{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "264679be-eb4c-405f-8ca5-66d0954d1199",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import analysis_visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac957981-27af-4793-b4e3-eeaec7d1d108",
   "metadata": {},
   "outputs": [],
   "source": [
    "pnd_file='../data/neuro_pnd.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea21ec1c-f21c-4a1e-a624-7f0caeec91c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>PND</th>\n",
       "      <th>Closest distance</th>\n",
       "      <th>Dc_zscore</th>\n",
       "      <th>Treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL1618018</td>\n",
       "      <td>GSM153404</td>\n",
       "      <td>1.586847</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-2.555190</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL1618018</td>\n",
       "      <td>GSM153407</td>\n",
       "      <td>1.708065</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-2.555190</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL1618018</td>\n",
       "      <td>GSM153408</td>\n",
       "      <td>1.679737</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-2.555190</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL1618018</td>\n",
       "      <td>GSM153410</td>\n",
       "      <td>1.712728</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-2.555190</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL1618018</td>\n",
       "      <td>GSM153411</td>\n",
       "      <td>1.615855</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-2.555190</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115323</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21227</td>\n",
       "      <td>1.650534</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115324</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21228</td>\n",
       "      <td>1.717595</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115325</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21229</td>\n",
       "      <td>1.643701</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115326</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21230</td>\n",
       "      <td>1.603160</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115327</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21233</td>\n",
       "      <td>1.705001</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115328 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID   gds_id          Drugs     gsm_id       PND  \\\n",
       "0       C0002395  GDS2519  CHEMBL1618018  GSM153404  1.586847   \n",
       "1       C0002395  GDS2519  CHEMBL1618018  GSM153407  1.708065   \n",
       "2       C0002395  GDS2519  CHEMBL1618018  GSM153408  1.679737   \n",
       "3       C0002395  GDS2519  CHEMBL1618018  GSM153410  1.712728   \n",
       "4       C0002395  GDS2519  CHEMBL1618018  GSM153411  1.615855   \n",
       "...          ...      ...            ...        ...       ...   \n",
       "115323  C0002395   GDS810   CHEMBL285802   GSM21227  1.650534   \n",
       "115324  C0002395   GDS810   CHEMBL285802   GSM21228  1.717595   \n",
       "115325  C0002395   GDS810   CHEMBL285802   GSM21229  1.643701   \n",
       "115326  C0002395   GDS810   CHEMBL285802   GSM21230  1.603160   \n",
       "115327  C0002395   GDS810   CHEMBL285802   GSM21233  1.705001   \n",
       "\n",
       "        Closest distance  Dc_zscore Treatment  \n",
       "0               0.400000  -2.555190   unknown  \n",
       "1               0.400000  -2.555190   unknown  \n",
       "2               0.400000  -2.555190   unknown  \n",
       "3               0.400000  -2.555190   unknown  \n",
       "4               0.400000  -2.555190   unknown  \n",
       "...                  ...        ...       ...  \n",
       "115323          0.142857  -5.294708   unknown  \n",
       "115324          0.142857  -5.294708   unknown  \n",
       "115325          0.142857  -5.294708   unknown  \n",
       "115326          0.142857  -5.294708   unknown  \n",
       "115327          0.142857  -5.294708   unknown  \n",
       "\n",
       "[115328 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neuro_pnd=pd.read_csv(pnd_file)\n",
    "neuro_pnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b239262a-ed3a-4d41-99e8-9bb1a0f71be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_pnd=analysis_visualization.filter_cd_zscore(neuro_pnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c384c129-22d5-4165-9ea0-e2a35261460a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter drugs to include only those with a PND smaller than the first quartile (Q1) PND of the known treatments\n",
    "drugs_with_disease = neuro_pnd[(neuro_pnd['Treatment'] == 'yes')]\n",
    "q1 = drugs_with_disease['PND'].quantile(0.25)\n",
    "final_unknown =filtered_pnd[(filtered_pnd['PND'] <= q1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebe8d784-38cf-411b-a1c6-e414a23754bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>PND</th>\n",
       "      <th>Closest distance</th>\n",
       "      <th>Dc_zscore</th>\n",
       "      <th>Treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL279516</td>\n",
       "      <td>GSM153411</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.064742</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL279516</td>\n",
       "      <td>GSM153454</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.064742</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL279516</td>\n",
       "      <td>GSM153490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.064742</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL279516</td>\n",
       "      <td>GSM153495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.064742</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2519</td>\n",
       "      <td>CHEMBL279516</td>\n",
