{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Sex-biased tissues per cell line per perturbagen\n",
    "--------------------------------------------------------------------------------\n",
    "## Heatmap 3 features by sex: Female\n",
    "\n",
    "###### 0 = no significant, no sex-bias tissue\n",
    "###### 1 = significant, Tissue GTEX =/ Tissue Clue\n",
    "###### 2 = significant, Tissue GTEX = Tissue Clue Sex-bias Female\n",
    "\n",
    "\n",
    "\n",
    "Author: Belén Otero Carrasco\n",
    "\n",
    "Last updated 07 December, 2023\n",
    "\n",
    "#### Signatures selection news (exemplar = 1 and TAS higher)\n",
    "\n",
    "--------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.0.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pkg_resources\n",
    "# Print version of cmapPy being used in current conda environment \n",
    "pkg_resources.get_distribution(\"cmapPy\").version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "from cmapPy.pandasGEXpress.parse import parse\n",
    "from scipy.stats import hypergeom\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signatures_ids_perturbagen(drug):\n",
    "    sig_pertu_ids = final_sig_original_filter[\"sig_id\"][final_sig_original_filter[\"pert_iname\"] == drug]\n",
    "    print(\"number of samples treated with this perturbagen:\")\n",
    "    return sig_pertu_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perturbagen_significant_genes(sig_pertu_ids,drug):\n",
    "    perturbagen_genes_exp= parse(\"./\", cid=sig_pertu_ids)\n",
    "    df_perturbagen_genes_exp =perturbagen_genes_exp.data_df\n",
    "    df_filter_significant = df_perturbagen_genes_exp[(df_perturbagen_genes_exp >2.0) | (df_perturbagen_genes_exp <-2.0)]\n",
    "    df_bool_sign = df_filter_significant.notna()\n",
    "    df_bool_sign[\"count\"] = df_bool_sign.sum(axis=1)\n",
    "    df_bool_sign = df_bool_sign.reset_index()\n",
    "    df_gene_change = df_bool_sign[df_bool_sign[\"count\"]> 0]\n",
    "    df_gene_change_id = df_gene_change[[\"rid\",\"count\"]]\n",
    "    df_gene_change_id[\"perturbagen_name\"] = drug\n",
    "    df_gene_change_id[\"Signature_id\"] = sig_pertu_ids\n",
    "    return df_gene_change_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_by_tissue(tissue_type):\n",
    "    data_gtex = pd.read_csv(\"signif.sbgenes.txt\", sep=\"\\t\")\n",
    "    filtered_df_tissue = data_gtex[data_gtex[\"tissue\"] == tissue_type]\n",
    "    return filtered_df_tissue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_by_ids_and_tissue(tissue_type):\n",
    "    data_total_genes_share = pd.read_csv(\"genes_hugo+ensembl.csv\", sep=\",\")\n",
    "    data_gtex_filter = data_gtex.merge(data_total_genes_share , on=\"gene\")\n",
    "    filtered_df_tissue_ids = data_gtex_filter[data_gtex_filter[\"tissue\"] == tissue_type]\n",
    "    return filtered_df_tissue_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate(disease_feature, diseases, features):\n",
    "   \n",
    "    \"\"\"Function that from a list of relationships disease-feature obtains the\n",
    "    feature matrix, so that rows are diseases and columns are features.\n",
    "    \"\"\"\n",
    "   \n",
    "   \n",
    "    dis_feat_dict = { i : list() for i in diseases}\n",
    "   \n",
    "    disease_feature_array = disease_feature.to_numpy()\n",
    "   \n",
    "    for [disease, feature] in disease_feature_array:\n",
    "       \n",
    "        if not feature in dis_feat_dict[disease]:\n",
    "            dis_feat_dict[disease].append(feature)\n",
    "           \n",
    "    bool_matr = [[0 for x in range(len(features))] for y in range(len(diseases))]\n",
    "   \n",
    "    count_dis = 0   \n",
    "    for dis in diseases:       \n",
    "        count_feat = 0       \n",
    "        for feat in features:           \n",
    "            if feat in dis_feat_dict[dis]:               \n",
    "                bool_matr[count_dis][count_feat] = 1               \n",
    "            count_feat += 1           \n",
    "        count_dis += 1\n",
    "   \n",
    "    feature_matrix = pd.DataFrame(bool_matr)\n",
    "    return feature_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Signature information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>cell_id</th>\n",
       "      <th>base_cell_id</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>tissue</th>\n",
       "      <th>abrev</th>\n",
       "      <th>subtype</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>pert_iname</th>\n",
       "      <th>tas</th>\n",
       "      <th>is_exemplar</th>\n",
       "      <th>Signature_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC018_A375_6H:BRD-K06817181-001-01-5:10</td>\n",
       "      <td>1,2,3,4,5,6-hexabromocyclohexane</td>\n",
       "      <td>0.282354</td>\n",
       "      <td>1</td>\n",
       "      <td>1,2,3,4,5,6-hexabromocyclohexane_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC017_A375_6H:BRD-K74430258-001-01-2:10</td>\n",
       "      <td>1,2-dichlorobenzene</td>\n",
       "      <td>0.057646</td>\n",
       "      <td>1</td>\n",
       "      <td>1,2-dichlorobenzene_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC010_A375_6H:BRD-K32795028-001-10-9:10</td>\n",
       "      <td>1-benzylimidazole</td>\n",
       "      <td>0.221885</td>\n",
       "      <td>1</td>\n",
       "      <td>1-benzylimidazole_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC018_A375_6H:BRD-A80928489-001-01-0:10</td>\n",
       "      <td>1-monopalmitin</td>\n",
       "      <td>0.118443</td>\n",
       "      <td>1</td>\n",
       "      <td>1-monopalmitin_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC018_A375_6H:BRD-K31491153-001-01-2:10</td>\n",
       "      <td>1-phenylbiguanide</td>\n",
       "      <td>0.313940</td>\n",
       "      <td>1</td>\n",
       "      <td>1-phenylbiguanide_A375_skin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cell_id base_cell_id sample_type primary_site  \\\n",
       "0    A375         A375       tumor         skin   \n",
       "1    A375         A375       tumor         skin   \n",
       "2    A375         A375       tumor         skin   \n",
       "3    A375         A375       tumor         skin   \n",
       "4    A375         A375       tumor         skin   \n",
       "\n",
       "                            tissue   abrev             subtype  \\\n",
       "0  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "1  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "2  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "3  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "4  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "\n",
       "                                     sig_id                        pert_iname  \\\n",
       "0  CPC018_A375_6H:BRD-K06817181-001-01-5:10  1,2,3,4,5,6-hexabromocyclohexane   \n",
       "1  CPC017_A375_6H:BRD-K74430258-001-01-2:10               1,2-dichlorobenzene   \n",
       "2  CPC010_A375_6H:BRD-K32795028-001-10-9:10                 1-benzylimidazole   \n",
       "3  CPC018_A375_6H:BRD-A80928489-001-01-0:10                    1-monopalmitin   \n",
       "4  CPC018_A375_6H:BRD-K31491153-001-01-2:10                 1-phenylbiguanide   \n",
       "\n",
       "        tas  is_exemplar                                Signature_id  \n",
       "0  0.282354            1  1,2,3,4,5,6-hexabromocyclohexane_A375_skin  \n",
       "1  0.057646            1               1,2-dichlorobenzene_A375_skin  \n",
       "2  0.221885            1                 1-benzylimidazole_A375_skin  \n",
       "3  0.118443            1                    1-monopalmitin_A375_skin  \n",
       "4  0.313940            1                 1-phenylbiguanide_A375_skin  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig_cell_info = pd.read_excel((\"new_signame_tas_exemplar.xlsx\"),engine='openpyxl')\n",
    "sig_cell_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_pertu_ids = sig_cell_info[\"sig_id\"][sig_cell_info[\"pert_iname\"] == \"tamoxifen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sig_pertu_ids.drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_pertu_ids = sig_pertu_ids.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids = sig_cell_info[[\"pert_iname\"]].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids = drugs_ids.values.ravel().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gene (row) annotations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pr_gene_id', 'pr_gene_symbol', 'pr_gene_title', 'pr_is_lm',\n",
       "       'pr_is_bing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_info = pd.read_csv(\"./GSE92742_Broad_LINCS_gene_info.txt\", sep=\"\\t\", dtype=str)\n",
    "gene_info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pertub_id = pd.read_csv(\"./GSE92742_Broad_LINCS_pert_info.txt\", sep=\"\\t\", dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_metrics_id = pd.read_csv(\"./GSE92742_Broad_LINCS_pert_metrics.txt\", sep=\"\\t\", dtype=str)\n",
    "per_metrics_id = per_metrics_id.replace({\"-666\":\"Unknown\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_total_genes_share = pd.read_csv(\"genes_hugo+ensembl.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex = pd.read_csv(\"signif.sbgenes.txt\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex['Sex-bias'] = data_gtex.apply(lambda row: 'Female' if row['effsize'] >= 0 else 'Male', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex-bias\n",
       "Female    52851\n",
       "Male      52013\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(data_gtex['Sex-bias'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex_tissues = data_gtex[\"tissue\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex_filter = data_gtex.merge(data_total_genes_share , on=\"gene\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>tissue</th>\n",
       "      <th>effsize</th>\n",
       "      <th>effsize_se</th>\n",
       "      <th>lfsr</th>\n",
       "      <th>Sex-bias</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>9.802124</td>\n",
       "      <td>0.071315</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>7503</td>\n",
       "      <td>XIST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>9.942181</td>\n",
       "      <td>0.072492</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>7503</td>\n",
       "      <td>XIST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>Adrenal_Gland</td>\n",
       "      <td>9.565248</td>\n",
       "      <td>0.120011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>7503</td>\n",
       "      <td>XIST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "      <td>10.274669</td>\n",
       "      <td>0.095769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>7503</td>\n",
       "      <td>XIST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>Artery_Coronary</td>\n",
       "      <td>9.680714</td>\n",
       "      <td>0.119024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>7503</td>\n",
       "      <td>XIST</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>62715</th>\n",
       "      <td>ENSG00000101132.9</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>-0.097052</td>\n",
       "      <td>0.048216</td>\n",
       "      <td>0.019016</td>\n",
       "      <td>Male</td>\n",
       "      <td>7623</td>\n",
       "      <td>5203</td>\n",
       "      <td>PFDN4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62716</th>\n",
       "      <td>ENSG00000196812.4</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>0.057226</td>\n",
       "      <td>0.028974</td>\n",
       "      <td>0.019223</td>\n",
       "      <td>Female</td>\n",
       "      <td>7624</td>\n",
       "      <td>80345</td>\n",
       "      <td>ZSCAN16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62717</th>\n",
       "      <td>ENSG00000111261.13</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>-0.043080</td>\n",
       "      <td>0.021107</td>\n",
       "      <td>0.021191</td>\n",
       "      <td>Male</td>\n",
       "      <td>7625</td>\n",
       "      <td>54682</td>\n",
       "      <td>MANSC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62718</th>\n",
       "      <td>ENSG00000163191.5</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>0.054503</td>\n",
       "      <td>0.026722</td>\n",
       "      <td>0.024280</td>\n",
       "      <td>Female</td>\n",
       "      <td>7626</td>\n",
       "      <td>6282</td>\n",
       "      <td>S100A11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62719</th>\n",
       "      <td>ENSG00000204610.12</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>-0.165408</td>\n",
       "      <td>0.082596</td>\n",
       "      <td>0.024650</td>\n",
       "      <td>Male</td>\n",
       "      <td>7627</td>\n",
       "      <td>89870</td>\n",
       "      <td>TRIM15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62720 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gene                    tissue    effsize  effsize_se  \\\n",
       "0      ENSG00000229807.10      Adipose_Subcutaneous   9.802124    0.071315   \n",
       "1      ENSG00000229807.10  Adipose_Visceral_Omentum   9.942181    0.072492   \n",
       "2      ENSG00000229807.10             Adrenal_Gland   9.565248    0.120011   \n",
       "3      ENSG00000229807.10              Artery_Aorta  10.274669    0.095769   \n",
       "4      ENSG00000229807.10           Artery_Coronary   9.680714    0.119024   \n",
       "...                   ...                       ...        ...         ...   \n",
       "62715   ENSG00000101132.9               Whole_Blood  -0.097052    0.048216   \n",
       "62716   ENSG00000196812.4               Whole_Blood   0.057226    0.028974   \n",
       "62717  ENSG00000111261.13               Whole_Blood  -0.043080    0.021107   \n",
       "62718   ENSG00000163191.5               Whole_Blood   0.054503    0.026722   \n",
       "62719  ENSG00000204610.12               Whole_Blood  -0.165408    0.082596   \n",
       "\n",
       "           lfsr Sex-bias  Unnamed: 0  gene_id gene_symbol  \n",
       "0      0.000000   Female           0     7503        XIST  \n",
       "1      0.000000   Female           0     7503        XIST  \n",
       "2      0.000000   Female           0     7503        XIST  \n",
       "3      0.000000   Female           0     7503        XIST  \n",
