GLM_model_global_iMSMS_compound.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27dd927e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from paths import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfab4162",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "warnings.filterwarnings(\"ignore\", message=\"`distplot` is a deprecated function\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"DeprecationWarning\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e0ff51",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "684a580e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PARENT_SAMPLE_NAME</th>\n",
       "      <th>35</th>\n",
       "      <th>50</th>\n",
       "      <th>55</th>\n",
       "      <th>62</th>\n",
       "      <th>93</th>\n",
       "      <th>98</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>117</th>\n",
       "      <th>...</th>\n",
       "      <th>999926106</th>\n",
       "      <th>999926107</th>\n",
       "      <th>999926108</th>\n",
       "      <th>999926109</th>\n",
       "      <th>999926111</th>\n",
       "      <th>999926119</th>\n",
       "      <th>999926158</th>\n",
       "      <th>999926162</th>\n",
       "      <th>999926193</th>\n",
       "      <th>999926220</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UCSF-06758</td>\n",
       "      <td>0.589314</td>\n",
       "      <td>-0.396834</td>\n",
       "      <td>-0.346427</td>\n",
       "      <td>-0.313341</td>\n",
       "      <td>0.851420</td>\n",
       "      <td>-0.739952</td>\n",
       "      <td>-0.544856</td>\n",
       "      <td>0.874154</td>\n",
       "      <td>-0.240343</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.682286</td>\n",
       "      <td>-0.511435</td>\n",
       "      <td>-0.405570</td>\n",
       "      <td>-0.296065</td>\n",
       "      <td>-1.399005</td>\n",
       "      <td>0.722791</td>\n",
       "      <td>-2.117685</td>\n",
       "      <td>-0.128829</td>\n",
       "      <td>0.164402</td>\n",
       "      <td>-0.501802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UCSF-06759</td>\n",
       "      <td>0.049593</td>\n",
       "      <td>0.139420</td>\n",
       "      <td>0.369308</td>\n",
       "      <td>-0.159880</td>\n",
       "      <td>0.238199</td>\n",
       "      <td>-0.249942</td>\n",
       "      <td>-0.490267</td>\n",
       "      <td>-0.172927</td>\n",
       "      <td>-1.654431</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140373</td>\n",
       "      <td>-0.085745</td>\n",
       "      <td>-0.288537</td>\n",
       "      <td>0.532617</td>\n",
       "      <td>-0.524474</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>-0.635392</td>\n",
       "      <td>-0.024504</td>\n",
       "      <td>-0.521084</td>\n",
       "      <td>0.346311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCSF-06760</td>\n",
       "      <td>-0.416035</td>\n",
       "      <td>-1.045957</td>\n",
       "      <td>-0.644778</td>\n",
       "      <td>-0.740068</td>\n",
       "      <td>0.530813</td>\n",
       "      <td>-0.135617</td>\n",
       "      <td>-0.911855</td>\n",
       "      <td>-0.459714</td>\n",
       "      <td>-1.654431</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345986</td>\n",
       "      <td>-0.028342</td>\n",
       "      <td>-0.215191</td>\n",
       "      <td>0.774885</td>\n",
       "      <td>-0.000414</td>\n",
       "      <td>-0.513820</td>\n",
       "      <td>-0.001856</td>\n",
       "      <td>-1.159971</td>\n",
       "      <td>0.273635</td>\n",
       "      <td>-0.517265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCSF-06761</td>\n",
       "      <td>-0.386523</td>\n",
       "      <td>0.151742</td>\n",
       "      <td>0.323162</td>\n",
       "      <td>0.580034</td>\n",
       "      <td>0.368174</td>\n",
       "      <td>0.307989</td>\n",
       "      <td>0.387318</td>\n",
       "      <td>0.176586</td>\n",
       "      <td>-0.504245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026680</td>\n",
       "      <td>-0.227256</td>\n",
       "      <td>-1.053303</td>\n",
       "      <td>-0.488734</td>\n",