       "      <td>GSM153502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.064742</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115278</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM107535</td>\n",
       "      <td>1.465846</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115295</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS4136</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM697327</td>\n",
       "      <td>1.490889</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115303</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS4136</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM697335</td>\n",
       "      <td>1.460556</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115308</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21205</td>\n",
       "      <td>1.484909</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115310</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL285802</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>1.388728</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-5.294708</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18396 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID   gds_id         Drugs     gsm_id       PND  \\\n",
       "216     C0002395  GDS2519  CHEMBL279516  GSM153411  0.000000   \n",
       "242     C0002395  GDS2519  CHEMBL279516  GSM153454  0.000000   \n",
       "251     C0002395  GDS2519  CHEMBL279516  GSM153490  0.000000   \n",
       "256     C0002395  GDS2519  CHEMBL279516  GSM153495  0.000000   \n",
       "259     C0002395  GDS2519  CHEMBL279516  GSM153502  0.000000   \n",
       "...          ...      ...           ...        ...       ...   \n",
       "115278  C0002395  GDS2795  CHEMBL285802  GSM107535  1.465846   \n",
       "115295  C0002395  GDS4136  CHEMBL285802  GSM697327  1.490889   \n",
       "115303  C0002395  GDS4136  CHEMBL285802  GSM697335  1.460556   \n",
       "115308  C0002395   GDS810  CHEMBL285802   GSM21205  1.484909   \n",
       "115310  C0002395   GDS810  CHEMBL285802   GSM21207  1.388728   \n",
       "\n",
       "        Closest distance  Dc_zscore Treatment  \n",
       "216             0.000000  -2.064742   unknown  \n",
       "242             0.000000  -2.064742   unknown  \n",
       "251             0.000000  -2.064742   unknown  \n",
       "256             0.000000  -2.064742   unknown  \n",
       "259             0.000000  -2.064742   unknown  \n",
       "...                  ...        ...       ...  \n",
       "115278          0.142857  -5.294708   unknown  \n",
       "115295          0.142857  -5.294708   unknown  \n",
       "115303          0.142857  -5.294708   unknown  \n",
       "115308          0.142857  -5.294708   unknown  \n",
       "115310          0.142857  -5.294708   unknown  \n",
       "\n",
       "[18396 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2484028a-753f-478a-94bb-2cf0f8ea03d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis_visualization.rep_pnd(neuro_pnd,final_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "61a54819-352c-4fe4-85fe-27dd2428ae88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CHEMBL282575', 'CHEMBL411', 'CHEMBL941', 'CHEMBL1336']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows from `final_unknown` where 'Closest distance' is zero because it is not meaningful\n",
    "final_unknown = final_unknown[final_unknown['Closest distance'] != 0]\n",
    "\n",
    "# Sort the remaining rows of `final_unknown` by 'PND' in ascending order.\n",
    "sorted_df = final_unknown.sort_values(by='PND')\n",
    "\n",
    "# Extract the distinct drugs with the lowest PND values.\n",
    "#We use `drop_duplicates` to keep only the first occurrence of each drug and `head(4)` to get the top 4 drugs.\n",
    "#These are the drugs with the lowest PND values after excluding those with a 'Closest distance' of zero.\n",
    "lowest_pnd_drugs = sorted_df.drop_duplicates(subset='Drugs').head(4)\n",
    "\n",
    "# Convert the DataFrame of the lowest PND drugs into a list of drug names.\n",
    "lowest_pnd_drugs = lowest_pnd_drugs['Drugs'].tolist()\n",
    "\n",
    "# Output the list of drugs with the lowest PND values.\n",
    "lowest_pnd_drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "add762ec-ec0e-4f77-bedf-3133d494947f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>PND</th>\n",
       "      <th>Closest distance</th>\n",
       "      <th>Dc_zscore</th>\n",
       "      <th>Treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55102</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.668762</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55095</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS4136</td>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GSM697335</td>\n",
       "      <td>1.088567</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.668762</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55065</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>1.095417</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.668762</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41428</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL411</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>1.048897</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-1.602619</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41421</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS4136</td>\n",
       "      <td>CHEMBL411</td>\n",
       "      <td>GSM697335</td>\n",
       "      <td>1.103897</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-1.602619</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41391</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>CHEMBL411</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>1.106898</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-1.602619</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54466</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>1.174558</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-3.851931</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54429</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>1.230510</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-3.851931</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54459</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS4136</td>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM697335</td>\n",
