Cluster_expression_hormone.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from google.cloud import bigquery as bq\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(53970, 19)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>case_barcode</th>\n",
       "      <th>sample_barcode</th>\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>normalized_count</th>\n",
       "      <th>transformed_count</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>sample_type_name</th>\n",
       "      <th>initial_response</th>\n",
       "      <th>recurrence_status</th>\n",
       "      <th>recurrence_log</th>\n",
       "      <th>subtype</th>\n",
       "      <th>ER</th>\n",
       "      <th>PR</th>\n",
       "      <th>Her2_IHC</th>\n",
       "      <th>Her2_ISH</th>\n",
       "      <th>Her2</th>\n",
       "      <th>triple_negative</th>\n",
       "      <th>subtype_sub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-5L-AAT0</td>\n",
       "      <td>TCGA-5L-AAT0-01A</td>\n",
       "      <td>TCGA-5L-AAT0-01A-12R-A41B-07</td>\n",
       "      <td>ADRA2C</td>\n",
       "      <td>39.8885</td>\n",
       "      <td>5.353623</td>\n",
       "      <td>1</td>\n",
       "      <td>Primary solid Tumor</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-5L-AAT0</td>\n",
       "      <td>TCGA-5L-AAT0-01A</td>\n",
       "      <td>TCGA-5L-AAT0-01A-12R-A41B-07</td>\n",
       "      <td>ANP32E</td>\n",
       "      <td>766.0947</td>\n",
       "      <td>9.583261</td>\n",
       "      <td>1</td>\n",
       "      <td>Primary solid Tumor</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-5L-AAT0</td>\n",
       "      <td>TCGA-5L-AAT0-01A</td>\n",
       "      <td>TCGA-5L-AAT0-01A-12R-A41B-07</td>\n",
       "      <td>ARL4C</td>\n",
       "      <td>993.6941</td>\n",
       "      <td>9.958109</td>\n",
       "      <td>1</td>\n",
       "      <td>Primary solid Tumor</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-5L-AAT0</td>\n",
       "      <td>TCGA-5L-AAT0-01A</td>\n",
       "      <td>TCGA-5L-AAT0-01A-12R-A41B-07</td>\n",
       "      <td>ASCL1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Primary solid Tumor</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-5L-AAT0</td>\n",
       "      <td>TCGA-5L-AAT0-01A</td>\n",
       "      <td>TCGA-5L-AAT0-01A-12R-A41B-07</td>\n",
       "      <td>ASS1</td>\n",
       "      <td>673.4125</td>\n",
       "      <td>9.397487</td>\n",
       "      <td>1</td>\n",
       "      <td>Primary solid Tumor</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LumA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_barcode    sample_barcode               aliquot_barcode gene_symbol  \\\n",
       "0  TCGA-5L-AAT0  TCGA-5L-AAT0-01A  TCGA-5L-AAT0-01A-12R-A41B-07      ADRA2C   \n",
       "1  TCGA-5L-AAT0  TCGA-5L-AAT0-01A  TCGA-5L-AAT0-01A-12R-A41B-07      ANP32E   \n",
       "2  TCGA-5L-AAT0  TCGA-5L-AAT0-01A  TCGA-5L-AAT0-01A-12R-A41B-07       ARL4C   \n",
       "3  TCGA-5L-AAT0  TCGA-5L-AAT0-01A  TCGA-5L-AAT0-01A-12R-A41B-07       ASCL1   \n",
       "4  TCGA-5L-AAT0  TCGA-5L-AAT0-01A  TCGA-5L-AAT0-01A-12R-A41B-07        ASS1   \n",
       "\n",
       "   normalized_count  transformed_count  sample_type     sample_type_name  \\\n",
       "0           39.8885           5.353623            1  Primary solid Tumor   \n",
       "1          766.0947           9.583261            1  Primary solid Tumor   \n",
       "2          993.6941           9.958109            1  Primary solid Tumor   \n",
       "3            0.0000           0.000000            1  Primary solid Tumor   \n",
       "4          673.4125           9.397487            1  Primary solid Tumor   \n",
       "\n",
       "  initial_response recurrence_status recurrence_log subtype        ER  \\\n",
       "0          Unknown               NaN            NaN    LumA  Positive   \n",
       "1          Unknown               NaN            NaN    LumA  Positive   \n",
       "2          Unknown               NaN            NaN    LumA  Positive   \n",
       "3          Unknown               NaN            NaN    LumA  Positive   \n",
       "4          Unknown               NaN            NaN    LumA  Positive   \n",
       "\n",
       "         PR Her2_IHC Her2_ISH Her2  triple_negative subtype_sub  \n",
       "0  Positive       1+      NaN  NaN              NaN        LumA  \n",
       "1  Positive       1+      NaN  NaN              NaN        LumA  \n",
       "2  Positive       1+      NaN  NaN              NaN        LumA  \n",
       "3  Positive       1+      NaN  NaN              NaN        LumA  \n",
       "4  Positive       1+      NaN  NaN              NaN        LumA  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hormone_expression_bigtable = pd.read_table(\"./datasets/hormone_expression_bigtable.IlluminaHiSeq.tsv\", sep=\"\\t\")\n",
    "print(hormone_expression_bigtable.shape)\n",
    "hormone_expression_bigtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample types\n",
      "Primary solid Tumor    480\n",
      "Solid Tissue Normal     33\n",
      "Metastatic               1\n",
      "Name: sample_type_name, dtype: int64\n",
      "\n",
      "\n",
      "Cases - Initial responses\n",
      "Unknown               431\n",
      "Complete response      40\n",
      "Progressive/Stable      9\n",
      "Name: initial_response, dtype: int64\n",
      "\n",
      "\n",
      "Cases - Recurrence status\n",
      "Unknown     423\n",
      "Occured      42\n",
      "Low risk      8\n",
      "Name: recurrence_status, dtype: int64\n",
      "\n",
      "\n",
      "Cases - Recurrence log\n",
      "Alive tumor free, last follow-up within risky period             376\n",
      "New tumor after/during treatment                                  30\n",
      "Drug start and end dates unknown                                  18\n",
      "Dead with tumor                                                   12\n",
      "Unknown neoplasm status                                           10\n",
      "Dead tumor free during risky period                                9\n",
      "Reported having tumor during follow-up but unknown recurrence      8\n",
      "Alive tumor free after risky period                                8\n",
      "Alive tumor free but unknown follow-up date                        2\n",
      "Name: recurrence_log, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Sample types\")\n",
    "print(hormone_expression_bigtable.drop_duplicates(subset=\"sample_barcode\").sample_type_name.value_counts())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Cases - Initial responses\")\n",
    "print(hormone_expression_bigtable.drop_duplicates(subset=\"case_barcode\").initial_response.value_counts())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Cases - Recurrence status\")\n",
    "print(hormone_expression_bigtable.drop_duplicates(subset=\"case_barcode\").recurrence_status.value_counts())\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Cases - Recurrence log\")\n",
    "print(hormone_expression_bigtable.drop_duplicates(subset=\"case_barcode\").recurrence_log.value_counts())\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette([\"#ff639c\", \"#5bc1b5\", \"#8cc8e2\", \"#edea3d\", \"#ff7e63\", \"#aee851\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Primary solid Tumor', 'Solid Tissue Normal', 'Metastatic'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hormone_expression_bigtable.drop_duplicates(subset=\"aliquot_barcode\")['sample_type_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Unknown', 'Complete response', 'Progressive/Stable'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hormone_expression_bigtable.drop_duplicates(subset=\"aliquot_barcode\")['initial_response'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Unknown', 'Low risk', 'Occured'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hormone_expression_bigtable.drop_duplicates(subset=\"aliquot_barcode\")['recurrence_status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Include normal\n",
    "## Initial response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 53)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th>TCGA-A2-A0CO-01A-13R-A22K-07</th>\n",
       "      <th>TCGA-A2-A0CR-01A-11R-A22K-07</th>\n",
       "      <th>TCGA-A2-A0EP-01A-52R-A22U-07</th>\n",
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       "      <th>TCGA-A8-A06R-01A-11R-A00Z-07</th>\n",
       "      <th>TCGA-A8-A07I-01A-11R-A00Z-07</th>\n",
       "      <th>TCGA-A8-A082-01A-11R-A00Z-07</th>\n",
       "      <th>...</th>\n",
       "      <th>TCGA-GM-A3XG-01A-31R-A24H-07</th>\n",
       "      <th>TCGA-GM-A3XN-01A-12R-A22U-07</th>\n",
       "      <th>TCGA-GM-A5PX-01A-12R-A28M-07</th>\n",
       "      <th>TCGA-LL-A50Y-01A-11R-A266-07</th>\n",
       "      <th>TCGA-LL-A73Z-01A-11R-A32P-07</th>\n",
       "      <th>TCGA-OL-A5S0-01A-11R-A28M-07</th>\n",
       "      <th>TCGA-S3-A6ZG-01A-22R-A32P-07</th>\n",
       "      <th>TCGA-S3-AA11-01A-31R-A41B-07</th>\n",
       "      <th>TCGA-V7-A7HQ-01A-11R-A33J-07</th>\n",
       "      <th>TCGA-WT-AB41-01A-11R-A41B-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>ADRA2C</th>\n",
       "      <td>7.371518</td>\n",
       "      <td>7.262014</td>\n",
       "      <td>7.054130</td>\n",
       "      <td>6.048299</td>\n",
       "      <td>3.039805</td>\n",
       "      <td>7.731880</td>\n",
       "      <td>5.561949</td>\n",
       "      <td>7.884440</td>\n",
       "      <td>6.450045</td>\n",
       "      <td>8.328226</td>\n",
       "      <td>...</td>\n",
       "      <td>6.034394</td>\n",
       "      <td>6.992436</td>\n",
       "      <td>7.017541</td>\n",
       "      <td>4.633699</td>\n",
       "      <td>6.362245</td>\n",
       "      <td>5.938330</td>\n",
       "      <td>3.235696</td>\n",
       "      <td>6.970269</td>\n",
       "      <td>7.721103</td>\n",
       "      <td>9.522046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANP32E</th>\n",
       "      <td>9.561221</td>\n",
       "      <td>9.829963</td>\n",
       "      <td>10.097180</td>\n",
       "      <td>9.781118</td>\n",
       "      <td>11.297024</td>\n",
       "      <td>8.128950</td>\n",
       "      <td>9.683484</td>\n",
       "      <td>8.871398</td>\n",
       "      <td>10.166508</td>\n",
       "      <td>9.805744</td>\n",
       "      <td>...</td>\n",
       "      <td>9.591270</td>\n",
       "      <td>10.365569</td>\n",
       "      <td>9.911534</td>\n",
       "      <td>9.741722</td>\n",
       "      <td>10.626102</td>\n",
       "      <td>10.873870</td>\n",
       "      <td>10.839075</td>\n",
       "      <td>9.483399</td>\n",
       "      <td>8.734465</td>\n",
       "      <td>10.252020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL4C</th>\n",
       "      <td>11.201284</td>\n",
       "      <td>11.275144</td>\n",
       "      <td>11.615422</td>\n",
       "      <td>10.084830</td>\n",
       "      <td>10.851740</td>\n",
       "      <td>8.161880</td>\n",
       "      <td>10.443259</td>\n",
       "      <td>10.215139</td>\n",
       "      <td>9.768760</td>\n",
       "      <td>8.460251</td>\n",
       "      <td>...</td>\n",
