figures_and_tables/table-data_info.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Top'></a>\n",
    "\n",
    "# MultiSurv results<a class='tocSkip'></a>\n",
    "\n",
    "Evaluation metric results for MultiSurv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%load_ext watermark\n",
    "\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Make modules in \"src\" dir visible\n",
    "project_dir = os.path.split(os.getcwd())[0]\n",
    "if project_dir not in sys.path:\n",
    "    sys.path.append(os.path.join(project_dir, 'src'))\n",
    "\n",
    "import dataset\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Data-modality-info\" data-toc-modified-id=\"Data-modality-info-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Data modality info</a></span><ul class=\"toc-item\"><li><span><a href=\"#Clinical\" data-toc-modified-id=\"Clinical-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Clinical</a></span></li><li><span><a href=\"#mRNA\" data-toc-modified-id=\"mRNA-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>mRNA</a></span></li><li><span><a href=\"#DNAm\" data-toc-modified-id=\"DNAm-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>DNAm</a></span></li><li><span><a href=\"#miRNA\" data-toc-modified-id=\"miRNA-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>miRNA</a></span></li><li><span><a href=\"#CNV\" data-toc-modified-id=\"CNV-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>CNV</a></span></li><li><span><a href=\"#WSI\" data-toc-modified-id=\"WSI-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>WSI</a></span></li></ul></li><li><span><a href=\"#Data-info-by-cancer-type\" data-toc-modified-id=\"Data-info-by-cancer-type-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Data info by cancer type</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = utils.INPUT_DATA_DIR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data modality info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_modalities = ['clinical', 'mRNA', 'DNAm', 'miRNA', 'CNV', 'wsi']\n",
    "\n",
    "info = {\n",
    "    'n': {modality: None for modality in data_modalities},\n",
    "    'continuous': {modality: None for modality in data_modalities},\n",
    "    'categorical': {modality: None for modality in data_modalities}\n",
    "    }\n",
    "info = pd.DataFrame.from_dict(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             n continuous categorical\n",
       "clinical  None       None        None\n",
       "mRNA      None       None        None\n",
       "DNAm      None       None        None\n",
       "miRNA     None       None        None\n",
       "CNV       None       None        None\n",
       "wsi       None       None        None"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clinical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>submitter_id</th>\n",
       "      <th>tumor_stage</th>\n",
       "      <th>age_at_diagnosis</th>\n",
       "      <th>prior_treatment</th>\n",
       "      <th>prior_malignancy</th>\n",
       "      <th>synchronous_malignancy</th>\n",
       "      <th>days_to_last_follow_up</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>vital_status</th>\n",
       "      <th>days_to_death</th>\n",
       "      <th>treatments_pharmaceutical_treatment_or_therapy</th>\n",
       "      <th>treatments_radiation_treatment_or_therapy</th>\n",
       "      <th>project_id</th>\n",
       "      <th>time</th>\n",
       "      <th>event</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-3B-A9HI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24984.0</td>\n",
       "      <td>No</td>\n",
       "      <td>no</td>\n",
       "      <td>No</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>male</td>\n",
       "      <td>white</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>SARC</td>\n",
       "      <td>4.167123</td>\n",
       "      <td>0</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-3B-A9HJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24962.0</td>\n",
       "      <td>No</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>male</td>\n",
       "      <td>white</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>SARC</td>\n",
       "      <td>3.024658</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-3B-A9HL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24628.0</td>\n",
       "      <td>No</td>\n",
       "      <td>no</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>white</td>\n",
       "      <td>Dead</td>\n",
       "      <td>599.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>SARC</td>\n",
       "      <td>1.641096</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-3B-A9HO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27664.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>959.0</td>\n",
       "      <td>male</td>\n",
       "      <td>white</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>SARC</td>\n",
       "      <td>2.627397</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-3B-A9HP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21094.0</td>\n",