       "      <td>1.234670</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-3.851931</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6236</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>1.175647</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-3.897939</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>1.226749</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-3.897939</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>C0002395</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-3.897939</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID   gds_id         Drugs     gsm_id       PND  Closest distance  \\\n",
       "55102  C0002395   GDS810  CHEMBL282575   GSM21207  1.037817          0.333333   \n",
       "55095  C0002395  GDS4136  CHEMBL282575  GSM697335  1.088567          0.333333   \n",
       "55065  C0002395  GDS2795  CHEMBL282575  GSM107525  1.095417          0.333333   \n",
       "41428  C0002395   GDS810     CHEMBL411   GSM21207  1.048897          0.250000   \n",
       "41421  C0002395  GDS4136     CHEMBL411  GSM697335  1.103897          0.250000   \n",
       "41391  C0002395  GDS2795     CHEMBL411  GSM107525  1.106898          0.250000   \n",
       "54466  C0002395   GDS810     CHEMBL941   GSM21207  1.174558          0.250000   \n",
       "54429  C0002395  GDS2795     CHEMBL941  GSM107525  1.230510          0.250000   \n",
       "54459  C0002395  GDS4136     CHEMBL941  GSM697335  1.234670          0.250000   \n",
       "6236   C0002395   GDS810    CHEMBL1336   GSM21207  1.175647          0.300000   \n",
       "6199   C0002395  GDS2795    CHEMBL1336  GSM107525  1.226749          0.300000   \n",
       "6198   C0002395  GDS2795    CHEMBL1336  GSM107523  1.232494          0.300000   \n",
       "\n",
       "       Dc_zscore Treatment  \n",
       "55102  -0.668762   unknown  \n",
       "55095  -0.668762   unknown  \n",
       "55065  -0.668762   unknown  \n",
       "41428  -1.602619   unknown  \n",
       "41421  -1.602619   unknown  \n",
       "41391  -1.602619   unknown  \n",
       "54466  -3.851931   unknown  \n",
       "54429  -3.851931   unknown  \n",
       "54459  -3.851931   unknown  \n",
       "6236   -3.897939   unknown  \n",
       "6199   -3.897939   unknown  \n",
       "6198   -3.897939   unknown  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get 3 different gsm_id for the given list of drugs with the lowest PND values\n",
    "gsm_df = analysis_visualization.get_gsm_ids_for_lowest_pnd_drugs(final_unknown, lowest_pnd_drugs, num_gsm=3)\n",
    "gsm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5359374-e494-4376-9199-9315f8ac5a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gds_id</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>id_ref</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>z_score</th>\n",
       "      <th>significant</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>protein_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GDS2519</td>\n",
       "      <td>GSM153404</td>\n",
       "      <td>200096_s_at</td>\n",
       "      <td>ATP6V0E1</td>\n",
       "      <td>2.7422</td>\n",
       "      <td>o</td>\n",
       "      <td>8992</td>\n",
       "      <td>O15342</td>\n",
       "      <td>ATPase H+ transporting V0 subunit e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GDS2519</td>\n",
       "      <td>GSM153404</td>\n",
       "      <td>200097_s_at</td>\n",
       "      <td>HNRNPK</td>\n",
       "      <td>2.9833</td>\n",
       "      <td>o</td>\n",
       "      <td>3190</td>\n",
       "      <td>P61978</td>\n",
       "      <td>heterogeneous nuclear ribonucleoprotein K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GDS2519</td>\n",
       "      <td>GSM153404</td>\n",
       "      <td>200801_x_at</td>\n",
       "      <td>ACTB</td>\n",
       "      <td>6.3641</td>\n",
       "      <td>o</td>\n",
       "      <td>60</td>\n",
       "      <td>P60709</td>\n",
       "      <td>actin beta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GDS2519</td>\n",
       "      <td>GSM153404</td>\n",
       "      <td>201925_s_at</td>\n",
       "      <td>CD55</td>\n",
       "      <td>3.1947</td>\n",
       "      <td>o</td>\n",
       "      <td>1604</td>\n",
       "      <td>P08174</td>\n",
       "      <td>CD55 molecule (Cromer blood group)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GDS2519</td>\n",
       "      <td>GSM153404</td>\n",
       "      <td>201998_at</td>\n",
       "      <td>ST6GAL1</td>\n",
       "      <td>2.9949</td>\n",
       "      <td>o</td>\n",
       "      <td>6480</td>\n",
       "      <td>P15907</td>\n",
       "      <td>ST6 beta-galactoside alpha-2,6-sialyltransfera...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16082</th>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21233</td>\n",
       "      <td>211000_s_at</td>\n",
       "      <td>IL6ST</td>\n",
       "      <td>2.6804</td>\n",
       "      <td>o</td>\n",
       "      <td>3572</td>\n",
       "      <td>P40189</td>\n",
       "      <td>interleukin 6 signal transducer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16083</th>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21233</td>\n",
       "      <td>211587_x_at</td>\n",
       "      <td>CHRNA3</td>\n",
       "      <td>3.0666</td>\n",
       "      <td>o</td>\n",
       "      <td>1136</td>\n",
       "      <td>P32297</td>\n",
       "      <td>cholinergic receptor nicotinic alpha 3 subunit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16084</th>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21233</td>\n",