       "      <td>10.601978</td>\n",
       "      <td>10.394763</td>\n",
       "      <td>9.318223</td>\n",
       "      <td>8.375083</td>\n",
       "      <td>11.027117</td>\n",
       "      <td>10.840359</td>\n",
       "      <td>8.771351</td>\n",
       "      <td>8.256225</td>\n",
       "      <td>9.452429</td>\n",
       "      <td>10.910269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCL1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.218626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.728312</td>\n",
       "      <td>7.424274</td>\n",
       "      <td>0.494876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.215927</td>\n",
       "      <td>1.137504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429750</td>\n",
       "      <td>3.925477</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.598175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.567229</td>\n",
       "      <td>7.767370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASS1</th>\n",
       "      <td>10.769460</td>\n",
       "      <td>10.606733</td>\n",
       "      <td>11.081560</td>\n",
       "      <td>10.417837</td>\n",
       "      <td>13.210058</td>\n",
       "      <td>8.736559</td>\n",
       "      <td>9.835311</td>\n",
       "      <td>13.206704</td>\n",
       "      <td>6.562009</td>\n",
       "      <td>8.016808</td>\n",
       "      <td>...</td>\n",
       "      <td>10.780222</td>\n",
       "      <td>11.401248</td>\n",
       "      <td>10.859516</td>\n",
       "      <td>9.556013</td>\n",
       "      <td>11.773173</td>\n",
       "      <td>12.519634</td>\n",
       "      <td>7.586168</td>\n",
       "      <td>11.488817</td>\n",
       "      <td>12.288174</td>\n",
       "      <td>8.588975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aliquot_barcode  TCGA-A2-A0CO-01A-13R-A22K-07  TCGA-A2-A0CR-01A-11R-A22K-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               7.371518                      7.262014   \n",
       "ANP32E                               9.561221                      9.829963   \n",
       "ARL4C                               11.201284                     11.275144   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                10.769460                     10.606733   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0EP-01A-52R-A22U-07  TCGA-A2-A0EW-01A-21R-A115-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               7.054130                      6.048299   \n",
       "ANP32E                              10.097180                      9.781118   \n",
       "ARL4C                               11.615422                     10.084830   \n",
       "ASCL1                                2.218626                      0.000000   \n",
       "ASS1                                11.081560                     10.417837   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A3Y0-01A-11R-A239-07  TCGA-A7-A3RF-01A-11R-A22K-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               3.039805                      7.731880   \n",
       "ANP32E                              11.297024                      8.128950   \n",
       "ARL4C                               10.851740                      8.161880   \n",
       "ASCL1                                0.728312                      7.424274   \n",
       "ASS1                                13.210058                      8.736559   \n",
       "\n",
       "aliquot_barcode  TCGA-A7-A5ZX-01A-12R-A29R-07  TCGA-A8-A06R-01A-11R-A00Z-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.561949                      7.884440   \n",
       "ANP32E                               9.683484                      8.871398   \n",
       "ARL4C                               10.443259                     10.215139   \n",
       "ASCL1                                0.494876                      0.000000   \n",
       "ASS1                                 9.835311                     13.206704   \n",
       "\n",
       "aliquot_barcode  TCGA-A8-A07I-01A-11R-A00Z-07  TCGA-A8-A082-01A-11R-A00Z-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.450045                      8.328226   \n",
       "ANP32E                              10.166508                      9.805744   \n",
       "ARL4C                                9.768760                      8.460251   \n",
       "ASCL1                                1.215927                      1.137504   \n",
       "ASS1                                 6.562009                      8.016808   \n",
       "\n",
       "aliquot_barcode              ...               TCGA-GM-A3XG-01A-31R-A24H-07  \\\n",
       "gene_symbol                  ...                                              \n",
       "ADRA2C                       ...                                   6.034394   \n",
       "ANP32E                       ...                                   9.591270   \n",
       "ARL4C                        ...                                  10.601978   \n",
       "ASCL1                        ...                                   0.429750   \n",
       "ASS1                         ...                                  10.780222   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A3XN-01A-12R-A22U-07  TCGA-GM-A5PX-01A-12R-A28M-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.992436                      7.017541   \n",
       "ANP32E                              10.365569                      9.911534   \n",
       "ARL4C                               10.394763                      9.318223   \n",
       "ASCL1                                3.925477                      0.000000   \n",
       "ASS1                                11.401248                     10.859516   \n",
       "\n",
       "aliquot_barcode  TCGA-LL-A50Y-01A-11R-A266-07  TCGA-LL-A73Z-01A-11R-A32P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               4.633699                      6.362245   \n",
       "ANP32E                               9.741722                     10.626102   \n",
       "ARL4C                                8.375083                     11.027117   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                 9.556013                     11.773173   \n",
       "\n",
       "aliquot_barcode  TCGA-OL-A5S0-01A-11R-A28M-07  TCGA-S3-A6ZG-01A-22R-A32P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.938330                      3.235696   \n",
       "ANP32E                              10.873870                     10.839075   \n",
       "ARL4C                               10.840359                      8.771351   \n",
       "ASCL1                                1.598175                      0.000000   \n",
       "ASS1                                12.519634                      7.586168   \n",
       "\n",
       "aliquot_barcode  TCGA-S3-AA11-01A-31R-A41B-07  TCGA-V7-A7HQ-01A-11R-A33J-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.970269                      7.721103   \n",
       "ANP32E                               9.483399                      8.734465   \n",
       "ARL4C                                8.256225                      9.452429   \n",
       "ASCL1                                0.000000                      2.567229   \n",
       "ASS1                                11.488817                     12.288174   \n",
       "\n",
       "aliquot_barcode  TCGA-WT-AB41-01A-11R-A41B-07  \n",
       "gene_symbol                                    \n",
       "ADRA2C                               9.522046  \n",
       "ANP32E                              10.252020  \n",
       "ARL4C                               10.910269  \n",
       "ASCL1                                7.767370  \n",
       "ASS1                                 8.588975  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_res = hormone_expression_bigtable.loc[hormone_expression_bigtable['initial_response'] != 'Unknown']\n",
    "expression_matrix = init_res.pivot(index='gene_symbol', columns='aliquot_barcode', values='transformed_count')\n",
    "\n",
    "print(expression_matrix.shape)\n",
    "expression_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Primary solid Tumor' 'Solid Tissue Normal']\n",
      "None\n",
      "['Complete response' 'Progressive/Stable']\n"
     ]
    }
   ],
   "source": [
    "color_matrix = pd.DataFrame()\n",
    "df = init_res.drop_duplicates(subset=\"aliquot_barcode\")\n",
    "\n",
    "print(print(df['sample_type_name'].unique()))\n",
    "print(df['initial_response'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Sample types</th>\n",
       "      <th>Initial response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0CO-01A-13R-A22K-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#8cc8e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0CR-01A-11R-A22K-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#8cc8e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0EP-01A-52R-A22U-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#8cc8e2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#edea3d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A3Y0-01A-11R-A239-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#8cc8e2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Sample types Initial response\n",
       "aliquot_barcode                                           \n",
       "TCGA-A2-A0CO-01A-13R-A22K-07      #ff639c          #8cc8e2\n",
       "TCGA-A2-A0CR-01A-11R-A22K-07      #ff639c          #8cc8e2\n",
       "TCGA-A2-A0EP-01A-52R-A22U-07      #ff639c          #8cc8e2\n",
       "TCGA-A2-A0EW-01A-21R-A115-07      #ff639c          #edea3d\n",
       "TCGA-A2-A3Y0-01A-11R-A239-07      #ff639c          #8cc8e2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = dict(zip(df['sample_type_name'].unique(), [\"#ff639c\", \"#5bc1b5\"]))\n",
    "color_matrix['Sample types'] = df['sample_type_name'].map(c)\n",
    "\n",
    "c = dict(zip(df['initial_response'].unique(), [\"#8cc8e2\", \"#edea3d\"]))\n",
    "color_matrix['Initial response'] = df['initial_response'].map(c)\n",
    "\n",
    "color_matrix.set_index(df['aliquot_barcode'], inplace=True)\n",
    "color_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = scipy.spatial.distance.squareform(1-expression_matrix.corr())\n",
    "linkage = scipy.cluster.hierarchy.linkage(distance, method='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1476 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.clustermap(data=expression_matrix.corr(), col_linkage=linkage, row_linkage=linkage,\n",
    "                   row_colors=color_matrix['Sample types'], col_colors = color_matrix.iloc[:,1:],\n",
    "                   cmap=\"BuPu\", vmax=1, figsize=(20,20.5), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal_aliquots = init_res.loc[(init_res['sample_type'] == 11), 'aliquot_barcode'].unique()\n",
    "\n",
    "g1 = sns.clustermap(data=expression_matrix.sort_values(by=list(normal_aliquots)), \n",
    "                    col_linkage=linkage, row_cluster = False,\n",
    "                    col_colors = color_matrix,\n",
    "                    cmap=\"Spectral_r\", vmin=0, vmax=18, figsize=(20,15), \n",
    "                    xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "g.savefig(\"./plots/cluster_expression.hormone.init_res.corrdata.png\")\n",