       "4      0.000000   Female           0     7503        XIST  \n",
       "...         ...      ...         ...      ...         ...  \n",
       "62715  0.019016     Male        7623     5203       PFDN4  \n",
       "62716  0.019223   Female        7624    80345     ZSCAN16  \n",
       "62717  0.021191     Male        7625    54682      MANSC1  \n",
       "62718  0.024280   Female        7626     6282     S100A11  \n",
       "62719  0.024650     Male        7627    89870      TRIM15  \n",
       "\n",
       "[62720 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_gtex_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex-bias\n",
       "Female    32823\n",
       "Male      29897\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(data_gtex_filter['Sex-bias'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids_check = [\"tamoxifen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [05:38<00:00, 338.02s/it]\n"
     ]
    }
   ],
   "source": [
    "signatures_per_perturbagen = []\n",
    "genes_significant_per_perturbagen = []\n",
    "p_values_per_tissue_perturbagen = []\n",
    "df = pd.DataFrame(columns=[\"Signature\",\"Perturbagen_name\",\"Tissue_type\", \"P-value\" ])\n",
    "overlap_genes = []\n",
    "\n",
    "for drug in tqdm(drugs_ids_check):\n",
    "    #print(drug)\n",
    "    for num_sig, sig in enumerate(sig_pertu_ids): \n",
    "        #print (num_sig, sig)\n",
    "        genes_significant = perturbagen_significant_genes(sig,drug)\n",
    "        gene_info = pd.read_csv(\"./GSE92742_Broad_LINCS_gene_info.txt\", sep=\"\\t\", dtype=str)\n",
    "        genes_sig_per_pertur = genes_significant.merge(gene_info, left_on=\"rid\", right_on=\"pr_gene_id\")\n",
    "        genes_sig_per_pertur = genes_sig_per_pertur[[\"perturbagen_name\",\"Signature_id\",\"pr_gene_id\",\"pr_gene_symbol\"]]\n",
    "        genes_significant_per_perturbagen.append(genes_sig_per_pertur)\n",
    "\n",
    "        for tissue in data_gtex_tissues:\n",
    "            #print(tissue)\n",
    "            tissue_type =filter_by_tissue(tissue)\n",
    "            tissue_type_filter = filter_by_ids_and_tissue(tissue)\n",
    "            overlap = genes_sig_per_pertur.merge(tissue_type_filter,left_on= \"pr_gene_symbol\",right_on=\"gene_symbol\")\n",
    "            overlap = overlap[[\"perturbagen_name\",\"Signature_id\",\"gene_symbol\",\"tissue\",\"Sex-bias\"]]\n",
    "            overlap_genes.append(overlap)\n",
    "\n",
    "            # parameters\n",
    "            population_total = 20000  #union Clue + gtex\n",
    "            num_genes_change_expression_perturbagen = len(genes_sig_per_pertur)  # Number of genes clue significant\n",
    "            num_genes_gtex_tissue = len(tissue_type) # GTEX tissue type n-genes\n",
    "            overlap_len = len(overlap)  #overlap\n",
    "            #test \n",
    "            #resultado = hypergeom.sf(overlap - 1, population_total, num_genes_change_expression_perturbagen, num_genes_gtex_tissue)\n",
    "            result = hypergeom.sf(overlap_len - 1, population_total,num_genes_gtex_tissue,num_genes_change_expression_perturbagen)\n",
    "            p_values_per_tissue_perturbagen.append(result)\n",
    "            #print(\"P-value:\", resultado, tissue, drug)\n",
    "            row = pd.DataFrame({\"P-value\": [result], \"Tissue_type\": [tissue], \"Perturbagen_name\": [drug],\"Signature\":[sig]})\n",
    "            df = pd.concat([df, row])\n",
    "    \n",
    "        \n",
    "      \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Signature\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>Tissue_type</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>0.288210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>0.466375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adrenal_Gland</td>\n",
       "      <td>0.579258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "      <td>0.934469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Artery_Coronary</td>\n",
       "      <td>0.872566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Small_Intestine_Terminal_Ileum</td>\n",
       "      <td>0.774316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Spleen</td>\n",
       "      <td>0.568827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Stomach</td>\n",
       "      <td>0.729772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Thyroid</td>\n",
       "      <td>0.962904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>0.917084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2464 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Signature Perturbagen_name  \\\n",
       "0   CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "0   CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "0   CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "0   CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "0   CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "..                                        ...              ...   \n",
       "0    CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "0    CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "0    CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "0    CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "0    CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "\n",
       "                       Tissue_type   P-value  \n",
       "0             Adipose_Subcutaneous  0.288210  \n",
       "0         Adipose_Visceral_Omentum  0.466375  \n",
       "0                    Adrenal_Gland  0.579258  \n",
       "0                     Artery_Aorta  0.934469  \n",
       "0                  Artery_Coronary  0.872566  \n",
       "..                             ...       ...  \n",
       "0   Small_Intestine_Terminal_Ileum  0.774316  \n",
       "0                           Spleen  0.568827  \n",
       "0                          Stomach  0.729772  \n",
       "0                          Thyroid  0.962904  \n",
       "0                      Whole_Blood  0.917084  \n",
       "\n",
       "[2464 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>tissue</th>\n",
       "      <th>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>SQSTM1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>HMGA2</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>IGF2BP2</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>CCNB1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>SUV39H1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76136</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>RBP4</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76137</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>COPZ2</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76138</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>RAP2A</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76139</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>PTGDS</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76140</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ZSCAN16</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76141 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      perturbagen_name                               Signature_id gene_symbol  \\\n",
       "0            tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10      SQSTM1   \n",
       "1            tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10       HMGA2   \n",
       "2            tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10     IGF2BP2   \n",
       "3            tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10       CCNB1   \n",
       "4            tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10     SUV39H1   \n",
       "...                ...                                        ...         ...   \n",
       "76136        tamoxifen   CPC015_SKB_24H:BRD-K43744935-048-01-6:10        RBP4   \n",
       "76137        tamoxifen   CPC015_SKB_24H:BRD-K43744935-048-01-6:10       COPZ2   \n",
       "76138        tamoxifen   CPC015_SKB_24H:BRD-K43744935-048-01-6:10       RAP2A   \n",
       "76139        tamoxifen   CPC015_SKB_24H:BRD-K43744935-048-01-6:10       PTGDS   \n",
       "76140        tamoxifen   CPC015_SKB_24H:BRD-K43744935-048-01-6:10     ZSCAN16   \n",
       "\n",
       "                     tissue Sex-bias  \n",
       "0      Adipose_Subcutaneous     Male  \n",
       "1      Adipose_Subcutaneous   Female  \n",
       "2      Adipose_Subcutaneous   Female  \n",
       "3      Adipose_Subcutaneous   Female  \n",
       "4      Adipose_Subcutaneous   Female  \n",
       "...                     ...      ...  \n",
       "76136           Whole_Blood   Female  \n",
       "76137           Whole_Blood   Female  \n",
       "76138           Whole_Blood     Male  \n",
       "76139           Whole_Blood     Male  \n",
       "76140           Whole_Blood   Female  \n",
       "\n",
       "[76141 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>tissue</th>\n",
       "      <th>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>SQSTM1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>HMGA2</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>IGF2BP2</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>CCNB1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>SUV39H1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>E2F1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>THBS2</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>ACOT11</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>DIDO1</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>TMEM144</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   perturbagen_name                               Signature_id gene_symbol  \\\n",
       "0         tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10      SQSTM1   \n",
       "1         tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10       HMGA2   \n",
       "2         tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10     IGF2BP2   \n",
       "3         tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10       CCNB1   \n",
       "4         tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10     SUV39H1   \n",
       "..              ...                                        ...         ...   \n",
       "70        tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10        E2F1   \n",
       "71        tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10       THBS2   \n",
       "72        tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10      ACOT11   \n",
       "73        tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10       DIDO1   \n",
       "74        tamoxifen  CPC005_A375_24H:BRD-K04210847-001-01-1:10     TMEM144   \n",
       "\n",
       "                  tissue Sex-bias  \n",
       "0   Adipose_Subcutaneous     Male  \n",
       "1   Adipose_Subcutaneous   Female  \n",
       "2   Adipose_Subcutaneous   Female  \n",
       "3   Adipose_Subcutaneous   Female  \n",
       "4   Adipose_Subcutaneous   Female  \n",
       "..                   ...      ...  \n",
       "70  Adipose_Subcutaneous   Female  \n",
       "71  Adipose_Subcutaneous     Male  \n",
       "72  Adipose_Subcutaneous   Female  \n",
       "73  Adipose_Subcutaneous     Male  \n",
       "74  Adipose_Subcutaneous   Female  \n",
       "\n",
       "[75 rows x 5 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_tissue = df_all[\n",
    "    (df_all.Signature_id == \"CPC005_A375_24H:BRD-K04210847-001-01-1:10\") &\n",
    "    (df_all.tissue == \"Adipose_Subcutaneous\") ]\n",
    "test_tissue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex-bias\n",
       "Male      39\n",
       "Female    36\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(test_tissue[\"Sex-bias\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### There are 75 overlap genes between gtex and clue but, \n",
    "### What is the way of sex-bias in these case? Male or Female? \n",
    "### There are 39 genes with sex bias male and 36 genes with sex bias female in this tissue\n",
    "### maybe the higher? male?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### the idea is create a heatmap with 4 colors \n",
    "#Heatmap 4 features\n",
    "#0 = no significant, no sex-bias tissue (beige)\n",
    "#1 = significant, Tissue GTEX =/ Tissue Clue (light green)\n",
    "#2 = significant, Tissue GTEX = Tissue Clue Sex-bias Male (dark green)\n",
    "#3 = significant, Tissue GTEX = Tissue Clue Sex-bias Female (dark purple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### other idea is create a heatmap with 5 colors \n",
    "#Heatmap 4 features\n",
    "#0 = no significant, no sex-bias tissue (beige)\n",
    "#1 = significant, Tissue GTEX =/ Tissue Clue Sex-bias Male (light green)\n",
    "#2 = significant, Tissue GTEX =/ Tissue Clue Sex-bias Female (light purple)\n",
    "#3 = significant, Tissue GTEX = Tissue Clue Sex-bias Male (dark green)\n",
    "#4 = significant, Tissue GTEX = Tissue Clue Sex-bias Female (dark purple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "abrev_tissue = pd.read_excel((\"mapping_tissues_GTEX_CLUE.xlsx\"),engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = df.merge(abrev_tissue, left_on=[\"Tissue_type\"],right_on=[\"tissue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = mapping[[\"Signature\",\"Perturbagen_name\",\"Tissue_type\",\"P-value\",\"primary_site\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>sig_id</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>primary_site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC018_A375_6H:BRD-K06817181-001-01-5:10</td>\n",
       "      <td>1,2,3,4,5,6-hexabromocyclohexane_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC017_A375_6H:BRD-K74430258-001-01-2:10</td>\n",
       "      <td>1,2-dichlorobenzene_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC010_A375_6H:BRD-K32795028-001-10-9:10</td>\n",
       "      <td>1-benzylimidazole_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC018_A375_6H:BRD-A80928489-001-01-0:10</td>\n",
       "      <td>1-monopalmitin_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC018_A375_6H:BRD-K31491153-001-01-2:10</td>\n",
       "      <td>1-phenylbiguanide_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258599</th>\n",
       "      <td>KDB008_SKL_96H:TRCN0000020197:-666</td>\n",
