3.explore-mutations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis of TCGA mutation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy\n",
    "import pandas\n",
    "import seaborn\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read TCGA datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8756, 20224)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join('data', 'mutation-matrix.tsv.bz2')\n",
    "mutation_df = pandas.read_table(path, index_col=0)\n",
    "mutation_df.columns.name = 'entrez_gene_id'\n",
    "mutation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_id</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>acronym</th>\n",
       "      <th>disease</th>\n",
       "      <th>age_diagnosed</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>ajcc_stage</th>\n",
       "      <th>clinical_stage</th>\n",
       "      <th>histological_type</th>\n",
       "      <th>...</th>\n",
       "      <th>dead</th>\n",
       "      <th>days_survived</th>\n",
       "      <th>disease_specific_survival_status</th>\n",
       "      <th>disease_specific_survival_days</th>\n",
       "      <th>disease_free_interval_status</th>\n",
       "      <th>disease_free_interval_days</th>\n",
       "      <th>progression_free_interval_status</th>\n",
       "      <th>progression_free_interval_days</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>n_mutations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-02-0047-01</td>\n",
       "      <td>TCGA-02-0047</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Primary Solid Tumor</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-02-0055-01</td>\n",
       "      <td>TCGA-02-0055</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Primary Solid Tumor</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sample_id    patient_id acronym                  disease  \\\n",
       "0  TCGA-02-0047-01  TCGA-02-0047     GBM  glioblastoma multiforme   \n",
       "1  TCGA-02-0055-01  TCGA-02-0055     GBM  glioblastoma multiforme   \n",
       "\n",
       "   age_diagnosed  gender   race ajcc_stage clinical_stage  \\\n",
       "0           78.0    Male  White        NaN            NaN   \n",
       "1           62.0  Female  White        NaN            NaN   \n",
       "\n",
       "                 histological_type     ...     dead  days_survived  \\\n",
       "0  Untreated primary (de novo) GBM     ...      1.0          448.0   \n",
       "1  Untreated primary (de novo) GBM     ...      1.0           76.0   \n",
       "\n",
       "  disease_specific_survival_status  disease_specific_survival_days  \\\n",
       "0                              1.0                           448.0   \n",
       "1                              1.0                            76.0   \n",
       "\n",
       "  disease_free_interval_status disease_free_interval_days  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "\n",
       "   progression_free_interval_status  progression_free_interval_days  \\\n",
       "0                               1.0                            57.0   \n",
       "1                               1.0                             6.0   \n",
       "\n",
       "           sample_type n_mutations  \n",