    "g1.savefig(\"./plots/cluster_expression.hormone.init_res.expressiondata.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrence status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 54)\n"
     ]
    },
    {
     "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>aliquot_barcode</th>\n",
       "      <th>TCGA-A2-A04N-01A-11R-A115-07</th>\n",
       "      <th>TCGA-A2-A04V-01A-21R-A034-07</th>\n",
       "      <th>TCGA-A2-A0CK-01A-11R-A22K-07</th>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <th>TCGA-A2-A0SV-01A-11R-A084-07</th>\n",
       "      <th>TCGA-A2-A0SW-01A-11R-A084-07</th>\n",
       "      <th>TCGA-A2-A0YC-01A-11R-A109-07</th>\n",
       "      <th>TCGA-A2-A25B-01A-11R-A169-07</th>\n",
       "      <th>TCGA-A2-A25E-01A-11R-A169-07</th>\n",
       "      <th>TCGA-A7-A13E-01A-11R-A12P-07</th>\n",
       "      <th>...</th>\n",
       "      <th>TCGA-E9-A1NF-01A-11R-A14D-07</th>\n",
       "      <th>TCGA-E9-A1NF-11A-73R-A14D-07</th>\n",
       "      <th>TCGA-EW-A1P0-01A-11R-A144-07</th>\n",
       "      <th>TCGA-GM-A2D9-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DA-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DC-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DM-01A-11R-A180-07</th>\n",
       "      <th>TCGA-GM-A2DN-01A-11R-A180-07</th>\n",
       "      <th>TCGA-LL-A73Z-01A-11R-A32P-07</th>\n",
       "      <th>TCGA-LQ-A4E4-01A-11R-A266-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>ADRA2C</th>\n",
       "      <td>5.412802</td>\n",
       "      <td>4.444813</td>\n",
       "      <td>6.541038</td>\n",
       "      <td>6.048299</td>\n",
       "      <td>6.377509</td>\n",
       "      <td>3.352377</td>\n",
       "      <td>3.563158</td>\n",
       "      <td>2.848358</td>\n",
       "      <td>4.918639</td>\n",
       "      <td>1.832769</td>\n",
       "      <td>...</td>\n",
       "      <td>5.299666</td>\n",
       "      <td>6.964089</td>\n",
       "      <td>3.756084</td>\n",
       "      <td>6.580773</td>\n",
       "      <td>5.816973</td>\n",
       "      <td>9.217166</td>\n",
       "      <td>1.732313</td>\n",
       "      <td>4.037918</td>\n",
       "      <td>6.362245</td>\n",
       "      <td>6.689829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANP32E</th>\n",
       "      <td>9.372962</td>\n",
       "      <td>10.669437</td>\n",
       "      <td>10.030458</td>\n",
       "      <td>9.781118</td>\n",
       "      <td>9.562843</td>\n",
       "      <td>9.542102</td>\n",
       "      <td>10.799081</td>\n",
       "      <td>11.367194</td>\n",
       "      <td>11.081927</td>\n",
       "      <td>11.732477</td>\n",
       "      <td>...</td>\n",
       "      <td>9.698123</td>\n",
       "      <td>9.802101</td>\n",
       "      <td>7.684026</td>\n",
       "      <td>9.453122</td>\n",
       "      <td>9.661606</td>\n",
       "      <td>10.929548</td>\n",
       "      <td>9.665320</td>\n",
       "      <td>10.816003</td>\n",
       "      <td>10.626102</td>\n",
       "      <td>9.658979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL4C</th>\n",
       "      <td>9.943622</td>\n",
       "      <td>9.345226</td>\n",
       "      <td>9.152490</td>\n",
       "      <td>10.084830</td>\n",
       "      <td>11.333493</td>\n",
       "      <td>9.840185</td>\n",
       "      <td>9.276834</td>\n",
       "      <td>9.365068</td>\n",
       "      <td>9.197618</td>\n",
       "      <td>10.415972</td>\n",
       "      <td>...</td>\n",
       "      <td>11.283361</td>\n",
       "      <td>10.381573</td>\n",
       "      <td>9.419215</td>\n",
       "      <td>9.322357</td>\n",
       "      <td>10.660715</td>\n",
       "      <td>8.938618</td>\n",
       "      <td>8.652597</td>\n",
       "      <td>9.851545</td>\n",
       "      <td>11.027117</td>\n",
       "      <td>7.734416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCL1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.859811</td>\n",
       "      <td>1.145417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.025419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.445038</td>\n",
       "      <td>3.197881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.695281</td>\n",
       "      <td>0.939151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.416407</td>\n",
       "      <td>6.609313</td>\n",
       "      <td>12.127080</td>\n",
       "      <td>7.042671</td>\n",
       "      <td>1.550063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.583712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASS1</th>\n",
       "      <td>10.309732</td>\n",
       "      <td>8.386607</td>\n",
       "      <td>10.685678</td>\n",
       "      <td>10.417837</td>\n",
       "      <td>11.599314</td>\n",
       "      <td>7.828981</td>\n",
       "      <td>8.429109</td>\n",
       "      <td>7.555266</td>\n",
       "      <td>7.217218</td>\n",
       "      <td>10.635957</td>\n",
       "      <td>...</td>\n",
       "      <td>10.840536</td>\n",
       "      <td>12.377656</td>\n",
       "      <td>13.220430</td>\n",
       "      <td>12.031642</td>\n",
       "      <td>9.843106</td>\n",
       "      <td>9.156216</td>\n",
       "      <td>7.706509</td>\n",
       "      <td>10.784490</td>\n",
       "      <td>11.773173</td>\n",
       "      <td>10.659422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aliquot_barcode  TCGA-A2-A04N-01A-11R-A115-07  TCGA-A2-A04V-01A-21R-A034-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.412802                      4.444813   \n",
       "ANP32E                               9.372962                     10.669437   \n",
       "ARL4C                                9.943622                      9.345226   \n",
       "ASCL1                                0.000000                      0.859811   \n",
       "ASS1                                10.309732                      8.386607   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0CK-01A-11R-A22K-07  TCGA-A2-A0EW-01A-21R-A115-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.541038                      6.048299   \n",
       "ANP32E                              10.030458                      9.781118   \n",
       "ARL4C                                9.152490                     10.084830   \n",
       "ASCL1                                1.145417                      0.000000   \n",
       "ASS1                                10.685678                     10.417837   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0SV-01A-11R-A084-07  TCGA-A2-A0SW-01A-11R-A084-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.377509                      3.352377   \n",
       "ANP32E                               9.562843                      9.542102   \n",
       "ARL4C                               11.333493                      9.840185   \n",
       "ASCL1                                0.000000                      2.025419   \n",
       "ASS1                                11.599314                      7.828981   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0YC-01A-11R-A109-07  TCGA-A2-A25B-01A-11R-A169-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               3.563158                      2.848358   \n",
       "ANP32E                              10.799081                     11.367194   \n",
       "ARL4C                                9.276834                      9.365068   \n",
       "ASCL1                                0.000000                      1.445038   \n",
       "ASS1                                 8.429109                      7.555266   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A25E-01A-11R-A169-07  TCGA-A7-A13E-01A-11R-A12P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               4.918639                      1.832769   \n",
       "ANP32E                              11.081927                     11.732477   \n",
       "ARL4C                                9.197618                     10.415972   \n",
       "ASCL1                                3.197881                      0.000000   \n",
       "ASS1                                 7.217218                     10.635957   \n",
       "\n",
       "aliquot_barcode              ...               TCGA-E9-A1NF-01A-11R-A14D-07  \\\n",
       "gene_symbol                  ...                                              \n",
       "ADRA2C                       ...                                   5.299666   \n",
       "ANP32E                       ...                                   9.698123   \n",
       "ARL4C                        ...                                  11.283361   \n",
       "ASCL1                        ...                                   0.695281   \n",
       "ASS1                         ...                                  10.840536   \n",
       "\n",
       "aliquot_barcode  TCGA-E9-A1NF-11A-73R-A14D-07  TCGA-EW-A1P0-01A-11R-A144-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.964089                      3.756084   \n",
       "ANP32E                               9.802101                      7.684026   \n",
       "ARL4C                               10.381573                      9.419215   \n",
       "ASCL1                                0.939151                      0.000000   \n",
       "ASS1                                12.377656                     13.220430   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2D9-01A-11R-A18M-07  TCGA-GM-A2DA-01A-11R-A18M-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.580773                      5.816973   \n",
       "ANP32E                               9.453122                      9.661606   \n",
       "ARL4C                                9.322357                     10.660715   \n",
       "ASCL1                                2.416407                      6.609313   \n",
       "ASS1                                12.031642                      9.843106   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2DC-01A-11R-A18M-07  TCGA-GM-A2DM-01A-11R-A180-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               9.217166                      1.732313   \n",
       "ANP32E                              10.929548                      9.665320   \n",
       "ARL4C                                8.938618                      8.652597   \n",
       "ASCL1                               12.127080                      7.042671   \n",
       "ASS1                                 9.156216                      7.706509   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2DN-01A-11R-A180-07  TCGA-LL-A73Z-01A-11R-A32P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               4.037918                      6.362245   \n",
       "ANP32E                              10.816003                     10.626102   \n",
       "ARL4C                                9.851545                     11.027117   \n",
       "ASCL1                                1.550063                      0.000000   \n",
       "ASS1                                10.784490                     11.773173   \n",
       "\n",
       "aliquot_barcode  TCGA-LQ-A4E4-01A-11R-A266-07  \n",
       "gene_symbol                                    \n",
