target_data_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:** Benoît BAILLIF\n",
    "\n",
    "**Purpose:** Process target data extracted from Pubchem and the Drug Repurposing Hub\n",
    "\n",
    "**Input:**\n",
    "- data/raw/\n",
    " - repurposing_drugs_20180907.txt : find the tubulin inhibitors / inactive compounds\n",
    "- data/processed/\n",
    " - pert_id_pubchem_cid.csv : mapping each used pert_id with one or more corresponding pubchem CID\n",
    " - pubchem_bioactivity_matrix_2018.csv : target x pubchem CID table (matrix like, including some target information)\n",
    " \n",
    "**Output:**\n",
    "- data/processed/\n",
    " - used_pertid_target_matrix.csv : final activity matrix to be used downstream, for used compounds\n",
    " - target_information.csv : table recording target information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data_directory = 'data/raw/'\n",
    "processed_data_directory = 'data/processed/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pubchem_bioactivity_matrix_path = processed_data_directory + 'pubchem_bioactivity_matrix_2018.csv'\n",
    "pert_id_pubchem_cid_map_path = processed_data_directory + 'pert_id_to_pubchem_cid.csv'\n",
    "brd_repurposing_path = raw_data_directory + 'repurposing_drugs_20180907.txt'\n",
    "cmp_info_cmap_path = processed_data_directory + 'cmp_info_cmap.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_information_path = processed_data_directory + \"target_information.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_pert_id_target_matrix_path = processed_data_directory + \"used_pert_id_target_matrix.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a binary activity matrix with unique pert_id and target gene symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pubchem_bioactivity_matrix = pd.read_csv(pubchem_bioactivity_matrix_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1625, 19657)\n"
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>388.0</td>\n",
       "      <td>arachidonate 12-lipoxygenase, 12S type [Source...</td>\n",
       "      <td>P18054</td>\n",
       "      <td>ALOX12</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
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       "<p>5 rows × 19657 columns</p>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0                                        description  \\\n",
       "0        47.0  arachidonate 15-lipoxygenase, type B [Source:H...   \n",
       "1       365.0  arachidonate 15-lipoxygenase [Source:HGNC Symb...   \n",
       "2       195.0  tumor protein p53 [Source:HGNC Symbol;Acc:HGNC...   \n",
       "3       388.0  arachidonate 12-lipoxygenase, 12S type [Source...   \n",
       "4       801.0  growth factor, augmenter of liver regeneration...   \n",
       "\n",
       "  uniprotswissprot external_gene_name  2853908  44825297  107867  2381  \\\n",
       "0           O15296            ALOX15B        2         0       0     0   \n",
       "1           P16050             ALOX15        2         0       0     0   \n",
       "2           P04637               TP53        2         0       0     0   \n",
       "3           P18054             ALOX12        2         0       0     0   \n",
       "4           P55789               GFER        2         0       1     0   \n",
       "\n",
       "   16746329  3182  ...  568763  405012  3594  114750  3792  107926  36687767  \\\n",
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       "\n",
       "[5 rows x 19657 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(pubchem_bioactivity_matrix.shape)\n",
    "pubchem_bioactivity_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>external_gene_name</th>\n",
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       "      <th>uniprotswissprot</th>\n",
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       "      <td>ALOX15B</td>\n",
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       "      <td>O15296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALOX15</td>\n",
       "      <td>arachidonate 15-lipoxygenase [Source:HGNC Symb...</td>\n",
       "      <td>P16050</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>TP53</td>\n",
       "      <td>tumor protein p53 [Source:HGNC Symbol;Acc:HGNC...</td>\n",
       "      <td>P04637</td>\n",
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       "      <th>3</th>\n",
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       "      <td>GFER</td>\n",
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       "      <td>P55789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  external_gene_name                                        description  \\\n",
       "0            ALOX15B  arachidonate 15-lipoxygenase, type B [Source:H...   \n",
       "1             ALOX15  arachidonate 15-lipoxygenase [Source:HGNC Symb...   \n",
       "2               TP53  tumor protein p53 [Source:HGNC Symbol;Acc:HGNC...   \n",
       "3             ALOX12  arachidonate 12-lipoxygenase, 12S type [Source...   \n",
       "4               GFER  growth factor, augmenter of liver regeneration...   \n",
       "\n",
       "  uniprotswissprot  \n",
       "0           O15296  \n",