       "      <td>ZNF589_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258600</th>\n",
       "      <td>KDB002_SKL_96H:TRCN0000236645:-666</td>\n",
       "      <td>ZNF629_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258601</th>\n",
       "      <td>KDB001_SKL_96H:TRCN0000150938:-666</td>\n",
       "      <td>ZW10_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258602</th>\n",
       "      <td>KDB007_SKL_96H:TRCN0000072242:-666</td>\n",
       "      <td>lacZ_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258603</th>\n",
       "      <td>KDB008_SKL_96H:TRCN0000145620:-666</td>\n",
       "      <td>pgw_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          sig_id  \\\n",
       "0       CPC018_A375_6H:BRD-K06817181-001-01-5:10   \n",
       "1       CPC017_A375_6H:BRD-K74430258-001-01-2:10   \n",
       "2       CPC010_A375_6H:BRD-K32795028-001-10-9:10   \n",
       "3       CPC018_A375_6H:BRD-A80928489-001-01-0:10   \n",
       "4       CPC018_A375_6H:BRD-K31491153-001-01-2:10   \n",
       "...                                          ...   \n",
       "258599        KDB008_SKL_96H:TRCN0000020197:-666   \n",
       "258600        KDB002_SKL_96H:TRCN0000236645:-666   \n",
       "258601        KDB001_SKL_96H:TRCN0000150938:-666   \n",
       "258602        KDB007_SKL_96H:TRCN0000072242:-666   \n",
       "258603        KDB008_SKL_96H:TRCN0000145620:-666   \n",
       "\n",
       "                                      Signature_id cell_id primary_site  \n",
       "0       1,2,3,4,5,6-hexabromocyclohexane_A375_skin    A375         skin  \n",
       "1                    1,2-dichlorobenzene_A375_skin    A375         skin  \n",
       "2                      1-benzylimidazole_A375_skin    A375         skin  \n",
       "3                         1-monopalmitin_A375_skin    A375         skin  \n",
       "4                      1-phenylbiguanide_A375_skin    A375         skin  \n",
       "...                                            ...     ...          ...  \n",
       "258599                           ZNF589_SKL_muscle     SKL       muscle  \n",
       "258600                           ZNF629_SKL_muscle     SKL       muscle  \n",
       "258601                             ZW10_SKL_muscle     SKL       muscle  \n",
       "258602                             lacZ_SKL_muscle     SKL       muscle  \n",
       "258603                              pgw_SKL_muscle     SKL       muscle  \n",
       "\n",
       "[173142 rows x 4 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_tissue_cell = sig_cell_info[[\"sig_id\",\"Signature_id\",\"cell_id\",\"primary_site\"]]\n",
    "info_tissue_cell = info_tissue_cell.drop_duplicates()\n",
    "info_tissue_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#info_tissue_cell = sig_cell_info[[\"sig_id\",\"Signature_id\",\"cell_id\",\"primary_site\",\"tissue\",\"subtype\"]]\n",
    "#info_tissue_cell = info_tissue_cell.drop_duplicates()\n",
    "#info_tissue_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_ = mapping.merge(info_tissue_cell, left_on=[\"Signature\"],right_on=[\"sig_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_['Significant'] = clue_tiss_sig_gtex_.apply(lambda row: 'No' if row['P-value'] > 0.05 else 'Yes', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_['Equal tissue']  = clue_tiss_sig_gtex_.apply(lambda x: str(x.primary_site_x) in str(x.primary_site_y), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clue_tiss_sig_gtex['Significant'] = clue_tiss_sig_gtex.apply(lambda row: 'No' if row['P-value'] > 0.05 else 'Yes', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Heatmap_values'] = df.apply(lambda row: 0 if row['Significant'] = \"No\" else 1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>Tissue_type</th>\n",
       "      <th>P-value</th>\n",
       "      <th>primary_site_x</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>primary_site_y</th>\n",
       "      <th>Significant</th>\n",
       "      <th>Equal tissue</th>\n",
       "      <th>Heatmap_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>0.288210</td>\n",
       "      <td>adipose</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>0.466375</td>\n",
       "      <td>adipose</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adrenal_Gland</td>\n",
       "      <td>0.579258</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "      <td>0.934469</td>\n",
       "      <td>vascular system</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Artery_Coronary</td>\n",
       "      <td>0.872566</td>\n",
       "      <td>vascular system</td>\n",
       "      <td>CPC005_A375_24H:BRD-K04210847-001-01-1:10</td>\n",
       "      <td>tamoxifen_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Small_Intestine_Terminal_Ileum</td>\n",
       "      <td>0.774316</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen_SKB_muscle</td>\n",
       "      <td>SKB</td>\n",
       "      <td>muscle</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Spleen</td>\n",
       "      <td>0.568827</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen_SKB_muscle</td>\n",
       "      <td>SKB</td>\n",
       "      <td>muscle</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2461</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Stomach</td>\n",
       "      <td>0.729772</td>\n",
       "      <td>stomach</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen_SKB_muscle</td>\n",
       "      <td>SKB</td>\n",
       "      <td>muscle</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2462</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Thyroid</td>\n",
       "      <td>0.962904</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen_SKB_muscle</td>\n",
       "      <td>SKB</td>\n",
       "      <td>muscle</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463</th>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Whole_Blood</td>\n",
       "      <td>0.917084</td>\n",
       "      <td>blood</td>\n",
       "      <td>CPC015_SKB_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen_SKB_muscle</td>\n",
       "      <td>SKB</td>\n",
       "      <td>muscle</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2464 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Signature Perturbagen_name  \\\n",
       "0     CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "1     CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "2     CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "3     CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "4     CPC005_A375_24H:BRD-K04210847-001-01-1:10        tamoxifen   \n",
       "...                                         ...              ...   \n",
       "2459   CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "2460   CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "2461   CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "2462   CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "2463   CPC015_SKB_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "\n",
       "                         Tissue_type   P-value   primary_site_x  \\\n",
       "0               Adipose_Subcutaneous  0.288210          adipose   \n",
       "1           Adipose_Visceral_Omentum  0.466375          adipose   \n",
       "2                      Adrenal_Gland  0.579258          Unknown   \n",
       "3                       Artery_Aorta  0.934469  vascular system   \n",
       "4                    Artery_Coronary  0.872566  vascular system   \n",
       "...                              ...       ...              ...   \n",
       "2459  Small_Intestine_Terminal_Ileum  0.774316          Unknown   \n",
       "2460                          Spleen  0.568827          Unknown   \n",
       "2461                         Stomach  0.729772          stomach   \n",
       "2462                         Thyroid  0.962904          Unknown   \n",
       "2463                     Whole_Blood  0.917084            blood   \n",
       "\n",
       "                                         sig_id          Signature_id cell_id  \\\n",
       "0     CPC005_A375_24H:BRD-K04210847-001-01-1:10   tamoxifen_A375_skin    A375   \n",
       "1     CPC005_A375_24H:BRD-K04210847-001-01-1:10   tamoxifen_A375_skin    A375   \n",
       "2     CPC005_A375_24H:BRD-K04210847-001-01-1:10   tamoxifen_A375_skin    A375   \n",
       "3     CPC005_A375_24H:BRD-K04210847-001-01-1:10   tamoxifen_A375_skin    A375   \n",
       "4     CPC005_A375_24H:BRD-K04210847-001-01-1:10   tamoxifen_A375_skin    A375   \n",
       "...                                         ...                   ...     ...   \n",
       "2459   CPC015_SKB_24H:BRD-K43744935-048-01-6:10  tamoxifen_SKB_muscle     SKB   \n",
       "2460   CPC015_SKB_24H:BRD-K43744935-048-01-6:10  tamoxifen_SKB_muscle     SKB   \n",
       "2461   CPC015_SKB_24H:BRD-K43744935-048-01-6:10  tamoxifen_SKB_muscle     SKB   \n",
       "2462   CPC015_SKB_24H:BRD-K43744935-048-01-6:10  tamoxifen_SKB_muscle     SKB   \n",
       "2463   CPC015_SKB_24H:BRD-K43744935-048-01-6:10  tamoxifen_SKB_muscle     SKB   \n",
       "\n",
       "     primary_site_y Significant  Equal tissue  Heatmap_values  \n",
       "0              skin          No         False               0  \n",
       "1              skin          No         False               0  \n",
       "2              skin          No         False               0  \n",
       "3              skin          No         False               0  \n",
       "4              skin          No         False               0  \n",
       "...             ...         ...           ...             ...  \n",
       "2459         muscle          No         False               0  \n",
       "2460         muscle          No         False               0  \n",
       "2461         muscle          No         False               0  \n",
       "2462         muscle          No         False               0  \n",
       "2463         muscle          No         False               0  \n",
       "\n",
       "[2464 rows x 12 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clue_tiss_sig_gtex_['Heatmap_values'] = np.where(\n",
    "    (clue_tiss_sig_gtex_[\"Significant\"] == \"Yes\") & (clue_tiss_sig_gtex_[\"Equal tissue\"] == True), 2,\n",
    "    np.where(\n",
    "        (clue_tiss_sig_gtex_[\"Significant\"] == \"Yes\") & (clue_tiss_sig_gtex_[\"Equal tissue\"] == False), 1, 0))\n",
    "clue_tiss_sig_gtex_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clue_tiss_sig_gtex[clue_tiss_sig_gtex[\"Significant\"]==\"Yes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_pivot = clue_tiss_sig_gtex_[[\"Signature_id\",\"Tissue_type\", \"Heatmap_values\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clue_tiss_sig_gtex_pivot = clue_tiss_sig_gtex_pivot.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(clue_tiss_sig_gtex_pivot[\"Signature_id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clue_tiss_sig_gtex_pivot.drop_duplicates(subset=[\"Signature_id\", \"Tissue_type\"])\n",
    "#clue_tiss_sig_gtex_pivot.groupby(by=[\"Signature_id\", \"Tissue_type\"])[\"Heatmap_values\"].sum().reset_index().sort_values(by=[\"Heatmap_values\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_dis = clue_tiss_sig_gtex_pivot.pivot(index='Signature_id', columns='Tissue_type', values='Heatmap_values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cmap = plt.get_cmap('RdYlBu', 3)\n",
    "fig, ax = plt.subplots(figsize=(25, 25))\n",
    "ax = sns.heatmap(group_dis,linewidth=.5,cmap= [\"#FDEBD0\",\"#82E0AA\",\"#117A65\"],vmin=0, vmax=2)#PiYG\n",
    "colorbar = ax.collections[0].colorbar\n",
    "colorbar.set_ticks([0, 1, 2])\n",
    "plt.title('Sex-biased tissues applying Tamoxifen', fontsize = 20) # title with fontsize 20\n",
    "plt.xlabel('Tissues', fontsize = 12) # x-axis label with fontsize 15\n",
    "plt.ylabel('Signatures', fontsize = 12) # y-axis label with fontsize 15\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"cell_line_tamoxifen_signatures_results.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = df.groupby(['Significant','Signature']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = result.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = result[[\"Significant\",\"Signature\",\"Tissue_type\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot = pd.pivot_table(data=result, index=['Signature'], columns=['Significant'], values='Tissue_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot = pivot.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table = pivot.sort_index(axis=1,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table_sort = pivot_table.sort_values(by='Yes',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(figsize=(16, 10))\n",
    "ax = pivot_table.plot.bar(stacked=True, color =['lightseagreen','tan'],figsize=(16, 10))#,#order_by= pivot_table[\"Yes\"] )\n",
    "plt.title('Number of significant tissue per signature (one cell line and Tamoxifen perturbagen)', fontsize = 20) # title with fontsize 20\n",
    "plt.bar_label(ax.containers[0])\n",
    "#plt.xlabel('Tissues', fontsize = 12) # x-axis label with fontsize 15\n",
    "#plt.ylabel('Signatures', fontsize = 12) # y-axis label with fontsize 15\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Number_of_sig_tissue_cell_line_tamoxifen.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(figsize=(16, 10))\n",
    "ax = pivot_table_sort.plot.bar(stacked=True, color =['lightseagreen','tan'],figsize=(16, 10))\n",
    "plt.title('Number of significant tissue per signature (one cell line and Tamoxifen perturbagen)', fontsize = 20) # title with fontsize 20\n",
    "plt.bar_label(ax.containers[0])\n",
    "#plt.xlabel('Tissues', fontsize = 12) # x-axis label with fontsize 15\n",
    "#plt.ylabel('Signatures', fontsize = 12) # y-axis label with fontsize 15\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"Number_of_sig_tissue_cell_line_tamoxifen.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Signature\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>sig_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>tissue</th>\n",