       "      <td>No</td>\n",
       "      <td>no</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>white</td>\n",
       "      <td>Dead</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>SARC</td>\n",
       "      <td>4.457534</td>\n",
       "      <td>1</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   submitter_id tumor_stage  age_at_diagnosis prior_treatment  \\\n",
       "0  TCGA-3B-A9HI         NaN           24984.0              No   \n",
       "1  TCGA-3B-A9HJ         NaN           24962.0              No   \n",
       "2  TCGA-3B-A9HL         NaN           24628.0              No   \n",
       "3  TCGA-3B-A9HO         NaN           27664.0             Yes   \n",
       "4  TCGA-3B-A9HP         NaN           21094.0              No   \n",
       "\n",
       "  prior_malignancy synchronous_malignancy  days_to_last_follow_up  gender  \\\n",
       "0               no                     No                  1521.0    male   \n",
       "1              yes                    NaN                  1104.0    male   \n",
       "2               no                     No                     NaN    male   \n",
       "3              yes                    NaN                   959.0    male   \n",
       "4               no                     No                     NaN  female   \n",
       "\n",
       "    race vital_status  days_to_death  \\\n",
       "0  white        Alive            NaN   \n",
       "1  white        Alive            NaN   \n",
       "2  white         Dead          599.0   \n",
       "3  white        Alive            NaN   \n",
       "4  white         Dead         1627.0   \n",
       "\n",
       "  treatments_pharmaceutical_treatment_or_therapy  \\\n",
       "0                                            yes   \n",
       "1                                             no   \n",
       "2                                            yes   \n",
       "3                                             no   \n",
       "4                                            yes   \n",
       "\n",
       "  treatments_radiation_treatment_or_therapy project_id      time  event  group  \n",
       "0                                       yes       SARC  4.167123      0    val  \n",
       "1                                       yes       SARC  3.024658      0  train  \n",
       "2                                        no       SARC  1.641096      1  train  \n",
       "3                                        no       SARC  2.627397      0  train  \n",
       "4                                       yes       SARC  4.457534      1    val  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinical_data = pd.read_csv('../data/clinical_data.tsv', sep='\\t')\n",
    "\n",
    "clinical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "modality = 'clinical'\n",
    "\n",
    "info['n'][modality] = len(clinical_data)\n",
    "info['categorical'][modality] = 9\n",
    "info['continuous'][modality] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>11081</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n continuous categorical\n",
       "clinical  11081          1           9\n",
       "mRNA       None       None        None\n",
       "DNAm       None       None        None\n",
       "miRNA      None       None        None\n",
       "CNV        None       None        None\n",
       "wsi        None       None        None"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mRNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluding 1188 patient(s) missing all data.\n",
      "Excluding 153 patient(s) missing all data.\n",
      "Excluding 135 patient(s) missing all data.\n",
      "Data modalities:\n",
      "   mRNA\n",
      "\n",
      "Dataset sizes (# patients):\n",
      "   train: 7692\n",
      "   val: 956\n",
      "   test: 957\n",
      "\n",
      "Batch size: 128\n"
     ]
    }
   ],
   "source": [
    "modality = 'mRNA'\n",
    "\n",
    "dataloaders = utils.get_dataloaders(\n",
    "    data_location=DATA,\n",
    "    labels_file='../data/labels.tsv',\n",
    "    modalities=[modality])\n",
    "\n",
    "n = 0\n",
    "\n",
    "for group in dataloaders:\n",
    "    n += len(dataloaders[group].dataset)\n",
    "\n",
    "info['n'][modality] = n\n",
    "info['categorical'][modality] = '-'\n",
    "info['continuous'][modality] = next(iter(dataloaders['train']))[0][modality].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>11081</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>9605</td>\n",
       "      <td>1000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n continuous categorical\n",
       "clinical  11081          1           9\n",
       "mRNA       9605       1000           -\n",
       "DNAm       None       None        None\n",
       "miRNA      None       None        None\n",
       "CNV        None       None        None\n",
       "wsi        None       None        None"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DNAm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluding 669 patient(s) missing all data.\n",
      "Excluding 79 patient(s) missing all data.\n",