       "1           P16050  \n",
       "2           P04637  \n",
       "3           P18054  \n",
       "4           P55789  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_information = pubchem_bioactivity_matrix[['external_gene_name', 'description', 'uniprotswissprot']]\n",
    "target_information = target_information.drop_duplicates()\n",
    "target_information.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_information.to_csv(target_information_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10015, 2)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pert_id</th>\n",
       "      <th>pubchem_cid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRD-A00100033</td>\n",
       "      <td>6842999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRD-A00267231</td>\n",
       "      <td>4043357</td>\n",
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       "      <th>2</th>\n",
       "      <td>BRD-A00420644</td>\n",
       "      <td>2853908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRD-A00474148</td>\n",
       "      <td>44825297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRD-A00520476</td>\n",
       "      <td>107867</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "         pert_id  pubchem_cid\n",
       "0  BRD-A00100033      6842999\n",
       "1  BRD-A00267231      4043357\n",
       "2  BRD-A00420644      2853908\n",
       "3  BRD-A00474148     44825297\n",
       "4  BRD-A00520476       107867"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pert_id_pubchem_cid_map = pd.read_csv(pert_id_pubchem_cid_map_path)\n",
    "print(pert_id_pubchem_cid_map.shape)\n",
    "pert_id_pubchem_cid_map.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_compound_matrix = pubchem_bioactivity_matrix.drop(['description', 'uniprotswissprot', 'Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge data for different targets coming from different target ids having the same symbol\n",
    "target_compound_matrix = target_compound_matrix.groupby('external_gene_name').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace pubchem_cid with corresponding pert_id\n",
    "compound_target_matrix = target_compound_matrix.T\n",
    "compound_target_matrix = compound_target_matrix.reset_index(drop=False)\n",
    "compound_target_matrix = compound_target_matrix.rename({'index' : 'pubchem_cid'}, axis=1)\n",
    "compound_target_matrix['pubchem_cid'] = compound_target_matrix['pubchem_cid'].astype(int)\n",
    "compound_target_matrix = compound_target_matrix.merge(pert_id_pubchem_cid_map, on='pubchem_cid')\n",
    "used_pert_id_target_matrix = compound_target_matrix.drop('pubchem_cid', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_pert_id_target_matrix = used_pert_id_target_matrix.groupby('pert_id').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove compounds/targets with no data\n",
    "used_pert_id_target_matrix = used_pert_id_target_matrix.replace({0 : np.nan, 1 : 0, 2 : 1})\n",
    "used_pert_id_target_matrix = used_pert_id_target_matrix.dropna(how='all', axis=0).dropna(how='all', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>AAK1</th>\n",
       "      <th>ABAT</th>\n",
       "      <th>ABCA1</th>\n",
       "      <th>ABCB1</th>\n",
       "      <th>ABCB11</th>\n",
       "      <th>ABCB6</th>\n",
       "      <th>ABCC1</th>\n",
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       "      <th>ABCC3</th>\n",
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       "      <th>YES1</th>\n",
       "      <th>YWHAB</th>\n",
       "      <th>YWHAG</th>\n",
       "      <th>ZAP70</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pert_id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BRD-A00100033</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00218260</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00267231</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00420644</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRD-A00474148</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1358 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAK1  ABAT  ABCA1  ABCB1  ABCB11  ABCB6  ABCC1  ABCC2  ABCC3  \\\n",
       "pert_id                                                                       \n",
       "BRD-A00100033   NaN   NaN    NaN    NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "BRD-A00218260   NaN   NaN    NaN    NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "BRD-A00267231   NaN   NaN    NaN    NaN     NaN    NaN    NaN    NaN    NaN   \n",
       "BRD-A00420644   NaN   NaN    NaN    0.0     NaN    0.0    0.0    NaN    NaN   \n",
       "BRD-A00474148   NaN   NaN    NaN    NaN     NaN    0.0    NaN    NaN    NaN   \n",
       "\n",
       "               ABCC5  ...  WEE2  WNT3A  WRN  XDH  XIAP  XPO1  YES1  YWHAB  \\\n",
       "pert_id               ...                                                   \n",
       "BRD-A00100033    NaN  ...   NaN    NaN  NaN  NaN   NaN   NaN   NaN    NaN   \n",
       "BRD-A00218260    NaN  ...   NaN    NaN  NaN  NaN   NaN   NaN   NaN    NaN   \n",
       "BRD-A00267231    NaN  ...   NaN    NaN  NaN  NaN   NaN   NaN   NaN    NaN   \n",