       "ADRA2C                               6.689829  \n",
       "ANP32E                               9.658979  \n",
       "ARL4C                                7.734416  \n",
       "ASCL1                                0.583712  \n",
       "ASS1                                10.659422  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recur = hormone_expression_bigtable.loc[(hormone_expression_bigtable['recurrence_status'] != 'Unknown') & \n",
    "                                     (hormone_expression_bigtable['recurrence_status'].isna() == False)]\n",
    "expression_matrix = recur.pivot(index='gene_symbol', columns='aliquot_barcode', values='transformed_count')\n",
    "\n",
    "print(expression_matrix.shape)\n",
    "expression_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Primary solid Tumor' 'Solid Tissue Normal']\n",
      "None\n",
      "['Low risk' 'Occured']\n"
     ]
    }
   ],
   "source": [
    "color_matrix = pd.DataFrame()\n",
    "df = recur.drop_duplicates(subset=\"aliquot_barcode\")\n",
    "\n",
    "print(print(df['sample_type_name'].unique()))\n",
    "print(df['recurrence_status'].unique())"
   ]
  },
  {
   "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>Sample types</th>\n",
       "      <th>Recurrence status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A04N-01A-11R-A115-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#aee851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A04V-01A-21R-A034-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0CK-01A-11R-A22K-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0SV-01A-11R-A084-07</th>\n",
       "      <td>#ff639c</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Sample types Recurrence status\n",
       "aliquot_barcode                                            \n",
       "TCGA-A2-A04N-01A-11R-A115-07      #ff639c           #aee851\n",
       "TCGA-A2-A04V-01A-21R-A034-07      #ff639c           #ff7e63\n",
       "TCGA-A2-A0CK-01A-11R-A22K-07      #ff639c           #ff7e63\n",
       "TCGA-A2-A0EW-01A-21R-A115-07      #ff639c           #ff7e63\n",
       "TCGA-A2-A0SV-01A-11R-A084-07      #ff639c           #ff7e63"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = dict(zip(df['sample_type_name'].unique(), [\"#ff639c\", \"#5bc1b5\"]))\n",
    "color_matrix['Sample types'] = df['sample_type_name'].map(c)\n",
    "\n",
    "c = dict(zip(df['recurrence_status'].unique(), [\"#aee851\", \"#ff7e63\"]))\n",
    "color_matrix['Recurrence status'] = df['recurrence_status'].map(c)\n",
    "\n",
    "color_matrix.set_index(df['aliquot_barcode'], inplace=True)\n",
    "color_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = scipy.spatial.distance.squareform(1-expression_matrix.corr())\n",
    "linkage = scipy.cluster.hierarchy.linkage(distance, method='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1476 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g2 = sns.clustermap(data=expression_matrix.corr(), col_linkage=linkage, row_linkage=linkage,\n",
    "                   row_colors=color_matrix['Sample types'], col_colors = color_matrix.iloc[:,1:],\n",
    "                   cmap=\"BuPu\", vmax=1, figsize=(20,20.5), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal_aliquots = recur.loc[(recur['sample_type'] == 11), 'aliquot_barcode'].unique()\n",
    "\n",
    "g3 = sns.clustermap(data=expression_matrix.sort_values(by=list(normal_aliquots)), \n",
    "                    col_linkage=linkage, row_cluster = False,\n",
    "                    col_colors = color_matrix,\n",
    "                    cmap=\"Spectral_r\", vmin=0, vmax=18, figsize=(20,15), \n",
    "                    xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2.savefig(\"./plots/cluster_expression.hormone.recur.corrdata.png\")\n",
    "g3.savefig(\"./plots/cluster_expression.hormone.recur.expressiondata.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exclude normal\n",
    "## Initial response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 49)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th>TCGA-A2-A0CO-01A-13R-A22K-07</th>\n",
       "      <th>TCGA-A2-A0CR-01A-11R-A22K-07</th>\n",
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       "      <th>TCGA-A8-A082-01A-11R-A00Z-07</th>\n",
       "      <th>...</th>\n",
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       "      <th>TCGA-V7-A7HQ-01A-11R-A33J-07</th>\n",
       "      <th>TCGA-WT-AB41-01A-11R-A41B-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADRA2C</th>\n",
       "      <td>7.371518</td>\n",
       "      <td>7.262014</td>\n",
       "      <td>7.054130</td>\n",
       "      <td>6.048299</td>\n",
       "      <td>3.039805</td>\n",
       "      <td>7.731880</td>\n",
       "      <td>5.561949</td>\n",
       "      <td>7.884440</td>\n",
       "      <td>6.450045</td>\n",
       "      <td>8.328226</td>\n",
       "      <td>...</td>\n",
       "      <td>6.034394</td>\n",
       "      <td>6.992436</td>\n",
       "      <td>7.017541</td>\n",
       "      <td>4.633699</td>\n",
       "      <td>6.362245</td>\n",
       "      <td>5.938330</td>\n",
       "      <td>3.235696</td>\n",
       "      <td>6.970269</td>\n",
       "      <td>7.721103</td>\n",
       "      <td>9.522046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANP32E</th>\n",
       "      <td>9.561221</td>\n",
       "      <td>9.829963</td>\n",
       "      <td>10.097180</td>\n",
       "      <td>9.781118</td>\n",
       "      <td>11.297024</td>\n",
       "      <td>8.128950</td>\n",
       "      <td>9.683484</td>\n",
       "      <td>8.871398</td>\n",
       "      <td>10.166508</td>\n",
       "      <td>9.805744</td>\n",
       "      <td>...</td>\n",
       "      <td>9.591270</td>\n",
       "      <td>10.365569</td>\n",
       "      <td>9.911534</td>\n",
       "      <td>9.741722</td>\n",
       "      <td>10.626102</td>\n",
       "      <td>10.873870</td>\n",
       "      <td>10.839075</td>\n",
       "      <td>9.483399</td>\n",
       "      <td>8.734465</td>\n",
       "      <td>10.252020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL4C</th>\n",
       "      <td>11.201284</td>\n",
       "      <td>11.275144</td>\n",
       "      <td>11.615422</td>\n",
       "      <td>10.084830</td>\n",
       "      <td>10.851740</td>\n",
       "      <td>8.161880</td>\n",
       "      <td>10.443259</td>\n",
       "      <td>10.215139</td>\n",
       "      <td>9.768760</td>\n",
       "      <td>8.460251</td>\n",
       "      <td>...</td>\n",
       "      <td>10.601978</td>\n",
       "      <td>10.394763</td>\n",
       "      <td>9.318223</td>\n",
       "      <td>8.375083</td>\n",
       "      <td>11.027117</td>\n",
       "      <td>10.840359</td>\n",
       "      <td>8.771351</td>\n",
       "      <td>8.256225</td>\n",
       "      <td>9.452429</td>\n",
       "      <td>10.910269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCL1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.218626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.728312</td>\n",
       "      <td>7.424274</td>\n",
       "      <td>0.494876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.215927</td>\n",
       "      <td>1.137504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429750</td>\n",
       "      <td>3.925477</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.598175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.567229</td>\n",
       "      <td>7.767370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASS1</th>\n",
       "      <td>10.769460</td>\n",
       "      <td>10.606733</td>\n",
       "      <td>11.081560</td>\n",
       "      <td>10.417837</td>\n",
       "      <td>13.210058</td>\n",
       "      <td>8.736559</td>\n",
       "      <td>9.835311</td>\n",
       "      <td>13.206704</td>\n",
       "      <td>6.562009</td>\n",
       "      <td>8.016808</td>\n",
       "      <td>...</td>\n",
       "      <td>10.780222</td>\n",
       "      <td>11.401248</td>\n",
       "      <td>10.859516</td>\n",
       "      <td>9.556013</td>\n",
       "      <td>11.773173</td>\n",
       "      <td>12.519634</td>\n",
       "      <td>7.586168</td>\n",
       "      <td>11.488817</td>\n",
       "      <td>12.288174</td>\n",
       "      <td>8.588975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aliquot_barcode  TCGA-A2-A0CO-01A-13R-A22K-07  TCGA-A2-A0CR-01A-11R-A22K-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               7.371518                      7.262014   \n",
       "ANP32E                               9.561221                      9.829963   \n",
       "ARL4C                               11.201284                     11.275144   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                10.769460                     10.606733   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0EP-01A-52R-A22U-07  TCGA-A2-A0EW-01A-21R-A115-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               7.054130                      6.048299   \n",
       "ANP32E                              10.097180                      9.781118   \n",
       "ARL4C                               11.615422                     10.084830   \n",
       "ASCL1                                2.218626                      0.000000   \n",
       "ASS1                                11.081560                     10.417837   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A3Y0-01A-11R-A239-07  TCGA-A7-A3RF-01A-11R-A22K-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               3.039805                      7.731880   \n",
       "ANP32E                              11.297024                      8.128950   \n",
       "ARL4C                               10.851740                      8.161880   \n",
       "ASCL1                                0.728312                      7.424274   \n",
       "ASS1                                13.210058                      8.736559   \n",
       "\n",
       "aliquot_barcode  TCGA-A7-A5ZX-01A-12R-A29R-07  TCGA-A8-A06R-01A-11R-A00Z-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.561949                      7.884440   \n",
       "ANP32E                               9.683484                      8.871398   \n",
       "ARL4C                               10.443259                     10.215139   \n",
       "ASCL1                                0.494876                      0.000000   \n",
       "ASS1                                 9.835311                     13.206704   \n",
       "\n",
       "aliquot_barcode  TCGA-A8-A07I-01A-11R-A00Z-07  TCGA-A8-A082-01A-11R-A00Z-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.450045                      8.328226   \n",
       "ANP32E                              10.166508                      9.805744   \n",
       "ARL4C                                9.768760                      8.460251   \n",
       "ASCL1                                1.215927                      1.137504   \n",
       "ASS1                                 6.562009                      8.016808   \n",
       "\n",
       "aliquot_barcode              ...               TCGA-GM-A3XG-01A-31R-A24H-07  \\\n",
       "gene_symbol                  ...                                              \n",