      "Excluding 76 patient(s) missing all data.\n",
      "Data modalities:\n",
      "   DNAm\n",
      "\n",
      "Dataset sizes (# patients):\n",
      "   train: 8211\n",
      "   val: 1030\n",
      "   test: 1016\n",
      "\n",
      "Batch size: 128\n"
     ]
    }
   ],
   "source": [
    "modality = 'DNAm'\n",
    "\n",
    "dataloaders = utils.get_dataloaders(\n",
    "    data_location=DATA,\n",
    "    labels_file='../data/labels.tsv',\n",
    "    modalities=[modality])\n",
    "\n",
    "n = 0\n",
    "\n",
    "for group in dataloaders:\n",
    "    n += len(dataloaders[group].dataset)\n",
    "\n",
    "info['n'][modality] = n\n",
    "info['categorical'][modality] = '-'\n",
    "info['continuous'][modality] = next(iter(dataloaders['train']))[0][modality].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>11081</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>9605</td>\n",
       "      <td>1000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>10257</td>\n",
       "      <td>5000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n continuous categorical\n",
       "clinical  11081          1           9\n",
       "mRNA       9605       1000           -\n",
       "DNAm      10257       5000           -\n",
       "miRNA      None       None        None\n",
       "CNV        None       None        None\n",
       "wsi        None       None        None"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## miRNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluding 1189 patient(s) missing all data.\n",
      "Excluding 147 patient(s) missing all data.\n",
      "Excluding 129 patient(s) missing all data.\n",
      "Data modalities:\n",
      "   miRNA\n",
      "\n",
      "Dataset sizes (# patients):\n",
      "   train: 7691\n",
      "   val: 962\n",
      "   test: 963\n",
      "\n",
      "Batch size: 128\n"
     ]
    }
   ],
   "source": [
    "modality = 'miRNA'\n",
    "\n",
    "dataloaders = utils.get_dataloaders(\n",
    "    data_location=DATA,\n",
    "    labels_file='../data/labels.tsv',\n",
    "    modalities=[modality])\n",
    "\n",
    "n = 0\n",
    "\n",
    "for group in dataloaders:\n",
    "    n += len(dataloaders[group].dataset)\n",
    "\n",
    "info['n'][modality] = n\n",
    "info['categorical'][modality] = '-'\n",
    "info['continuous'][modality] = next(iter(dataloaders['train']))[0][modality].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>11081</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>9605</td>\n",
       "      <td>1000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>10257</td>\n",
       "      <td>5000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>9616</td>\n",
       "      <td>1881</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n continuous categorical\n",
       "clinical  11081          1           9\n",
       "mRNA       9605       1000           -\n",
       "DNAm      10257       5000           -\n",
       "miRNA      9616       1881           -\n",
       "CNV        None       None        None\n",
       "wsi        None       None        None"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluding 615 patient(s) missing all data.\n",
      "Excluding 72 patient(s) missing all data.\n",
      "Excluding 69 patient(s) missing all data.\n",
      "Data modalities:\n",
      "   CNV\n",
      "\n",
      "Dataset sizes (# patients):\n",
      "   train: 8265\n",
      "   val: 1037\n",
      "   test: 1023\n",
      "\n",
      "Batch size: 128\n"
     ]
    }
   ],
   "source": [
    "modality = 'CNV'\n",
    "\n",
    "dataloaders = utils.get_dataloaders(\n",
    "    data_location=DATA,\n",
    "    labels_file='../data/labels.tsv',\n",
    "    modalities=[modality])\n",
    "\n",
    "n = 0\n",
    "\n",
    "for group in dataloaders:\n",
    "    n += len(dataloaders[group].dataset)\n",
    "\n",
    "info['n'][modality] = n\n",
    "info['categorical'][modality] = next(iter(dataloaders['train']))[0][modality].shape[1]\n",
    "info['continuous'][modality] = '-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>11081</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>9605</td>\n",
       "      <td>1000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>10257</td>\n",
       "      <td>5000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>9616</td>\n",
       "      <td>1881</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>10325</td>\n",
       "      <td>-</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n continuous categorical\n",
       "clinical  11081          1           9\n",
       "mRNA       9605       1000           -\n",
       "DNAm      10257       5000           -\n",
       "miRNA      9616       1881           -\n",
       "CNV       10325          -        2000\n",
       "wsi        None       None        None"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WSI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excluding 2192 patient(s) missing all data.\n",
      "Excluding 279 patient(s) missing all data.\n",
      "Excluding 234 patient(s) missing all data.\n",