       "BRD-A00420644    NaN  ...   NaN    NaN  0.0  NaN   NaN   NaN   NaN    NaN   \n",
       "BRD-A00474148    NaN  ...   NaN    NaN  NaN  NaN   NaN   NaN   NaN    NaN   \n",
       "\n",
       "               YWHAG  ZAP70  \n",
       "pert_id                      \n",
       "BRD-A00100033    NaN    NaN  \n",
       "BRD-A00218260    NaN    NaN  \n",
       "BRD-A00267231    NaN    NaN  \n",
       "BRD-A00420644    0.0    NaN  \n",
       "BRD-A00474148    NaN    NaN  \n",
       "\n",
       "[5 rows x 1358 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "used_pert_id_target_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of annotations for 7803 (DMSO excluded) : 517779\n",
      "Previous result (2018) : 512406 annotations if DMSO included\n"
     ]
    }
   ],
   "source": [
    "n_annotations = used_pert_id_target_matrix.count().sum()\n",
    "n_annotated_compounds = used_pert_id_target_matrix.shape[0]\n",
    "print('Total number of annotations for ' + str(n_annotated_compounds) + ' (DMSO excluded) : ' + str(n_annotations))\n",
    "\n",
    "print('Previous result (2018) : 512406 annotations if DMSO included')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add tubulin inhibitor information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default utf-8 encoding does not work\n",
    "brd_repurposing_data = pd.read_csv(brd_repurposing_path, sep='\\t', skiprows=9, encoding='latin_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>pert_iname</th>\n",
       "      <th>clinical_phase</th>\n",
       "      <th>moa</th>\n",
       "      <th>target</th>\n",
       "      <th>disease_area</th>\n",
       "      <th>indication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[sar9,met(o2)11]-substance-p</td>\n",
       "      <td>Preclinical</td>\n",
       "      <td>tachykinin antagonist</td>\n",
       "      <td>TACR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-1070722</td>\n",
       "      <td>Preclinical</td>\n",
       "      <td>glycogen synthase kinase inhibitor</td>\n",
       "      <td>GSK3A|GSK3B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A-1120</td>\n",
       "      <td>Preclinical</td>\n",
       "      <td>retinoid receptor ligand</td>\n",
       "      <td>RBP4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-317491</td>\n",
       "      <td>Preclinical</td>\n",
       "      <td>purinergic receptor antagonist</td>\n",
       "      <td>P2RX3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A-33903</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     pert_iname clinical_phase  \\\n",
       "0  [sar9,met(o2)11]-substance-p    Preclinical   \n",
       "1                     A-1070722    Preclinical   \n",
       "2                        A-1120    Preclinical   \n",
       "3                      A-317491    Preclinical   \n",
       "4                       A-33903        Phase 2   \n",
       "\n",
       "                                  moa       target disease_area indication  \n",
       "0               tachykinin antagonist        TACR1          NaN        NaN  \n",
       "1  glycogen synthase kinase inhibitor  GSK3A|GSK3B          NaN        NaN  \n",
       "2            retinoid receptor ligand         RBP4          NaN        NaN  \n",
       "3      purinergic receptor antagonist        P2RX3          NaN        NaN  \n",
       "4                                 NaN          NaN          NaN        NaN  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brd_repurposing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_tubb_active = brd_repurposing_data['target'].str.contains('TUBB', na = False)\n",
    "is_tubulin_inhibitor = brd_repurposing_data['moa'].str.contains('tubul.*inhibitor', na = False)\n",
    "tubb_active_pert_inames = brd_repurposing_data[is_tubb_active | is_tubulin_inhibitor]['pert_iname'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_info_cmap = pd.read_csv(cmp_info_cmap_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_tubb_active = cmp_info[\"pert_iname\"].isin(tubb_active_pert_inames)\n",
    "is_used_compound = cmp_info['used_compound'] == 1\n",
    "tubb_cmp_info = cmp_info[is_tubb_active & is_used_compound]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "tubb_active_pert_ids = tubb_cmp_info['pert_id'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "for pert_id in tubb_active_pert_ids :\n",
    "    used_pert_id_target_matrix.loc[pert_id,'TUBB'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of annotations for 7825 (DMSO excluded, and adding tubulin inhibitors) : 517825\n",
      "Previous result (2018) : 512406 annotations (DMSO included)\n"
     ]
    }
   ],
   "source": [
    "n_annotations = used_pert_id_target_matrix.count().sum()\n",
    "n_annotated_compounds = used_pert_id_target_matrix.shape[0]\n",
    "print('Total number of annotations for ' + str(n_annotated_compounds) + ' (DMSO excluded, and adding tubulin inhibitors) : ' + str(n_annotations))\n",
    "\n",
    "print('Previous result (2018) : 512406 annotations (DMSO included)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_pert_id_target_matrix.to_csv(used_pert_id_target_matrix_path)"
   ]
  }
 ],
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