Visualize_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(context=\"poster\", style='ticks')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>sig-DMD</th>\n",
       "      <th>pair</th>\n",
       "      <th>pairs_number</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sig-DMD_1</td>\n",
       "      <td>nontrip_trip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sig-DMD_2</td>\n",
       "      <td>nontrip_trip,normal_nontrip,normal_trip</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sig-DMD_3</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sig-DMD_4</td>\n",
       "      <td>ers_err,normal_nontrip,normal_trip</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sig-DMD_5</td>\n",
       "      <td>ers_err,normal_nontrip,normal_trip</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sig-DMD                                     pair  pairs_number BRCA  \\\n",
       "0  sig-DMD_1                 nontrip_trip,normal_trip             2   No   \n",
       "1  sig-DMD_2  nontrip_trip,normal_nontrip,normal_trip             3  Yes   \n",
       "2  sig-DMD_3               normal_nontrip,normal_trip             2  Yes   \n",
       "3  sig-DMD_4       ers_err,normal_nontrip,normal_trip             3  Yes   \n",
       "4  sig-DMD_5       ers_err,normal_nontrip,normal_trip             3  Yes   \n",
       "\n",
       "  nonTN-BRCA TN-BRCA EndoR-BRCA  \n",
       "0         No     Yes         No  \n",
       "1        Yes     Yes         No  \n",
       "2         No      No         No  \n",
       "3         No      No        Yes  \n",
       "4         No      No        Yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = pd.read_table(\"./preliminary/sig-DMD.union.includeERR.reference\", sep=\"\\t\", header=None, usecols=[3, 5, 6])\n",
    "meta.columns = ['sig-DMD', 'pair', 'pairs_number']\n",
    "\n",
    "meta['BRCA'] = meta['pair'].apply(lambda x: 'Yes' if ('normal_trip' in x.split(\",\")) & \n",
    "                                  ('normal_nontrip' in x.split(\",\")) else \"No\")\n",
    "meta['nonTN-BRCA'] = meta['pair'].apply(lambda x: 'Yes' if ('nontrip_trip' in x.split(\",\")) & \n",
    "                                        ('normal_nontrip' in x.split(\",\")) else \"No\")\n",
    "meta['TN-BRCA'] = meta['pair'].apply(lambda x: 'Yes' if ('nontrip_trip' in x.split(\",\")) & \n",
    "                                     ('normal_trip' in x.split(\",\")) else \"No\")\n",
    "meta['EndoR-BRCA'] = meta['pair'].apply(lambda x: 'Yes' if ('ers_err' in x.split(\",\")) else \"No\")\n",
    "\n",
    "meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2905, 7)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sig-DMD</th>\n",
       "      <th>pair</th>\n",
       "      <th>pairs_number</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sig-DMD_1</td>\n",
       "      <td>nontrip_trip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sig-DMD_10</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sig-DMD_100</td>\n",
       "      <td>nontrip_trip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sig-DMD_1000</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sig-DMD_1001</td>\n",
       "      <td>nontrip_trip,normal_nontrip</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sig-DMD                         pair  pairs_number BRCA nonTN-BRCA  \\\n",
       "0     sig-DMD_1     nontrip_trip,normal_trip             2   No         No   \n",
       "1    sig-DMD_10   normal_nontrip,normal_trip             2  Yes         No   \n",
       "2   sig-DMD_100     nontrip_trip,normal_trip             2   No         No   \n",
       "3  sig-DMD_1000   normal_nontrip,normal_trip             2  Yes         No   \n",
       "4  sig-DMD_1001  nontrip_trip,normal_nontrip             2   No        Yes   \n",
       "\n",
       "  TN-BRCA EndoR-BRCA  \n",
       "0     Yes         No  \n",
       "1      No         No  \n",
       "2     Yes         No  \n",
       "3      No         No  \n",
       "4      No         No  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methylation = pd.read_table(\"./datasets/methylation_sigDMD.HM450.tsv\", sep=\"\\t\", usecols=['sig-DMD'])\n",
    "methylation = methylation.drop_duplicates()\n",
    "\n",
    "methylation = pd.merge(methylation, meta, how='inner', on='sig-DMD')\n",
    "\n",
    "print(methylation.shape)\n",
    "methylation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2886, 7)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sig-DMD</th>\n",
       "      <th>pair</th>\n",
       "      <th>pairs_number</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sig-DMD_563</td>\n",
       "      <td>ers_err,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sig-DMD_1083</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sig-DMD_1086</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sig-DMD_3701</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sig-DMD_3915</td>\n",
       "      <td>nontrip_trip,normal_nontrip,normal_trip</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sig-DMD                                     pair  pairs_number BRCA  \\\n",
       "0   sig-DMD_563                      ers_err,normal_trip             2   No   \n",
       "1  sig-DMD_1083               normal_nontrip,normal_trip             2  Yes   \n",