       "      <td>1.394431</td>\n",
       "      <td>0.809892</td>\n",
       "      <td>-0.817902</td>\n",
       "      <td>-2.311686</td>\n",
       "      <td>-2.726539</td>\n",
       "      <td>-0.139303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UCSF-06762</td>\n",
       "      <td>-0.352323</td>\n",
       "      <td>-0.064386</td>\n",
       "      <td>0.709778</td>\n",
       "      <td>0.691759</td>\n",
       "      <td>0.359623</td>\n",
       "      <td>-0.057788</td>\n",
       "      <td>-0.368919</td>\n",
       "      <td>0.571490</td>\n",
       "      <td>0.070276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085673</td>\n",
       "      <td>-0.212734</td>\n",
       "      <td>-0.409145</td>\n",
       "      <td>-1.118993</td>\n",
       "      <td>-0.428157</td>\n",
       "      <td>-0.219333</td>\n",
       "      <td>-0.668027</td>\n",
       "      <td>-2.311686</td>\n",
       "      <td>-1.176159</td>\n",
       "      <td>-0.038116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1554 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PARENT_SAMPLE_NAME        35        50        55        62        93  \\\n",
       "0         UCSF-06758  0.589314 -0.396834 -0.346427 -0.313341  0.851420   \n",
       "1         UCSF-06759  0.049593  0.139420  0.369308 -0.159880  0.238199   \n",
       "2         UCSF-06760 -0.416035 -1.045957 -0.644778 -0.740068  0.530813   \n",
       "3         UCSF-06761 -0.386523  0.151742  0.323162  0.580034  0.368174   \n",
       "4         UCSF-06762 -0.352323 -0.064386  0.709778  0.691759  0.359623   \n",
       "\n",
       "         98       111       112       117  ...  999926106  999926107  \\\n",
       "0 -0.739952 -0.544856  0.874154 -0.240343  ...  -0.682286  -0.511435   \n",
       "1 -0.249942 -0.490267 -0.172927 -1.654431  ...   0.140373  -0.085745   \n",
       "2 -0.135617 -0.911855 -0.459714 -1.654431  ...   0.345986  -0.028342   \n",
       "3  0.307989  0.387318  0.176586 -0.504245  ...   0.026680  -0.227256   \n",
       "4 -0.057788 -0.368919  0.571490  0.070276  ...   0.085673  -0.212734   \n",
       "\n",
       "   999926108  999926109  999926111  999926119  999926158  999926162  \\\n",
       "0  -0.405570  -0.296065  -1.399005   0.722791  -2.117685  -0.128829   \n",
       "1  -0.288537   0.532617  -0.524474   0.027085  -0.635392  -0.024504   \n",
       "2  -0.215191   0.774885  -0.000414  -0.513820  -0.001856  -1.159971   \n",
       "3  -1.053303  -0.488734   1.394431   0.809892  -0.817902  -2.311686   \n",
       "4  -0.409145  -1.118993  -0.428157  -0.219333  -0.668027  -2.311686   \n",
       "\n",
       "   999926193  999926220  \n",
       "0   0.164402  -0.501802  \n",
       "1  -0.521084   0.346311  \n",
       "2   0.273635  -0.517265  \n",
       "3  -2.726539  -0.139303  \n",
       "4  -1.176159  -0.038116  \n",
       "\n",
       "[5 rows x 1554 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = \"serum\"\n",
    "\n",
    "if sample == \"serum\":\n",
    "    filename = GLOBAL_SERUM_DATA_FILENAME\n",
    "else:\n",
    "    filename = GLOBAL_STOOL_DATA_FILENAME\n",
    "    \n",
    "\n",
    "file_path = os.path.join(DATA_ROOT_PATH, filename)\n",
    "\n",
    "sheet_name = [\"Chemical Annotation\", \"Sample Meta Data\", \"Log Transformed Data\"]\n",
    "\n",
    "analyte_metadata = pd.read_excel(file_path, engine='openpyxl', sheet_name=sheet_name[0])\n",
    "patient_metadata = pd.read_excel(file_path, engine='openpyxl', sheet_name=sheet_name[1])\n",
    "data = pd.read_excel(file_path, engine='openpyxl', sheet_name=sheet_name[2])\n",
    "\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a44486e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PARENT_SAMPLE_NAME', 'CLIENT_IDENTIFIER', 'NEG', 'POLAR', 'POS EARLY',\n",
       "       'POS LATE', 'AGE', 'BMI', 'BOX_NUMBER', 'CLIENT_MATRIX',\n",
       "       'CLIENT_SAMPLE_ID', 'CLIENT_SAMPLE_NUMBER', 'GENDER', 'GROUP_NAME',\n",
       "       'GROUP_NAME_2', 'GROUP_NAME_3', 'RACE__ETHNICITY', 'SAMPLE_AMOUNT',\n",
       "       'SAMPLE_AMOUNT_UNITS', 'SAMPLE_BOX_LOCATION', 'SAMPLE_DESCRIPTION',\n",