       "      <td>211813_x_at</td>\n",
       "      <td>DCN</td>\n",
       "      <td>3.0475</td>\n",
       "      <td>o</td>\n",
       "      <td>1634</td>\n",
       "      <td>P07585</td>\n",
       "      <td>decorin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16085</th>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21233</td>\n",
       "      <td>213603_s_at</td>\n",
       "      <td>RAC2</td>\n",
       "      <td>2.6776</td>\n",
       "      <td>o</td>\n",
       "      <td>5880</td>\n",
       "      <td>P15153</td>\n",
       "      <td>Rac family small GTPase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16086</th>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21233</td>\n",
       "      <td>215552_s_at</td>\n",
       "      <td>ESR1</td>\n",
       "      <td>3.3458</td>\n",
       "      <td>o</td>\n",
       "      <td>2099</td>\n",
       "      <td>P03372</td>\n",
       "      <td>estrogen receptor 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16087 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gds_id     gsm_id       id_ref gene_symbol  z_score significant  \\\n",
       "0      GDS2519  GSM153404  200096_s_at    ATP6V0E1   2.7422           o   \n",
       "1      GDS2519  GSM153404  200097_s_at      HNRNPK   2.9833           o   \n",
       "2      GDS2519  GSM153404  200801_x_at        ACTB   6.3641           o   \n",
       "3      GDS2519  GSM153404  201925_s_at        CD55   3.1947           o   \n",
       "4      GDS2519  GSM153404    201998_at     ST6GAL1   2.9949           o   \n",
       "...        ...        ...          ...         ...      ...         ...   \n",
       "16082   GDS810   GSM21233  211000_s_at       IL6ST   2.6804           o   \n",
       "16083   GDS810   GSM21233  211587_x_at      CHRNA3   3.0666           o   \n",
       "16084   GDS810   GSM21233  211813_x_at         DCN   3.0475           o   \n",
       "16085   GDS810   GSM21233  213603_s_at        RAC2   2.6776           o   \n",
       "16086   GDS810   GSM21233  215552_s_at        ESR1   3.3458           o   \n",
       "\n",
       "       gene_id protein_id                                          gene_name  \n",
       "0         8992     O15342               ATPase H+ transporting V0 subunit e1  \n",
       "1         3190     P61978          heterogeneous nuclear ribonucleoprotein K  \n",
       "2           60     P60709                                         actin beta  \n",
       "3         1604     P08174                 CD55 molecule (Cromer blood group)  \n",
       "4         6480     P15907  ST6 beta-galactoside alpha-2,6-sialyltransfera...  \n",
       "...        ...        ...                                                ...  \n",
       "16082     3572     P40189                    interleukin 6 signal transducer  \n",
       "16083     1136     P32297     cholinergic receptor nicotinic alpha 3 subunit  \n",
       "16084     1634     P07585                                            decorin  \n",
       "16085     5880     P15153                          Rac family small GTPase 2  \n",
       "16086     2099     P03372                                estrogen receptor 1  \n",
       "\n",
       "[16087 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the file containing PEEPs data\n",
    "peep_file='../data/genes_gds_alzheimer_filtered_dup.csv'\n",
    "peeps=pd.read_csv(peep_file)\n",
    "peeps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1677a3b1-abb7-47c0-bfbf-dbeefff0c734",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create an empty dataframe to store the final results\n",
    "final_df = pd.DataFrame()\n",
    "\n",
    "# Iterate through each unique drug in the 'Drugs' column of gsm_df\n",
    "for drug in gsm_df['Drugs'].unique():\n",
    "    # Get the gsm_id values for the current drug\n",
    "    gsm_ids = gsm_df[gsm_df['Drugs'] == drug]['gsm_id'].unique()\n",
    "    \n",
    "    # Filter the peeps dataframe to include only rows with the selected gsm_id values\n",
    "    filtered_peeps = peeps[peeps['gsm_id'].isin(gsm_ids)]\n",
    "    \n",
    "    #Find the gene symbols that are present in all the gsm_id values for the current drug\n",
    "    gene_counts = filtered_peeps['gene_symbol'].value_counts()\n",
    "    common_genes = gene_counts[gene_counts == len(gsm_ids)].index\n",
    "    \n",
    "    # Filter the dataframe to include only the common genes\n",
    "    common_genes_df = filtered_peeps[filtered_peeps['gene_symbol'].isin(common_genes)]\n",
    "    \n",
    "    # Check if the 'significant' column has the same value for each gene_symbol across all gsm_id values\n",
    "    significant_genes = common_genes_df.groupby('gene_symbol').filter(lambda x: x['significant'].nunique() == 1)\n",
    "    \n",
    "    # Add drug and PND information to the significant_genes dataframe\n",
    "    for gsm_id in gsm_ids:\n",
    "        gsm_info = gsm_df[gsm_df['gsm_id'] == gsm_id][['gds_id', 'gsm_id', 'PND']].iloc[0]\n",
    "        subset = significant_genes[significant_genes['gsm_id'] == gsm_id].copy()\n",
    "        subset['Drugs'] = drug\n",
    "        subset['PND'] = gsm_info['PND']\n",
    "        \n",
    "        # Append the results to the final dataframe\n",
    "        final_df = pd.concat([final_df, subset])\n",
    "\n",
    "desired_columns = ['Drugs', 'gds_id', 'gsm_id', 'gene_symbol', 'gene_id', 'PND'] + [col for col in peeps.columns if col not in ['gds_id', 'gsm_id', 'gene_symbol', 'gene_id']]\n",
    "final_df = final_df[desired_columns]\n",
    "# Reset the index of the final dataframe\n",
    "final_df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "741f9ffa-37de-47d0-8ba7-4fa12d859a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>PND</th>\n",
       "      <th>id_ref</th>\n",
       "      <th>z_score</th>\n",
       "      <th>significant</th>\n",