      "Data modalities:\n",
      "   wsi\n",
      "\n",
      "Dataset sizes (# patients):\n",
      "   train: 6688\n",
      "   val: 830\n",
      "   test: 858\n",
      "\n",
      "Batch size: 32\n"
     ]
    }
   ],
   "source": [
    "modality = 'wsi'\n",
    "\n",
    "dataloaders = utils.get_dataloaders(\n",
    "    data_location=DATA,\n",
    "    labels_file='../data/labels.tsv',\n",
    "    modalities=[modality],\n",
    "    wsi_patch_size=299,\n",
    "    n_wsi_patches=5)\n",
    "\n",
    "n = 0\n",
    "\n",
    "for group in dataloaders:\n",
    "    n += len(dataloaders[group].dataset)\n",
    "\n",
    "info['n'][modality] = n\n",
    "info['categorical'][modality] = '-'\n",
    "info['continuous'][modality] = r' 299 $\\times$ 299 pixels'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>continuous</th>\n",
       "      <th>categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clinical</th>\n",
       "      <td>11081</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mRNA</th>\n",
       "      <td>9605</td>\n",
       "      <td>1000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNAm</th>\n",
       "      <td>10257</td>\n",
       "      <td>5000</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miRNA</th>\n",
       "      <td>9616</td>\n",
       "      <td>1881</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNV</th>\n",
       "      <td>10325</td>\n",
       "      <td>-</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wsi</th>\n",
       "      <td>8376</td>\n",
       "      <td>299 $\\times$ 299 pixels</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n                continuous categorical\n",
       "clinical  11081                         1           9\n",
       "mRNA       9605                      1000           -\n",
       "DNAm      10257                      5000           -\n",
       "miRNA      9616                      1881           -\n",
       "CNV       10325                         -        2000\n",
       "wsi        8376   299 $\\times$ 299 pixels           -"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data info by cancer type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>submitter_id</th>\n",
       "      <th>project_id</th>\n",
       "      <th>time</th>\n",
       "      <th>event</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-3B-A9HI</td>\n",
       "      <td>SARC</td>\n",
       "      <td>4.167123</td>\n",
       "      <td>0</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-3B-A9HJ</td>\n",
       "      <td>SARC</td>\n",
       "      <td>3.024658</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-3B-A9HL</td>\n",
       "      <td>SARC</td>\n",
       "      <td>1.641096</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-3B-A9HO</td>\n",
       "      <td>SARC</td>\n",
       "      <td>2.627397</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-3B-A9HP</td>\n",
       "      <td>SARC</td>\n",
       "      <td>4.457534</td>\n",
       "      <td>1</td>\n",
       "      <td>val</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   submitter_id project_id      time  event  group\n",
       "0  TCGA-3B-A9HI       SARC  4.167123      0    val\n",
       "1  TCGA-3B-A9HJ       SARC  3.024658      0  train\n",
       "2  TCGA-3B-A9HL       SARC  1.641096      1  train\n",
       "3  TCGA-3B-A9HO       SARC  2.627397      0  train\n",
       "4  TCGA-3B-A9HP       SARC  4.457534      1    val"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = pd.read_csv('../data/labels.tsv', sep='\\t')\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_types = sorted(labels.project_id.unique())\n",
    "cancer_types += ['All']\n",
    "columns = ['n', '% censored', 'min_time', 'max_time', 'median_time']\n",
    "\n",
    "cancer_type_info = {}\n",
    "\n",
    "for column in columns:\n",
    "    cancer_type_info[column] = {cancer_type: None for cancer_type in cancer_types}\n",
    "    \n",
    "cancer_type_info = pd.DataFrame.from_dict(cancer_type_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stats(data, in_days=True):\n",
    "    stats = data.min(), data.max(), data.median()\n",
    "#     stats = tuple([round(x , 2) for x in stats])\n",
    "    \n",
    "    if in_days:\n",
    "        stats = tuple([x * 365 for x in stats])\n",
    "\n",
    "    return stats\n",
    "\n",
    "for cancer_type in sorted(labels.project_id.unique()):\n",
    "    cancer_labels = labels.loc[labels.project_id == cancer_type]\n",
    "    cancer_type_info['n'][cancer_type] = len(cancer_labels)\n",
    "\n",
    "    n_cens = len(cancer_labels.loc[cancer_labels.event == 0])\n",
    "    percent_cens = (n_cens * 100 / len(cancer_labels))\n",
    "    cancer_type_info['% censored'][cancer_type] = round(percent_cens, 1)\n",
    "\n",
    "    (cancer_type_info['min_time'][cancer_type],\n",
    "     cancer_type_info['max_time'][cancer_type],\n",
    "     cancer_type_info['median_time'][cancer_type]) = get_stats(cancer_labels['time'], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All cancer types\n",
    "cancer_labels = labels\n",
