quadrant_plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:** Benoît BAILLIF\n",
    "\n",
    "**Purpose:** Produce figures for chemical and biological spaces plots\n",
    "\n",
    "**Input:**\n",
    "- data/processed/\n",
    " - cmp_info_cmap.csv : info on used compounds\n",
    " - used_pert_id_target_matrix.csv : find targets for each used compound\n",
    " - signatures/\n",
    " \n",
    "**Output:** \n",
    "- plots/Figures 6 and Supplementary Figure 2 : space plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas            as pd\n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib\n",
    "import os\n",
    "import time\n",
    "\n",
    "from rdkit           import Chem\n",
    "from rdkit.Chem      import AllChem\n",
    "from sklearn.metrics import pairwise_distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_data_directory = 'data/processed/'\n",
    "sigs_directory = processed_data_directory + 'signatures/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_info_cmap_path = processed_data_directory + 'cmp_info_cmap.csv'\n",
    "used_pert_id_target_matrix_path = processed_data_directory + 'used_pert_id_target_matrix.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plots_directory = 'plots/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure6_path = plots_directory + 'Figure 6.tif'\n",
    "supplementary_figure_2_path = plots_directory + 'Supplementary Figure 2.tif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_lines = ['MCF7', 'HA1E', 'HT29', 'A549', 'HCC515', 'PC3', 'VCAP', 'A375']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21220, 6)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pert_iname</th>\n",
       "      <th>is_touchstone</th>\n",
       "      <th>inchi_key</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>pubchem_cid</th>\n",
       "      <th>used_compound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pert_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>hemado</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                        pert_iname  is_touchstone  \\\n",
       "pert_id                                             \n",
       "BRD-A00100033           nifurtimox            1.0   \n",
       "BRD-A00150179  5-hydroxytryptophan            0.0   \n",
       "BRD-A00267231               hemado            1.0   \n",
       "BRD-A00420644              SA-3676            0.0   \n",
       "BRD-A00474148        BRD-A00474148            0.0   \n",
       "\n",
       "                                 inchi_key  \\\n",
       "pert_id                                      \n",
       "BRD-A00100033  ARFHIAQFJWUCFH-UHFFFAOYSA-N   \n",
       "BRD-A00150179  QSHLMQDRPXXYEE-UHFFFAOYSA-N   \n",
       "BRD-A00267231  KOCIMZNSNPOGOP-UHFFFAOYSA-N   \n",
       "BRD-A00420644  ASCBUEVCEVGOFP-UHFFFAOYSA-N   \n",
       "BRD-A00474148  RCGAUPRLRFZAMS-UHFFFAOYSA-N   \n",
       "\n",
       "                                                canonical_smiles  pubchem_cid  \\\n",
       "pert_id                                                                         \n",
       "BRD-A00100033          CC1CS(=O)(=O)CCN1N=Cc1ccc([N+](=O)[O-])o1    6842999.0   \n",
       "BRD-A00150179                     NC(Cc1c[nH]c2cccc(O)c12)C(=O)O     589768.0   \n",
       "BRD-A00267231          CCCCC#Cc1nc(NC)c2ncn(C3OC(CO)C(O)C3O)c2n1    4043357.0   \n",
       "BRD-A00420644               CCN1c2ccccc2NC2N=C(OC)C(c3ccccc3)C21    2853908.0   \n",
       "BRD-A00474148  O=C1Cc2cc([S+](=O)([O-])N3CCN(c4ccc(O)cc4)CC3)...   44825297.0   \n",
       "\n",
       "               used_compound  \n",
       "pert_id                       \n",