       "      <th>protein_id</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>DNM2</td>\n",
       "      <td>1785</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>202253_s_at</td>\n",
       "      <td>3.6879</td>\n",
       "      <td>o</td>\n",
       "      <td>P50570</td>\n",
       "      <td>dynamin 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>BMP1</td>\n",
       "      <td>649</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>202701_at</td>\n",
       "      <td>5.1284</td>\n",
       "      <td>o</td>\n",
       "      <td>P13497</td>\n",
       "      <td>bone morphogenetic protein 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>PTPN1</td>\n",
       "      <td>5770</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>202716_at</td>\n",
       "      <td>3.4236</td>\n",
       "      <td>o</td>\n",
       "      <td>P18031</td>\n",
       "      <td>protein tyrosine phosphatase non-receptor type 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>IGF1R</td>\n",
       "      <td>3480</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>203628_at</td>\n",
       "      <td>5.6898</td>\n",
       "      <td>o</td>\n",
       "      <td>P08069</td>\n",
       "      <td>insulin like growth factor 1 receptor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>IL23A</td>\n",
       "      <td>51561</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>220054_at</td>\n",
       "      <td>3.9142</td>\n",
       "      <td>o</td>\n",
       "      <td>Q9NPF7</td>\n",
       "      <td>interleukin 23 subunit alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>MELTF</td>\n",
       "      <td>4241</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>223723_at</td>\n",
       "      <td>5.2922</td>\n",
       "      <td>o</td>\n",
       "      <td>P08582</td>\n",
       "      <td>melanotransferrin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>ARRB1</td>\n",
       "      <td>408</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>228444_at</td>\n",
       "      <td>13.8469</td>\n",
       "      <td>o</td>\n",
       "      <td>P49407</td>\n",
       "      <td>arrestin beta 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>MBP</td>\n",
       "      <td>4155</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>228938_at</td>\n",
       "      <td>7.3370</td>\n",
       "      <td>o</td>\n",
       "      <td>P02686</td>\n",
       "      <td>myelin basic protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>405 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Drugs   gds_id     gsm_id gene_symbol gene_id       PND  \\\n",
       "0    CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "1    CHEMBL282575   GDS810   GSM21207        DNM2    1785  1.037817   \n",
       "2    CHEMBL282575   GDS810   GSM21207        BMP1     649  1.037817   \n",
       "3    CHEMBL282575   GDS810   GSM21207       PTPN1    5770  1.037817   \n",
       "4    CHEMBL282575   GDS810   GSM21207       IGF1R    3480  1.037817   \n",
       "..            ...      ...        ...         ...     ...       ...   \n",
       "400    CHEMBL1336  GDS2795  GSM107523       IL23A   51561  1.232494   \n",
       "401    CHEMBL1336  GDS2795  GSM107523       MELTF    4241  1.232494   \n",
       "402    CHEMBL1336  GDS2795  GSM107523       ARRB1     408  1.232494   \n",
       "403    CHEMBL1336  GDS2795  GSM107523         MBP    4155  1.232494   \n",
       "404    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "\n",
       "          id_ref  z_score significant protein_id  \\\n",
       "0      160020_at   4.7079           o     P50281   \n",
       "1    202253_s_at   3.6879           o     P50570   \n",
       "2      202701_at   5.1284           o     P13497   \n",
       "3      202716_at   3.4236           o     P18031   \n",
       "4      203628_at   5.6898           o     P08069   \n",
       "..           ...      ...         ...        ...   \n",
       "400    220054_at   3.9142           o     Q9NPF7   \n",
       "401    223723_at   5.2922           o     P08582   \n",
       "402    228444_at  13.8469           o     P49407   \n",
       "403    228938_at   7.3370           o     P02686   \n",
       "404    237142_at   4.4084           o     Q07869   \n",
       "\n",
       "                                            gene_name  \n",
       "0                          matrix metallopeptidase 14  \n",
       "1                                           dynamin 2  \n",
       "2                        bone morphogenetic protein 1  \n",
       "3    protein tyrosine phosphatase non-receptor type 1  \n",
       "4               insulin like growth factor 1 receptor  \n",
       "..                                                ...  \n",
       "400                      interleukin 23 subunit alpha  \n",
       "401                                 melanotransferrin  \n",
       "402                                   arrestin beta 1  \n",
       "403                              myelin basic protein  \n",
       "404  peroxisome proliferator activated receptor alpha  \n",
       "\n",
       "[405 rows x 11 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "80bf697e-bd65-4a34-9814-afcf499afa5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drugs\n",
       "CHEMBL1336      51\n",
       "CHEMBL282575    28\n",
       "CHEMBL411       28\n",
       "CHEMBL941       28\n",
       "Name: gene_symbol, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_gene_symbols = final_df.groupby('Drugs')['gene_symbol'].unique()\n",
    "\n",
    "# Get the number of different gene symbols related to each drug\n",
    "lengths_unique_gene_symbols = unique_gene_symbols.apply(len)\n",
    "lengths_unique_gene_symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "38e5ae77-39e7-4888-86a5-51ed6f0cc614",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the file containing the pathways and relation bewteen genes and pathways\n",
    "gen_pat=pd.read_csv('../data/data_00_raw_data_links_gen_pat.tsv',sep='\\t')\n",