       "ADRA2C                       ...                                   6.034394   \n",
       "ANP32E                       ...                                   9.591270   \n",
       "ARL4C                        ...                                  10.601978   \n",
       "ASCL1                        ...                                   0.429750   \n",
       "ASS1                         ...                                  10.780222   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A3XN-01A-12R-A22U-07  TCGA-GM-A5PX-01A-12R-A28M-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.992436                      7.017541   \n",
       "ANP32E                              10.365569                      9.911534   \n",
       "ARL4C                               10.394763                      9.318223   \n",
       "ASCL1                                3.925477                      0.000000   \n",
       "ASS1                                11.401248                     10.859516   \n",
       "\n",
       "aliquot_barcode  TCGA-LL-A50Y-01A-11R-A266-07  TCGA-LL-A73Z-01A-11R-A32P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               4.633699                      6.362245   \n",
       "ANP32E                               9.741722                     10.626102   \n",
       "ARL4C                                8.375083                     11.027117   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                 9.556013                     11.773173   \n",
       "\n",
       "aliquot_barcode  TCGA-OL-A5S0-01A-11R-A28M-07  TCGA-S3-A6ZG-01A-22R-A32P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.938330                      3.235696   \n",
       "ANP32E                              10.873870                     10.839075   \n",
       "ARL4C                               10.840359                      8.771351   \n",
       "ASCL1                                1.598175                      0.000000   \n",
       "ASS1                                12.519634                      7.586168   \n",
       "\n",
       "aliquot_barcode  TCGA-S3-AA11-01A-31R-A41B-07  TCGA-V7-A7HQ-01A-11R-A33J-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.970269                      7.721103   \n",
       "ANP32E                               9.483399                      8.734465   \n",
       "ARL4C                                8.256225                      9.452429   \n",
       "ASCL1                                0.000000                      2.567229   \n",
       "ASS1                                11.488817                     12.288174   \n",
       "\n",
       "aliquot_barcode  TCGA-WT-AB41-01A-11R-A41B-07  \n",
       "gene_symbol                                    \n",
       "ADRA2C                               9.522046  \n",
       "ANP32E                              10.252020  \n",
       "ARL4C                               10.910269  \n",
       "ASCL1                                7.767370  \n",
       "ASS1                                 8.588975  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_res = hormone_expression_bigtable.loc[(hormone_expression_bigtable['sample_type'] == 1) & \n",
    "                                     (hormone_expression_bigtable['initial_response'] != 'Unknown')]\n",
    "expression_matrix = init_res.pivot(index='gene_symbol', columns='aliquot_barcode', values='transformed_count')\n",
    "\n",
    "print(expression_matrix.shape)\n",
    "expression_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Complete response' 'Progressive/Stable']\n"
     ]
    }
   ],
   "source": [
    "color_matrix = pd.DataFrame()\n",
    "df = init_res.drop_duplicates(subset=\"aliquot_barcode\")\n",
    "\n",
    "print(df['initial_response'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Initial response\n",
       "aliquot_barcode                              \n",
       "TCGA-A2-A0CO-01A-13R-A22K-07          #8cc8e2\n",
       "TCGA-A2-A0CR-01A-11R-A22K-07          #8cc8e2\n",
       "TCGA-A2-A0EP-01A-52R-A22U-07          #8cc8e2\n",
       "TCGA-A2-A0EW-01A-21R-A115-07          #edea3d\n",
       "TCGA-A2-A3Y0-01A-11R-A239-07          #8cc8e2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = dict(zip(df['initial_response'].unique(), [\"#8cc8e2\", \"#edea3d\"]))\n",
    "color_matrix['Initial response'] = df['initial_response'].map(c)\n",
    "\n",
    "color_matrix.set_index(df['aliquot_barcode'], inplace=True)\n",
    "color_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = scipy.spatial.distance.squareform(1-expression_matrix.corr())\n",
    "linkage = scipy.cluster.hierarchy.linkage(distance, method='complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1476 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g4 = sns.clustermap(data=expression_matrix.corr(), col_linkage=linkage, row_linkage=linkage,\n",
    "                   row_colors=color_matrix, col_colors = color_matrix,\n",
    "                   cmap=\"BuPu\", vmax=1, figsize=(20,20.5), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "complete_aliquots = init_res.loc[(init_res['initial_response'] == 'Complete response'), 'aliquot_barcode'].unique()\n",
    "\n",
    "g5 = sns.clustermap(data=expression_matrix.sort_values(by=list(complete_aliquots)), \n",
    "                    col_linkage=linkage, row_cluster = False,\n",
    "                    col_colors = color_matrix,\n",
    "                    cmap=\"Spectral_r\", vmin=0, vmax=18, figsize=(20,15), \n",
    "                    xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "g4.savefig(\"./plots/cluster_expression.hormone.excl_norm.init_res.corrdata.png\")\n",
    "g5.savefig(\"./plots/cluster_expression.hormone.excl_norm.init_res.expressiondata.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrence status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 50)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>aliquot_barcode</th>\n",
       "      <th>TCGA-A2-A04N-01A-11R-A115-07</th>\n",
       "      <th>TCGA-A2-A04V-01A-21R-A034-07</th>\n",
       "      <th>TCGA-A2-A0CK-01A-11R-A22K-07</th>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <th>TCGA-A2-A0SV-01A-11R-A084-07</th>\n",
       "      <th>TCGA-A2-A0SW-01A-11R-A084-07</th>\n",
       "      <th>TCGA-A2-A0YC-01A-11R-A109-07</th>\n",
       "      <th>TCGA-A2-A25B-01A-11R-A169-07</th>\n",
       "      <th>TCGA-A2-A25E-01A-11R-A169-07</th>\n",
       "      <th>TCGA-A7-A13E-01A-11R-A12P-07</th>\n",
       "      <th>...</th>\n",
       "      <th>TCGA-E9-A1N6-01A-11R-A144-07</th>\n",
       "      <th>TCGA-E9-A1NF-01A-11R-A14D-07</th>\n",
       "      <th>TCGA-EW-A1P0-01A-11R-A144-07</th>\n",
       "      <th>TCGA-GM-A2D9-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DA-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DC-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DM-01A-11R-A180-07</th>\n",
       "      <th>TCGA-GM-A2DN-01A-11R-A180-07</th>\n",
       "      <th>TCGA-LL-A73Z-01A-11R-A32P-07</th>\n",
       "      <th>TCGA-LQ-A4E4-01A-11R-A266-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>ADRA2C</th>\n",
       "      <td>5.412802</td>\n",
       "      <td>4.444813</td>\n",
       "      <td>6.541038</td>\n",
       "      <td>6.048299</td>\n",
       "      <td>6.377509</td>\n",
       "      <td>3.352377</td>\n",
       "      <td>3.563158</td>\n",
       "      <td>2.848358</td>\n",
       "      <td>4.918639</td>\n",
       "      <td>1.832769</td>\n",
       "      <td>...</td>\n",
       "      <td>4.355961</td>\n",
       "      <td>5.299666</td>\n",
       "      <td>3.756084</td>\n",
       "      <td>6.580773</td>\n",
       "      <td>5.816973</td>\n",
       "      <td>9.217166</td>\n",
       "      <td>1.732313</td>\n",
       "      <td>4.037918</td>\n",
       "      <td>6.362245</td>\n",
       "      <td>6.689829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANP32E</th>\n",
       "      <td>9.372962</td>\n",
       "      <td>10.669437</td>\n",
       "      <td>10.030458</td>\n",
       "      <td>9.781118</td>\n",
       "      <td>9.562843</td>\n",
       "      <td>9.542102</td>\n",
       "      <td>10.799081</td>\n",
       "      <td>11.367194</td>\n",
       "      <td>11.081927</td>\n",
       "      <td>11.732477</td>\n",
       "      <td>...</td>\n",
       "      <td>10.063238</td>\n",
       "      <td>9.698123</td>\n",
       "      <td>7.684026</td>\n",
       "      <td>9.453122</td>\n",
       "      <td>9.661606</td>\n",
       "      <td>10.929548</td>\n",
       "      <td>9.665320</td>\n",
       "      <td>10.816003</td>\n",
       "      <td>10.626102</td>\n",
       "      <td>9.658979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL4C</th>\n",
       "      <td>9.943622</td>\n",
       "      <td>9.345226</td>\n",
       "      <td>9.152490</td>\n",
       "      <td>10.084830</td>\n",
       "      <td>11.333493</td>\n",
       "      <td>9.840185</td>\n",
       "      <td>9.276834</td>\n",
       "      <td>9.365068</td>\n",
       "      <td>9.197618</td>\n",
       "      <td>10.415972</td>\n",
       "      <td>...</td>\n",
       "      <td>10.125916</td>\n",
       "      <td>11.283361</td>\n",
       "      <td>9.419215</td>\n",
       "      <td>9.322357</td>\n",
       "      <td>10.660715</td>\n",
       "      <td>8.938618</td>\n",
       "      <td>8.652597</td>\n",
       "      <td>9.851545</td>\n",
       "      <td>11.027117</td>\n",
       "      <td>7.734416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCL1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.859811</td>\n",
       "      <td>1.145417</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.025419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.445038</td>\n",
       "      <td>3.197881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.695281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.416407</td>\n",
       "      <td>6.609313</td>\n",
       "      <td>12.127080</td>\n",
       "      <td>7.042671</td>\n",
       "      <td>1.550063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.583712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASS1</th>\n",
       "      <td>10.309732</td>\n",
       "      <td>8.386607</td>\n",
       "      <td>10.685678</td>\n",
       "      <td>10.417837</td>\n",
       "      <td>11.599314</td>\n",
       "      <td>7.828981</td>\n",
       "      <td>8.429109</td>\n",
       "      <td>7.555266</td>\n",
       "      <td>7.217218</td>\n",
       "      <td>10.635957</td>\n",
       "      <td>...</td>\n",
       "      <td>12.100029</td>\n",
       "      <td>10.840536</td>\n",
       "      <td>13.220430</td>\n",
       "      <td>12.031642</td>\n",
       "      <td>9.843106</td>\n",
       "      <td>9.156216</td>\n",
       "      <td>7.706509</td>\n",
       "      <td>10.784490</td>\n",
       "      <td>11.773173</td>\n",
       "      <td>10.659422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aliquot_barcode  TCGA-A2-A04N-01A-11R-A115-07  TCGA-A2-A04V-01A-21R-A034-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.412802                      4.444813   \n",
       "ANP32E                               9.372962                     10.669437   \n",