       "BRD-A00100033              1  \n",
       "BRD-A00150179              0  \n",
       "BRD-A00267231              1  \n",
       "BRD-A00420644              1  \n",
       "BRD-A00474148              1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp_info_cmap = pd.read_csv(cmp_info_cmap_path, index_col='pert_id')\n",
    "print(cmp_info_cmap.shape)\n",
    "cmp_info_cmap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_cmp_info_cmap = cmp_info_cmap[cmp_info_cmap['used_compound'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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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",
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       "      <th>ABCC3</th>\n",
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       "    <tr>\n",
       "      <th>pert_id</th>\n",
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      "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",
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       "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",
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       "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "used_pert_id_target_matrix = pd.read_csv(used_pert_id_target_matrix_path, index_col='pert_id')\n",
    "print(used_pert_id_target_matrix.shape)\n",
    "used_pert_id_target_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_sigs = {}\n",
    "for cell_line in cell_lines :\n",
    "    d_sigs[cell_line] = pd.read_csv(sigs_directory + cell_line + '_used_signatures.csv', index_col='pert_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute quadrant plots for active compounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "molecules = used_cmp_info_cmap['canonical_smiles'].apply(Chem.MolFromSmiles)\n",
    "fps = molecules.apply(lambda mol : AllChem.GetMorganFingerprintAsBitVect(mol, radius=3, nBits=1024))\n",
    "binary_fps = np.stack(fps)\n",
    "binary_fps_df = pd.DataFrame(binary_fps, index=used_cmp_info_cmap.index).astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCF7\n",
      "NR3C1\n",
      "TUBB\n",
      "DRD1\n",
      "A549\n",
      "NR3C1\n",
      "TUBB\n",
      "DRD1\n",
      "PC3\n",
      "NR3C1\n",
      "TUBB\n",
      "DRD1\n",
      "0.5185329914093018\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "targets = ['NR3C1', 'TUBB', 'DRD1']\n",
    "\n",
    "distance_table = pd.DataFrame(columns=['perturbagen_1', 'perturbagen_2', 'smiles_1', 'smiles_2', 'target', \n",
    "                                         'cell_line', 'ECFP6_dice_dist', 'signature_cosine_dist'])\n",
    "\n",
    "actives = []\n",
    "\n",
    "for cell_line in ['MCF7', 'A549', 'PC3'] :\n",
    "    print(cell_line)\n",
    "    \n",
    "    cell_line_sigs = d_sigs[cell_line]\n",
    "\n",
    "    for target in targets :\n",
    "        print(target)\n",
    "        \n",
    "        actives = used_pert_id_target_matrix[used_pert_id_target_matrix[target] == 1].index\n",
    "        actives = actives[actives.isin(cell_line_sigs.index)]\n",
    "            \n",
    "        active_fps = binary_fps_df.loc[actives, ]\n",
    "        active_sigs = cell_line_sigs.loc[actives, ]\n",
    "\n",
    "        fp_dists = pairwise_distances(active_fps.values, metric='dice')\n",
    "        # using index.copy() to avoid using the same index object\n",
    "        fp_dists = pd.DataFrame(fp_dists, index=active_fps.index.copy(), columns=active_fps.index.copy())\n",
    "        fp_dists.index.name = 'perturbagen_1'\n",
    "        fp_dists.columns.name = 'perturbagen_2'\n",
    "        fp_dists = fp_dists.reset_index().melt('perturbagen_1')\n",