       "0  Primary Solid Tumor          64  \n",
       "1  Primary Solid Tumor          51  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join('data', 'samples.tsv')\n",
    "sample_df = pandas.read_table(path)\n",
    "sample_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of mutations counts for genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entrez_gene_id</th>\n",
       "      <th>n_mutations</th>\n",
       "      <th>n_mutations_log1p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>4.060443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>226</td>\n",
       "      <td>5.424950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  entrez_gene_id  n_mutations  n_mutations_log1p\n",
       "0              1           57           4.060443\n",
       "1              2          226           5.424950"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_df = mutation_df.sum(axis='rows').rename('n_mutations').reset_index()\n",
    "gene_df['n_mutations_log1p'] = numpy.log1p(gene_df.n_mutations)\n",
    "gene_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9DDycupCEuJhpmhw6M0BpsZNAZand4aRMY42HI2cH6eyfYE1Thd3hiBwlKztFTugdmqJveJqNK6twOHJ72mGieJlIyitiKSSRi5xwKFZW2XJFtc2RpJZMQRSpIIlc5ITDZ63545tXVdkcSWrFL1PXIc2zxBIkVSMXwk6hcITjrUM01nioyuFl+bPt3N8BgLe0iLNdo+fv77i60c6wRA6SEbnIeifOjRAMR9m8Or9G43GVXjfTwQjTQZm5IhZHErnIekdiZZVNeVZWiYu3tB0eC9ocichVkshF1jvaMojL6WBdU6XdoaRFpS+WyMdnbI5E5CpJ5CKrjU0GaesdZ21TBe6i3Lwa0EIqvdbVgiSRi8WSRC6y2rHWIQA2rPTbHEn6VHjcGMDwuJRWxOJIIhdZLZ7INzbnZ30cwOl04PO4GR6bwTTl4lri8kkiF1ntaMsgpcUumuuyr3VoKlV63QTDUaZm5GpB4vJJIhdZq2/YWpa/fkVlXi3Lv5TzM1ekTi4WQRK5yFqFUFaJOz9zZUwSubh8srJTZK2jLdb88fGp4PlVj/kqPnNlSEbkYhFkRC6yUtQ0OdY6RFmxi3KP2+5w0q68zI3DgBGZuSIWQRK5yErtveOMTYaoqy7DMPK7Pg7gcBiUe9wMj88QlZkr4jJJIhdZKV4fr68uszmSzKn0FROOmAyMZPbaoSL3SSIXWeloSzyR589l3RYSn7kiLW3F5ZJELrJOOBJFnxuivrqMspLCOR8fP+HZ0SdXCxKXRxK5yDpnOkcJhqJsXJn/0w4T+X0yIheLI4lcZJ34tMONzfnbX+VSvKVFOB2GXPZNXDZJ5CLrHG0dwjBArSisRG4YBpVeN10Dk0SiUbvDETlEErnIKlMzYc52jrKqvryg6uNxld5iwpEovUNTdocicogkcpFVTpwbJhI1C66sElcRr5NLeUVcBknkIqvEpx1uKLATnXH+2MyVTjnhKS6DJHKRVY62DuJ2OVjTWG53KLaIzyVvl0QuLkPhFSFFVtq5v4OpmTAdfRPUV5fx8uFuu0OyRVmJi9Jip8wlF5dFRuQia3QNTAKFtSx/NsMwaKjx0DM4RSgsM1dEciSRi6zRfT6RF86y/EtprPESNU26ByftDkXkCEnkIiuYpknnwATuIgdV5cV2h2Or5bVeANp6xmyOROQKSeQiK4xNhpicDlNfVRhta+fTXG9dn7SlSxK5SI4kcpEV4vXxugIvqwAsD3hxOgxaekbtDkXkCEnkIit0DVjT7Qr5RGecu8hJQ42Hcz3jslRfJEUSubBdOBKlq38SX1lRQVzWLRnNdT6CYeu4CLGQpOaRK6UeALYBJvAJrfVrCc/dCnwJiAAauFdrLcMIkbRT7SOEIlFW1xTmIqBLaa7z8dLBLs52j9IUO/kpxFwWHJErpW4B1mqtrwfuBR6atck3gfdprbcDPuDOlEcp8tqhMwMANAakPh7XXG99qLV0ywlPsbBkSiu3AY8BaK2PAn6lVOLQ6a1a6/bY7T6gOrUhinx36MwADodBXZXUx+OaAh6cDoNWSeQiCckk8jqsBB3XE3sMAK31KIBSqh64A3gylQGK/DY4Ok173wR1VaW4nHLKJq7I5aQx4KGtZ5xwRCqVYn7J1MhnT+o1sGrl5ymlaoH/AO7TWg/MtzO/vwyXy3lZQS5VIODL6Ovlgmw5Jm+ctt4uqxsr8XlLbI3F7tePi//frG+upq1nnOkorKqz5/8rW94n2SQbj0kyibyDhBE40ACc72gUK7M8BXxaa/3MQjsbGsrsWfhAwEdfn3w9TZRNx2T3gU4AasqLGRufti0On7fE1tdPFP+/qau0PljeONqNtyjz31ay6X2SLew8JvN9gCTz7ngGeB+AUmor0Km1TvyXfBV4QGv91FKCFIUnHIlytGWQ2spSmXZ4CW+u8JSFQWJ+C47Itda7lVJ7lVK7gShwn1LqHmAEeBr4CLBWKXVv7Ee+r7X+ZroCFvnjaMsQ08EIN11ZY3coWakp4KW4yIk+N2x3KCLLJTWPXGt9/6yHDiTcLuwOR2LR9upeAN6qAnQOyIUUZnM5HaxdXsHhM4MMj8+cv+iEELPJhSWELSLRKPtO9lPhcbOmqUISeYKd+zvO33YXWRMDfvbiGf7wtzbYFZLIcjLfS9jixLkRxqdCbF0XwFHg3Q7nUx+bWy+9ycV8JJELWySWVcTc/OXFuF2O8xfdEOJSJJGLjIuaJm+c6MNT4kItr7Q7nKzmMAyWVZUxPhWif2TK7nBElpJELjLuTOcow+NBtq4NyGrOJMRbFxxvldkr4tLkt0hk3KtHewApqySrLtaj/VjrkM2RiGwliVxkVDgS5dfHevCWFrFpVZXd4eSESq+bEreT421DmKa58A+IgiOJXGTU0ZYhxiZDXLdhmZRVkmQYBnXVZQyNzdDWM253OCILyW+SyKg9R6w2Pds2L7M5ktyycpm1XP+14702RyKykSwIEhkzNRPmjRN9+MqKaOsZ41yvjC6T1RjwUFzk5PXjvfzOLasxZO69SCAjcpExb5zoIxiOsrqhXBLRZXI5HVy1ppre4Sn5ABQXkUQuMuaVWFlldYNcm3MxrlG1gJRXxMWktCLSJrFnyPhkiKMtQwQqS/GVScvaxdhyRTXuIgevH+/lt2+W8op4k4zIRUac6hgBYN3yCpsjyV3FRU6uuqKGniEpr4gLSSIXaRc1TU61j1DkcrBiWfZdJitX7NzfQVmJ9SX6kZ2nLvjGIwqbJHKRdp39E0zOhFlV76PIJW+5pWgKeHAXOTjTOUo0KouDhEV+q0TanWq3yiprm6RB1lI5nQ5W1ZczNROhs196uAuLJHKRVlMzYc71jlNVXkx1RXZcpT7XrWm0zjPEzzsIIYlcpNXpjhFME9Y0yUnOVKkqL6bS66a9d5yxyaDd4YgsIIlcpI1pmpxsH8HpMFhdL3PHU8UwDNY0VRA1Yc+RHrvDEVlAErlIm56hKcYmQ6ys852/9qRIDWt1LOw61GV3KCILSCIXafPmSU4pq6RaidvF8lov53rHae0eszscYTNJ5CItJqZDtHaPUV5WRK2/1O5w8tIVsZOeMioXkshFWuw50kMkarJmeaUsJU+TxhoP5R43e450EwpH7Q5H2EgSuUi5qGnyq73tOAy4QhpkpY3DYXDDpjompsMcONVvdzjCRpLIRcodPjNA9+Akq+rLKS2WvmzptP3KekDKK4VOErlIuadfPQfAhma/zZHkv8YaD6vqyzl0ZoChsRm7wxE2kUQuUupc7zjHWofYsNJPVbms5Ey3nfs7qPWXYJrwg1+eYOf+DmmmVYAkkYuUeua1NgB+49rlNkdSOFbW+TAMONM1ancowiaSyEXK9A9PsedID3VVZWy5otrucApGidtFY42HwdEZhselvFKIJJGLlHn0pTNEoibv3t6MQ6YcZtSq2Oygs50yKi9EkshFSrT1jLHnSA8rlnl528ZldodTcJbXenE5Dc52jWGa0qe80EgiFynxyM7TmMD7d6yR0bgNXE7r6kvjUyH6hqfsDkdkmCRysWSHzwxw5Owgm5r9bFpVZXc4BWt1rLxyplN6rxSapFZrKKUeALYBJvAJrfVrCc+VAN8ENmqtr0lLlCJrTU6HefgXx3EYBu+/dY3d4RS0uuoySoudtHSPEo5EcTllnFYoFvyfVkrdAqzVWl8P3As8NGuTrwD70hCbyAE//NVJBkdnuOuGlXJhZZs5DIPmunKCoSiHzwzaHY7IoGQ+sm8DHgPQWh8F/EqpxAYanwIeTUNsIsvtP9XPrkNdrFjm5a4bmu0OR/Dm7JU9R7ttjkRkUjKJvA7oS7jfE3sMAK21FOQK0PhUiH976jgup8G9d22Ur/FZorq8mHKPm30n+5maCdsdjsiQZGrks6cgGFi18kXx+8twuTJ7tZhAQL7yz7bUY/Lwd19nZCLIPe/cyNaN9ZfcxufNrSX6uRbvXNavrOLVo92c7BrjtmtXLGlf8rtzsWw8Jskk8g4SRuBAA7Do721DQ5OL/dFFCQR89PXJl4ZESzkmO/d30NI1yosHughUlhAOh+fc19j49FLCzCiftySn4p1PY411IY9n97Rw5RIal8nvzsXsPCbzfYAkk8ifAT4H/ItSaivQKeWUwjU1E+bXR3txOgy2b6nHYRjSpCnL+MrcXNFYztHWIQZHp6V5WQFYsLCptd4N7FVK7QYeBO5TSt2jlLobQCn1CPBD66baqZT6QFojFrYxTZNXjvQwE4rwFhWg3OO2OyQxh5uubMA04aWD0qe8ECQ1j1xrff+shw4kPPf+lEYkstbuw920945TV1XG+hWVdocj5vG2DbX84FcneelgJ++6oRmHQ1bb5jO5fIu4pNnlkompEE+83EKR08ENm+vkOpxZrsTtYtvGZbywv5PDZwe5UrpR5jWZMyYWFDVNdh3qIhSOcs2GAN6yIrtDEkm4+aoGAF6Qcxh5TxK5WNDBUwP0DE6xvNbLmsYKu8MRSWqu87Gi1suBUwPSpzzPSSIX8+oamODg6QE8JS5u2CIllVyxc38HLxzopL6mjKhp8vBTx+0OSaSRJHIxp7HJIC8d6MIw4OarGyguyuxCLrF0qxsqcBc5ON42xHRQVnrmK0nk4pImp8M8+1o708EI166vJVBZandIYhGKXA42rPQTDEV58YBMRcxXksjFRSamQ/zy9XOMT4W48opq1q9c/OpAYT+1wo/LafD0q22EI1G7wxFpINMPC0h8SuHs5eg7rm48f3smGOHrjxxkeDyIWlHJVWtk2lquK3E7WdtUybHWIfYc6eHGKy/dG0fkLhmRi/PCkSjfeOwQpzpGWFXv420bauXkZp7Y2OzH6TD4z1daZFSehySRC8Bafv/tJ49z+MwgW1ZXs31LvSTxPOIpLWLH1Y30Dk3x3N52u8MRKSaJXADw7GvneOVIN6sbyvn43ZtlSXcees9NqygrdvHEyy2MT4XsDkekkCRywbGWQX78/GkqPG7uu3uLTDPMU97SIt69vZnJmTCPv3TW7nBECkkiL3DjUyH+6fEjGAbcd/cW/L5iu0MSafT2tzaxzF/K8/s6aOuRbtT5QhJ5AQtHouzc18H4VIgP3rGONU2y/D6f7dzfwa5DXWxeXU3UNPn6Tw7y3BtSL88HksgLlGma7DnSw+DoDDdfVc8tVzfYHZLIkMaAhysayxkam+HQmQG7wxEpIIm8QB1tGeJM5yg1FSV88A4lM1QKzLXraykrcXHw9ACt3VJiyXWSyAuQbhtir+6jtNjJLVsbKHLJ26DQuIucXL+pDtOEf3niCFMz0ocll8nKzgLT0TfB82+043Y5uP2a5XhKiuSamwWqMeBhY7Ofoy1D/NsvjvOn794k38xylCTyAnKyfZhfH+nFMAxufWujzFARvGVdgGA4yqvHelnbVMltb22yOySxCJLIs9ClRsiJ/VAu10wwwo+eO8krh3twFzm4c1szlR65yo8Ah8PgY+/ZzF99+1V++KuTNAU8qBXSJC3XSCJfglQn3Mt9rYVebyYUYee+Dp7c08rYZAi/r5gdWxtoqPVd0DRLFDa/r5iPv3czf//D/Xzj0cN8+r9eQ620Lc4pksjzzNRMGN02zGvHe9l3so/pYITSYifv3t6Mt6wIl1NObIoLxQcJ126oZc+RHr7073v54h9vszkqcTkkkWdIukbvM6EIpzpGON46xPHWIc52jRE1TQBqKkq445rl3HHtcrylclJTzG/d8kqGx2c43jrMNx49xBc+dqPdIYkkSSLPIaZpMj4VYng8yMDIND2Dk3z/2ROEI1bidhgGqxp8rF/hJ2qa1FSUYBgGr+temyMXueIaVcv4ZIijLUM8+ON9fOj2tTKTJQdIIs9yAyPTnO4cYWBkmuGxIKFZvaRX1vnYsMLP+pV+1jZVUFps/ZfK6FsshsNhcPPVDew50sPze9spKXLw/h1r7A5LLEASeZZq7x1n/6l+BkdnADAMqPC4qfQW4/dZfwL+Ut5x7QqbIxX5xuV08Ofvu5K/+/4+ntrTRqnbxV03NNsdlpiHJPIsMzoR5MX9nbR0j2EYsLzWy9rlFdRXl+F0yIlKkRnlZW7++qM38Bf/+CI/e/EMxW4nd1yz3O6wxBwkkS9SOBJlcHSy+kV/AAANG0lEQVQab2kR7kX27zZNk3DEPL+v5/d18PKhbmZCEWoqSrhhcx2VsmhH2GDn/g583hJuuqqBp19t4we/PMnJ9mE+/t4tdocmLkES+SKMTAR56GcHOd0xClgN+zeu8rM+iYUUE9Mh9p+0SiaPPH+KqZnIBc9XlRdz1Zpq1q2oxCEnmYTNyj1u7rh2Oc++1s7rx/t47KUzvOfGVXICNMtIIr9MbT1jPPjTgwyMzlBfXQZA/8g0rx7tZWomwi1XNVzyTW6aJqc7RnnteC+hcBSX02CZv4yKejdFTgfFbifXqFq2rqvhpYNdmf5nCTGnSm8xd15nJfMnXm5hcGyGD92xbtHfREXqSSK/DH3DU3zlB/uYmA7z2zevxlPqwjAMxiaD/PL1dg6dHuC7z5zgA7evvWDhzXQwzIsHumjtHqPI6eD6TctY01TBrVulr4XIDb4yN3det4LXjvWy62AXLV1jfPzuzdRVldkdmkDa2CYtGIrwjZ8dYmI6zEfeobjrhubzI+/4m9zvK2bnvg6++N299AxOEolGaesZ42++s5fW7jFq/aW868Zm1i6vlK+mIueUlbj41Iffwo6rG2jvG+ez//oqP9l5mslpaYFrNxmRJ8E0Tb77tKatd5ybr2pgx9aLV2SWFru487oVtPWM8fKhbj7zrV9jmhCJWot11IpKrl1fK1enFzmtyOXkI3euZ/1KPz967hRP7mnlhf0d3HhlPdu31NMU8NodYkGSRJ6Ex3ed5eXD3ayq9/HBO9bNuV2Ry8EfvXMjm1ZV8Z+7Wyl1O6n1l7J1bYDx6VAGIxYiPRIXmv3mthUcax3iyNlBnn71HE+/eo4Kr5uGag/1NWW898bVeEuly2YmJJXIlVIPANsAE/iE1vq1hOduB74IRIAntdZ/nY5A7WCaJo/vOssTL7cQqCzhvru3JHU1nW0b69i2se6Cx2Slpcg3LqeDLaur2djsp713gtMdI3QNTHJsfIhjrUM8t7eDWn8pq+vLWVVfTnO9j8YaD2UlktxTbcFErpS6BVirtb5eKbUR+DZwXcIm/wi8A+gAdimlfqq1PpqWaDNocHSax146y65DXdRWlvIXH9hKVXmJ3WEJkXWcDgcr63ysrPMRiUTpHZ6ie3CK/uEpBkam2TPUw56jPee3r/S6aQx4aazxUOsvpcpXQlW5tVrZW1ok548WIZkR+W3AYwBa66NKKb9SqlxrPaqUWg0Maq3PASilfh7bPi2JfGomTCgcxTRNTMA0rVEzCbdNiD1ngglBDAYGJojdPf94/Hbsx4maJsFQhM7+Cc50jvLrYz2EIyYNNR7+x+9eJUlciCQ4nQ7qqz3UV3sA63dsbDJE/8gUg6MzDI8HGR6f4cjZQY6cHbzo590uB57SIkqLXZQWOyktdlFW7Lrg7wtvO3G5HLgcDpwOA6fTwOEwWPCjYIEPi7mejTgcDA1PLbBrA8Pg/AeSw7Bez+kw0lZqSiaR1wF7E+73xB4bjf3dl/BcN3BFyqJLsP9UPw/+5CBmOnZ+CbWVpbxrezPbNi2TpfFCLJJhGJR73JR73KxuePPxYCjCyHiQsakQk9MhJqbDTEyHmZwOEQxFmZieig3a7Is9Hd570yrevX1VyvebTCKf/eFkwPl8Ot9zlxQI+Bb1vemOgI87rk/9AViK99+x3vZt07kPIURuSGao2YE18o5rwBp5X+q5RkCWJQohRAYlk8ifAd4HoJTaCnRqrccAtNYtQLlSqlkp5QLuim0vhBAiQwwziSKUUurLwM1AFLgP2AqMaK0fVUrdDPxtbNOfaq3/Pl3BCiGEuFhSiVwIIUT2kukYQgiR4ySRCyFEjsvrXivztRYoVEqpDwJ/AYSBz2itn7Q5pIxTSr0VeBw4FXvokNb6z2LPvQP4hda6YJYXKqVKgSPA54FfAd8FnFgz0D6stZ5RSv0e8D+xzpP9Smv9l3bFmw5Kqb8DbsLKiV/CWtT4TazccQL4mNY6rJT6AnAr1iD4Ua313ymlKoDvAxXAOPABrfXFq53SKG9H5ImtBYB7gYdsDsl2Sqlq4LPAjVgzjN5rb0S28QI/0VrviP2JJ/ES4P9QeFNoPw0MxG5/HviG1vomoAX4Q6VUGdaEhtuA64HbY+068oJS6lZgcyxX3An8A9a/90ta61uANuB3lVKbgbdrrbcD24E/UErVAZ8EdmqtbwSeAP53pv8NeZvImdVaAPArpcrtDcl2twO/1FqPaa27tNZ/YndANvHN8fingG8AwQzGYiul1HpgI/Dz2EM7sJIRWN9abtdaTwJbYu8bEyvpV2c61jR6EXh/7PYQ4AHWAa/GHnsa+A1gBChRShUDJVjfTiaxcs2jsW0fx/o9y6h8TuSz2wfEWwsUsmbAUEr9SCn1klLqNrsDsokXuFEp9ZRS6kWl1K1KqXXAVVrrR+wOLsO+CvyPhPserfVM7HY3UA8QXzsSG5U2A3syGGNaaa0jWuuJ2N17gSeBg8A7Y4+9A1gW6yn1CNAa+/PPWuvZrUrOH7NMyudEftntAwqAATQBHwTuAb6tlCqYWnCCA8Dntda/ifWL+2/A17kwoeU9pdRHgFe01mcTHk78Hbngd0YptRb4AVYNOO8a7Cul3gP8EfDfgP+FVU55DitPGrEmgXcDq4E1wEeVUrVcmGtsyTP5fLJzvtYChaoH2K21DgOnlVJjQADotTeszNJaHwOOxW6fUErNYH11/p5SCqBeKfVCrD6az94JrFZK3YX1AT8DTCilSrXWUyS03FBKNWGVKj+std5vV8DpEjvJ/ZfAnVrrEawyyl0Jz9UD1wK/jpWaUEodBDbzZq4ZwaY2JfmcyJ8BPgf8y+zWAgXsGeBhpdTfAlVYJYZ+e0PKPKXUHwJerfU/xk5WuYFSrXUw9nxLASRxtNa/F7+tlPorrJObNwC/A/x77O9fxDb5FtbMjTcyG2X6xWadfAXrfMBg7LHPAa9qrX8O/AHWTJ5u4JNKKQfWrJ4twBms36v3A1/gwmOWMXm9snN2awGt9QGbQ7KdUupPgd8HyoAvaK2fWOBH8o5Syg98D+uDrBj4XOI0zFgib7YpPFskJPKnge9gncxrxUpiq4D9vHnyD+Br+fLeUUr9CfBXWNMM4z6LldyDwPNa68/Etv0ccAdWCeVHWut/UEp5sT74qoFh4EOxUX3G5HUiF0KIQpDPJzuFEKIgSCIXQogcJ4lcCCFynCRyIYTIcZLIhRAix0kiFzlPKdWglHp7Ett9KPb31UqpB9MfmTWVUSm1ZpE/61ZK/YNSyoxdSlGIS5JELvLBrcC8iVwp1Qh8FEBrvT/e8TDLfQ2r54cQ85JPeZEWSqkdwP1AO7AJCGEtf56cY/txrJVx78JaaflF4I8BhbWi8Bml1E6sRUy/VEo1A7uwekj/DVYvjEGshT7fxXpvVwBf11p/B6tf9Bal1HeAf43t58ZYs6x/xhrUuID7tda7lFIPA51Yq/fWAd+K9Z6+FfgyVte7EuDPk+lzr5RyYrVHfStWL47ntNafifW6eQirPWwncBKY0Fp/GviU1npUKfWtWftqif17rgNqgE9qrZ9fKAaRv2RELtLpeqxkdD0QweoiNxcP8Hqs1/ME8C6t9W8Bfw18bK4fijV8ehj4rtb6a1g9dR7SWr8dq1fG12KbfhbrAhIfmbWLB4F/0lrviL3OdxKeW621fhdWH5b4hRQ+ibWq8VasxmPJdrr7XawVktuxVhv/Rqxn/m3A27D6ePweb3bcI9ZZby4DWuvbsBp9fTXJGESekkQu0umY1jrekKsVq7/LfHbF/m4HdifcrryM1+wEfl8ptQv4IQv3zb4OeBZAa30IKFdK1cSe2xl7vDX2uBNrJPw3SqmvYrU2TXaZ+nVYveBNrXUEeAkreV8NvJjQSvXpJPcX3+5lrH7iooBJIhfpFJ51f6GWueE5bsd/LrGfhHuOfXwBOBm7Wss759gm0eweFYltSC+KX2v9I+Aa4NfA/1VKfTGJ17iU+Os4ZsUQSfLn47+70p5ZSCIXOWUUWB67nXhyMwqUxm4vA07Hbn8AiMau6JK4TaI9xEo+sS6ZA1rrgUtsR2ybzwFOrfWPgU9glY+S8Qpwh1LKiM1AuSX22seBbbHHy5i//JQo/u+/ETkhWvDkZKfIJQ8B/6yU+gAXtgp9CfiRUmoyts2DsamG/4p1MeHvA38CVCulnsU6ORr3Z7F9fhQoAj68QAwngWeVUsNYA6HPJhn7I1gtYndhtUB9TGv9ciyp/z7wOta1IXcT+yaglPoZb5ajfqWUatdafzB2v1Ep9XOsPuIfTzIGkaek+6EQNor1wn4v8B2ttamUegL4gdb6B/P8TAtW7+xTmYlSZDsZkYuMUEqVAk/N8fSXtdYZb8afKkqpx7GmOs72sNb64QV+fAxrJssnlFJTWD2xC+26oWKJZEQuhBA5Tk52CiFEjpNELoQQOU4SuRBC5DhJ5EIIkeMkkQshRI6TRC6EEDnu/wMi2+QFENbkpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb955037b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.distplot(gene_df.n_mutations_log1p)\n",
    "xticks = ax.get_xticks()\n",
    "xticklabels = numpy.expm1(xticks).round().astype(int)\n",
    "axis_texts = ax.set_xticklabels(xticklabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(gene_df.n_mutations == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of mutations counts for samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_id</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>acronym</th>\n",
       "      <th>disease</th>\n",
       "      <th>age_diagnosed</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>ajcc_stage</th>\n",
       "      <th>clinical_stage</th>\n",
       "      <th>histological_type</th>\n",
       "      <th>...</th>\n",
       "      <th>days_survived</th>\n",
       "      <th>disease_specific_survival_status</th>\n",
       "      <th>disease_specific_survival_days</th>\n",
       "      <th>disease_free_interval_status</th>\n",
       "      <th>disease_free_interval_days</th>\n",
       "      <th>progression_free_interval_status</th>\n",
       "      <th>progression_free_interval_days</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>n_mutations</th>\n",