       "      <th>subtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC018_A375_6H:BRD-K06817181-001-01-5:10</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>malignant melanoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC017_A375_6H:BRD-K74430258-001-01-2:10</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>malignant melanoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC010_A375_6H:BRD-K32795028-001-10-9:10</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>malignant melanoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC018_A375_6H:BRD-A80928489-001-01-0:10</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>malignant melanoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC018_A375_6H:BRD-K31491153-001-01-2:10</td>\n",
       "      <td>A375</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>malignant melanoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264162</th>\n",
       "      <td>KDB008_SKL_96H:TRCN0000020197:-666</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264163</th>\n",
       "      <td>KDB002_SKL_96H:TRCN0000236645:-666</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264164</th>\n",
       "      <td>KDB001_SKL_96H:TRCN0000150938:-666</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264165</th>\n",
       "      <td>KDB007_SKL_96H:TRCN0000072242:-666</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264166</th>\n",
       "      <td>KDB008_SKL_96H:TRCN0000145620:-666</td>\n",
       "      <td>SKL</td>\n",
       "      <td>muscle</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264167 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          sig_id cell_id primary_site  \\\n",
       "0       CPC018_A375_6H:BRD-K06817181-001-01-5:10    A375         skin   \n",
       "1       CPC017_A375_6H:BRD-K74430258-001-01-2:10    A375         skin   \n",
       "2       CPC010_A375_6H:BRD-K32795028-001-10-9:10    A375         skin   \n",
       "3       CPC018_A375_6H:BRD-A80928489-001-01-0:10    A375         skin   \n",
       "4       CPC018_A375_6H:BRD-K31491153-001-01-2:10    A375         skin   \n",
       "...                                          ...     ...          ...   \n",
       "264162        KDB008_SKL_96H:TRCN0000020197:-666     SKL       muscle   \n",
       "264163        KDB002_SKL_96H:TRCN0000236645:-666     SKL       muscle   \n",
       "264164        KDB001_SKL_96H:TRCN0000150938:-666     SKL       muscle   \n",
       "264165        KDB007_SKL_96H:TRCN0000072242:-666     SKL       muscle   \n",
       "264166        KDB008_SKL_96H:TRCN0000145620:-666     SKL       muscle   \n",
       "\n",
       "                                 tissue                               subtype  \n",
       "0       Skin_Not_Sun_Exposed_Suprapubic                    malignant melanoma  \n",
       "1       Skin_Not_Sun_Exposed_Suprapubic                    malignant melanoma  \n",
       "2       Skin_Not_Sun_Exposed_Suprapubic                    malignant melanoma  \n",
       "3       Skin_Not_Sun_Exposed_Suprapubic                    malignant melanoma  \n",
       "4       Skin_Not_Sun_Exposed_Suprapubic                    malignant melanoma  \n",
       "...                                 ...                                   ...  \n",
       "264162                  Muscle_Skeletal  normal primary skeletal muscle cells  \n",
       "264163                  Muscle_Skeletal  normal primary skeletal muscle cells  \n",
       "264164                  Muscle_Skeletal  normal primary skeletal muscle cells  \n",
       "264165                  Muscle_Skeletal  normal primary skeletal muscle cells  \n",
       "264166                  Muscle_Skeletal  normal primary skeletal muscle cells  \n",
       "\n",
       "[264167 rows x 5 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_tissue_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex = df.merge(info_tissue_cell, left_on=\"Signature\",right_on=\"sig_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## join df with info cell line\n",
    "## then, selected only the significant registers in df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_sig  = clue_tiss_sig_gtex[clue_tiss_sig_gtex[\"Significant\"]==\"Yes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_sig['Significance equal tissue']  = clue_tiss_sig_gtex_sig.apply(lambda x: str(x.Tissue_type) in str(x.tissue), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>Tissue_type</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Significant</th>\n",
       "      <th>Significant_</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>tissue</th>\n",
       "      <th>subtype</th>\n",
       "      <th>Significance equal tissue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>CPC006_A673_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Brain_Nucleus_accumbens_basal_ganglia</td>\n",
       "      <td>0.027830</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_A673_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>A673</td>\n",
       "      <td>bone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ewing's sarcoma</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>CPC006_CORL23_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Cells_Cultured_fibroblasts</td>\n",
       "      <td>0.035913</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_CORL23_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>CORL23</td>\n",
       "      <td>lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>non small cell lung cancer| large cell carcinoma</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Cells_Cultured_fibroblasts</td>\n",
       "      <td>0.027632</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>DV90</td>\n",
       "      <td>lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>non small cell lung cancer| adenocarcinoma</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Kidney_Cortex</td>\n",
       "      <td>0.048627</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>DV90</td>\n",
       "      <td>lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>non small cell lung cancer| adenocarcinoma</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Minor_Salivary_Gland</td>\n",
       "      <td>0.009112</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>DV90</td>\n",
       "      <td>lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>non small cell lung cancer| adenocarcinoma</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>0.039366</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ASC</td>\n",
       "      <td>adipose</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>normal primary adipocyte stem cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3162</th>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Breast_Mammary_Tissue</td>\n",
       "      <td>0.005854</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ASC</td>\n",
       "      <td>adipose</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>normal primary adipocyte stem cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3163</th>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Breast_Mammary_Tissue</td>\n",
       "      <td>0.005854</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ASC</td>\n",
       "      <td>adipose</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>normal primary adipocyte stem cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3182</th>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Kidney_Cortex</td>\n",
       "      <td>0.041273</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ASC</td>\n",
       "      <td>adipose</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>normal primary adipocyte stem cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3183</th>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Kidney_Cortex</td>\n",
       "      <td>0.041273</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ASC</td>\n",
       "      <td>adipose</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>normal primary adipocyte stem cells</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Signature Perturbagen_name  \\\n",
       "147     CPC006_A673_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "284   CPC006_CORL23_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "372     CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "381     CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "384     CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "...                                             ...              ...   \n",
       "3125       CPC015_ASC_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "3162       CPC015_ASC_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "3163       CPC015_ASC_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "3182       CPC015_ASC_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "3183       CPC015_ASC_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "\n",
       "                                Tissue_type   P-value Significant  \\\n",
       "147   Brain_Nucleus_accumbens_basal_ganglia  0.027830         Yes   \n",
       "284              Cells_Cultured_fibroblasts  0.035913         Yes   \n",
       "372              Cells_Cultured_fibroblasts  0.027632         Yes   \n",
       "381                           Kidney_Cortex  0.048627         Yes   \n",
       "384                    Minor_Salivary_Gland  0.009112         Yes   \n",
       "...                                     ...       ...         ...   \n",
       "3125                   Adipose_Subcutaneous  0.039366         Yes   \n",
       "3162                  Breast_Mammary_Tissue  0.005854         Yes   \n",
       "3163                  Breast_Mammary_Tissue  0.005854         Yes   \n",
       "3182                          Kidney_Cortex  0.041273         Yes   \n",
       "3183                          Kidney_Cortex  0.041273         Yes   \n",
       "\n",
       "      Significant_                                         sig_id cell_id  \\\n",
       "147              1    CPC006_A673_6H:BRD-K93754473-001-06-0:28.39    A673   \n",
       "284              1  CPC006_CORL23_6H:BRD-K93754473-001-06-0:28.39  CORL23   \n",
       "372              1    CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39    DV90   \n",
       "381              1    CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39    DV90   \n",
       "384              1    CPC006_DV90_6H:BRD-K93754473-001-06-0:28.39    DV90   \n",
       "...            ...                                            ...     ...   \n",
       "3125             1       CPC015_ASC_24H:BRD-K43744935-048-01-6:10     ASC   \n",
       "3162             1       CPC015_ASC_24H:BRD-K43744935-048-01-6:10     ASC   \n",
       "3163             1       CPC015_ASC_24H:BRD-K43744935-048-01-6:10     ASC   \n",
       "3182             1       CPC015_ASC_24H:BRD-K43744935-048-01-6:10     ASC   \n",
       "3183             1       CPC015_ASC_24H:BRD-K43744935-048-01-6:10     ASC   \n",
       "\n",
       "     primary_site                    tissue  \\\n",
       "147          bone                       NaN   \n",
       "284          lung                      Lung   \n",
       "372          lung                      Lung   \n",
       "381          lung                      Lung   \n",
       "384          lung                      Lung   \n",
       "...           ...                       ...   \n",
       "3125      adipose  Adipose_Visceral_Omentum   \n",
       "3162      adipose      Adipose_Subcutaneous   \n",
       "3163      adipose  Adipose_Visceral_Omentum   \n",
       "3182      adipose      Adipose_Subcutaneous   \n",
       "3183      adipose  Adipose_Visceral_Omentum   \n",
       "\n",
       "                                               subtype  \\\n",
       "147                                    ewing's sarcoma   \n",
       "284   non small cell lung cancer| large cell carcinoma   \n",
       "372         non small cell lung cancer| adenocarcinoma   \n",
       "381         non small cell lung cancer| adenocarcinoma   \n",
       "384         non small cell lung cancer| adenocarcinoma   \n",
       "...                                                ...   \n",
       "3125               normal primary adipocyte stem cells   \n",
       "3162               normal primary adipocyte stem cells   \n",
       "3163               normal primary adipocyte stem cells   \n",
       "3182               normal primary adipocyte stem cells   \n",
       "3183               normal primary adipocyte stem cells   \n",
       "\n",
       "      Significance equal tissue  \n",
       "147                       False  \n",
       "284                       False  \n",
       "372                       False  \n",
       "381                       False  \n",
       "384                       False  \n",
       "...                         ...  \n",
       "3125                      False  \n",
       "3162                      False  \n",
       "3163                      False  \n",
       "3182                      False  \n",
       "3183                      False  \n",
       "\n",
       "[164 rows x 12 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clue_tiss_sig_gtex_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>Tissue_type</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Significant</th>\n",
       "      <th>Significant_</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>tissue</th>\n",
       "      <th>subtype</th>\n",
       "      <th>Significance equal tissue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>CPC006_SKMEL1_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Skin_Sun_Exposed_Lower_leg</td>\n",
       "      <td>0.045031</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_SKMEL1_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>SKMEL1</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Sun_Exposed_Lower_leg</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>CPC006_WSUDLCL2_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Cells_EBV.transformed_lymphocytes</td>\n",
       "      <td>0.017128</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC006_WSUDLCL2_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>WSUDLCL2</td>\n",
       "      <td>haematopoietic and lymphoid tissue</td>\n",
       "      <td>Cells_EBV.transformed_lymphocytes</td>\n",
       "      <td>lymphoma| B-cell| non-hodgkin's| diffuse large...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Brain_Caudate_basal_ganglia</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>NPC</td>\n",
       "      <td>central nervous system</td>\n",
       "      <td>Brain_Caudate_basal_ganglia</td>\n",