    "        fp_dists = fp_dists.rename({'value' : 'ECFP6_dice_dist'}, axis=1)\n",
    "\n",
    "        sig_dists = pairwise_distances(active_sigs.values, metric='cosine')\n",
    "        sig_dists = pd.DataFrame(sig_dists, index=active_sigs.index.copy(), columns=active_sigs.index.copy())\n",
    "        sig_dists.index.name = 'perturbagen_1'\n",
    "        sig_dists.columns.name = 'perturbagen_2'\n",
    "        sig_dists = sig_dists.reset_index().melt('perturbagen_1')\n",
    "        sig_dists = sig_dists.rename({'value' : 'signature_cosine_dist'}, axis=1)\n",
    "\n",
    "        all_dists = fp_dists.merge(sig_dists, left_on=['perturbagen_1', 'perturbagen_2'], \n",
    "                                       right_on=['perturbagen_1', 'perturbagen_2'])\n",
    "\n",
    "        all_dists = all_dists.merge(pd.DataFrame(cmp_info_cmap['canonical_smiles']).reset_index(), how='inner', \n",
    "                                                    left_on=['perturbagen_1'], right_on=['pert_id'])\n",
    "        all_dists = all_dists.drop('pert_id', axis=1)\n",
    "        all_dists = all_dists.rename({'canonical_smiles' : 'smiles_1'}, axis=1)\n",
    "\n",
    "        all_dists = all_dists.merge(pd.DataFrame(cmp_info_cmap['canonical_smiles']).reset_index(), how='inner', \n",
    "                                                    left_on=['perturbagen_2'], right_on=['pert_id'])\n",
    "        all_dists = all_dists.drop('pert_id', axis=1)\n",
    "        all_dists = all_dists.rename({'canonical_smiles' : 'smiles_2'}, axis=1)\n",
    "        all_dists['target'] = target\n",
    "        all_dists['cell_line'] = cell_line\n",
    "        distance_table = pd.concat([distance_table, all_dists], axis=0)\n",
    "                \n",
    "end = time.time()\n",
    "print (end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_pairs = distance_table['perturbagen_1'].copy() + '__' + distance_table['perturbagen_2'].copy()\n",
    "unique_pairs = unique_pairs.apply(lambda s : '_'.join(sorted(s.split('__'))))\n",
    "distance_table['unique_pair'] = unique_pairs + '__' + distance_table['cell_line'] + '__' + distance_table['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_table = distance_table.drop_duplicates(subset=['unique_pair'])\n",
    "distance_table = distance_table[distance_table['perturbagen_1'] != distance_table['perturbagen_2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>perturbagen_1</th>\n",
       "      <th>perturbagen_2</th>\n",
       "      <th>smiles_1</th>\n",
       "      <th>smiles_2</th>\n",
       "      <th>target</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>ECFP6_dice_dist</th>\n",
       "      <th>signature_cosine_dist</th>\n",
       "      <th>unique_pair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRD-K00824317</td>\n",
       "      <td>BRD-A34299591</td>\n",
       "      <td>CC1(C)O[C@@H]2C[C@H]3[C@@H]4C[C@H](F)C5=CC(=O)...</td>\n",
       "      <td>CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...</td>\n",
       "      <td>NR3C1</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>0.530864</td>\n",
       "      <td>0.758737</td>\n",
       "      <td>BRD-A34299591_BRD-K00824317__MCF7__NR3C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRD-K02407574</td>\n",
       "      <td>BRD-A34299591</td>\n",
       "      <td>CCCCc1ccc2[nH]c(NC(=O)OC)nc2c1</td>\n",
       "      <td>CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...</td>\n",
       "      <td>NR3C1</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>0.824818</td>\n",
       "      <td>1.167193</td>\n",