       "2  sig-DMD_1086               normal_nontrip,normal_trip             2  Yes   \n",
       "3  sig-DMD_3701               normal_nontrip,normal_trip             2  Yes   \n",
       "4  sig-DMD_3915  nontrip_trip,normal_nontrip,normal_trip             3  Yes   \n",
       "\n",
       "  nonTN-BRCA TN-BRCA EndoR-BRCA  \n",
       "0         No      No        Yes  \n",
       "1         No      No         No  \n",
       "2         No      No         No  \n",
       "3         No      No         No  \n",
       "4        Yes     Yes         No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meth_expr = pd.read_table(\"./datasets/gene_sigDMD_corr.tsv\", sep=\"\\t\")\n",
    "\n",
    "meth_expr_tested = pd.merge(pd.DataFrame(meth_expr['sig-DMD'].unique(),columns=['sig-DMD']), meta, \n",
    "                            how='inner', on='sig-DMD')\n",
    "\n",
    "print(meth_expr_tested.shape)\n",
    "meth_expr_tested.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1499, 7)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sig-DMD</th>\n",
       "      <th>pair</th>\n",
       "      <th>pairs_number</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sig-DMD_3915</td>\n",
       "      <td>nontrip_trip,normal_nontrip,normal_trip</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sig-DMD_2188</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sig-DMD_4336</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sig-DMD_4337</td>\n",
       "      <td>nontrip_trip,normal_nontrip,normal_trip</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sig-DMD_3809</td>\n",
       "      <td>normal_nontrip,normal_trip</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sig-DMD                                     pair  pairs_number BRCA  \\\n",
       "0  sig-DMD_3915  nontrip_trip,normal_nontrip,normal_trip             3  Yes   \n",
       "1  sig-DMD_2188               normal_nontrip,normal_trip             2  Yes   \n",
       "2  sig-DMD_4336               normal_nontrip,normal_trip             2  Yes   \n",
       "3  sig-DMD_4337  nontrip_trip,normal_nontrip,normal_trip             3  Yes   \n",
       "4  sig-DMD_3809               normal_nontrip,normal_trip             2  Yes   \n",
       "\n",
       "  nonTN-BRCA TN-BRCA EndoR-BRCA  \n",
       "0        Yes     Yes         No  \n",
       "1         No      No         No  \n",
       "2         No      No         No  \n",
       "3        Yes     Yes         No  \n",
       "4         No      No         No  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meth_expr_corr = pd.merge(meth_expr.loc[(meth_expr['FDR_BH_p'] < 0.01) & \n",
    "                                        (meth_expr['abs_corr_coef'] >= 0.2), ['sig-DMD']].drop_duplicates(), \n",
    "                          meta, how='inner', on='sig-DMD')\n",
    "\n",
    "print(meth_expr_corr.shape)\n",
    "meth_expr_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 3)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>set</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>3286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Union</td>\n",
       "      <td>Total</td>\n",
       "      <td>4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>HM450k data available</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>HM450k data available</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>HM450k data available</td>\n",
       "      <td>857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>HM450k data available</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Union</td>\n",
       "      <td>HM450k data available</td>\n",
       "      <td>2905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>1917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Union</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>2886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Union</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>1499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group                                            set  number\n",
       "0         BRCA                                          Total    3286\n",
       "1   nonTN-BRCA                                          Total     926\n",
       "2      TN-BRCA                                          Total     950\n",
       "3   EndoR-BRCA                                          Total     149\n",
       "4        Union                                          Total    4375\n",
       "5         BRCA                          HM450k data available    1934\n",
       "6   nonTN-BRCA                          HM450k data available     878\n",
       "7      TN-BRCA                          HM450k data available     857\n",
       "8   EndoR-BRCA                          HM450k data available     143\n",
       "9        Union                          HM450k data available    2905\n",
       "10        BRCA  Tested for methylation-expression correlation    1917\n",
       "11  nonTN-BRCA  Tested for methylation-expression correlation     877\n",
       "12     TN-BRCA  Tested for methylation-expression correlation     854\n",
       "13  EndoR-BRCA  Tested for methylation-expression correlation     143\n",
       "14       Union  Tested for methylation-expression correlation    2886\n",