       "      <td>normal stem fibroblast-derived iPScs</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2639</th>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Brain_Nucleus_accumbens_basal_ganglia</td>\n",
       "      <td>0.014830</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>NPC</td>\n",
       "      <td>central nervous system</td>\n",
       "      <td>Brain_Nucleus_accumbens_basal_ganglia</td>\n",
       "      <td>normal stem fibroblast-derived iPScs</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Brain_Putamen_basal_ganglia</td>\n",
       "      <td>0.005646</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>NPC</td>\n",
       "      <td>central nervous system</td>\n",
       "      <td>Brain_Putamen_basal_ganglia</td>\n",
       "      <td>normal stem fibroblast-derived iPScs</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3124</th>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>0.039366</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>CPC015_ASC_24H:BRD-K43744935-048-01-6:10</td>\n",
       "      <td>ASC</td>\n",
       "      <td>adipose</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>normal primary adipocyte stem cells</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Signature Perturbagen_name  \\\n",
       "1749    CPC006_SKMEL1_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "2353  CPC006_WSUDLCL2_6H:BRD-K93754473-001-06-0:28.39        tamoxifen   \n",
       "2534          NMH002_NPC_6H:BRD-K93754473-048-14-5:10        tamoxifen   \n",
       "2639          NMH002_NPC_6H:BRD-K93754473-048-14-5:10        tamoxifen   \n",
       "2654          NMH002_NPC_6H:BRD-K93754473-048-14-5:10        tamoxifen   \n",
       "3124         CPC015_ASC_24H:BRD-K43744935-048-01-6:10        tamoxifen   \n",
       "\n",
       "                                Tissue_type   P-value Significant  \\\n",
       "1749             Skin_Sun_Exposed_Lower_leg  0.045031         Yes   \n",
       "2353      Cells_EBV.transformed_lymphocytes  0.017128         Yes   \n",
       "2534            Brain_Caudate_basal_ganglia  0.020288         Yes   \n",
       "2639  Brain_Nucleus_accumbens_basal_ganglia  0.014830         Yes   \n",
       "2654            Brain_Putamen_basal_ganglia  0.005646         Yes   \n",
       "3124                   Adipose_Subcutaneous  0.039366         Yes   \n",
       "\n",
       "      Significant_                                           sig_id   cell_id  \\\n",
       "1749             1    CPC006_SKMEL1_6H:BRD-K93754473-001-06-0:28.39    SKMEL1   \n",
       "2353             1  CPC006_WSUDLCL2_6H:BRD-K93754473-001-06-0:28.39  WSUDLCL2   \n",
       "2534             1          NMH002_NPC_6H:BRD-K93754473-048-14-5:10       NPC   \n",
       "2639             1          NMH002_NPC_6H:BRD-K93754473-048-14-5:10       NPC   \n",
       "2654             1          NMH002_NPC_6H:BRD-K93754473-048-14-5:10       NPC   \n",
       "3124             1         CPC015_ASC_24H:BRD-K43744935-048-01-6:10       ASC   \n",
       "\n",
       "                            primary_site  \\\n",
       "1749                                skin   \n",
       "2353  haematopoietic and lymphoid tissue   \n",
       "2534              central nervous system   \n",
       "2639              central nervous system   \n",
       "2654              central nervous system   \n",
       "3124                             adipose   \n",
       "\n",
       "                                     tissue  \\\n",
       "1749             Skin_Sun_Exposed_Lower_leg   \n",
       "2353      Cells_EBV.transformed_lymphocytes   \n",
       "2534            Brain_Caudate_basal_ganglia   \n",
       "2639  Brain_Nucleus_accumbens_basal_ganglia   \n",
       "2654            Brain_Putamen_basal_ganglia   \n",
       "3124                   Adipose_Subcutaneous   \n",
       "\n",
       "                                                subtype  \\\n",
       "1749                                 malignant melanoma   \n",
       "2353  lymphoma| B-cell| non-hodgkin's| diffuse large...   \n",
       "2534               normal stem fibroblast-derived iPScs   \n",
       "2639               normal stem fibroblast-derived iPScs   \n",
       "2654               normal stem fibroblast-derived iPScs   \n",
       "3124                normal primary adipocyte stem cells   \n",
       "\n",
       "      Significance equal tissue  \n",
       "1749                       True  \n",
       "2353                       True  \n",
       "2534                       True  \n",
       "2639                       True  \n",
       "2654                       True  \n",
       "3124                       True  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clue_tiss_sig_gtex_sig[clue_tiss_sig_gtex_sig[\"Significance equal tissue\"]== True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 6 cases where GTEX tissue = CLUE  tissue primary site "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_tissue_df= count_tissue_df.reset_index()\n",
    "count_tissue_df = count_tissue_df[[\"Tissue_type\", \"count\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>tissue</th>\n",
       "      <th>abrev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>ADPSBQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>ADPVSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adrenal_Gland</td>\n",
       "      <td>ADRNLG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Artery_Aorta</td>\n",
       "      <td>ARTAORT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Artery_Coronary</td>\n",
       "      <td>ARTCRN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tissue    abrev\n",
       "0      Adipose_Subcutaneous   ADPSBQ\n",
       "1  Adipose_Visceral_Omentum   ADPVSC\n",
       "2             Adrenal_Gland   ADRNLG\n",
       "3              Artery_Aorta  ARTAORT\n",
       "4           Artery_Coronary   ARTCRN"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abrev_tissue = pd.read_excel((\"abrev_tissue.xlsx\"),engine='openpyxl')\n",
    "abrev_tissue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "abrev_tissu_all_data = abrev_tissue.merge(count_tissue_df,left_on=\"tissue\",right_on=\"Tissue_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "my_colors = [\"#FF6600\",\"#FFAA00\",\"#33DD33\",\"#FF5555\",\"#FFAA99\",\"#FF0000\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\n",
    "             \"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#EEEE00\",\"#33CCCC\",\"#AAEEFF\",\"#CC66FF\",\n",
    "             \"#EEBB77\",\"#CC9955\",\"#8B7355\",\"#552200\",\"#BB9988\",\"#9900FF\",\"#660099\",\"#22FFDD\",\"#AABB66\",\"#99FF00\",\"#99BB88\",\n",
    "             \"#AAAAFF\",\"#FFD700\",\"#995522\",\"#AAFF99\",\"#0000FF\",\"#7777FF\",\"#555522\",\"#778855\",\"#FFDD99\",\"#006600\",\"#FF00BB\"] \n",
    "sns.set_palette( my_colors )\n",
    "sns.barplot(data=abrev_tissu_all_data, x=\"abrev\", y=\"count\",orient='v').set_title('Number of significant signatures applying Tamoxifen per tissue')\n",
    "plt.xticks(rotation=90)\n",
    "#sns.despine(top=False, bottom=True)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#ax.legend(loc=(0.6,0), title='', fontsize=12)\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"tamoxifen_significant_signatures.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "signature_information = sig_info.merge(signatures_signi,left_on=\"sig_id\",right_on=\"Signature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pert_idose\n",
       "30 µM    92\n",
       "10 µM    33\n",
       "1 µM      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(signature_information[\"pert_idose\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pert_itime\n",
       "6 h     97\n",
       "24 h    29\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.value_counts(signature_information[\"pert_itime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_count = pd.value_counts(signature_information[\"cell_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_count_df = pd.DataFrame(cell_count)\n",
    "cell_count_df= cell_count_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_info = pd.read_csv(\"./GSE92742_Broad_LINCS_cell_info.txt\", sep=\"\\t\", dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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>cell_id</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>base_cell_id</th>\n",
       "      <th>precursor_cell_id</th>\n",
       "      <th>modification</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>subtype</th>\n",
       "      <th>original_growth_pattern</th>\n",
       "      <th>provider_catalog_id</th>\n",
       "      <th>original_source_vendor</th>\n",
       "      <th>donor_age</th>\n",
       "      <th>donor_sex</th>\n",
       "      <th>donor_ethnicity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A375</td>\n",
       "      <td>cell line</td>\n",
       "      <td>A375</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CRL-1619</td>\n",
       "      <td>ATCC</td>\n",
       "      <td>54</td>\n",
       "      <td>F</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A375.311</td>\n",
       "      <td>cell line</td>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>genetically modified to stably express Cas9 pr...</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CRL-1619</td>\n",
       "      <td>ATCC</td>\n",
       "      <td>54</td>\n",
       "      <td>F</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A549</td>\n",
       "      <td>cell line</td>\n",
       "      <td>A549</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>tumor</td>\n",
       "      <td>lung</td>\n",
       "      <td>non small cell lung cancer| carcinoma</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CCL-185</td>\n",
       "      <td>ATCC</td>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>Caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A549.311</td>\n",
       "      <td>cell line</td>\n",
       "      <td>A549</td>\n",
       "      <td>A549</td>\n",
       "      <td>genetically  modified to stably express Cas9 p...</td>\n",
       "      <td>tumor</td>\n",
       "      <td>lung</td>\n",
       "      <td>non small cell lung cancer| carcinoma</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CCL-185</td>\n",
       "      <td>ATCC</td>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>Caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A673</td>\n",
       "      <td>cell line</td>\n",
       "      <td>A673</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>tumor</td>\n",
       "      <td>bone</td>\n",
       "      <td>ewing's sarcoma</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CRL-1598</td>\n",
       "      <td>ATCC</td>\n",
       "      <td>-666</td>\n",
       "      <td>F</td>\n",
       "      <td>-666</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>CD34</td>\n",
       "      <td>primary</td>\n",
       "      <td>CD34</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>normal</td>\n",
       "      <td>bone</td>\n",
       "      <td>bone marrow</td>\n",
       "      <td>suspension</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>PHH</td>\n",
       "      <td>primary</td>\n",
       "      <td>PHH</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>primary</td>\n",
       "      <td>liver</td>\n",
       "      <td>normal primary liver</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>CellzDirect</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>SKB</td>\n",
       "      <td>primary</td>\n",
       "      <td>SKB</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>normal</td>\n",
       "      <td>muscle</td>\n",
       "      <td>myoblast</td>\n",
       "      <td>-666</td>\n",
       "      <td>CC-2580</td>\n",
       "      <td>Lonza</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>SKL</td>\n",
       "      <td>primary</td>\n",
       "      <td>SKL</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>primary</td>\n",
       "      <td>muscle</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CC-2561</td>\n",
       "      <td>LONZA</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>SKL.C</td>\n",
       "      <td>primary</td>\n",
       "      <td>SKL</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>primary</td>\n",
       "      <td>muscle</td>\n",
       "      <td>normal primary skeletal muscle cells</td>\n",
       "      <td>adherent</td>\n",
       "      <td>CC-2561</td>\n",
       "      <td>LONZA</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "      <td>-666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cell_id  cell_type base_cell_id precursor_cell_id  \\\n",
       "0       A375  cell line         A375              -666   \n",
       "1   A375.311  cell line         A375              A375   \n",
       "2       A549  cell line         A549              -666   \n",
       "3   A549.311  cell line         A549              A549   \n",
       "4       A673  cell line         A673              -666   \n",
       "..       ...        ...          ...               ...   \n",
       "93      CD34    primary         CD34              -666   \n",
       "94       PHH    primary          PHH              -666   \n",
       "95       SKB    primary          SKB              -666   \n",
       "96       SKL    primary          SKL              -666   \n",
       "97     SKL.C    primary          SKL              -666   \n",
       "\n",
       "                                         modification sample_type  \\\n",
       "0                                                -666       tumor   \n",
       "1   genetically modified to stably express Cas9 pr...       tumor   \n",
       "2                                                -666       tumor   \n",
       "3   genetically  modified to stably express Cas9 p...       tumor   \n",
       "4                                                -666       tumor   \n",
       "..                                                ...         ...   \n",
       "93                                               -666      normal   \n",
       "94                                               -666     primary   \n",
       "95                                               -666      normal   \n",
       "96                                               -666     primary   \n",