       "      <td>BRD-A34299591_BRD-K02407574__MCF7__NR3C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRD-K03981224</td>\n",
       "      <td>BRD-A34299591</td>\n",
       "      <td>C#C[C@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C...</td>\n",
       "      <td>CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...</td>\n",
       "      <td>NR3C1</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>0.702128</td>\n",
       "      <td>0.911739</td>\n",
       "      <td>BRD-A34299591_BRD-K03981224__MCF7__NR3C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRD-K09295900</td>\n",
       "      <td>BRD-A34299591</td>\n",
       "      <td>C[C@]12C[C@H](O)[C@H]3[C@@H](CCC4=CC(=O)C=C[C@...</td>\n",
       "      <td>CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...</td>\n",
       "      <td>NR3C1</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>0.430303</td>\n",
       "      <td>1.039730</td>\n",
       "      <td>BRD-A34299591_BRD-K09295900__MCF7__NR3C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BRD-K10799896</td>\n",
       "      <td>BRD-A34299591</td>\n",
       "      <td>CCC(=O)O[C@]1(C(=O)CCl)[C@@H](C)C[C@H]2[C@@H]3...</td>\n",
       "      <td>CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...</td>\n",
       "      <td>NR3C1</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>0.617284</td>\n",
       "      <td>0.834606</td>\n",
       "      <td>BRD-A34299591_BRD-K10799896__MCF7__NR3C1</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>8552</th>\n",
       "      <td>BRD-K97440753</td>\n",
       "      <td>BRD-K97158071</td>\n",
       "      <td>CC(C)[C@@]1(NC(=O)[C@@H]2C[C@@H]3c4cccc5[nH]cc...</td>\n",
       "      <td>O=C(CCCN1CC=C(n2c(=O)[nH]c3ccccc32)CC1)c1ccc(F...</td>\n",
       "      <td>DRD1</td>\n",
       "      <td>PC3</td>\n",
       "      <td>0.775401</td>\n",
       "      <td>0.838584</td>\n",
       "      <td>BRD-K97158071_BRD-K97440753__PC3__DRD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8553</th>\n",
       "      <td>BRD-K99792991</td>\n",
       "      <td>BRD-K97158071</td>\n",
       "      <td>Oc1c(Cl)cc(Cl)c(Cl)c1Cc1c(O)c(Cl)cc(Cl)c1Cl</td>\n",
       "      <td>O=C(CCCN1CC=C(n2c(=O)[nH]c3ccccc32)CC1)c1ccc(F...</td>\n",
       "      <td>DRD1</td>\n",
       "      <td>PC3</td>\n",
       "      <td>0.918367</td>\n",
       "      <td>0.978273</td>\n",
       "      <td>BRD-K97158071_BRD-K99792991__PC3__DRD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8646</th>\n",
       "      <td>BRD-K97440753</td>\n",
       "      <td>BRD-K97309399</td>\n",
       "      <td>CC(C)[C@@]1(NC(=O)[C@@H]2C[C@@H]3c4cccc5[nH]cc...</td>\n",
       "      <td>CN1CCN(CC/C=C2\\c3ccccc3Sc3ccc(S(=O)(=O)N(C)C)c...</td>\n",
       "      <td>DRD1</td>\n",
       "      <td>PC3</td>\n",
       "      <td>0.805405</td>\n",
       "      <td>0.840809</td>\n",
       "      <td>BRD-K97309399_BRD-K97440753__PC3__DRD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8647</th>\n",
       "      <td>BRD-K99792991</td>\n",
       "      <td>BRD-K97309399</td>\n",
       "      <td>Oc1c(Cl)cc(Cl)c(Cl)c1Cc1c(O)c(Cl)cc(Cl)c1Cl</td>\n",
       "      <td>CN1CCN(CC/C=C2\\c3ccccc3Sc3ccc(S(=O)(=O)N(C)C)c...</td>\n",
       "      <td>DRD1</td>\n",
       "      <td>PC3</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.700614</td>\n",
       "      <td>BRD-K97309399_BRD-K99792991__PC3__DRD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8741</th>\n",
       "      <td>BRD-K99792991</td>\n",
       "      <td>BRD-K97440753</td>\n",
       "      <td>Oc1c(Cl)cc(Cl)c(Cl)c1Cc1c(O)c(Cl)cc(Cl)c1Cl</td>\n",
       "      <td>CC(C)[C@@]1(NC(=O)[C@@H]2C[C@@H]3c4cccc5[nH]cc...</td>\n",
       "      <td>DRD1</td>\n",