    "pat=pd.read_csv('../data/data_00_raw_data_nodes_pat.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a9cd8524-a040-4e4b-b3b0-4f74ea722d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>PND</th>\n",
       "      <th>id_ref</th>\n",
       "      <th>z_score</th>\n",
       "      <th>significant</th>\n",
       "      <th>protein_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>pathway_id</th>\n",
       "      <th>pathway_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP4754</td>\n",
       "      <td>IL18 signaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP5283</td>\n",
       "      <td>Chronic hyperglycemia impairment of neuron fun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP2324</td>\n",
       "      <td>AGE/RAGE pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP129</td>\n",
       "      <td>Matrix metalloproteinases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP3888</td>\n",
       "      <td>VEGFA-VEGFR2 signaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP299</td>\n",
       "      <td>Nuclear receptors in lipid metabolism and toxi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP236</td>\n",
       "      <td>Adipogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP170</td>\n",
       "      <td>Nuclear receptors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP1541</td>\n",
       "      <td>Energy metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP4396</td>\n",
       "      <td>Nonalcoholic fatty liver disease</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1368 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Drugs   gds_id     gsm_id gene_symbol gene_id       PND  \\\n",
       "0     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "1     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "2     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "3     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "4     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "...            ...      ...        ...         ...     ...       ...   \n",
       "1363    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1364    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1365    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1366    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1367    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "\n",
       "         id_ref  z_score significant protein_id  \\\n",
       "0     160020_at   4.7079           o     P50281   \n",
       "1     160020_at   4.7079           o     P50281   \n",
       "2     160020_at   4.7079           o     P50281   \n",
       "3     160020_at   4.7079           o     P50281   \n",
       "4     160020_at   4.7079           o     P50281   \n",
       "...         ...      ...         ...        ...   \n",
       "1363  237142_at   4.4084           o     Q07869   \n",
       "1364  237142_at   4.4084           o     Q07869   \n",
       "1365  237142_at   4.4084           o     Q07869   \n",
       "1366  237142_at   4.4084           o     Q07869   \n",
       "1367  237142_at   4.4084           o     Q07869   \n",
       "\n",
       "                                             gene_name pathway_id  \\\n",
       "0                           matrix metallopeptidase 14     WP4754   \n",
       "1                           matrix metallopeptidase 14     WP5283   \n",
       "2                           matrix metallopeptidase 14     WP2324   \n",
       "3                           matrix metallopeptidase 14      WP129   \n",
       "4                           matrix metallopeptidase 14     WP3888   \n",
       "...                                                ...        ...   \n",
       "1363  peroxisome proliferator activated receptor alpha      WP299   \n",
       "1364  peroxisome proliferator activated receptor alpha      WP236   \n",
       "1365  peroxisome proliferator activated receptor alpha      WP170   \n",
       "1366  peroxisome proliferator activated receptor alpha     WP1541   \n",
       "1367  peroxisome proliferator activated receptor alpha     WP4396   \n",
       "\n",
       "                                           pathway_name  \n",
       "0                                        IL18 signaling  \n",
       "1     Chronic hyperglycemia impairment of neuron fun...  \n",
       "2                                      AGE/RAGE pathway  \n",
       "3                             Matrix metalloproteinases  \n",
       "4                                VEGFA-VEGFR2 signaling  \n",
       "...                                                 ...  \n",
       "1363  Nuclear receptors in lipid metabolism and toxi...  \n",
       "1364                                       Adipogenesis  \n",
       "1365                                  Nuclear receptors  \n",
       "1366                                  Energy metabolism  \n",
       "1367                   Nonalcoholic fatty liver disease  \n",
       "\n",
       "[1368 rows x 13 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Step 1: Convert pathway_id in gen_pat and pat dataframes to string type\n",
    "gen_pat['pathway_id'] = gen_pat['pathway_id'].astype(str)\n",
    "pat['pathway_id'] = pat['pathway_id'].astype(str)\n",
    "\n",
    "#Step 2: Rename columns in the pat dataframe\n",
    "pat.rename(columns={'id': 'pathway_id', 'name': 'pathway_name'}, inplace=True)\n",
    "\n",
    "#Step 3: Merge gen_pat with pat to get the complete pathway information\n",
    "gen_pat_combined = pd.merge(gen_pat, pat, on='pathway_id', how='left')\n",
    "\n",
    "#Step 4: Convert gene_id in final_df to string type if necessary\n",
    "final_df['gene_id'] = final_df['gene_id'].astype(str)\n",
    "\n",
    "#Step 5: Merge gen_pat_combined with final_df using gene_id to get pathway_id and pathway_name for each gene_id\n",
    "final_df_with_pathways = pd.merge(final_df, gen_pat_combined, on='gene_id', how='left')\n",