       "      <th>n_mutations_log1p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-02-0047-01</td>\n",
       "      <td>TCGA-02-0047</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Primary Solid Tumor</td>\n",
       "      <td>64</td>\n",
       "      <td>4.174387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-02-0055-01</td>\n",
       "      <td>TCGA-02-0055</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Primary Solid Tumor</td>\n",
       "      <td>51</td>\n",
       "      <td>3.951244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sample_id    patient_id acronym                  disease  \\\n",
       "0  TCGA-02-0047-01  TCGA-02-0047     GBM  glioblastoma multiforme   \n",
       "1  TCGA-02-0055-01  TCGA-02-0055     GBM  glioblastoma multiforme   \n",
       "\n",
       "   age_diagnosed  gender   race ajcc_stage clinical_stage  \\\n",
       "0           78.0    Male  White        NaN            NaN   \n",
       "1           62.0  Female  White        NaN            NaN   \n",
       "\n",
       "                 histological_type        ...        days_survived  \\\n",
       "0  Untreated primary (de novo) GBM        ...                448.0   \n",
       "1  Untreated primary (de novo) GBM        ...                 76.0   \n",
       "\n",
       "   disease_specific_survival_status disease_specific_survival_days  \\\n",
       "0                               1.0                          448.0   \n",
       "1                               1.0                           76.0   \n",
       "\n",
       "   disease_free_interval_status disease_free_interval_days  \\\n",
       "0                           NaN                        NaN   \n",
       "1                           NaN                        NaN   \n",
       "\n",
       "  progression_free_interval_status  progression_free_interval_days  \\\n",
       "0                              1.0                            57.0   \n",
       "1                              1.0                             6.0   \n",
       "\n",
       "           sample_type n_mutations n_mutations_log1p  \n",
       "0  Primary Solid Tumor          64          4.174387  \n",
       "1  Primary Solid Tumor          51          3.951244  \n",
       "\n",
       "[2 rows x 39 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df = sample_df.merge(\n",
    "    mutation_df.sum(axis='columns').rename('n_mutations').reset_index()\n",
    ")\n",
    "sample_df['n_mutations_log1p'] = numpy.log1p(sample_df.n_mutations)\n",
    "sample_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb9ca3aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mutations per sample\n",
    "ax = seaborn.distplot(sample_df.n_mutations_log1p)\n",
    "xticks = ax.get_xticks()\n",
    "xticklabels = numpy.expm1(xticks).round().astype(int)\n",
    "axis_texts = ax.set_xticklabels(xticklabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnosis age versus mutation count for samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb9a68c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = seaborn.jointplot('n_mutations_log1p', 'age_diagnosed', data=sample_df, kind='hex')\n",