       "      <td>PC3</td>\n",
       "      <td>0.888112</td>\n",
       "      <td>0.925906</td>\n",
       "      <td>BRD-K97440753_BRD-K99792991__PC3__DRD1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18851 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      perturbagen_1  perturbagen_2  \\\n",
       "1     BRD-K00824317  BRD-A34299591   \n",
       "2     BRD-K02407574  BRD-A34299591   \n",
       "3     BRD-K03981224  BRD-A34299591   \n",
       "4     BRD-K09295900  BRD-A34299591   \n",
       "5     BRD-K10799896  BRD-A34299591   \n",
       "...             ...            ...   \n",
       "8552  BRD-K97440753  BRD-K97158071   \n",
       "8553  BRD-K99792991  BRD-K97158071   \n",
       "8646  BRD-K97440753  BRD-K97309399   \n",
       "8647  BRD-K99792991  BRD-K97309399   \n",
       "8741  BRD-K99792991  BRD-K97440753   \n",
       "\n",
       "                                               smiles_1  \\\n",
       "1     CC1(C)O[C@@H]2C[C@H]3[C@@H]4C[C@H](F)C5=CC(=O)...   \n",
       "2                        CCCCc1ccc2[nH]c(NC(=O)OC)nc2c1   \n",
       "3     C#C[C@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C...   \n",
       "4     C[C@]12C[C@H](O)[C@H]3[C@@H](CCC4=CC(=O)C=C[C@...   \n",
       "5     CCC(=O)O[C@]1(C(=O)CCl)[C@@H](C)C[C@H]2[C@@H]3...   \n",
       "...                                                 ...   \n",
       "8552  CC(C)[C@@]1(NC(=O)[C@@H]2C[C@@H]3c4cccc5[nH]cc...   \n",
       "8553        Oc1c(Cl)cc(Cl)c(Cl)c1Cc1c(O)c(Cl)cc(Cl)c1Cl   \n",
       "8646  CC(C)[C@@]1(NC(=O)[C@@H]2C[C@@H]3c4cccc5[nH]cc...   \n",
       "8647        Oc1c(Cl)cc(Cl)c(Cl)c1Cc1c(O)c(Cl)cc(Cl)c1Cl   \n",
       "8741        Oc1c(Cl)cc(Cl)c(Cl)c1Cc1c(O)c(Cl)cc(Cl)c1Cl   \n",
       "\n",
       "                                               smiles_2 target cell_line  \\\n",
       "1     CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...  NR3C1      MCF7   \n",
       "2     CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...  NR3C1      MCF7   \n",
       "3     CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...  NR3C1      MCF7   \n",
       "4     CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...  NR3C1      MCF7   \n",
       "5     CCCC1O[C@@H]2C[C@H]3[C@@H]4CCC5=CC(=O)C=C[C@]5...  NR3C1      MCF7   \n",
       "...                                                 ...    ...       ...   \n",
       "8552  O=C(CCCN1CC=C(n2c(=O)[nH]c3ccccc32)CC1)c1ccc(F...   DRD1       PC3   \n",
       "8553  O=C(CCCN1CC=C(n2c(=O)[nH]c3ccccc32)CC1)c1ccc(F...   DRD1       PC3   \n",
       "8646  CN1CCN(CC/C=C2\\c3ccccc3Sc3ccc(S(=O)(=O)N(C)C)c...   DRD1       PC3   \n",
       "8647  CN1CCN(CC/C=C2\\c3ccccc3Sc3ccc(S(=O)(=O)N(C)C)c...   DRD1       PC3   \n",
       "8741  CC(C)[C@@]1(NC(=O)[C@@H]2C[C@@H]3c4cccc5[nH]cc...   DRD1       PC3   \n",
       "\n",
       "      ECFP6_dice_dist  signature_cosine_dist  \\\n",
       "1            0.530864               0.758737   \n",
       "2            0.824818               1.167193   \n",
       "3            0.702128               0.911739   \n",
       "4            0.430303               1.039730   \n",
       "5            0.617284               0.834606   \n",
       "...               ...                    ...   \n",
       "8552         0.775401               0.838584   \n",
       "8553         0.918367               0.978273   \n",
       "8646         0.805405               0.840809   \n",
       "8647         0.875000               0.700614   \n",
       "8741         0.888112               0.925906   \n",
       "\n",
       "                                   unique_pair  \n",