       "       'TREATMENT', 'TREATMENT_2', 'VOL_EXTRACTED'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_metadata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a64a9afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['717', '716', '718', '715', '765', '764', '714'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# patient_metadata[\"VOL_EXTRACTED\"].unique()\n",
    "patient_metadata.loc[:, \"site_code\"] = patient_metadata[\"CLIENT_SAMPLE_ID\"].apply(lambda x:x[0:3])\n",
    "patient_metadata.site_code.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "378bf828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21,)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_metadata[(patient_metadata.site_code==\"765\") & (patient_metadata.GROUP_NAME==\"MS\")][\"CLIENT_SAMPLE_ID\"].unique().shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a71b1691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2100/500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a4bd9c",
   "metadata": {},
   "source": [
    "## Analyte selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f4363d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_metabolomics_compound_spoke_map = pd.read_csv(os.path.join(OUTPUT_PATH, \"global_metabolomics_compound_spoke_map.csv\"))\n",
    "\n",
    "analyte_columns = list(data.columns)\n",
    "analyte_columns.remove(\"PARENT_SAMPLE_NAME\")\n",
    "\n",
    "analyte_columns_selected = global_metabolomics_compound_spoke_map[global_metabolomics_compound_spoke_map.CHEM_ID.isin(analyte_columns)][\"CHEM_ID\"].unique()\n",
    "\n",
    "data_with_analyte_columns_selected = data[[\"PARENT_SAMPLE_NAME\"]+list(analyte_columns_selected)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd7f239",
   "metadata": {},
   "source": [
    "## Merging patient metadata with analyte concentration data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc0721d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PARENT_SAMPLE_NAME</th>\n",
       "      <th>35</th>\n",
       "      <th>50</th>\n",
       "      <th>62</th>\n",
       "      <th>93</th>\n",
       "      <th>98</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>117</th>\n",
       "      <th>144</th>\n",
       "      <th>...</th>\n",
       "      <th>BMI</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>CLIENT_SAMPLE_ID</th>\n",
       "      <th>CLIENT_MATRIX</th>\n",
       "      <th>TREATMENT</th>\n",
       "      <th>SAMPLE_AMOUNT_UNITS</th>\n",
       "      <th>house</th>\n",
       "      <th>site</th>\n",
       "      <th>Disease_label</th>\n",
       "      <th>Gender_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UCSF-06758</td>\n",
       "      <td>0.589314</td>\n",
       "      <td>-0.396834</td>\n",
       "      <td>-0.313341</td>\n",
       "      <td>0.851420</td>\n",
       "      <td>-0.739952</td>\n",
       "      <td>-0.544856</td>\n",
       "      <td>0.874154</td>\n",
       "      <td>-0.240343</td>\n",
       "      <td>0.562101</td>\n",
       "      <td>...</td>\n",
       "      <td>19.422970</td>\n",
       "      <td>F</td>\n",
       "      <td>71701-0004</td>\n",
       "      <td>serum</td>\n",
       "      <td>Treated</td>\n",
       "      <td>uL</td>\n",
       "      <td>7170004</td>\n",
       "      <td>717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UCSF-06759</td>\n",
       "      <td>0.049593</td>\n",
       "      <td>0.139420</td>\n",
       "      <td>-0.159880</td>\n",
       "      <td>0.238199</td>\n",
       "      <td>-0.249942</td>\n",
       "      <td>-0.490267</td>\n",
       "      <td>-0.172927</td>\n",
       "      <td>-1.654431</td>\n",
       "      <td>-0.540335</td>\n",
       "      <td>...</td>\n",
       "      <td>25.501750</td>\n",
       "      <td>F</td>\n",
       "      <td>71601-0150</td>\n",
       "      <td>serum</td>\n",
       "      <td>Off</td>\n",
       "      <td>uL</td>\n",
       "      <td>7160150</td>\n",
       "      <td>716</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCSF-06760</td>\n",
       "      <td>-0.416035</td>\n",
       "      <td>-1.045957</td>\n",