    "xticks = grid.ax_marg_x.get_xticks()\n",
    "xticklabels = numpy.expm1(xticks).round().astype(int)\n",
    "axis_texts = grid.ax_marg_x.set_xticklabels(xticklabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutation frequency by disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disease</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adrenocortical cancer</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bladder urothelial carcinoma</th>\n",
       "      <td>0.009926</td>\n",
       "      <td>0.034739</td>\n",
       "      <td>0.007444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brain lower grade glioma</th>\n",
       "      <td>0.001961</td>\n",
       "      <td>0.007843</td>\n",
       "      <td>0.001961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     1         2         9\n",
       "disease                                                   \n",
       "adrenocortical cancer         0.000000  0.000000  0.000000\n",
       "bladder urothelial carcinoma  0.009926  0.034739  0.007444\n",
       "brain lower grade glioma      0.001961  0.007843  0.001961"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes = mutation_df.columns.tolist()\n",
    "verbose_mutation_df = sample_df.merge(mutation_df.reset_index())\n",
    "mutation_freq_df = verbose_mutation_df.groupby('disease').apply(lambda df: df[genes].mean(axis='rows')).assign(\n",
    "    n_mutations = verbose_mutation_df.groupby('disease').apply(len)\n",
    ")\n",
    "mutation_freq_df.iloc[:3, :3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_id</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>acronym</th>\n",
       "      <th>disease</th>\n",
       "      <th>age_diagnosed</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>ajcc_stage</th>\n",
       "      <th>clinical_stage</th>\n",
       "      <th>histological_type</th>\n",
       "      <th>...</th>\n",
       "      <th>105372481</th>\n",
       "      <th>105375355</th>\n",
       "      <th>105379427</th>\n",
       "      <th>106479339</th>\n",
       "      <th>106479938</th>\n",
       "      <th>106660612</th>\n",
       "      <th>107133494</th>\n",
       "      <th>107133516</th>\n",
       "      <th>107984923</th>\n",
       "      <th>109731405</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-02-0047-01</td>\n",
       "      <td>TCGA-02-0047</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-02-0055-01</td>\n",
       "      <td>TCGA-02-0055</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-02-2483-01</td>\n",
       "      <td>TCGA-02-2483</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-02-2485-01</td>\n",
       "      <td>TCGA-02-2485</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black Or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-02-2486-01</td>\n",
       "      <td>TCGA-02-2486</td>\n",
       "      <td>GBM</td>\n",
       "      <td>glioblastoma multiforme</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Untreated primary (de novo) GBM</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 20263 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sample_id    patient_id acronym                  disease  \\\n",
       "0  TCGA-02-0047-01  TCGA-02-0047     GBM  glioblastoma multiforme   \n",