       "15        BRCA              Methylation-expression correlated     997\n",
       "16  nonTN-BRCA              Methylation-expression correlated     523\n",
       "17     TN-BRCA              Methylation-expression correlated     406\n",
       "18  EndoR-BRCA              Methylation-expression correlated      83\n",
       "19       Union              Methylation-expression correlated    1499"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = ['BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA']\n",
    "total = [len(meta.loc[meta[x]=='Yes', x]) for x in group]\n",
    "meth = [len(methylation.loc[methylation[x]=='Yes', x]) for x in group]\n",
    "meth_tested = [len(meth_expr_tested.loc[meth_expr_tested[x]=='Yes', x]) for x in group]\n",
    "meth_corr = [len(meth_expr_corr.loc[meth_expr_corr[x]=='Yes', x]) for x in group]\n",
    "\n",
    "group.append('Union')\n",
    "total.append(meta.shape[0])\n",
    "meth.append(methylation.shape[0])\n",
    "meth_tested.append(meth_expr_tested.shape[0])\n",
    "meth_corr.append(meth_expr_corr.shape[0])\n",
    "\n",
    "data = list(zip(group, [\"Total\"]*len(group), total)) + \\\n",
    "        list(zip(group, [\"HM450k data available\"]*len(group), meth)) + \\\n",
    "        list(zip(group, [\"Tested for methylation-expression correlation\"]*len(group), meth_tested)) + \\\n",
    "        list(zip(group, [\"Methylation-expression correlated\"]*len(group), meth_corr))\n",
    "\n",
    "numbers_meth = pd.DataFrame(data, columns=['group', 'set', 'number'])\n",
    "print(numbers_meth.shape)\n",
    "numbers_meth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "g = sns.barplot(data=numbers_meth, x='group', y='number', hue='set', \n",
    "                order=['Union', 'BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA'],\n",
    "                palette='GnBu_d')\n",
    "g.set_xticklabels(['Union', 'BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA'], rotation=30)\n",
    "g.set_xlabel(\"DMV signature type\")\n",
    "g.set_ylabel(\"Number of signatures\")\n",
    "\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "fig.savefig(\"plots/number_signatures.svg\", transparent=True, bbox_inches=\"tight\")"
   ]
  },
  {
   "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",
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       "\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>group</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>Union</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HM450k data available</th>\n",
       "      <td>1934</td>\n",
       "      <td>143</td>\n",
       "      <td>857</td>\n",
       "      <td>2905</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methylation-expression correlated</th>\n",
       "      <td>997</td>\n",
       "      <td>83</td>\n",
       "      <td>406</td>\n",
       "      <td>1499</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tested for methylation-expression correlation</th>\n",
       "      <td>1917</td>\n",
       "      <td>143</td>\n",
       "      <td>854</td>\n",
       "      <td>2886</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>3286</td>\n",
       "      <td>149</td>\n",
       "      <td>950</td>\n",
       "      <td>4375</td>\n",
       "      <td>926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group                                          BRCA  EndoR-BRCA  TN-BRCA  \\\n",
       "set                                                                        \n",
       "HM450k data available                          1934         143      857   \n",
       "Methylation-expression correlated               997          83      406   \n",
       "Tested for methylation-expression correlation  1917         143      854   \n",
       "Total                                          3286         149      950   \n",
       "\n",
       "group                                          Union  nonTN-BRCA  \n",
       "set                                                               \n",
       "HM450k data available                           2905         878  \n",
       "Methylation-expression correlated               1499         523  \n",
       "Tested for methylation-expression correlation   2886         877  \n",
       "Total                                           4375         926  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot(index=numbers_meth['set'], columns=numbers_meth['group'], values=numbers_meth['number'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3518, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>gene_symbol</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1CF</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AACS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARS2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AASS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AATF</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_symbol BRCA TN-BRCA nonTN-BRCA EndoR-BRCA\n",
       "0        A1CF  Yes      No         No        Yes\n",
       "1        AACS  Yes      No         No         No\n",
       "2       AARS2  Yes      No         No         No\n",
       "3        AASS  Yes     Yes        Yes         No\n",
       "4        AATF  Yes      No         No         No"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = pd.read_table(\"./metadata/associated_genes_list.tsv\", sep=\"\\t\")\n",