       "      <td>-0.740068</td>\n",
       "      <td>0.530813</td>\n",
       "      <td>-0.135617</td>\n",
       "      <td>-0.911855</td>\n",
       "      <td>-0.459714</td>\n",
       "      <td>-1.654431</td>\n",
       "      <td>-0.672326</td>\n",
       "      <td>...</td>\n",
       "      <td>34.970939</td>\n",
       "      <td>M</td>\n",
       "      <td>71602-0150</td>\n",
       "      <td>serum</td>\n",
       "      <td>Control</td>\n",
       "      <td>uL</td>\n",
       "      <td>7160150</td>\n",
       "      <td>716</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCSF-06761</td>\n",
       "      <td>-0.386523</td>\n",
       "      <td>0.151742</td>\n",
       "      <td>0.580034</td>\n",
       "      <td>0.368174</td>\n",
       "      <td>0.307989</td>\n",
       "      <td>0.387318</td>\n",
       "      <td>0.176586</td>\n",
       "      <td>-0.504245</td>\n",
       "      <td>0.127822</td>\n",
       "      <td>...</td>\n",
       "      <td>26.544467</td>\n",
       "      <td>M</td>\n",
       "      <td>71601-0164</td>\n",
       "      <td>serum</td>\n",
       "      <td>Treated</td>\n",
       "      <td>uL</td>\n",
       "      <td>7160164</td>\n",
       "      <td>716</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UCSF-06762</td>\n",
       "      <td>-0.352323</td>\n",
       "      <td>-0.064386</td>\n",
       "      <td>0.691759</td>\n",
       "      <td>0.359623</td>\n",
       "      <td>-0.057788</td>\n",
       "      <td>-0.368919</td>\n",
       "      <td>0.571490</td>\n",
       "      <td>0.070276</td>\n",
       "      <td>-0.425296</td>\n",
       "      <td>...</td>\n",
       "      <td>31.176140</td>\n",
       "      <td>F</td>\n",
       "      <td>71602-0164</td>\n",
       "      <td>serum</td>\n",
       "      <td>Control</td>\n",
       "      <td>uL</td>\n",
       "      <td>7160164</td>\n",
       "      <td>716</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PARENT_SAMPLE_NAME        35        50        62        93        98  \\\n",
       "0         UCSF-06758  0.589314 -0.396834 -0.313341  0.851420 -0.739952   \n",
       "1         UCSF-06759  0.049593  0.139420 -0.159880  0.238199 -0.249942   \n",
       "2         UCSF-06760 -0.416035 -1.045957 -0.740068  0.530813 -0.135617   \n",
       "3         UCSF-06761 -0.386523  0.151742  0.580034  0.368174  0.307989   \n",
       "4         UCSF-06762 -0.352323 -0.064386  0.691759  0.359623 -0.057788   \n",
       "\n",
       "        111       112       117       144  ...        BMI  GENDER  \\\n",
       "0 -0.544856  0.874154 -0.240343  0.562101  ...  19.422970       F   \n",
       "1 -0.490267 -0.172927 -1.654431 -0.540335  ...  25.501750       F   \n",
       "2 -0.911855 -0.459714 -1.654431 -0.672326  ...  34.970939       M   \n",
       "3  0.387318  0.176586 -0.504245  0.127822  ...  26.544467       M   \n",
       "4 -0.368919  0.571490  0.070276 -0.425296  ...  31.176140       F   \n",
       "\n",
       "   CLIENT_SAMPLE_ID  CLIENT_MATRIX  TREATMENT  SAMPLE_AMOUNT_UNITS    house  \\\n",
       "0        71701-0004          serum    Treated                   uL  7170004   \n",
       "1        71601-0150          serum        Off                   uL  7160150   \n",
       "2        71602-0150          serum    Control                   uL  7160150   \n",
       "3        71601-0164          serum    Treated                   uL  7160164   \n",
       "4        71602-0164          serum    Control                   uL  7160164   \n",
       "\n",
       "   site  Disease_label  Gender_label  \n",
       "0   717              1             0  \n",
       "1   716              1             0  \n",
       "2   716              0             1  \n",
       "3   716              1             1  \n",
       "4   716              0             0  \n",
       "\n",
       "[5 rows x 1220 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_metadata_columns = [\"PARENT_SAMPLE_NAME\", \"CLIENT_IDENTIFIER\", \"GROUP_NAME\", \"AGE\", \"BMI\", \"GENDER\", \"CLIENT_SAMPLE_ID\", \"CLIENT_MATRIX\", \"TREATMENT\", \"SAMPLE_AMOUNT_UNITS\"]\n",
