbar_title='column scaled\\nexpression', \n",
    "                  show = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe972d9a-658f-4d09-8f98-47af89886551",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7f5c971-5f2a-45da-b2fb-57258a52d6dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "toc-showcode": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}