       "97                                               -666     primary   \n",
       "\n",
       "   primary_site                                subtype  \\\n",
       "0          skin                     malignant melanoma   \n",
       "1          skin                     malignant melanoma   \n",
       "2          lung  non small cell lung cancer| carcinoma   \n",
       "3          lung  non small cell lung cancer| carcinoma   \n",
       "4          bone                        ewing's sarcoma   \n",
       "..          ...                                    ...   \n",
       "93         bone                            bone marrow   \n",
       "94        liver                   normal primary liver   \n",
       "95       muscle                               myoblast   \n",
       "96       muscle   normal primary skeletal muscle cells   \n",
       "97       muscle   normal primary skeletal muscle cells   \n",
       "\n",
       "   original_growth_pattern provider_catalog_id original_source_vendor  \\\n",
       "0                 adherent            CRL-1619                   ATCC   \n",
       "1                 adherent            CRL-1619                   ATCC   \n",
       "2                 adherent             CCL-185                   ATCC   \n",
       "3                 adherent             CCL-185                   ATCC   \n",
       "4                 adherent            CRL-1598                   ATCC   \n",
       "..                     ...                 ...                    ...   \n",
       "93              suspension                -666                   -666   \n",
       "94                    -666                -666            CellzDirect   \n",
       "95                    -666             CC-2580                  Lonza   \n",
       "96                adherent             CC-2561                  LONZA   \n",
       "97                adherent             CC-2561                  LONZA   \n",
       "\n",
       "   donor_age donor_sex donor_ethnicity  \n",
       "0         54         F            -666  \n",
       "1         54         F            -666  \n",
       "2         58         M       Caucasian  \n",
       "3         58         M       Caucasian  \n",
       "4       -666         F            -666  \n",
       "..       ...       ...             ...  \n",
       "93      -666      -666            -666  \n",
       "94      -666      -666            -666  \n",
       "95      -666      -666            -666  \n",
       "96      -666      -666            -666  \n",
       "97      -666      -666            -666  \n",
       "\n",
       "[98 rows x 14 columns]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_info = cell_info[[\"cell_id\",\"cell_type\",\"sample_type\",\"subtype\",\"donor_sex\",\"donor_age\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "sign_cell_information = cell_info.merge(cell_count_df, on=\"cell_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "sns.barplot(data=sign_cell_information, x=\"cell_id\", y=\"count\",orient='v',hue = \"sample_type\").set_title('Number of significant signatures per cell_lines')\n",
    "plt.xticks(rotation=90)\n",
    "#sns.despine(top=False, bottom=True)\n",
    "#ax.bar_label(ax.containers[0])\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#ax.legend(loc=(0.6,0), title='', fontsize=12)\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"tamoxifen_significant_signatures.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = pd.concat(overlap_genes,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>tissue</th>\n",
       "      <th>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57876</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57895</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57935</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57969</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Artery_Coronary</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58038</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Amygdala</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58060</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Anterior_cingulate_cortex_BA24</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58082</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Caudate_basal_ganglia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58121</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Cortex</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58144</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Frontal_Cortex_BA9</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58165</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Hippocampus</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58190</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Hypothalamus</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58213</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Nucleus_accumbens_basal_ganglia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58232</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Putamen_basal_ganglia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58256</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Spinal_cord_cervical_c.1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58282</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Substantia_nigra</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58359</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Colon_Sigmoid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58402</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Esophagus_Gastroesophageal_Junction</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58441</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Esophagus_Muscularis</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58550</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58574</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Nerve_Tibial</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58729</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Stomach</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74353</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74370</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74394</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74416</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Artery_Coronary</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74461</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Amygdala</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74478</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Anterior_cingulate_cortex_BA24</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74494</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Caudate_basal_ganglia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74522</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Cortex</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74535</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Frontal_Cortex_BA9</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74549</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Hippocampus</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74568</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Hypothalamus</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74584</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Nucleus_accumbens_basal_ganglia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74600</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Putamen_basal_ganglia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74618</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Spinal_cord_cervical_c.1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74634</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Brain_Substantia_nigra</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74683</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Colon_Sigmoid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74716</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Esophagus_Gastroesophageal_Junction</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74743</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Esophagus_Muscularis</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74808</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Muscle_Skeletal</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74825</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Nerve_Tibial</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74918</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>Stomach</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      perturbagen_name                                    Signature_id  \\\n",
       "57876        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "57895        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "57935        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "57969        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58038        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58060        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58082        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58121        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58144        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58165        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58190        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58213        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58232        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58256        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58282        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58359        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58402        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58441        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58550        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58574        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "58729        tamoxifen  CPC006_NCIH596_6H:BRD-K93754473-001-06-0:28.39   \n",
       "74353        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74370        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74394        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74416        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74461        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74478        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74494        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74522        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74535        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74549        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74568        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74584        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74600        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74618        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74634        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74683        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74716        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74743        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74808        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74825        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "74918        tamoxifen            AML001_CD34_6H:BRD-K93754473:13.4584   \n",
       "\n",
       "      gene_symbol                                 tissue Sex-bias  \n",
       "57876         AK4                   Adipose_Subcutaneous   Female  \n",
       "57895         AK4               Adipose_Visceral_Omentum   Female  \n",
       "57935         AK4                           Artery_Aorta   Female  \n",
       "57969         AK4                        Artery_Coronary   Female  \n",
       "58038         AK4                         Brain_Amygdala     Male  \n",
       "58060         AK4   Brain_Anterior_cingulate_cortex_BA24     Male  \n",
       "58082         AK4            Brain_Caudate_basal_ganglia     Male  \n",
       "58121         AK4                           Brain_Cortex     Male  \n",
       "58144         AK4               Brain_Frontal_Cortex_BA9     Male  \n",
       "58165         AK4                      Brain_Hippocampus     Male  \n",
       "58190         AK4                     Brain_Hypothalamus     Male  \n",
       "58213         AK4  Brain_Nucleus_accumbens_basal_ganglia     Male  \n",
       "58232         AK4            Brain_Putamen_basal_ganglia     Male  \n",
       "58256         AK4         Brain_Spinal_cord_cervical_c.1     Male  \n",
       "58282         AK4                 Brain_Substantia_nigra     Male  \n",
       "58359         AK4                          Colon_Sigmoid   Female  \n",
       "58402         AK4    Esophagus_Gastroesophageal_Junction   Female  \n",
       "58441         AK4                   Esophagus_Muscularis   Female  \n",
       "58550         AK4                        Muscle_Skeletal   Female  \n",
       "58574         AK4                           Nerve_Tibial   Female  \n",
       "58729         AK4                                Stomach   Female  \n",
       "74353         AK4                   Adipose_Subcutaneous   Female  \n",
       "74370         AK4               Adipose_Visceral_Omentum   Female  \n",
       "74394         AK4                           Artery_Aorta   Female  \n",
       "74416         AK4                        Artery_Coronary   Female  \n",
       "74461         AK4                         Brain_Amygdala     Male  \n",
       "74478         AK4   Brain_Anterior_cingulate_cortex_BA24     Male  \n",
       "74494         AK4            Brain_Caudate_basal_ganglia     Male  \n",
       "74522         AK4                           Brain_Cortex     Male  \n",
       "74535         AK4               Brain_Frontal_Cortex_BA9     Male  \n",
       "74549         AK4                      Brain_Hippocampus     Male  \n",
       "74568         AK4                     Brain_Hypothalamus     Male  \n",
       "74584         AK4  Brain_Nucleus_accumbens_basal_ganglia     Male  \n",
       "74600         AK4            Brain_Putamen_basal_ganglia     Male  \n",
       "74618         AK4         Brain_Spinal_cord_cervical_c.1     Male  \n",