       "ARL4C                                9.943622                      9.345226   \n",
       "ASCL1                                0.000000                      0.859811   \n",
       "ASS1                                10.309732                      8.386607   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0CK-01A-11R-A22K-07  TCGA-A2-A0EW-01A-21R-A115-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.541038                      6.048299   \n",
       "ANP32E                              10.030458                      9.781118   \n",
       "ARL4C                                9.152490                     10.084830   \n",
       "ASCL1                                1.145417                      0.000000   \n",
       "ASS1                                10.685678                     10.417837   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0SV-01A-11R-A084-07  TCGA-A2-A0SW-01A-11R-A084-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.377509                      3.352377   \n",
       "ANP32E                               9.562843                      9.542102   \n",
       "ARL4C                               11.333493                      9.840185   \n",
       "ASCL1                                0.000000                      2.025419   \n",
       "ASS1                                11.599314                      7.828981   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A0YC-01A-11R-A109-07  TCGA-A2-A25B-01A-11R-A169-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               3.563158                      2.848358   \n",
       "ANP32E                              10.799081                     11.367194   \n",
       "ARL4C                                9.276834                      9.365068   \n",
       "ASCL1                                0.000000                      1.445038   \n",
       "ASS1                                 8.429109                      7.555266   \n",
       "\n",
       "aliquot_barcode  TCGA-A2-A25E-01A-11R-A169-07  TCGA-A7-A13E-01A-11R-A12P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               4.918639                      1.832769   \n",
       "ANP32E                              11.081927                     11.732477   \n",
       "ARL4C                                9.197618                     10.415972   \n",
       "ASCL1                                3.197881                      0.000000   \n",
       "ASS1                                 7.217218                     10.635957   \n",
       "\n",
       "aliquot_barcode              ...               TCGA-E9-A1N6-01A-11R-A144-07  \\\n",
       "gene_symbol                  ...                                              \n",
       "ADRA2C                       ...                                   4.355961   \n",
       "ANP32E                       ...                                  10.063238   \n",
       "ARL4C                        ...                                  10.125916   \n",
       "ASCL1                        ...                                   0.000000   \n",
       "ASS1                         ...                                  12.100029   \n",
       "\n",
       "aliquot_barcode  TCGA-E9-A1NF-01A-11R-A14D-07  TCGA-EW-A1P0-01A-11R-A144-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.299666                      3.756084   \n",
       "ANP32E                               9.698123                      7.684026   \n",
       "ARL4C                               11.283361                      9.419215   \n",
       "ASCL1                                0.695281                      0.000000   \n",
       "ASS1                                10.840536                     13.220430   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2D9-01A-11R-A18M-07  TCGA-GM-A2DA-01A-11R-A18M-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.580773                      5.816973   \n",
       "ANP32E                               9.453122                      9.661606   \n",
       "ARL4C                                9.322357                     10.660715   \n",
       "ASCL1                                2.416407                      6.609313   \n",
       "ASS1                                12.031642                      9.843106   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2DC-01A-11R-A18M-07  TCGA-GM-A2DM-01A-11R-A180-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               9.217166                      1.732313   \n",
       "ANP32E                              10.929548                      9.665320   \n",
       "ARL4C                                8.938618                      8.652597   \n",
       "ASCL1                               12.127080                      7.042671   \n",
       "ASS1                                 9.156216                      7.706509   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2DN-01A-11R-A180-07  TCGA-LL-A73Z-01A-11R-A32P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               4.037918                      6.362245   \n",
       "ANP32E                              10.816003                     10.626102   \n",
       "ARL4C                                9.851545                     11.027117   \n",
       "ASCL1                                1.550063                      0.000000   \n",
       "ASS1                                10.784490                     11.773173   \n",
       "\n",
       "aliquot_barcode  TCGA-LQ-A4E4-01A-11R-A266-07  \n",
       "gene_symbol                                    \n",
       "ADRA2C                               6.689829  \n",
       "ANP32E                               9.658979  \n",
       "ARL4C                                7.734416  \n",
       "ASCL1                                0.583712  \n",
       "ASS1                                10.659422  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recur = hormone_expression_bigtable.loc[(hormone_expression_bigtable['sample_type'] == 1) & \n",
    "                                  (hormone_expression_bigtable['recurrence_status'] != 'Unknown') & \n",
    "                                  (hormone_expression_bigtable['recurrence_status'].isna() == False)]\n",
    "expression_matrix = recur.pivot(index='gene_symbol', columns='aliquot_barcode', values='transformed_count')\n",
    "\n",
    "print(expression_matrix.shape)\n",
    "expression_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Low risk' 'Occured']\n"
     ]
    }
   ],
   "source": [
    "color_matrix = pd.DataFrame()\n",
    "df = recur.drop_duplicates(subset=\"aliquot_barcode\")\n",
    "\n",
    "print(df['recurrence_status'].unique())"
   ]
  },
  {
   "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>Recurrence status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A04N-01A-11R-A115-07</th>\n",
       "      <td>#aee851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A04V-01A-21R-A034-07</th>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0CK-01A-11R-A22K-07</th>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0SV-01A-11R-A084-07</th>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Recurrence status\n",
       "aliquot_barcode                               \n",
       "TCGA-A2-A04N-01A-11R-A115-07           #aee851\n",
       "TCGA-A2-A04V-01A-21R-A034-07           #ff7e63\n",
       "TCGA-A2-A0CK-01A-11R-A22K-07           #ff7e63\n",
       "TCGA-A2-A0EW-01A-21R-A115-07           #ff7e63\n",
       "TCGA-A2-A0SV-01A-11R-A084-07           #ff7e63"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = dict(zip(df['recurrence_status'].unique(), [\"#aee851\", \"#ff7e63\"]))\n",
    "color_matrix['Recurrence status'] = df['recurrence_status'].map(c)\n",
    "\n",
    "color_matrix.set_index(df['aliquot_barcode'], inplace=True)\n",
    "color_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = scipy.spatial.distance.squareform(1-expression_matrix.corr())\n",
    "linkage = scipy.cluster.hierarchy.linkage(distance, method='complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1476 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g6 = sns.clustermap(data=expression_matrix.corr(), col_linkage=linkage, row_linkage=linkage,\n",
    "                   row_colors=color_matrix, col_colors = color_matrix,\n",
    "                   cmap=\"BuPu\", vmax=1, figsize=(20,20.5), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lowrisk_aliquots = recur.loc[(recur['recurrence_status'] == 'Low risk'), 'aliquot_barcode'].unique()\n",
    "\n",
    "g7 = sns.clustermap(data=expression_matrix.sort_values(by=list(lowrisk_aliquots)), \n",
    "                    col_linkage=linkage, row_cluster = False,\n",
    "                   col_colors = color_matrix,\n",
    "                   cmap=\"Spectral_r\", vmin=0, vmax=18, figsize=(20,15), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "g6.savefig(\"./plots/cluster_expression.hormone.excl_norm.recur.corrdata.png\")\n",
    "g7.savefig(\"./plots/cluster_expression.hormone.excl_norm.recur.expressiondata.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete + Low-risk vs. Progressive/Stable + Relapsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 11)\n"
     ]
    },
    {
     "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>aliquot_barcode</th>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <th>TCGA-A7-A3RF-01A-11R-A22K-07</th>\n",
       "      <th>TCGA-A8-A08O-01A-21R-A056-07</th>\n",
       "      <th>TCGA-D8-A73W-01A-22R-A352-07</th>\n",
       "      <th>TCGA-E2-A1IE-01A-11R-A13Q-07</th>\n",
       "      <th>TCGA-E9-A1N6-01A-11R-A144-07</th>\n",
       "      <th>TCGA-GM-A2DA-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DC-01A-11R-A18M-07</th>\n",
       "      <th>TCGA-GM-A2DM-01A-11R-A180-07</th>\n",
       "      <th>TCGA-GM-A2DN-01A-11R-A180-07</th>\n",
       "      <th>TCGA-LL-A73Z-01A-11R-A32P-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>ADRA2C</th>\n",
       "      <td>6.048299</td>\n",
       "      <td>7.731880</td>\n",
       "      <td>3.403309</td>\n",
       "      <td>3.350115</td>\n",
       "      <td>8.306813</td>\n",
       "      <td>4.355961</td>\n",
       "      <td>5.816973</td>\n",
       "      <td>9.217166</td>\n",
       "      <td>1.732313</td>\n",
       "      <td>4.037918</td>\n",
       "      <td>6.362245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANP32E</th>\n",
       "      <td>9.781118</td>\n",
       "      <td>8.128950</td>\n",
       "      <td>10.356002</td>\n",
       "      <td>9.623196</td>\n",
       "      <td>8.359199</td>\n",
       "      <td>10.063238</td>\n",
       "      <td>9.661606</td>\n",
       "      <td>10.929548</td>\n",
       "      <td>9.665320</td>\n",
       "      <td>10.816003</td>\n",
       "      <td>10.626102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL4C</th>\n",
       "      <td>10.084830</td>\n",
       "      <td>8.161880</td>\n",
       "      <td>9.864767</td>\n",
       "      <td>9.603949</td>\n",
       "      <td>7.748289</td>\n",
       "      <td>10.125916</td>\n",
       "      <td>10.660715</td>\n",
       "      <td>8.938618</td>\n",
       "      <td>8.652597</td>\n",
       "      <td>9.851545</td>\n",