       "1  TCGA-02-0055-01  TCGA-02-0055     GBM  glioblastoma multiforme   \n",
       "2  TCGA-02-2483-01  TCGA-02-2483     GBM  glioblastoma multiforme   \n",
       "3  TCGA-02-2485-01  TCGA-02-2485     GBM  glioblastoma multiforme   \n",
       "4  TCGA-02-2486-01  TCGA-02-2486     GBM  glioblastoma multiforme   \n",
       "\n",
       "   age_diagnosed  gender                       race ajcc_stage clinical_stage  \\\n",
       "0           78.0    Male                      White        NaN            NaN   \n",
       "1           62.0  Female                      White        NaN            NaN   \n",
       "2           43.0    Male                      Asian        NaN            NaN   \n",
       "3           53.0    Male  Black Or African American        NaN            NaN   \n",
       "4           64.0    Male                      White        NaN            NaN   \n",
       "\n",
       "                 histological_type    ...    105372481  105375355 105379427  \\\n",
       "0  Untreated primary (de novo) GBM    ...            0          0         0   \n",
       "1  Untreated primary (de novo) GBM    ...            0          0         0   \n",
       "2  Untreated primary (de novo) GBM    ...            0          0         0   \n",
       "3  Untreated primary (de novo) GBM    ...            0          0         0   \n",
       "4  Untreated primary (de novo) GBM    ...            0          0         0   \n",
       "\n",
       "   106479339 106479938 106660612  107133494  107133516 107984923 109731405  \n",
       "0          0         0         0          0          0         0         0  \n",
       "1          0         0         0          0          0         0         0  \n",
       "2          0         0         0          0          0         0         0  \n",
       "3          0         0         0          0          0         0         0  \n",
       "4          0         0         0          0          0         0         0  \n",
       "\n",
       "[5 rows x 20263 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verbose_mutation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb878fffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_subset = {\n",
    "    '7157': 'TP53', # tumor protein p53\n",
    "    '7428': 'VHL', # von Hippel-Lindau tumor suppressor\n",
    "    '29126': 'CD274', # CD274 molecule\n",
    "    '672': 'BRCA1', # BRCA1, DNA repair associated\n",
    "    '675': 'BRCA2', # BRCA2, DNA repair associated\n",
    "    '238': 'ALK', # anaplastic lymphoma receptor tyrosine kinase\n",
    "    '4221': 'MEN1', # menin 1\n",
    "    '5979': 'RET', # ret proto-oncogene \n",
    "}\n",
    "plot_df = (mutation_freq_df\n",
    "    .query(\"n_mutations > 100\")\n",
    "    [list(gene_subset)]\n",
    "    .rename(columns=gene_subset)\n",
    ")\n",
    "\n",
    "# Convert to percent of max mutation rate for gene\n",
    "plot_df = 100 * plot_df.divide(plot_df.max())\n",
    "ax = seaborn.heatmap(plot_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:cognoma-cancer-data]",
   "language": "python",
   "name": "conda-env-cognoma-cancer-data-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}