       "74634         AK4                 Brain_Substantia_nigra     Male  \n",
       "74683         AK4                          Colon_Sigmoid   Female  \n",
       "74716         AK4    Esophagus_Gastroesophageal_Junction   Female  \n",
       "74743         AK4                   Esophagus_Muscularis   Female  \n",
       "74808         AK4                        Muscle_Skeletal   Female  \n",
       "74825         AK4                           Nerve_Tibial   Female  \n",
       "74918         AK4                                Stomach   Female  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all[df_all[\"gene_symbol\"]==\"AK4\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AARS</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>ACOX2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>ACP5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AK4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>AML001_CD34_6H:BRD-K93754473:13.4584</td>\n",
       "      <td>AMDHD2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9413</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>ZFPL1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9414</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>ZMYM2</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9415</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>ZNF280D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9416</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>ZNF442</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9417</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>NMH002_NPC_6H:BRD-K93754473-048-14-5:10</td>\n",
       "      <td>ZZEF1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9418 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     perturbagen_name                             Signature_id gene_symbol  \\\n",
       "0           tamoxifen     AML001_CD34_6H:BRD-K93754473:13.4584        AARS   \n",
       "1           tamoxifen     AML001_CD34_6H:BRD-K93754473:13.4584       ACOX2   \n",
       "2           tamoxifen     AML001_CD34_6H:BRD-K93754473:13.4584        ACP5   \n",
       "3           tamoxifen     AML001_CD34_6H:BRD-K93754473:13.4584         AK4   \n",
       "4           tamoxifen     AML001_CD34_6H:BRD-K93754473:13.4584      AMDHD2   \n",
       "...               ...                                      ...         ...   \n",
       "9413        tamoxifen  NMH002_NPC_6H:BRD-K93754473-048-14-5:10       ZFPL1   \n",
       "9414        tamoxifen  NMH002_NPC_6H:BRD-K93754473-048-14-5:10       ZMYM2   \n",
       "9415        tamoxifen  NMH002_NPC_6H:BRD-K93754473-048-14-5:10     ZNF280D   \n",
       "9416        tamoxifen  NMH002_NPC_6H:BRD-K93754473-048-14-5:10      ZNF442   \n",
       "9417        tamoxifen  NMH002_NPC_6H:BRD-K93754473-048-14-5:10       ZZEF1   \n",
       "\n",
       "      Sex-bias  \n",
       "0            3  \n",
       "1            9  \n",
       "2           23  \n",
       "3           21  \n",
       "4            2  \n",
       "...        ...  \n",
       "9413         1  \n",
       "9414        25  \n",
       "9415         4  \n",
       "9416         4  \n",
       "9417         1  \n",
       "\n",
       "[9418 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.groupby([\"perturbagen_name\", \"Signature_id\", \"gene_symbol\"])[\"Sex-bias\"].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Signature_id\n",
       "CPC006_HCT116_6H:BRD-K93754473-001-06-0:28.39      18219\n",
       "CPC006_LOVO_6H:BRD-K93754473-001-06-0:28.39        16436\n",
       "CPC006_JHUEM2_6H:BRD-K93754473-001-06-0:28.39       7948\n",
       "NMH002_NEU_6H:BRD-K93754473-001-16-9:10             2782\n",
       "CPC005_A375_24H:BRD-K04210847-001-01-1:10           2317\n",
       "                                                   ...  \n",
       "CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39         108\n",
       "CPC006_NCIH508_6H:BRD-K93754473-001-06-0:28.39       108\n",
       "CPC006_NCIH1694_6H:BRD-K93754473-001-06-0:28.39      105\n",
       "CPC006_T3M10_6H:BRD-K93754473-001-06-0:28.39          81\n",
       "CPC006_NOMO1_6H:BRD-K93754473-001-06-0:28.39          79\n",
       "Name: count, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df_all[\"Signature_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "prueba = df_all[df_all[\"Signature_id\"]==\"CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>tissue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14343</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>GDPD5</td>\n",
       "      <td>Adipose_Subcutaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14344</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>CEBPA</td>\n",
       "      <td>Adipose_Visceral_Omentum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14345</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>CEBPA</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14346</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>SPAG4</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14347</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>GDPD5</td>\n",
       "      <td>Artery_Aorta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14446</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>TRAP1</td>\n",
       "      <td>Thyroid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14447</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>WASF3</td>\n",
       "      <td>Thyroid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14448</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>GDPD5</td>\n",
       "      <td>Thyroid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14449</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>NFIL3</td>\n",
       "      <td>Whole_Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14450</th>\n",
       "      <td>tamoxifen</td>\n",
       "      <td>CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39</td>\n",
       "      <td>INTS3</td>\n",
       "      <td>Whole_Blood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      perturbagen_name                                  Signature_id  \\\n",
       "14343        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14344        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14345        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14346        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14347        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "...                ...                                           ...   \n",
       "14446        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14447        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14448        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14449        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "14450        tamoxifen  CPC006_HCC15_6H:BRD-K93754473-001-06-0:28.39   \n",
       "\n",
       "      gene_symbol                    tissue  \n",
       "14343       GDPD5      Adipose_Subcutaneous  \n",
       "14344       CEBPA  Adipose_Visceral_Omentum  \n",
       "14345       CEBPA              Artery_Aorta  \n",
       "14346       SPAG4              Artery_Aorta  \n",
       "14347       GDPD5              Artery_Aorta  \n",
       "...           ...                       ...  \n",
       "14446       TRAP1                   Thyroid  \n",
       "14447       WASF3                   Thyroid  \n",
       "14448       GDPD5                   Thyroid  \n",
       "14449       NFIL3               Whole_Blood  \n",
       "14450       INTS3               Whole_Blood  \n",
       "\n",
       "[108 rows x 4 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "pru = prueba[[\"gene_symbol\",\"tissue\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiss_escape = pru[\"tissue\"]\n",
    "tiss_escape = tiss_escape.drop_duplicates()\n",
    "tiss_escape = tiss_escape.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_escape = pru[\"gene_symbol\"]\n",
    "gene_escape = gene_escape.drop_duplicates()\n",
    "gene_escape = gene_escape.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease_escape_matrix = generate(pru,gene_escape,tiss_escape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease_escape_matrix.columns = tiss_escape\n",
    "disease_escape_matrix.index = gene_escape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease_escape_matrix.to_csv(\"matriz_tamoxifen_dos.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_matrix = disease_escape_matrix.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_matrix.to_csv(\"matriz_tamoxifen.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repurposing data\n",
    "\n",
    "DB00675 (DBID: tamoxifen)\tMalignant epithelial neoplasm of female breast (CUI: C3163805)\tApproved\t\n",
    "DB00675 (DBID: tamoxifen)\tInvasive Ductal Breast Carcinoma (CUI: C1134719)\tApproved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## estrategies for repurposing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#escape genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_genes = pd.read_excel((\"escape_genes_Oliva2020.xlsx\"),engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_sex_bias_tamoxifen = escape_genes.merge(genes_all_tissues, left_on=\"HUGO_gene_id\", right_on=\"gene_symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSEMBL_gene_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Genetype</th>\n",
       "      <th>Sum of relative sex predictivity</th>\n",
       "      <th>Avg of relative sex predictivity (*100)</th>\n",
       "      <th>#Tissues predictivity</th>\n",
       "      <th>Female-biased</th>\n",
       "      <th>Male-biased</th>\n",
       "      <th>Female-biased (median)</th>\n",
       "      <th>Male-biased (median)</th>\n",
       "      <th>Reported Escapee?</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>tissue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>XIST</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>27.913224</td>\n",
       "      <td>63.439145</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>9.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>XIST</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000005889.15</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.571802</td>\n",
       "      <td>1.681771</td>\n",
       "      <td>34.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000198034.10</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.437481</td>\n",
       "      <td>2.083242</td>\n",
       "      <td>21.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000006757.11</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.284667</td>\n",
       "      <td>1.293940</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000126012.11</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.282845</td>\n",
       "      <td>1.131381</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ENSG00000130021.13</td>\n",
       "      <td>PUDP</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.244160</td>\n",
       "      <td>0.739878</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PUDP</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENSG00000169249.12</td>\n",
       "      <td>ZRSR2</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.202390</td>\n",
       "      <td>1.065212</td>\n",
       "      <td>19.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ZRSR2</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENSG00000173674.10</td>\n",
       "      <td>EIF1AX</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.123401</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>15.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>EIF1AX</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENSG00000215301.9</td>\n",
       "      <td>DDX3X</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.033712</td>\n",
       "      <td>0.421399</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>DDX3X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENSG00000130741.10</td>\n",
       "      <td>EIF2S3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.002818</td>\n",
       "      <td>0.040263</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>EIF2S3</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ENSG00000186312.10</td>\n",
       "      <td>CA5BP1</td>\n",
       "      <td>transcribed_unprocessed_pseudogene</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.069600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>CA5BP1</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ENSG00000072501.17</td>\n",
       "      <td>SMC1A</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.062003</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>SMC1A</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ENSG00000130985.16</td>\n",
       "      <td>UBA1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.083839</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>UBA1</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENSG00000086712.12</td>\n",
       "      <td>TXLNG</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>0.032986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>TXLNG</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ENSG00000124486.12</td>\n",
       "      <td>USP9X</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.034813</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USP9X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ENSG00000102309.12</td>\n",
       "      <td>PIN4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.016223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PIN4</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ENSEMBL_gene_id HUGO_gene_id                            Genetype  \\\n",
       "0   ENSG00000229807.10         XIST                             lincRNA   \n",
       "1   ENSG00000005889.15          ZFX                      protein_coding   \n",
       "2   ENSG00000198034.10        RPS4X                      protein_coding   \n",
       "3   ENSG00000006757.11       PNPLA4                      protein_coding   \n",
       "4   ENSG00000126012.11        KDM5C                      protein_coding   \n",
       "5   ENSG00000130021.13         PUDP                      protein_coding   \n",
       "6   ENSG00000169249.12        ZRSR2                      protein_coding   \n",