       "      <td>11.027117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCL1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.424274</td>\n",
       "      <td>7.420915</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.618956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.609313</td>\n",
       "      <td>12.127080</td>\n",
       "      <td>7.042671</td>\n",
       "      <td>1.550063</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASS1</th>\n",
       "      <td>10.417837</td>\n",
       "      <td>8.736559</td>\n",
       "      <td>8.507015</td>\n",
       "      <td>11.840373</td>\n",
       "      <td>9.038086</td>\n",
       "      <td>12.100029</td>\n",
       "      <td>9.843106</td>\n",
       "      <td>9.156216</td>\n",
       "      <td>7.706509</td>\n",
       "      <td>10.784490</td>\n",
       "      <td>11.773173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "aliquot_barcode  TCGA-A2-A0EW-01A-21R-A115-07  TCGA-A7-A3RF-01A-11R-A22K-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.048299                      7.731880   \n",
       "ANP32E                               9.781118                      8.128950   \n",
       "ARL4C                               10.084830                      8.161880   \n",
       "ASCL1                                0.000000                      7.424274   \n",
       "ASS1                                10.417837                      8.736559   \n",
       "\n",
       "aliquot_barcode  TCGA-A8-A08O-01A-21R-A056-07  TCGA-D8-A73W-01A-22R-A352-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               3.403309                      3.350115   \n",
       "ANP32E                              10.356002                      9.623196   \n",
       "ARL4C                                9.864767                      9.603949   \n",
       "ASCL1                                7.420915                      0.000000   \n",
       "ASS1                                 8.507015                     11.840373   \n",
       "\n",
       "aliquot_barcode  TCGA-E2-A1IE-01A-11R-A13Q-07  TCGA-E9-A1N6-01A-11R-A144-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               8.306813                      4.355961   \n",
       "ANP32E                               8.359199                     10.063238   \n",
       "ARL4C                                7.748289                     10.125916   \n",
       "ASCL1                                7.618956                      0.000000   \n",
       "ASS1                                 9.038086                     12.100029   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2DA-01A-11R-A18M-07  TCGA-GM-A2DC-01A-11R-A18M-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               5.816973                      9.217166   \n",
       "ANP32E                               9.661606                     10.929548   \n",
       "ARL4C                               10.660715                      8.938618   \n",
       "ASCL1                                6.609313                     12.127080   \n",
       "ASS1                                 9.843106                      9.156216   \n",
       "\n",
       "aliquot_barcode  TCGA-GM-A2DM-01A-11R-A180-07  TCGA-GM-A2DN-01A-11R-A180-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               1.732313                      4.037918   \n",
       "ANP32E                               9.665320                     10.816003   \n",
       "ARL4C                                8.652597                      9.851545   \n",
       "ASCL1                                7.042671                      1.550063   \n",
       "ASS1                                 7.706509                     10.784490   \n",
       "\n",
       "aliquot_barcode  TCGA-LL-A73Z-01A-11R-A32P-07  \n",
       "gene_symbol                                    \n",
       "ADRA2C                               6.362245  \n",
       "ANP32E                              10.626102  \n",
       "ARL4C                               11.027117  \n",
       "ASCL1                                0.000000  \n",
       "ASS1                                11.773173  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restricted = hormone_expression_bigtable.loc[(hormone_expression_bigtable['sample_type'] == 1) &\n",
    "                                             (hormone_expression_bigtable['recurrence_status'] != 'Unknown') & \n",
    "                                             (hormone_expression_bigtable['recurrence_status'].isna() == False) & \n",
    "                                             (hormone_expression_bigtable['initial_response'] != 'Unknown')]\n",
    "expression_matrix = restricted.pivot(index='gene_symbol', columns='aliquot_barcode', values='transformed_count')\n",
    "\n",
    "print(expression_matrix.shape)\n",
    "expression_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Progressive/Stable' 'Complete response']\n",
      "['Occured' 'Low risk']\n"
     ]
    }
   ],
   "source": [
    "color_matrix = pd.DataFrame()\n",
    "df = restricted.drop_duplicates(subset=\"aliquot_barcode\")\n",
    "\n",
    "print(df['initial_response'].unique())\n",
    "print(df['recurrence_status'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>recurrence_status</th>\n",
       "      <th>Low risk</th>\n",
       "      <th>Occured</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_response</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Complete response</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Progressive/Stable</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "recurrence_status   Low risk  Occured  All\n",
       "initial_response                          \n",
       "Complete response          3        0    3\n",
       "Progressive/Stable         0        8    8\n",
       "All                        3        8   11"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['initial_response'], df['recurrence_status'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LumA      9\n",
       "Normal    1\n",
       "LumB      1\n",
       "Name: subtype_sub, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subtype_sub'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Initial response</th>\n",
       "      <th>Recurrence status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A2-A0EW-01A-21R-A115-07</th>\n",
       "      <td>#edea3d</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A3RF-01A-11R-A22K-07</th>\n",
       "      <td>#edea3d</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A08O-01A-21R-A056-07</th>\n",
       "      <td>#edea3d</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-D8-A73W-01A-22R-A352-07</th>\n",
       "      <td>#edea3d</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-E2-A1IE-01A-11R-A13Q-07</th>\n",
       "      <td>#edea3d</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Initial response Recurrence status\n",
       "aliquot_barcode                                                \n",
       "TCGA-A2-A0EW-01A-21R-A115-07          #edea3d           #ff7e63\n",
       "TCGA-A7-A3RF-01A-11R-A22K-07          #edea3d           #ff7e63\n",
       "TCGA-A8-A08O-01A-21R-A056-07          #edea3d           #ff7e63\n",
       "TCGA-D8-A73W-01A-22R-A352-07          #edea3d           #ff7e63\n",
       "TCGA-E2-A1IE-01A-11R-A13Q-07          #edea3d           #ff7e63"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = dict(zip(df['initial_response'].unique(), [\"#edea3d\", \"#8cc8e2\"]))\n",
    "color_matrix['Initial response'] = df['initial_response'].map(c)\n",
    "\n",
    "c = dict(zip(df['recurrence_status'].unique(), [\"#ff7e63\", \"#aee851\"]))\n",
    "color_matrix['Recurrence status'] = df['recurrence_status'].map(c)\n",
    "\n",
    "color_matrix.set_index(df['aliquot_barcode'], inplace=True)\n",
    "color_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = scipy.spatial.distance.squareform(1-expression_matrix.corr())\n",
    "linkage = scipy.cluster.hierarchy.linkage(distance, method='complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1476 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g8 = sns.clustermap(data=expression_matrix.corr(), col_linkage=linkage, row_linkage=linkage,\n",
    "                   row_colors=color_matrix, col_colors = color_matrix,\n",
    "                   cmap=\"BuPu\", vmax=1, figsize=(20,20.5), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g9 = sns.clustermap(data=expression_matrix, col_linkage=linkage, row_cluster = True,\n",
    "                   col_colors = color_matrix,\n",
    "                   cmap=\"Spectral_r\", vmin=0, vmax=18, figsize=(20,15), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "g8.savefig(\"./plots/cluster_expression.hormone.excl_norm.init_recur.corrdata.png\")\n",
    "g9.savefig(\"./plots/cluster_expression.hormone.excl_norm.init_recur.expressiondata.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normal tissues only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 33)\n"
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th>TCGA-A7-A0D9-11A-53R-A089-07</th>\n",
       "      <th>TCGA-A7-A0DB-11A-33R-A089-07</th>\n",
       "      <th>TCGA-A7-A0DC-11A-41R-A089-07</th>\n",
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       "      <th>TCGA-BH-A0B8-11A-41R-A089-07</th>\n",
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       "      <th>TCGA-E9-A1NF-11A-73R-A14D-07</th>\n",
       "      <th>TCGA-E9-A1NG-11A-52R-A14M-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>ADRA2C</th>\n",
       "      <td>6.329656</td>\n",
       "      <td>6.743967</td>\n",
       "      <td>6.864976</td>\n",
       "      <td>7.144808</td>\n",
       "      <td>6.970193</td>\n",
       "      <td>6.356271</td>\n",
       "      <td>6.366060</td>\n",
       "      <td>4.812837</td>\n",
       "      <td>6.362896</td>\n",
       "      <td>7.268351</td>\n",
       "      <td>...</td>\n",
       "      <td>5.563994</td>\n",
       "      <td>6.956900</td>\n",
       "      <td>7.397868</td>\n",
       "      <td>6.828312</td>\n",
       "      <td>4.808848</td>\n",
       "      <td>3.919521</td>\n",
       "      <td>6.025831</td>\n",
       "      <td>7.216198</td>\n",
       "      <td>6.964089</td>\n",
       "      <td>6.686049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANP32E</th>\n",
       "      <td>9.166989</td>\n",
       "      <td>9.995184</td>\n",
       "      <td>10.141016</td>\n",
       "      <td>9.352723</td>\n",
       "      <td>9.274543</td>\n",
       "      <td>9.842412</td>\n",
       "      <td>10.110755</td>\n",
       "      <td>10.216136</td>\n",
       "      <td>10.049434</td>\n",
       "      <td>9.778098</td>\n",
       "      <td>...</td>\n",
       "      <td>10.063120</td>\n",
       "      <td>9.904411</td>\n",
       "      <td>9.728791</td>\n",
       "      <td>9.761114</td>\n",
       "      <td>10.287002</td>\n",
       "      <td>10.471928</td>\n",
       "      <td>10.118482</td>\n",
       "      <td>9.656397</td>\n",
       "      <td>9.802101</td>\n",
       "      <td>9.977503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARL4C</th>\n",
       "      <td>8.767611</td>\n",
       "      <td>10.130703</td>\n",
       "      <td>10.140420</td>\n",