    "cancer_type = 'All'\n",
    "\n",
    "cancer_type_info['n'][cancer_type] = len(cancer_labels)\n",
    "\n",
    "n_cens = len(cancer_labels.loc[cancer_labels.event == 0])\n",
    "percent_cens = (n_cens * 100 / len(cancer_labels))\n",
    "cancer_type_info['% censored'][cancer_type] = round(percent_cens, 1)\n",
    "\n",
    "(cancer_type_info['min_time'][cancer_type],\n",
    " cancer_type_info['max_time'][cancer_type],\n",
    " cancer_type_info['median_time'][cancer_type]) = get_stats(cancer_labels['time'], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>n</th>\n",
       "      <th>% censored</th>\n",
       "      <th>min_time</th>\n",
       "      <th>max_time</th>\n",
       "      <th>median_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>92</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>4673</td>\n",
       "      <td>1182.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLCA</th>\n",
       "      <td>408</td>\n",
       "      <td>55.9</td>\n",
       "      <td>0</td>\n",
       "      <td>5050</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRCA</th>\n",
       "      <td>1095</td>\n",
       "      <td>86.2</td>\n",
       "      <td>0</td>\n",
       "      <td>8605</td>\n",
       "      <td>825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESC</th>\n",
       "      <td>307</td>\n",
       "      <td>76.5</td>\n",
       "      <td>0</td>\n",
       "      <td>6408</td>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHOL</th>\n",
       "      <td>48</td>\n",
       "      <td>54.2</td>\n",
       "      <td>10</td>\n",
       "      <td>1976</td>\n",
       "      <td>678.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COAD</th>\n",
       "      <td>458</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0</td>\n",
       "      <td>4502</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DLBC</th>\n",
       "      <td>48</td>\n",
       "      <td>81.2</td>\n",
       "      <td>0</td>\n",
       "      <td>6425</td>\n",
       "      <td>811.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESCA</th>\n",
       "      <td>185</td>\n",
       "      <td>58.4</td>\n",
       "      <td>0</td>\n",
       "      <td>3714</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBM</th>\n",
       "      <td>592</td>\n",
       "      <td>17.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3881</td>\n",
       "      <td>368.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HNSC</th>\n",
       "      <td>526</td>\n",
       "      <td>57.6</td>\n",
       "      <td>1</td>\n",
       "      <td>6417</td>\n",
       "      <td>644.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KICH</th>\n",
       "      <td>112</td>\n",
       "      <td>89.3</td>\n",
       "      <td>6</td>\n",
       "      <td>5132</td>\n",
       "      <td>1471.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIRC</th>\n",
       "      <td>537</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>4537</td>\n",
       "      <td>1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIRP</th>\n",
       "      <td>290</td>\n",
       "      <td>84.8</td>\n",
       "      <td>0</td>\n",
       "      <td>5925</td>\n",
       "      <td>767.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAML</th>\n",
       "      <td>186</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2861</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGG</th>\n",
       "      <td>512</td>\n",
       "      <td>75.6</td>\n",
       "      <td>0</td>\n",
       "      <td>6423</td>\n",
       "      <td>677.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIHC</th>\n",
       "      <td>375</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0</td>\n",
       "      <td>3675</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUAD</th>\n",
       "      <td>513</td>\n",
       "      <td>64.1</td>\n",
       "      <td>0</td>\n",
       "      <td>7248</td>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUSC</th>\n",
       "      <td>498</td>\n",
       "      <td>56.8</td>\n",
       "      <td>0</td>\n",
       "      <td>5287</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESO</th>\n",
       "      <td>85</td>\n",
       "      <td>14.1</td>\n",
       "      <td>20</td>\n",
       "      <td>2790</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OV</th>\n",
       "      <td>582</td>\n",
       "      <td>40.2</td>\n",
       "      <td>8</td>\n",
       "      <td>5481</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAAD</th>\n",
       "      <td>185</td>\n",
       "      <td>45.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2741</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCPG</th>\n",
       "      <td>179</td>\n",
       "      <td>96.6</td>\n",
       "      <td>2</td>\n",
       "      <td>9634</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAD</th>\n",
       "      <td>498</td>\n",
       "      <td>98</td>\n",
       "      <td>23</td>\n",
       "      <td>5024</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>READ</th>\n",