       "7   ENSG00000173674.10       EIF1AX                      protein_coding   \n",
       "8    ENSG00000215301.9        DDX3X                      protein_coding   \n",
       "9   ENSG00000130741.10       EIF2S3                      protein_coding   \n",
       "10  ENSG00000186312.10       CA5BP1  transcribed_unprocessed_pseudogene   \n",
       "11  ENSG00000072501.17        SMC1A                      protein_coding   \n",
       "12  ENSG00000130985.16         UBA1                      protein_coding   \n",
       "13  ENSG00000086712.12        TXLNG                      protein_coding   \n",
       "14  ENSG00000124486.12        USP9X                      protein_coding   \n",
       "15  ENSG00000102309.12         PIN4                      protein_coding   \n",
       "\n",
       "    Sum of relative sex predictivity  Avg of relative sex predictivity (*100)  \\\n",
       "0                          27.913224                                63.439145   \n",
       "1                           0.571802                                 1.681771   \n",
       "2                           0.437481                                 2.083242   \n",
       "3                           0.284667                                 1.293940   \n",
       "4                           0.282845                                 1.131381   \n",
       "5                           0.244160                                 0.739878   \n",
       "6                           0.202390                                 1.065212   \n",
       "7                           0.123401                                 0.822676   \n",
       "8                           0.033712                                 0.421399   \n",
       "9                           0.002818                                 0.040263   \n",
       "10                          0.002784                                 0.069600   \n",
       "11                          0.001860                                 0.062003   \n",
       "12                          0.001677                                 0.083839   \n",
       "13                          0.001319                                 0.032986   \n",
       "14                          0.000348                                 0.034813   \n",
       "15                          0.000162                                 0.016223   \n",
       "\n",
       "    #Tissues predictivity  Female-biased  Male-biased  Female-biased (median)  \\\n",
       "0                    44.0             44            0                   9.514   \n",
       "1                    34.0             44            0                   0.581   \n",
       "2                    21.0             44            0                   0.485   \n",
       "3                    22.0             44            0                   0.419   \n",
       "4                    25.0             44            0                   0.448   \n",
       "5                    33.0             44            0                   0.562   \n",
       "6                    19.0             44            0                   0.419   \n",
       "7                    15.0             44            0                   0.460   \n",
       "8                     8.0             44            0                   0.419   \n",
       "9                     7.0             44            0                   0.288   \n",
       "10                    4.0             44            0                   0.282   \n",
       "11                    3.0             44            0                   0.239   \n",
       "12                    2.0             44            0                   0.168   \n",
       "13                    4.0             44            0                   0.305   \n",
       "14                    1.0             44            0                   0.159   \n",
       "15                    1.0             44            0                   0.070   \n",
       "\n",
       "    Male-biased (median)  Reported Escapee? gene_symbol  perturbagen_name  \\\n",
       "0                    NaN                  1        XIST                44   \n",
       "1                    NaN                  1         ZFX                44   \n",
       "2                    NaN                  1       RPS4X                44   \n",
       "3                    NaN                  1      PNPLA4                44   \n",
       "4                    NaN                  1       KDM5C                44   \n",
       "5                    NaN                  1        PUDP                44   \n",
       "6                    NaN                  1       ZRSR2                44   \n",
       "7                    NaN                  1      EIF1AX                44   \n",
       "8                    NaN                  1       DDX3X                44   \n",
       "9                    NaN                  1      EIF2S3                44   \n",
       "10                   NaN                  1      CA5BP1                44   \n",
       "11                   NaN                  1       SMC1A                44   \n",
       "12                   NaN                  1        UBA1                44   \n",
       "13                   NaN                  1       TXLNG                44   \n",
       "14                   NaN                  1       USP9X                44   \n",
       "15                   NaN                  1        PIN4                44   \n",
       "\n",
       "    tissue  \n",
       "0       44  \n",
       "1       44  \n",
       "2       44  \n",
       "3       44  \n",
       "4       44  \n",
       "5       44  \n",
       "6       44  \n",
       "7       44  \n",
       "8       44  \n",
       "9       44  \n",
       "10      44  \n",
       "11      44  \n",
       "12      44  \n",
       "13      44  \n",
       "14      44  \n",
       "15      44  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escape_sex_bias_tamoxifen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 16/17 son escape genes, all have female sex-bias, present female-biased in 44 tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_sex_bias_tamoxifen_all_genes = escape_genes.merge(df_more_impot_genes, left_on=\"HUGO_gene_id\", right_on=\"gene_symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "escape_genes_filter = escape_sex_bias_tamoxifen_all_genes[escape_sex_bias_tamoxifen_all_genes[\"Reported Escapee?\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] > escape_genes_filter['Female-biased'], 'Gene-bias'] = 1 \n",
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] < escape_genes_filter['Female-biased'], 'Gene-bias'] = 2\n",
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] == escape_genes_filter['Female-biased'], 'Gene-bias'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSEMBL_gene_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Genetype</th>\n",
       "      <th>Sum of relative sex predictivity</th>\n",
       "      <th>Avg of relative sex predictivity (*100)</th>\n",
       "      <th>#Tissues predictivity</th>\n",
       "      <th>Female-biased</th>\n",
       "      <th>Male-biased</th>\n",
       "      <th>Female-biased (median)</th>\n",
       "      <th>Male-biased (median)</th>\n",
       "      <th>Reported Escapee?</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>tissue</th>\n",
       "      <th>Gene-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>XIST</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>27.913224</td>\n",
       "      <td>63.439145</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>9.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>XIST</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000005889.15</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.571802</td>\n",
       "      <td>1.681771</td>\n",
       "      <td>34.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000198034.10</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.437481</td>\n",
       "      <td>2.083242</td>\n",
       "      <td>21.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000006757.11</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.284667</td>\n",
       "      <td>1.293940</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000126012.11</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.282845</td>\n",
       "      <td>1.131381</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>ENSG00000186310.9</td>\n",
       "      <td>NAP1L3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NAP1L3</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>ENSG00000189221.9</td>\n",
       "      <td>MAOA</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>1</td>\n",
       "      <td>MAOA</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>ENSG00000089820.15</td>\n",
       "      <td>ARHGAP4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>1</td>\n",
       "      <td>ARHGAP4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>ENSG00000102178.12</td>\n",
       "      <td>UBL4A</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>1</td>\n",
       "      <td>UBL4A</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>ENSG00000189108.12</td>\n",
       "      <td>IL1RAPL2</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>IL1RAPL2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ENSEMBL_gene_id HUGO_gene_id        Genetype  \\\n",
       "0    ENSG00000229807.10         XIST         lincRNA   \n",
       "1    ENSG00000005889.15          ZFX  protein_coding   \n",
       "2    ENSG00000198034.10        RPS4X  protein_coding   \n",
       "3    ENSG00000006757.11       PNPLA4  protein_coding   \n",
       "4    ENSG00000126012.11        KDM5C  protein_coding   \n",
       "..                  ...          ...             ...   \n",
       "241   ENSG00000186310.9       NAP1L3  protein_coding   \n",
       "243   ENSG00000189221.9         MAOA  protein_coding   \n",
       "244  ENSG00000089820.15      ARHGAP4  protein_coding   \n",
       "246  ENSG00000102178.12        UBL4A  protein_coding   \n",
       "248  ENSG00000189108.12     IL1RAPL2  protein_coding   \n",
       "\n",
       "     Sum of relative sex predictivity  \\\n",
       "0                           27.913224   \n",
       "1                            0.571802   \n",
       "2                            0.437481   \n",
       "3                            0.284667   \n",
       "4                            0.282845   \n",
       "..                                ...   \n",
       "241                               NaN   \n",
       "243                               NaN   \n",
       "244                               NaN   \n",
       "246                               NaN   \n",
       "248                               NaN   \n",
       "\n",
       "     Avg of relative sex predictivity (*100)  #Tissues predictivity  \\\n",
       "0                                  63.439145                   44.0   \n",
       "1                                   1.681771                   34.0   \n",
       "2                                   2.083242                   21.0   \n",
       "3                                   1.293940                   22.0   \n",
       "4                                   1.131381                   25.0   \n",
       "..                                       ...                    ...   \n",
       "241                                      NaN                    NaN   \n",
       "243                                      NaN                    NaN   \n",
       "244                                      NaN                    NaN   \n",
       "246                                      NaN                    NaN   \n",
       "248                                      NaN                    NaN   \n",
       "\n",
       "     Female-biased  Male-biased  Female-biased (median)  Male-biased (median)  \\\n",
       "0               44            0                   9.514                   NaN   \n",
       "1               44            0                   0.581                   NaN   \n",
       "2               44            0                   0.485                   NaN   \n",
       "3               44            0                   0.419                   NaN   \n",
       "4               44            0                   0.448                   NaN   \n",
       "..             ...          ...                     ...                   ...   \n",
       "241             26            0                   0.105                   NaN   \n",
       "243              1            2                   0.049                -0.070   \n",
       "244              0            2                     NaN                -0.030   \n",
       "246              1           18                   0.065                -0.021   \n",
       "248              1            0                   0.381                   NaN   \n",
       "\n",
       "     Reported Escapee? gene_symbol  perturbagen_name  tissue  Gene-bias  \n",
       "0                    1        XIST                44      44        2.0  \n",
       "1                    1         ZFX                44      44        2.0  \n",
       "2                    1       RPS4X                44      44        2.0  \n",
       "3                    1      PNPLA4                44      44        2.0  \n",
       "4                    1       KDM5C                44      44        2.0  \n",
       "..                 ...         ...               ...     ...        ...  \n",
       "241                  1      NAP1L3                26      26        2.0  \n",
       "243                  1        MAOA                 3       3        1.0  \n",
       "244                  1     ARHGAP4                 2       2        1.0  \n",
       "246                  1       UBL4A                19      19        1.0  \n",
       "248                  1    IL1RAPL2                 1       1        2.0  \n",
       "\n",
       "[161 rows x 15 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escape_genes_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene-bias\n",
       "2.0    99\n",
       "1.0    56\n",
       "0.0     6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(escape_genes_filter[\"Gene-bias\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 161/5070 escape genes, more female-biased "
   ]
  }
 ],
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