       "1     BRD-A34299591_BRD-K00824317__MCF7__NR3C1  \n",
       "2     BRD-A34299591_BRD-K02407574__MCF7__NR3C1  \n",
       "3     BRD-A34299591_BRD-K03981224__MCF7__NR3C1  \n",
       "4     BRD-A34299591_BRD-K09295900__MCF7__NR3C1  \n",
       "5     BRD-A34299591_BRD-K10799896__MCF7__NR3C1  \n",
       "...                                        ...  \n",
       "8552    BRD-K97158071_BRD-K97440753__PC3__DRD1  \n",
       "8553    BRD-K97158071_BRD-K99792991__PC3__DRD1  \n",
       "8646    BRD-K97309399_BRD-K97440753__PC3__DRD1  \n",
       "8647    BRD-K97309399_BRD-K99792991__PC3__DRD1  \n",
       "8741    BRD-K97440753_BRD-K99792991__PC3__DRD1  \n",
       "\n",
       "[18851 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "icol = 1\n",
    "fig, axs = plt.subplots(3,3, dpi=300, sharex='col', sharey='row', gridspec_kw={'hspace': 0.1, 'wspace': 0.1}, figsize=(18/2.54,18/2.54))\n",
    "(ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9) = axs\n",
    "lax = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9]\n",
    "figure_subletter = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']\n",
    "\n",
    "#fig.suptitle('Quadrant plot', fontsize=16, y=0.93)\n",
    "i = 0\n",
    "for target in ['NR3C1', 'TUBB', 'DRD1'] :\n",
    "    for cell_line in ['A549', 'MCF7', 'PC3'] :\n",
    "        \n",
    "        cax = lax[i]\n",
    "        sub_df = quadrant_data[(quadrant_data['cell_line'] == cell_line) & (quadrant_data['activity'] == target)]\n",
    "        cax.scatter(sub_df['ECFP6_dice_dist'], sub_df['signature_cosine_dist'], s=1)\n",
    "        cax.axhline(0.5, color='k', linestyle='--', linewidth=1)\n",
    "        cax.axvline(0.5, color='k', linestyle='--', linewidth=1)\n",
    "        cax.tick_params(labelsize=7)\n",
    "        cax.set_xlim([-0.05, 1.05])\n",
    "        cax.set_ylim([-0.05, 2.05])\n",
    "        \n",
    "        subgroup_total = group_total[(group_total['cell_line'] == cell_line) & (group_total['activity'] == target)]\n",
    "        if (subgroup_total.shape[0] > 0) :\n",
    "            q1_val = subgroup_total['Q1'].values[0] / 2\n",
    "            q1_pourcent = round(subgroup_total['Q1 percent'].values[0])\n",
    "            q2_val = subgroup_total['Q2'].values[0] / 2\n",
    "            q2_pourcent = round(subgroup_total['Q2 percent'].values[0])\n",
    "            q3_val = subgroup_total['Q3'].values[0] / 2\n",
    "            q3_pourcent = round(subgroup_total['Q3 percent'].values[0])\n",
    "            q4_val = subgroup_total['Q4'].values[0] / 2\n",
    "            q4_pourcent = round(subgroup_total['Q4 percent'].values[0])\n",
    "            cax.text(0,2, figure_subletter[i], horizontalalignment='left', verticalalignment='top', fontsize=10)\n",
    "            cax.text(0,1.8, str(q1_val) + ' (' + str(q1_pourcent) + ' %)', horizontalalignment='left', verticalalignment='top', fontsize=6)\n",
    "            cax.text(1,1.8, str(q2_val) + ' (' + str(q2_pourcent) + ' %)', horizontalalignment='right', verticalalignment='top', fontsize=6)\n",
    "            cax.text(0,0, str(q3_val) + ' (' + str(q3_pourcent) + ' %)', horizontalalignment='left', verticalalignment='bottom', fontsize=6)\n",
    "            cax.text(1,0, str(q4_val) + ' (' + str(q4_pourcent) + ' %)', horizontalalignment='right', verticalalignment='bottom', fontsize=6)\n",
    "        \n",
    "        i = i + 1\n",
    "        \n",
    "# Create a big subplot\n",
    "ax = fig.add_subplot(111, frameon=False)\n",
    "# hide tick and tick label of the big axes\n",
    "plt.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')\n",
    "\n",
    "ax.set_xlabel('Morgan fingerprint Dice distance', fontsize=12)\n",