    "patient_metadata_selected_columns = patient_metadata[selected_metadata_columns]\n",
    "patient_metadata_selected_columns.loc[:, 'house'] = (patient_metadata_selected_columns['CLIENT_SAMPLE_ID'].str[:3] + patient_metadata_selected_columns['CLIENT_SAMPLE_ID'].str[-4:])\n",
    "patient_metadata_selected_columns.loc[:, 'site'] = patient_metadata_selected_columns.loc[:, 'CLIENT_SAMPLE_ID'].str[:3]\n",
    "le = LabelEncoder()\n",
    "patient_metadata_selected_columns.loc[:, 'Disease_label'] = le.fit_transform(patient_metadata_selected_columns['GROUP_NAME'])\n",
    "patient_metadata_selected_columns.loc[:, 'Gender_label'] = le.fit_transform(patient_metadata_selected_columns['GENDER'])\n",
    "\n",
    "data_with_patient_metadata = pd.merge(data_with_analyte_columns_selected, patient_metadata_selected_columns, on=\"PARENT_SAMPLE_NAME\")\n",
    "data_with_patient_metadata.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be70c75e",
   "metadata": {},
   "source": [
    "## Reversing the log transformation to the analyte concentration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "05f77da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_patient_metadata_reverse_log_transformed = data_with_patient_metadata.copy()\n",
    "for analyte in analyte_columns_selected:\n",
    "    analyte_concentration = data_with_patient_metadata_reverse_log_transformed[analyte].values\n",
    "    data_with_patient_metadata_reverse_log_transformed.loc[:, analyte] = np.exp(analyte_concentration)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf3a2d9",
   "metadata": {},
   "source": [
    "## Distribution of concentration values of first 8 analytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "38b94c2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 4x4 subplot grid\n",
    "fig, axes = plt.subplots(4, 2, figsize=(12, 12))\n",
    "\n",
    "# Flatten the subplot grid for easier indexing\n",
    "axes = axes.flatten()\n",
    "\n",
    "df = data_with_patient_metadata\n",
    "for i, analyte in enumerate(analyte_columns_selected[0:8]):\n",
    "    analyte_name = analyte_metadata[analyte_metadata[\"CHEM_ID\"]==analyte][\"CHEMICAL_NAME\"].values[0]\n",
    "    # Select the data for the current analyte\n",
    "    subset = df[analyte]\n",
    "    # Compute the skewness of the data\n",
    "    skewness = round(subset.skew(), 2)\n",
    "    # Plot the histogram and kde plot\n",
    "    sns.distplot(subset, hist=True, kde=True, ax=axes[i], \n",
    "                 kde_kws={'color': 'r', 'lw': 3},\n",
    "                 hist_kws={'color': 'gray', 'edgecolor': 'gray', 'linewidth': 1})\n",
    "    # Set the title and axis labels\n",
    "    axes[i].set_title(f\"{analyte_name}, skewness={skewness}\")\n",
    "    axes[i].set_xlabel('Concentration')\n",
    "    axes[i].set_ylabel('Density')\n",
    "    \n",
    "# Remove the unused subplots\n",
    "for i in range(8, len(axes)):\n",
    "    fig.delaxes(axes[i])\n",
    "    \n",
    "# Adjust the spacing between the subplots\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.5)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# print('Saving the figure ...')\n",
    "# fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"distribution_of_iMSMS_global_compounds_concentration_full_data_{}_sample_log_transformed.svg\".format(sample))\n",
    "# fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "661a65fa",
   "metadata": {},
   "source": [
    "## Distribution of concentration values of first 8 analytes after reversing log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fcaa9481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 4x4 subplot grid\n",
    "fig, axes = plt.subplots(4, 2, figsize=(12, 12))\n",
    "\n",
    "# Flatten the subplot grid for easier indexing\n",
    "axes = axes.flatten()\n",
    "\n",
    "df = data_with_patient_metadata_reverse_log_transformed\n",
    "for i, analyte in enumerate(analyte_columns_selected[0:8]):\n",
    "    analyte_name = analyte_metadata[analyte_metadata[\"CHEM_ID\"]==analyte][\"CHEMICAL_NAME\"].values[0]\n",
    "    # Select the data for the current analyte\n",
