repurposing.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 28,
   "id": "b987b5e5-3a96-46dc-ad64-08edeb477a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "from tabulate import tabulate\n",
    "from networkx.algorithms import bipartite\n",
    "import random\n",
    "from scipy.stats import norm\n",
    "from itertools import combinations\n",
    "import re\n",
    "from itertools import product\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4b1e17-fa87-42d9-a28d-e53aebb77943",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c5bf61fe-29af-40b6-a85e-f5453c2b4b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "proximity = pd.read_csv(\"../results/Proximity_results.csv\", sep = \",\")\n",
    "dge = pd.read_csv(\"../results/Filtering_by_DGE_results.csv\", sep = \",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a70afe4-73a7-4436-a8d9-14deba773032",
   "metadata": {},
   "source": [
    "### Filtering of drug repurposing candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "06d790a3-6c11-4724-9ed3-0c764083243f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering of schizophrenia data\n",
    "proximity_schizo = proximity[proximity[\"ID\"] == \"C0036341\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ae6a91-08a8-4bab-97c2-b47070875c48",
   "metadata": {},
   "source": [
    "#### 1) Proximal drugs to schizophrenia module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "493872db-4d45-4ca6-a613-d0adafacd5c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "proximal_drugs_schizo = proximity_schizo[(proximity_schizo[\"Dc_zscore\"] <= -0.15) & \n",
    "                                        (proximity_schizo[\"Treatment\"] == \"unknown\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3e28c3-f78d-4d20-9745-3d28dad6838a",
   "metadata": {},
   "source": [
    "#### 2) Distance to schizophrenia module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1f33226f-a0b8-4d76-be20-f82ca3851d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q1 and median of closest distance for treatment drugs\n",
    "\n",
    "treatment_schizo = proximity_schizo[proximity_schizo[\"Treatment\"] == \"yes\"]\n",
    "Q1_treatment = treatment_schizo[\"Closest distance\"].quantile(0.25)\n",
    "median_treatment = treatment_schizo[\"Closest distance\"].quantile(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "79a8a797-2089-4c60-bcb3-690de1bdbec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unknown drugs with distance to schizophrenia module between Q1 and nedian of treatment drugs\n",
    "\n",
    "closest_drugs_schizo = proximal_drugs_schizo[(proximal_drugs_schizo[\"Closest distance\"] >= Q1_treatment) &\n",
    "                                       (proximal_drugs_schizo[\"Closest distance\"] <= median_treatment)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f6a702-06ee-4d94-8982-1ff4659b8dbc",
   "metadata": {},
   "source": [
    "#### 3) Targets with significant DGE in schizophrenia and correlated with its co-expression module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0665674b-9fbd-4855-9785-7a93b1907540",
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_in_dge = dge[\"Drugs\"].unique()\n",
    "drugs_schizo_filtered =  closest_drugs_schizo[(closest_drugs_schizo[\"Drugs\"].isin(drugs_in_dge))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "418c6fb7-4a32-407e-bb35-9c13804d1dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "proximity_schizo_filtered = drugs_schizo_filtered.sort_values(by=\"Dc_zscore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c6dbe376-fca9-43c1-9c76-cad14964f703",
   "metadata": {},
   "outputs": [],
   "source": [
    "proximity_schizo_filtered = proximity_schizo_filtered.drop('ID', axis=1).drop('Treatment', axis=1).drop('Dc_std', axis=1).drop('Dc_mean', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "05f3dce7-b0ea-4ce3-b06a-52c52f3bb7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "proximity_schizo_filtered.to_csv(\"../results/Repurposing candidates schizophrenia.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a5a03c-76c7-4817-a596-2e89724302d4",
   "metadata": {},
   "source": [