    "print(meta.shape)\n",
    "meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3452, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1CF</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AACS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARS2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AASS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AATF</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_symbol BRCA TN-BRCA nonTN-BRCA EndoR-BRCA\n",
       "0        A1CF  Yes      No         No        Yes\n",
       "1        AACS  Yes      No         No         No\n",
       "2       AARS2  Yes      No         No         No\n",
       "3        AASS  Yes     Yes        Yes         No\n",
       "4        AATF  Yes      No         No         No"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression = pd.read_table(\"./datasets/expression.IlluminaHiSeq.tsv\", sep=\"\\t\", \n",
    "                           usecols=['gene_symbol'])\n",
    "expression = expression.drop_duplicates()\n",
    "\n",
    "expression = pd.merge(expression, meta, how='inner', on='gene_symbol')\n",
    "\n",
    "print(expression.shape)\n",
    "expression.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3186, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1CF</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AACS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARS2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AASS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AATF</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_symbol BRCA TN-BRCA nonTN-BRCA EndoR-BRCA\n",
       "0        A1CF  Yes      No         No        Yes\n",
       "1        AACS  Yes      No         No         No\n",
       "2       AARS2  Yes      No         No         No\n",
       "3        AASS  Yes     Yes        Yes         No\n",
       "4        AATF  Yes      No         No         No"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr_meth = pd.read_table(\"./datasets/gene_sigDMD_corr.tsv\", sep=\"\\t\")\n",
    "\n",
    "expr_meth_tested = pd.merge(pd.DataFrame(expr_meth['gene_symbol'].unique(),columns=['gene_symbol']), meta, \n",
    "                            how='inner', on='gene_symbol')\n",
    "\n",
    "print(expr_meth_tested.shape)\n",
    "expr_meth_tested.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1295, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>gene_symbol</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AATF</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABCA2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABCB5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABCG1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_symbol BRCA TN-BRCA nonTN-BRCA EndoR-BRCA\n",
       "0        AASS  Yes     Yes        Yes         No\n",
       "1        AATF  Yes      No         No         No\n",
       "2       ABCA2  Yes     Yes        Yes         No\n",
       "3       ABCB5  Yes      No         No         No\n",
       "4       ABCG1   No      No        Yes         No"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr_meth_corr = pd.merge(expr_meth.loc[(expr_meth['FDR_BH_p'] < 0.01) & \n",
    "                                        (expr_meth['abs_corr_coef'] >= 0.2), ['gene_symbol']].drop_duplicates(), \n",
    "                          meta, how='inner', on='gene_symbol')\n",
    "\n",
    "print(expr_meth_corr.shape)\n",
    "expr_meth_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>set</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>2351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>1306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>Total</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Union</td>\n",
       "      <td>Total</td>\n",
       "      <td>3518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>RNA-Seq data available</td>\n",
       "      <td>2302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>RNA-Seq data available</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>RNA-Seq data available</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>RNA-Seq data available</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Union</td>\n",
       "      <td>RNA-Seq data available</td>\n",
       "      <td>3452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>2136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>1205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Union</td>\n",
       "      <td>Tested for methylation-expression correlation</td>\n",
       "      <td>3186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Union</td>\n",
       "      <td>Methylation-expression correlated</td>\n",
       "      <td>1295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group                                            set  number\n",
       "0         BRCA                                          Total    2351\n",
       "1   nonTN-BRCA                                          Total    1309\n",
       "2      TN-BRCA                                          Total    1306\n",
       "3   EndoR-BRCA                                          Total     229\n",
       "4        Union                                          Total    3518\n",