    "    subset = df[analyte]\n",
    "    # Compute the skewness of the data\n",
    "    skewness = round(subset.skew(), 2)\n",
    "    # Plot the histogram and kde plot\n",
    "    sns.distplot(subset, hist=True, kde=True, ax=axes[i], \n",
    "                 kde_kws={'color': 'r', 'lw': 3},\n",
    "                 hist_kws={'color': 'gray', 'edgecolor': 'gray', 'linewidth': 1})\n",
    "    # Set the title and axis labels\n",
    "    axes[i].set_title(f\"{analyte_name}, skewness={skewness}\")\n",
    "    axes[i].set_xlabel('Concentration')\n",
    "    axes[i].set_ylabel('Density')\n",
    "    \n",
    "# Remove the unused subplots\n",
    "for i in range(8, len(axes)):\n",
    "    fig.delaxes(axes[i])\n",
    "    \n",
    "# Adjust the spacing between the subplots\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.5)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# print('Saving the figure ...')\n",
    "# fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"distribution_of_iMSMS_global_compounds_concentration_full_data_{}_reverse_log_transformed.svg\".format(sample))\n",
    "# fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd76b6f3",
   "metadata": {},
   "source": [
    "## Computing skewness for all analytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf5a0526",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1206/1206 [00:00<00:00, 1440.86it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_with_patient_metadata\n",
    "analyte_skewness_list = []\n",
    "for analyte in tqdm(analyte_columns_selected):\n",
    "    analyte_name = analyte_metadata[analyte_metadata[\"CHEM_ID\"]==analyte][\"CHEMICAL_NAME\"].values[0]\n",
    "    subset = df[analyte]\n",
    "    skewness = round(subset.skew(), 2)\n",
    "    analyte_skewness_list.append((analyte, analyte_name, skewness))\n",
    "    \n",
    "analyte_skewness_df = pd.DataFrame(analyte_skewness_list, columns=[\"CHEM_ID\", \"CHEMICAL_NAME\", \"skewness\"])\n",
    "\n",
    "\n",
    "FIGSIZE = (10, 4)\n",
    "FONTSIZE = 20\n",
    "\n",
    "fig, ax = plt.subplots(figsize=FIGSIZE)\n",
    "sns.distplot(analyte_skewness_df[\"skewness\"], hist=True, kde=False, ax=ax, \n",
    "             kde_kws={'color': 'r', 'lw': 3},\n",
    "             hist_kws={'color': 'gray', 'edgecolor': 'gray', 'linewidth': 1})\n",
    "\n",
    "\n",
    "# remove the top and right spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# set the x label and title\n",
    "plt.xlabel('Skewness',fontsize=FONTSIZE)\n",
    "plt.ylabel('Count',fontsize=FONTSIZE)\n",
    "\n",
    "plt.xticks(fontsize=FONTSIZE-5)\n",
    "plt.yticks(fontsize=FONTSIZE-5)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print('Saving the figure ...')\n",
    "# fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"skewness_distribution_of_iMSMS_global_compounds_full_data_{}_log_transformed.svg\".format(sample))\n",
    "# fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d23bdd8",
   "metadata": {},
   "source": [
    "## Computing skewness for all analytes after reversing log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf2fec76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1206/1206 [00:00<00:00, 1342.65it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data_with_patient_metadata_reverse_log_transformed\n",
    "analyte_skewness_list_reverse_log_transformed = []\n",
    "for analyte in tqdm(analyte_columns_selected):\n",
    "    analyte_name = analyte_metadata[analyte_metadata[\"CHEM_ID\"]==analyte][\"CHEMICAL_NAME\"].values[0]\n",
    "    subset = df[analyte]\n",
    "    skewness = round(subset.skew(), 2)\n",
    "    analyte_skewness_list_reverse_log_transformed.append((analyte, analyte_name, skewness))\n",
    "    \n",
    "analyte_skewness_df_reverse_log_transformed = pd.DataFrame(analyte_skewness_list_reverse_log_transformed, columns=[\"CHEM_ID\", \"CHEMICAL_NAME\", \"skewness\"])\n",
    "\n",
    "\n",
    "FIGSIZE = (10, 4)\n",
    "FONTSIZE = 20\n",
    "\n",
    "fig, ax = plt.subplots(figsize=FIGSIZE)\n",
    "sns.distplot(analyte_skewness_df_reverse_log_transformed[\"skewness\"], hist=True, kde=False, ax=ax, \n",