       "      <td>9.503859</td>\n",
       "      <td>10.967476</td>\n",
       "      <td>8.462580</td>\n",
       "      <td>10.936821</td>\n",
       "      <td>10.199774</td>\n",
       "      <td>10.500236</td>\n",
       "      <td>8.785992</td>\n",
       "      <td>...</td>\n",
       "      <td>8.968092</td>\n",
       "      <td>10.356491</td>\n",
       "      <td>8.200677</td>\n",
       "      <td>10.688131</td>\n",
       "      <td>11.566673</td>\n",
       "      <td>10.967478</td>\n",
       "      <td>12.274797</td>\n",
       "      <td>10.481395</td>\n",
       "      <td>10.381573</td>\n",
       "      <td>10.756167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASCL1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.323889</td>\n",
       "      <td>3.392496</td>\n",
       "      <td>0.902730</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612400</td>\n",
       "      <td>0.672652</td>\n",
       "      <td>1.593975</td>\n",
       "      <td>0.948451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.939151</td>\n",
       "      <td>0.533264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASS1</th>\n",
       "      <td>12.687534</td>\n",
       "      <td>12.728685</td>\n",
       "      <td>11.819102</td>\n",
       "      <td>11.891328</td>\n",
       "      <td>12.505365</td>\n",
       "      <td>13.559786</td>\n",
       "      <td>11.637454</td>\n",
       "      <td>10.486406</td>\n",
       "      <td>11.447914</td>\n",
       "      <td>12.638608</td>\n",
       "      <td>...</td>\n",
       "      <td>13.182231</td>\n",
       "      <td>11.257493</td>\n",
       "      <td>14.838582</td>\n",
       "      <td>11.990911</td>\n",
       "      <td>11.092244</td>\n",
       "      <td>11.083869</td>\n",
       "      <td>11.737176</td>\n",
       "      <td>12.386615</td>\n",
       "      <td>12.377656</td>\n",
       "      <td>11.968884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aliquot_barcode  TCGA-A7-A0D9-11A-53R-A089-07  TCGA-A7-A0DB-11A-33R-A089-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.329656                      6.743967   \n",
       "ANP32E                               9.166989                      9.995184   \n",
       "ARL4C                                8.767611                     10.130703   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                12.687534                     12.728685   \n",
       "\n",
       "aliquot_barcode  TCGA-A7-A0DC-11A-41R-A089-07  TCGA-A7-A13E-11A-61R-A12P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.864976                      7.144808   \n",
       "ANP32E                              10.141016                      9.352723   \n",
       "ARL4C                               10.140420                      9.503859   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                11.819102                     11.891328   \n",
       "\n",
       "aliquot_barcode  TCGA-A7-A13F-11A-42R-A12P-07  TCGA-A7-A13G-11A-51R-A13Q-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.970193                      6.356271   \n",
       "ANP32E                               9.274543                      9.842412   \n",
       "ARL4C                               10.967476                      8.462580   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                12.505365                     13.559786   \n",
       "\n",
       "aliquot_barcode  TCGA-AC-A2FB-11A-13R-A17B-07  TCGA-AC-A2FF-11A-13R-A17B-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.366060                      4.812837   \n",
       "ANP32E                              10.110755                     10.216136   \n",
       "ARL4C                               10.936821                     10.199774   \n",
       "ASCL1                                1.323889                      3.392496   \n",
       "ASS1                                11.637454                     10.486406   \n",
       "\n",
       "aliquot_barcode  TCGA-BH-A0AY-11A-23R-A089-07  TCGA-BH-A0B8-11A-41R-A089-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.362896                      7.268351   \n",
       "ANP32E                              10.049434                      9.778098   \n",
       "ARL4C                               10.500236                      8.785992   \n",
       "ASCL1                                0.902730                      0.000000   \n",
       "ASS1                                11.447914                     12.638608   \n",
       "\n",
       "aliquot_barcode              ...               TCGA-E2-A15I-11A-32R-A137-07  \\\n",
       "gene_symbol                  ...                                              \n",
       "ADRA2C                       ...                                   5.563994   \n",
       "ANP32E                       ...                                  10.063120   \n",
       "ARL4C                        ...                                   8.968092   \n",
       "ASCL1                        ...                                   0.000000   \n",
       "ASS1                         ...                                  13.182231   \n",
       "\n",
       "aliquot_barcode  TCGA-E2-A15M-11A-22R-A12D-07  TCGA-E2-A1BC-11A-32R-A12P-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.956900                      7.397868   \n",
       "ANP32E                               9.904411                      9.728791   \n",
       "ARL4C                               10.356491                      8.200677   \n",
       "ASCL1                                0.000000                      0.000000   \n",
       "ASS1                                11.257493                     14.838582   \n",
       "\n",
       "aliquot_barcode  TCGA-E2-A1IG-11A-22R-A144-07  TCGA-E9-A1N4-11A-33R-A14M-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               6.828312                      4.808848   \n",
       "ANP32E                               9.761114                     10.287002   \n",
       "ARL4C                               10.688131                     11.566673   \n",
       "ASCL1                                0.612400                      0.672652   \n",
       "ASS1                                11.990911                     11.092244   \n",
       "\n",
       "aliquot_barcode  TCGA-E9-A1N5-11A-41R-A14D-07  TCGA-E9-A1N6-11A-32R-A144-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               3.919521                      6.025831   \n",
       "ANP32E                              10.471928                     10.118482   \n",
       "ARL4C                               10.967478                     12.274797   \n",
       "ASCL1                                1.593975                      0.948451   \n",
       "ASS1                                11.083869                     11.737176   \n",
       "\n",
       "aliquot_barcode  TCGA-E9-A1NA-11A-33R-A144-07  TCGA-E9-A1NF-11A-73R-A14D-07  \\\n",
       "gene_symbol                                                                   \n",
       "ADRA2C                               7.216198                      6.964089   \n",
       "ANP32E                               9.656397                      9.802101   \n",
       "ARL4C                               10.481395                     10.381573   \n",
       "ASCL1                                0.000000                      0.939151   \n",
       "ASS1                                12.386615                     12.377656   \n",
       "\n",
       "aliquot_barcode  TCGA-E9-A1NG-11A-52R-A14M-07  \n",
       "gene_symbol                                    \n",
       "ADRA2C                               6.686049  \n",
       "ANP32E                               9.977503  \n",
       "ARL4C                               10.756167  \n",
       "ASCL1                                0.533264  \n",
       "ASS1                                11.968884  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_normal = hormone_expression_bigtable.loc[hormone_expression_bigtable['sample_type'] == 11]\n",
    "expression_matrix = only_normal.pivot(index='gene_symbol', columns='aliquot_barcode', values='transformed_count')\n",
    "\n",
    "print(expression_matrix.shape)\n",
    "expression_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7485735068608894"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression_matrix.corr().min().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Unknown' 'Complete response' 'Progressive/Stable']\n",
      "['Unknown' 'Occured']\n"
     ]
    }
   ],
   "source": [
    "color_matrix = pd.DataFrame()\n",
    "df = only_normal.drop_duplicates(subset=\"aliquot_barcode\")\n",
    "\n",
    "print(df['initial_response'].unique())\n",
    "print(df['recurrence_status'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Initial response</th>\n",
       "      <th>Recurrence status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aliquot_barcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A0D9-11A-53R-A089-07</th>\n",
       "      <td>#ffffff</td>\n",
       "      <td>#ffffff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A0DB-11A-33R-A089-07</th>\n",
       "      <td>#ffffff</td>\n",
       "      <td>#ffffff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A0DC-11A-41R-A089-07</th>\n",
       "      <td>#ffffff</td>\n",
       "      <td>#ffffff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A13E-11A-61R-A12P-07</th>\n",
       "      <td>#ffffff</td>\n",
       "      <td>#ff7e63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A13F-11A-42R-A12P-07</th>\n",
       "      <td>#ffffff</td>\n",
       "      <td>#ffffff</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Initial response Recurrence status\n",
       "aliquot_barcode                                                \n",
       "TCGA-A7-A0D9-11A-53R-A089-07          #ffffff           #ffffff\n",
       "TCGA-A7-A0DB-11A-33R-A089-07          #ffffff           #ffffff\n",
       "TCGA-A7-A0DC-11A-41R-A089-07          #ffffff           #ffffff\n",
       "TCGA-A7-A13E-11A-61R-A12P-07          #ffffff           #ff7e63\n",
       "TCGA-A7-A13F-11A-42R-A12P-07          #ffffff           #ffffff"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = dict(zip(df['initial_response'].unique(), [\"#ffffff\", \"#8cc8e2\", \"#edea3d\"]))\n",
    "color_matrix['Initial response'] = df['initial_response'].map(c)\n",
    "\n",
    "c = dict(zip(df['recurrence_status'].unique(), [\"#ffffff\", \"#ff7e63\"]))\n",
    "color_matrix['Recurrence status'] = df['recurrence_status'].map(c)\n",
    "\n",
    "color_matrix.set_index(df['aliquot_barcode'], inplace=True)\n",
    "color_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance = scipy.spatial.distance.squareform(1-expression_matrix.corr())\n",
    "linkage = scipy.cluster.hierarchy.linkage(distance, method='complete')\n",
    "\n",
    "gg = sns.clustermap(data=expression_matrix.corr(), col_linkage=linkage, row_linkage=linkage,\n",
    "                   row_colors=color_matrix, col_colors = color_matrix,\n",
    "                   cmap=\"BuPu\", vmin=0.5, vmax=1, figsize=(20,20), \n",
    "                   xticklabels=False, yticklabels=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}