    "### MeSH Pharmacological Actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73e12501-9ea1-4d08-a87a-88a30036d1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = pd.read_csv(\"../files/drug_categories.csv\", sep = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b7c78db-7596-4759-a5e1-50d9e121abb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_drugs = pd.merge(proximity_schizo_filtered, dge[['Drugs', 'Gene symbol']], on='Drugs')\n",
    "merged_drugs = pd.merge(merged_drugs, categories[['drug_id', 'class_name', 'type']], left_on='Drugs', right_on='drug_id')\n",
    "\n",
    "drugs_classification = merged_drugs[merged_drugs['class_name'].str.contains('Agents')]\n",
    "\n",
    "drugs_classification = drugs_classification[['Drugs', 'Closest distance', 'Dc_zscore', 'Gene symbol', 'class_name', 'type']]\n",
    "drugs_classification.columns = ['Drugs', 'Closest distance', 'Proximity', 'Gene target', 'Classification', 'Type']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "21abf2e1-c9df-47ab-86a7-0bcf5523b0ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Drugs  Closest distance   Proximity Gene target  \\\n",
      "1       CHEMBL623          0.300000 -115.579629       KCNH2   \n",
      "8       CHEMBL113          0.363636 -103.512846      PIK3CB   \n",
      "11      CHEMBL113          0.363636 -103.512846      PIK3CB   \n",
      "15      CHEMBL941          0.500000  -94.879347        DDR1   \n",
      "20  CHEMBL1628227          0.352941  -90.844253       KCNH2   \n",
      "28       CHEMBL83          0.428571  -85.950381       KCNH2   \n",
      "30       CHEMBL83          0.428571  -85.950381       KCNH2   \n",
      "34    CHEMBL23588          0.500000  -77.736571       PPARA   \n",
      "35      CHEMBL640          0.333333  -75.756252       KCNH2   \n",
      "39      CHEMBL709          0.500000  -74.365765       KCNH2   \n",
      "43   CHEMBL278819          0.333333  -65.014945        MAOA   \n",
      "44      CHEMBL652          0.500000  -51.717537       KCNH2   \n",
      "47      CHEMBL413          0.333333  -22.765103        FGF2   \n",
      "49      CHEMBL413          0.333333  -22.765103        FGF2   \n",
      "50      CHEMBL413          0.333333  -22.765103        FGF2   \n",
      "53       CHEMBL43          0.500000   -8.866920       KCNH2   \n",
      "\n",
      "                              Classification    Type  \n",
      "1   Antidepressive Agents, Second-Generation  MESHPA  \n",
      "8    Anti-Inflammatory Agents, Non-Steroidal  MESHPA  \n",
      "11                      Antimutagenic Agents  MESHPA  \n",
      "15                     Antineoplastic Agents  MESHPA  \n",
      "20          Antidepressive Agents, Tricyclic  MESHPA  \n",
      "28           Antineoplastic Agents, Hormonal  MESHPA  \n",
      "30          Bone Density Conservation Agents  MESHPA  \n",
      "34                  Anti-Inflammatory Agents  MESHPA  \n",
      "35                    Anti-Arrhythmia Agents  MESHPA  \n",
      "39                         Urological Agents  MESHPA  \n",
      "43                     Antidepressive Agents  MESHPA  \n",
      "44                    Anti-Arrhythmia Agents  MESHPA  \n",
      "47                     Anti-Bacterial Agents  MESHPA  \n",
      "49                         Antifungal Agents  MESHPA  \n",
      "50                  Immunosuppressive Agents  MESHPA  \n",
      "53                     Antineoplastic Agents  MESHPA  \n"
     ]
    }
   ],
   "source": [
    "merged_drugs = pd.merge(proximity_schizo_filtered, dge[['Drugs', 'Gene symbol']], on='Drugs')\n",
    "merged_drugs = pd.merge(merged_drugs, categories[['drug_id', 'class_name', 'type']], left_on='Drugs', right_on='drug_id')\n",
    "\n",
    "drugs_classification = merged_drugs[merged_drugs['class_name'].str.contains('Agents')]\n",
    "\n",
    "drugs_classification = drugs_classification[['Drugs', 'Closest distance', 'Dc_zscore', 'Gene symbol', 'class_name', 'type']]\n",
    "drugs_classification.columns = ['Drugs', 'Closest distance', 'Proximity', 'Gene target', 'Classification', 'Type']\n",
    "\n",
    "\n",
    "print(drugs_classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "447d822e-7e21-45fa-993e-53659fda6fc5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3898d83-c1ed-4c95-88d5-eca1ae6debe6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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