       "5         BRCA                         RNA-Seq data available    2302\n",
       "6   nonTN-BRCA                         RNA-Seq data available    1289\n",
       "7      TN-BRCA                         RNA-Seq data available    1287\n",
       "8   EndoR-BRCA                         RNA-Seq data available     226\n",
       "9        Union                         RNA-Seq data available    3452\n",
       "10        BRCA  Tested for methylation-expression correlation    2136\n",
       "11  nonTN-BRCA  Tested for methylation-expression correlation    1257\n",
       "12     TN-BRCA  Tested for methylation-expression correlation    1205\n",
       "13  EndoR-BRCA  Tested for methylation-expression correlation     221\n",
       "14       Union  Tested for methylation-expression correlation    3186\n",
       "15        BRCA              Methylation-expression correlated     922\n",
       "16  nonTN-BRCA              Methylation-expression correlated     582\n",
       "17     TN-BRCA              Methylation-expression correlated     472\n",
       "18  EndoR-BRCA              Methylation-expression correlated     105\n",
       "19       Union              Methylation-expression correlated    1295"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = ['BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA']\n",
    "total = [len(meta.loc[meta[x]=='Yes', x]) for x in group]\n",
    "expr = [len(expression.loc[expression[x]=='Yes', x]) for x in group]\n",
    "expr_tested = [len(expr_meth_tested.loc[expr_meth_tested[x]=='Yes', x]) for x in group]\n",
    "expr_corr = [len(expr_meth_corr.loc[expr_meth_corr[x]=='Yes', x]) for x in group]\n",
    "\n",
    "group.append('Union')\n",
    "total.append(meta.shape[0])\n",
    "expr.append(expression.shape[0])\n",
    "expr_tested.append(expr_meth_tested.shape[0])\n",
    "expr_corr.append(expr_meth_corr.shape[0])\n",
    "\n",
    "\n",
    "data = list(zip(group, [\"Total\"]*len(group), total)) + \\\n",
    "        list(zip(group, [\"RNA-Seq data available\"]*len(group), expr)) + \\\n",
    "        list(zip(group, [\"Tested for methylation-expression correlation\"]*len(group), expr_tested)) + \\\n",
    "        list(zip(group, [\"Methylation-expression correlated\"]*len(group), expr_corr))\n",
    "numbers_expr = pd.DataFrame(data, columns=['group', 'set', 'number'])\n",
    "print(numbers_expr.shape)\n",
    "numbers_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "g = sns.barplot(data=numbers_expr, x='group', y='number', hue='set', \n",
    "                order=['Union', 'BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA'],\n",
    "                palette='GnBu_d')\n",
    "g.set_xticklabels(['Union', 'BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA'], rotation=30)\n",
    "g.set_xlabel(\"DMV signature type\")\n",
    "g.set_ylabel(\"Number of genes\")\n",
    "\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "fig.savefig(\"plots/number_genes.svg\", transparent=True, bbox_inches=\"tight\")"
   ]
  },
  {
   "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>group</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>Union</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Methylation-expression correlated</th>\n",
       "      <td>922</td>\n",
       "      <td>105</td>\n",
       "      <td>472</td>\n",
       "      <td>1295</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA-Seq data available</th>\n",
       "      <td>2302</td>\n",
       "      <td>226</td>\n",
       "      <td>1287</td>\n",
       "      <td>3452</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tested for methylation-expression correlation</th>\n",
       "      <td>2136</td>\n",
       "      <td>221</td>\n",
       "      <td>1205</td>\n",
       "      <td>3186</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>2351</td>\n",
       "      <td>229</td>\n",
       "      <td>1306</td>\n",
       "      <td>3518</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group                                          BRCA  EndoR-BRCA  TN-BRCA  \\\n",
       "set                                                                        \n",
       "Methylation-expression correlated               922         105      472   \n",
       "RNA-Seq data available                         2302         226     1287   \n",
       "Tested for methylation-expression correlation  2136         221     1205   \n",
       "Total                                          2351         229     1306   \n",
       "\n",
       "group                                          Union  nonTN-BRCA  \n",
       "set                                                               \n",
       "Methylation-expression correlated               1295         582  \n",
       "RNA-Seq data available                          3452        1289  \n",
       "Tested for methylation-expression correlation   3186        1257  \n",
       "Total                                           3518        1309  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot(index=numbers_expr['set'], columns=numbers_expr['group'], values=numbers_expr['number'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlated genes - signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1899, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>gene_symbol</th>\n",