    "final_df_with_pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1a510926-01b2-4f79-bc61-85b62d334314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>PND</th>\n",
       "      <th>id_ref</th>\n",
       "      <th>z_score</th>\n",
       "      <th>significant</th>\n",
       "      <th>protein_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>pathway_id</th>\n",
       "      <th>pathway_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP4754</td>\n",
       "      <td>IL18 signaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP5283</td>\n",
       "      <td>Chronic hyperglycemia impairment of neuron fun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP2324</td>\n",
       "      <td>AGE/RAGE pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP129</td>\n",
       "      <td>Matrix metalloproteinases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL282575</td>\n",
       "      <td>GDS810</td>\n",
       "      <td>GSM21207</td>\n",
       "      <td>MMP14</td>\n",
       "      <td>4323</td>\n",
       "      <td>1.037817</td>\n",
       "      <td>160020_at</td>\n",
       "      <td>4.7079</td>\n",
       "      <td>o</td>\n",
       "      <td>P50281</td>\n",
       "      <td>matrix metallopeptidase 14</td>\n",
       "      <td>WP3888</td>\n",
       "      <td>VEGFA-VEGFR2 signaling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP299</td>\n",
       "      <td>Nuclear receptors in lipid metabolism and toxi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP236</td>\n",
       "      <td>Adipogenesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP170</td>\n",
       "      <td>Nuclear receptors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP1541</td>\n",
       "      <td>Energy metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>5465</td>\n",
       "      <td>1.232494</td>\n",
       "      <td>237142_at</td>\n",
       "      <td>4.4084</td>\n",
       "      <td>o</td>\n",
       "      <td>Q07869</td>\n",
       "      <td>peroxisome proliferator activated receptor alpha</td>\n",
       "      <td>WP4396</td>\n",
       "      <td>Nonalcoholic fatty liver disease</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1164 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Drugs   gds_id     gsm_id gene_symbol gene_id       PND  \\\n",
       "0     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "1     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "2     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "3     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "4     CHEMBL282575   GDS810   GSM21207       MMP14    4323  1.037817   \n",
       "...            ...      ...        ...         ...     ...       ...   \n",
       "1363    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1364    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1365    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1366    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "1367    CHEMBL1336  GDS2795  GSM107523       PPARA    5465  1.232494   \n",
       "\n",
       "         id_ref  z_score significant protein_id  \\\n",
       "0     160020_at   4.7079           o     P50281   \n",
       "1     160020_at   4.7079           o     P50281   \n",
       "2     160020_at   4.7079           o     P50281   \n",
       "3     160020_at   4.7079           o     P50281   \n",
       "4     160020_at   4.7079           o     P50281   \n",
       "...         ...      ...         ...        ...   \n",
       "1363  237142_at   4.4084           o     Q07869   \n",
       "1364  237142_at   4.4084           o     Q07869   \n",
       "1365  237142_at   4.4084           o     Q07869   \n",
       "1366  237142_at   4.4084           o     Q07869   \n",
       "1367  237142_at   4.4084           o     Q07869   \n",
       "\n",
       "                                             gene_name pathway_id  \\\n",
       "0                           matrix metallopeptidase 14     WP4754   \n",
       "1                           matrix metallopeptidase 14     WP5283   \n",
       "2                           matrix metallopeptidase 14     WP2324   \n",
       "3                           matrix metallopeptidase 14      WP129   \n",
       "4                           matrix metallopeptidase 14     WP3888   \n",
       "...                                                ...        ...   \n",
       "1363  peroxisome proliferator activated receptor alpha      WP299   \n",
       "1364  peroxisome proliferator activated receptor alpha      WP236   \n",
       "1365  peroxisome proliferator activated receptor alpha      WP170   \n",
       "1366  peroxisome proliferator activated receptor alpha     WP1541   \n",
       "1367  peroxisome proliferator activated receptor alpha     WP4396   \n",
       "\n",
       "                                           pathway_name  \n",
       "0                                        IL18 signaling  \n",
       "1     Chronic hyperglycemia impairment of neuron fun...  \n",
       "2                                      AGE/RAGE pathway  \n",
       "3                             Matrix metalloproteinases  \n",
       "4                                VEGFA-VEGFR2 signaling  \n",
       "...                                                 ...  \n",
       "1363  Nuclear receptors in lipid metabolism and toxi...  \n",
       "1364                                       Adipogenesis  \n",
       "1365                                  Nuclear receptors  \n",
       "1366                                  Energy metabolism  \n",
       "1367                   Nonalcoholic fatty liver disease  \n",
       "\n",
       "[1164 rows x 13 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter by the ones without known pathways\n",
    "final_df_with_pathways_filt=final_df_with_pathways.dropna(subset=['pathway_id'])\n",