    "ax.set_ylabel('Signature cosine distance', fontsize=12, labelpad=20)\n",
    "\n",
    "ax1.set_title('A549', fontsize=8)\n",
    "ax2.set_title('MCF7', fontsize=8)\n",
    "ax3.set_title('PC3', fontsize=8)\n",
    "\n",
    "ax1.set_ylabel('NR3C1', fontsize=8)\n",
    "ax4.set_ylabel('TUBB', fontsize=8)\n",
    "ax7.set_ylabel('DRD1', fontsize=8)\n",
    "\n",
    "plt.savefig('Figure 6.tif', bbox_inches='tight', pil_kwargs={'compression' : 'LZW'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04689764976501465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ben\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py:1735: DataConversionWarning: Data was converted to boolean for metric dice\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "targets = ['NR3C1', 'TUBB', 'DRD1']\n",
    "\n",
    "distance_table = pd.DataFrame(columns=['perturbagen_1', 'perturbagen_2', 'smiles_1', 'smiles_2', 'activity', \n",
    "                                         'cell_line', 'ECFP6_dice_dist', 'signature_cosine_dist'])\n",
    "\n",
    "cell_line = 'MCF7'\n",
    "    \n",
    "cell_line_sigs = d_sigs[cell_line]\n",
    "cell_line_fps = binary_fps_df.loc[cell_line_sigs.index, ]\n",
    "\n",
    "target = 'NR3C1'\n",
    "        \n",
    "actives = used_pert_id_target_matrix[used_pert_id_target_matrix[target] == 1].index\n",
    "        \n",
    "if len(actives) :\n",
    "            \n",
    "    active_fps = cell_line_fps.loc[actives, ]\n",
    "    active_sigs = cell_line_sigs.loc[actives, ]\n",
    "\n",
    "    global fp_dists\n",
    "    fp_dists = pairwise_distances(active_fps.values, metric='dice')\n",
    "    fp_dists = pd.DataFrame(fp_dists, index=active_fps.index.copy(), columns=active_fps.index.copy())\n",
    "    fp_dists.index.name = 'perturbagen_1'\n",
    "    fp_dists.columns.name = 'perturbagen_2'\n",
    "    #fp_dists = fp_dists.reset_index().melt('perturbagen_1')\n",
    "    #fp_dists = fp_dists.rename({'value' : 'ECFP6_dice_dist'}, axis=1)\n",
    "                \n",
    "end = time.time()\n",
    "print (end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_dists = pairwise_distances(active_sigs.values, metric='cosine')\n",
    "sig_dists = pd.DataFrame(sig_dists, index=active_sigs.index, columns=active_sigs.index)\n",
    "sig_dists.index.name = 'perturbagen_1'\n",
    "sig_dists.columns.name = 'perturbagen_2'\n",
    "sig_dists = sig_dists.reset_index().melt('perturbagen_1')\n",
    "sig_dists = sig_dists.rename({'value' : 'signature_cosine_dist'}, axis=1)\n",
    "\n",
    "all_dists = fp_dists.merge(sig_dists, left_on=['perturbagen_1', 'perturbagen_2'], \n",
    "                               right_on=['perturbagen_1', 'perturbagen_2'])\n",
    "\n",
    "all_dists = all_dists.merge(pd.DataFrame(cmp_info_cmap['canonical_smiles']).reset_index(), how='inner', \n",
    "                                            left_on=['perturbagen_1'], right_on=['pert_id'])\n",
    "all_dists = all_dists.drop('pert_id', axis=1)\n",
    "all_dists = all_dists.rename({'canonical_smiles' : 'smiles_1'}, axis=1)\n",
    "\n",
    "all_dists = all_dists.merge(pd.DataFrame(cmp_info_cmap['canonical_smiles']).reset_index(), how='inner', \n",
    "                                            left_on=['perturbagen_2'], right_on=['pert_id'])\n",
    "all_dists = all_dists.drop('pert_id', axis=1)\n",
    "all_dists = all_dists.rename({'canonical_smiles' : 'smiles_2'}, axis=1)\n",
    "all_dists['activity'] = moa\n",
    "all_dists['cell_line'] = cell_line\n",
    "distance_table = pd.concat([distance_table, all_dists], axis=0)"
   ]
  }
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