       "      <th>sig-DMD</th>\n",
       "      <th>BRCA</th>\n",
       "      <th>TN-BRCA</th>\n",
       "      <th>nonTN-BRCA</th>\n",
       "      <th>EndoR-BRCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AASS</td>\n",
       "      <td>sig-DMD_3915</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AATF</td>\n",
       "      <td>sig-DMD_2188</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABCA2</td>\n",
       "      <td>sig-DMD_4336</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABCA2</td>\n",
       "      <td>sig-DMD_4337</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABCB5</td>\n",
       "      <td>sig-DMD_3809</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_symbol       sig-DMD BRCA TN-BRCA nonTN-BRCA EndoR-BRCA\n",
       "0        AASS  sig-DMD_3915  Yes     Yes        Yes         No\n",
       "1        AATF  sig-DMD_2188  Yes      No         No         No\n",
       "2       ABCA2  sig-DMD_4336  Yes     Yes        Yes         No\n",
       "3       ABCA2  sig-DMD_4337  Yes     Yes        Yes         No\n",
       "4       ABCB5  sig-DMD_3809  Yes      No         No         No"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigdf = pd.read_table(\"./datasets/correlated_gene_bigtable.tsv\", sep=\"\\t\", usecols=[0,1,9,10,11,12])\n",
    "bigdf.columns = ['gene_symbol', 'sig-DMD', 'BRCA', 'TN-BRCA', 'nonTN-BRCA', 'EndoR-BRCA']\n",
    "print(bigdf.shape)\n",
    "bigdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import groupby\n",
    "\n",
    "groups = ['Union', 'BRCA', 'nonTN-BRCA', 'TN-BRCA', 'EndoR-BRCA']\n",
    "gene_per_sig = []\n",
    "sig_per_gene = []\n",
    "\n",
    "for gr in groups:\n",
    "    if (gr != \"Union\"):\n",
    "        sigs = list(bigdf.loc[bigdf[gr]=='Yes', 'sig-DMD'])\n",
    "        genes = list(bigdf.loc[bigdf[gr]=='Yes', 'gene_symbol'])\n",
    "    else:\n",
    "        sigs = list(bigdf['sig-DMD'])\n",
    "        genes = list(bigdf['gene_symbol'])    \n",
    "    \n",
    "    gps = [len(list(group)) for key, group in groupby(sigs)]\n",
    "    for i in [1, 2]:\n",
    "        count = len([x for x in gps if x==i])\n",
    "        gene_per_sig.append((gr, str(i), count))\n",
    "    \n",
    "    spg = [len(list(group)) for key, group in groupby(genes)]\n",
    "    for i in [1, 2, 3, 4]:\n",
    "        if (i==4):\n",
    "            count = len([x for x in spg if x>3])\n",
    "            sig_per_gene.append((gr, \">= 4\", count))\n",
    "        else:\n",
    "            count = len([x for x in spg if x==i])\n",
    "            sig_per_gene.append((gr, str(i), count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gps_df = pd.DataFrame(gene_per_sig, columns=['type', 'group', 'count'])\n",
    "spg_df = pd.DataFrame(sig_per_gene, columns=['type', 'group', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>group</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Union</td>\n",
       "      <td>1</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Union</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>1463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         type group  count\n",
       "0       Union     1   1863\n",
       "1       Union     2     18\n",
       "2        BRCA     1   1463\n",
       "3        BRCA     2     11\n",
       "4  nonTN-BRCA     1    775\n",
       "5  nonTN-BRCA     2      7\n",
       "6     TN-BRCA     1    644\n",
       "7     TN-BRCA     2      3\n",
       "8  EndoR-BRCA     1    161\n",
       "9  EndoR-BRCA     2      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gps_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Union</td>\n",
       "      <td>1</td>\n",
       "      <td>1092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Union</td>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>Union</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Union</td>\n",
       "      <td>&gt;= 4</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BRCA</td>\n",
       "      <td>&gt;= 4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nonTN-BRCA</td>\n",
       "      <td>&gt;= 4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TN-BRCA</td>\n",
       "      <td>&gt;= 4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EndoR-BRCA</td>\n",
       "      <td>&gt;= 4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          type group  count\n",
       "0        Union     1   1092\n",
       "1        Union     2    139\n",
       "2        Union     3     28\n",
       "3        Union  >= 4     36\n",
       "4         BRCA     1    746\n",
       "5         BRCA     2    117\n",
       "6         BRCA     3     25\n",
       "7         BRCA  >= 4     34\n",
       "8   nonTN-BRCA     1    477\n",
       "9   nonTN-BRCA     2     74\n",
       "10  nonTN-BRCA     3     17\n",
       "11  nonTN-BRCA  >= 4     14\n",
       "12     TN-BRCA     1    384\n",
       "13     TN-BRCA     2     60\n",
       "14     TN-BRCA     3     15\n",
       "15     TN-BRCA  >= 4     13\n",
       "16  EndoR-BRCA     1     78\n",
       "17  EndoR-BRCA     2     19\n",
       "18  EndoR-BRCA     3      4\n",
       "19  EndoR-BRCA  >= 4      4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spg_df"
   ]
  }
 ],
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