    "             kde_kws={'color': 'r', 'lw': 3},\n",
    "             hist_kws={'color': 'gray', 'edgecolor': 'gray', 'linewidth': 1})\n",
    "\n",
    "\n",
    "# remove the top and right spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# set the x label and title\n",
    "plt.xlabel('Skewness',fontsize=FONTSIZE)\n",
    "plt.ylabel('Count',fontsize=FONTSIZE)\n",
    "\n",
    "plt.xticks(fontsize=FONTSIZE-5)\n",
    "plt.yticks(fontsize=FONTSIZE-5)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print('Saving the figure ...')\n",
    "# fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"skewness_distribution_of_iMSMS_global_compounds_full_data_{}_reverse_log_transformed.svg\".format(sample))\n",
    "# fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aba3250e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving the figure ...\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "k = 3 is selected for Tukey Fences based on the following Ref:\n",
    "    https://community.ibm.com/community/user/ai-datascience/blogs/moloy-de1/2021/03/23/points-to-ponder\n",
    "'''\n",
    "\n",
    "FIG_SIZE = (8,3)\n",
    "FONTSIZE = 15\n",
    "\n",
    "data_ = data_with_patient_metadata_reverse_log_transformed.copy()\n",
    "analyte = analyte_columns_selected[16]\n",
    "analyte_name = analyte_metadata[analyte_metadata[\"CHEM_ID\"]==analyte][\"CHEMICAL_NAME\"].values[0]\n",
    "sample_unit = data_[\"SAMPLE_AMOUNT_UNITS\"].values[0]\n",
    "analyte_concentration_values = data_[analyte]\n",
    "# Calculate quartiles and IQR\n",
    "q1 = analyte_concentration_values.quantile(0.25)\n",
    "q3 = analyte_concentration_values.quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "# Define Tukey's fences\n",
    "k = 3\n",
    "lower_fence = q1 - k * iqr\n",
    "upper_fence = q3 + k * iqr\n",
    "# Filter non-outliers\n",
    "non_outliers = data_[(data_[analyte] >= lower_fence) & (data_[analyte] <= upper_fence)]\n",
    "house_counts = non_outliers.groupby(\"house\").size()\n",
    "\n",
    "# Create a 4x4 subplot grid\n",
    "fig, axes = plt.subplots(figsize=FIG_SIZE)\n",
    "\n",
    "\n",
    "skewness = round(analyte_concentration_values.skew(), 2)\n",
    "# Plot the histogram and kde plot\n",
    "sns.distplot(analyte_concentration_values, hist=True, kde=True, ax=axes, \n",
    "             kde_kws={'color': 'r', 'lw': 3},\n",
    "             hist_kws={'color': 'gray', 'edgecolor': 'gray', 'linewidth': 1})\n",
    "\n",
    "# Add vertical bars for lower and upper fences\n",
    "# axes.axvline(q1, color='blue', linestyle='--', label='Lower Fence', linewidth=3)\n",
    "# axes.axvline(q3, color='blue', linestyle='--', label='Upper Fence', linewidth=3)\n",
    "\n",
    "axes.axvline(lower_fence, color='magenta', linestyle='--', label='Lower Fence', linewidth=3)\n",
    "axes.axvline(upper_fence, color='magenta', linestyle='--', label='Upper Fence', linewidth=3)\n",
    "\n",
    "\n",
    "# Set the title and axis labels\n",
    "# axes.set_title(f\"{analyte_name}\", fontsize=FONTSIZE)\n",
    "axes.set_xlabel('Concentration', fontsize=FONTSIZE)\n",
    "axes.set_ylabel('Density', fontsize=FONTSIZE)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.xticks(fontsize=FONTSIZE)\n",
    "plt.yticks(fontsize=FONTSIZE)\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "print('Saving the figure ...')\n",
    "fig_filename = os.path.join(OUTPUT_PATH, \"figures\", \"tukey_fence_demo_3_using_reverse_log_transformed_data_of_{}_analyte_of_{}_sample.svg\".format(analyte, sample))\n",
    "fig.savefig(fig_filename, format='svg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "8dbaf60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from scipy.stats import zscore\n",
    "# data_to_plot = data_with_patient_metadata_reverse_log_transformed_nan_removed[analyte_columns_selected]\n",
    "# data_zscore = data_to_plot.apply(zscore)\n",
    "# data_zscore_T = data_zscore.T\n",
    "# sns.heatmap(data_zscore_T, cmap='coolwarm', annot=True, fmt='.2f')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105d9f8c",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}