    "final_df_with_pathways_filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c8793f13-7d45-4201-aed1-d65a60cb8070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drugs</th>\n",
       "      <th>gsm_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>gds_id</th>\n",
       "      <th>pathway_id</th>\n",
       "      <th>pathway_name</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>ARRB1</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP382</td>\n",
       "      <td>MAPK signaling</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>AVPR2</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP24</td>\n",
       "      <td>Peptide GPCRs</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>BCL2L11</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP673</td>\n",
       "      <td>ErbB signaling</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>BMP1</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP236</td>\n",
       "      <td>Adipogenesis</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL1336</td>\n",
       "      <td>GSM107523</td>\n",
       "      <td>CASP2</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP254</td>\n",
       "      <td>Apoptosis</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>NOS1</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP2447</td>\n",
       "      <td>Amyotrophic lateral sclerosis (ALS)</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP299</td>\n",
       "      <td>Nuclear receptors in lipid metabolism and toxi...</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>PTPN1</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP2037</td>\n",
       "      <td>Prolactin signaling</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>SIRT3</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP1541</td>\n",
       "      <td>Energy metabolism</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>GSM107525</td>\n",
       "      <td>TBX2</td>\n",
       "      <td>GDS2795</td>\n",
       "      <td>WP304</td>\n",
       "      <td>Kit receptor signaling</td>\n",
       "      <td>o</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drugs     gsm_id gene_symbol   gds_id pathway_id  \\\n",
       "0    CHEMBL1336  GSM107523       ARRB1  GDS2795      WP382   \n",
       "1    CHEMBL1336  GSM107523       AVPR2  GDS2795       WP24   \n",
       "2    CHEMBL1336  GSM107523     BCL2L11  GDS2795      WP673   \n",
       "3    CHEMBL1336  GSM107523        BMP1  GDS2795      WP236   \n",
       "4    CHEMBL1336  GSM107523       CASP2  GDS2795      WP254   \n",
       "..          ...        ...         ...      ...        ...   \n",
       "170   CHEMBL941  GSM107525        NOS1  GDS2795     WP2447   \n",
       "171   CHEMBL941  GSM107525       PPARA  GDS2795      WP299   \n",
       "172   CHEMBL941  GSM107525       PTPN1  GDS2795     WP2037   \n",
       "173   CHEMBL941  GSM107525       SIRT3  GDS2795     WP1541   \n",
       "174   CHEMBL941  GSM107525        TBX2  GDS2795      WP304   \n",
       "\n",
       "                                          pathway_name significant  \n",
       "0                                       MAPK signaling           o  \n",
       "1                                        Peptide GPCRs           o  \n",
       "2                                       ErbB signaling           o  \n",
       "3                                         Adipogenesis           o  \n",
       "4                                            Apoptosis           o  \n",
       "..                                                 ...         ...  \n",
       "170                Amyotrophic lateral sclerosis (ALS)           o  \n",
       "171  Nuclear receptors in lipid metabolism and toxi...           o  \n",
       "172                                Prolactin signaling           o  \n",
       "173                                  Energy metabolism           o  \n",
       "174                             Kit receptor signaling           o  \n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the columns to specify the criteria for grouping the data.\n",
    "group_columns = ['Drugs', 'gsm_id', 'gene_symbol']\n",
    "\n",
    "#Group the final_df_with_pathways_filt DataFrame by the specified columns.\n",
    "#Within each group, select the first occurrence of each combination of 'gds_id', 'pathway_id', 'pathway_name', and 'significant'.\n",
    "pathways_by_group = final_df_with_pathways_filt.groupby(group_columns)[['gds_id','pathway_id', 'pathway_name','significant']].first().reset_index()\n",
    "\n",
    "# Remove duplicate rows from `pathways_by_group` based on the combination of 'Drugs', 'gene_symbol', and 'pathway_id' to ensure that each drug-gene-pathway combination is unique in the filtered DataFrame.\n",
    "pathways_by_group_filt = pathways_by_group.drop_duplicates(subset=['Drugs','gene_symbol','pathway_id'])\n",
    "\n",
    "#Show the filtered DataFrame that now contains unique rows for each combination of 'Drugs', 'gene_symbol', and 'pathway_id'.\n",
    "pathways_by_group_filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "533769b2-c106-4ce6-b921-cb7f4bc9daec",
   "metadata": {},
   "outputs": [],
   "source": [
    "pathways_by_group_filt.to_csv('../results/dru_gene_pat_final.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3b6e8181-7060-4591-b4fa-5faeee61d3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drugs\n",
       "CHEMBL1336      28\n",
       "CHEMBL282575    13\n",
       "CHEMBL411       13\n",
       "CHEMBL941       13\n",
       "Name: gene_symbol, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the dataFrame by the Drugs column and for each drug, count the number of different gene symbols associated with it.\n",
    "unique_gene_symbols_per_drug = pathways_by_group_filt.groupby('Drugs')['gene_symbol'].nunique()\n",
    "\n",
    "#Show the count of unique gene symbols for each drug.\n",
    "unique_gene_symbols_per_drug"
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}