       "      <td>169</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>3932</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARC</th>\n",
       "      <td>261</td>\n",
       "      <td>62.1</td>\n",
       "      <td>0</td>\n",
       "      <td>5723</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKCM</th>\n",
       "      <td>459</td>\n",
       "      <td>51.9</td>\n",
       "      <td>0</td>\n",
       "      <td>11252</td>\n",
       "      <td>1124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STAD</th>\n",
       "      <td>434</td>\n",
       "      <td>60.8</td>\n",
       "      <td>0</td>\n",
       "      <td>3720</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGCT</th>\n",
       "      <td>134</td>\n",
       "      <td>97</td>\n",
       "      <td>3</td>\n",
       "      <td>7437</td>\n",
       "      <td>1261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THCA</th>\n",
       "      <td>507</td>\n",
       "      <td>96.8</td>\n",
       "      <td>0</td>\n",
       "      <td>5423</td>\n",
       "      <td>944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THYM</th>\n",
       "      <td>123</td>\n",
       "      <td>92.7</td>\n",
       "      <td>14</td>\n",
       "      <td>4575</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCEC</th>\n",
       "      <td>546</td>\n",
       "      <td>83.3</td>\n",
       "      <td>0</td>\n",
       "      <td>6859</td>\n",
       "      <td>897.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCS</th>\n",
       "      <td>57</td>\n",
       "      <td>38.6</td>\n",
       "      <td>0</td>\n",
       "      <td>4269</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVM</th>\n",
       "      <td>80</td>\n",
       "      <td>71.2</td>\n",
       "      <td>4</td>\n",
       "      <td>2600</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>11081</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0</td>\n",
       "      <td>11252</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          n % censored min_time max_time median_time\n",
       "ACC      92         63        0     4673      1182.5\n",
       "BLCA    408       55.9        0     5050         536\n",
       "BRCA   1095       86.2        0     8605         825\n",
       "CESC    307       76.5        0     6408         638\n",
       "CHOL     48       54.2       10     1976       678.5\n",
       "COAD    458       77.7        0     4502         657\n",
       "DLBC     48       81.2        0     6425       811.5\n",
       "ESCA    185       58.4        0     3714         400\n",
       "GBM     592       17.1        0     3881       368.5\n",
       "HNSC    526       57.6        1     6417       644.5\n",
       "KICH    112       89.3        6     5132      1471.5\n",
       "KIRC    537         67        0     4537        1175\n",
       "KIRP    290       84.8        0     5925       767.5\n",
       "LAML    186       35.5        0     2861         365\n",
       "LGG     512       75.6        0     6423       677.5\n",
       "LIHC    375       64.8        0     3675         601\n",
       "LUAD    513       64.1        0     7248         656\n",
       "LUSC    498       56.8        0     5287         663\n",
       "MESO     85       14.1       20     2790         527\n",
       "OV      582       40.2        8     5481        1002\n",
       "PAAD    185       45.9        0     2741         467\n",
       "PCPG    179       96.6        2     9634         755\n",
       "PRAD    498         98       23     5024         932\n",
       "READ    169         84        0     3932         609\n",
       "SARC    261       62.1        0     5723         938\n",
       "SKCM    459       51.9        0    11252        1124\n",
       "STAD    434       60.8        0     3720         424\n",
       "TGCT    134         97        3     7437        1261\n",
       "THCA    507       96.8        0     5423         944\n",
       "THYM    123       92.7       14     4575        1253\n",
       "UCEC    546       83.3        0     6859       897.5\n",
       "UCS      57       38.6        0     4269         597\n",
       "UVM      80       71.2        4     2600         784\n",
       "All   11081       67.5        0    11252         714"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_type_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Watermark<a class='tocSkip'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas 1.0.1\n",
      "\n",
      "CPython 3.6.7\n",
      "IPython 7.11.1\n",
      "\n",
      "last updated: Tue Jul 28 2020\n"
     ]
    }
   ],
   "source": [
    "%watermark --iversions\n",
    "%watermark -v\n",
    "print()\n",
    "%watermark -u -n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top of the page](#Top)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "197px",
    "width": "372px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "180px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
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 "nbformat": 4,
 "nbformat_minor": 2
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