data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(file_path):\n",
    "    df = pd.read_csv(file_path, delimiter='\\t')\n",
    "    df = df.T\n",
    "    df = df.rename_axis('case_id').reset_index()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>A1BG</th>\n",
       "      <th>A2M</th>\n",
       "      <th>A2ML1</th>\n",
       "      <th>A4GALT</th>\n",
       "      <th>AAAS</th>\n",
       "      <th>AACS</th>\n",
       "      <th>AADAT</th>\n",
       "      <th>AAED1</th>\n",
       "      <th>AAGAB</th>\n",
       "      <th>...</th>\n",
       "      <th>ZSWIM8</th>\n",
       "      <th>ZSWIM9</th>\n",
       "      <th>ZW10</th>\n",
       "      <th>ZWILCH</th>\n",
       "      <th>ZWINT</th>\n",
       "      <th>ZXDC</th>\n",
       "      <th>ZYG11B</th>\n",
       "      <th>ZYX</th>\n",
       "      <th>ZZEF1</th>\n",
       "      <th>ZZZ3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S001</td>\n",
       "      <td>-1.180</td>\n",
       "      <td>-0.8630</td>\n",
       "      <td>-0.8020</td>\n",
       "      <td>0.2220</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.6650</td>\n",
       "      <td>1.2800</td>\n",
       "      <td>-0.3390</td>\n",
       "      <td>0.4120</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.08770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02290</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.332</td>\n",
       "      <td>-0.43300</td>\n",
       "      <td>-1.020</td>\n",
       "      <td>-0.12300</td>\n",
       "      <td>-0.0859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S002</td>\n",
       "      <td>-0.685</td>\n",
       "      <td>-1.0700</td>\n",
       "      <td>-0.6840</td>\n",
       "      <td>0.9840</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>0.3340</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>0.1390</td>\n",
       "      <td>1.3300</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>0.7370</td>\n",
       "      <td>-0.564</td>\n",
       "      <td>-0.00461</td>\n",
       "      <td>-1.130</td>\n",
       "      <td>-0.07570</td>\n",
       "      <td>-0.4730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S003</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>-1.3200</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.2400</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>-0.0213</td>\n",
       "      <td>-0.0479</td>\n",
       "      <td>0.4190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00112</td>\n",
       "      <td>-0.1450</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>-0.1160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-0.07400</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>-0.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S005</td>\n",
       "      <td>-1.670</td>\n",
       "      <td>-1.1900</td>\n",
       "      <td>-0.4430</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>-0.0993</td>\n",
       "      <td>0.7570</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>-0.9290</td>\n",
       "      <td>0.2290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07250</td>\n",
       "      <td>-0.0552</td>\n",
       "      <td>-0.07140</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>-0.398</td>\n",
       "      <td>-0.07520</td>\n",
       "      <td>-0.797</td>\n",
       "      <td>-0.03010</td>\n",
       "      <td>-0.4670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S006</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>-0.0206</td>\n",
       "      <td>-0.5370</td>\n",
       "      <td>0.3110</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>-1.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5650</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.22000</td>\n",
       "      <td>-0.5620</td>\n",
       "      <td>0.9370</td>\n",
       "      <td>-0.646</td>\n",
       "      <td>0.20700</td>\n",
       "      <td>-1.850</td>\n",
       "      <td>-0.17600</td>\n",
       "      <td>0.0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>S099</td>\n",
       "      <td>-1.070</td>\n",
       "      <td>-0.7120</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>-0.0471</td>\n",
       "      <td>0.2270</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>1.2100</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.6820</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.06990</td>\n",
       "      <td>-0.4010</td>\n",
       "      <td>0.55700</td>\n",
       "      <td>0.8270</td>\n",
       "      <td>0.3480</td>\n",
       "      <td>-0.187</td>\n",
       "      <td>-0.10700</td>\n",
       "      <td>-0.830</td>\n",
       "      <td>0.06200</td>\n",
       "      <td>-0.5280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>S100</td>\n",
       "      <td>-1.280</td>\n",
       "      <td>-0.7360</td>\n",
       "      <td>-0.5520</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.2730</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.5360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.13100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00364</td>\n",
       "      <td>0.7630</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>0.01320</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>-0.35700</td>\n",
       "      <td>0.0299</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>S101</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>-0.3200</td>\n",
       "      <td>3.1700</td>\n",
       "      <td>-0.9070</td>\n",
       "      <td>0.0317</td>\n",
       "      <td>-0.0425</td>\n",
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       "      <td>-0.2730</td>\n",
       "      <td>-0.0747</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17900</td>\n",
       "      <td>-0.5210</td>\n",
       "      <td>0.05230</td>\n",
       "      <td>0.5150</td>\n",
       "      <td>0.5850</td>\n",
       "      <td>0.138</td>\n",
       "      <td>-0.19000</td>\n",
       "      <td>-0.966</td>\n",
       "      <td>-0.00627</td>\n",
       "      <td>-0.2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>S102</td>\n",
       "      <td>0.266</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>-0.0655</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>0.3980</td>\n",
       "      <td>-0.1340</td>\n",
       "      <td>0.4610</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.14000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.01220</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.5530</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.06420</td>\n",
       "      <td>-0.437</td>\n",
       "      <td>0.10400</td>\n",
       "      <td>-0.4980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>S103</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.5890</td>\n",
       "      <td>0.5870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0835</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>-0.2150</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>-0.1460</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02250</td>\n",
       "      <td>0.2980</td>\n",
       "      <td>-0.12400</td>\n",
       "      <td>0.1780</td>\n",
       "      <td>0.1470</td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.13700</td>\n",
       "      <td>1.240</td>\n",
       "      <td>0.00682</td>\n",
       "      <td>-0.0233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 11000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id   A1BG     A2M   A2ML1  A4GALT    AAAS    AACS   AADAT   AAED1  \\\n",
       "0     S001 -1.180 -0.8630 -0.8020  0.2220  0.2560  0.6650  1.2800 -0.3390   \n",
       "1     S002 -0.685 -1.0700 -0.6840  0.9840  0.1350  0.3340  1.3000  0.1390   \n",
       "2     S003 -0.528 -1.3200  0.4350     NaN -0.2400  1.0400 -0.0213 -0.0479   \n",
       "3     S005 -1.670 -1.1900 -0.4430  0.2430 -0.0993  0.7570  0.7400 -0.9290   \n",
       "4     S006 -0.374 -0.0206 -0.5370  0.3110  0.3750  0.0131 -1.1000     NaN   \n",
       "..     ...    ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "90    S099 -1.070 -0.7120  0.0462 -0.0471  0.2270  1.3500  1.2100  0.0048   \n",
       "91    S100 -1.280 -0.7360 -0.5520  0.1140  0.2730  1.0700  0.8500     NaN   \n",
       "92    S101 -0.290 -0.3200  3.1700 -0.9070  0.0317 -0.0425     NaN -0.2730   \n",
       "93    S102  0.266  1.3900 -0.0655  0.4700  0.3980 -0.1340  0.4610  1.0400   \n",
       "94    S103  0.692  0.5890  0.5870     NaN -0.0835  0.1720 -0.2150  0.0140   \n",
       "\n",
       "     AAGAB  ...   ZSWIM8  ZSWIM9     ZW10  ZWILCH   ZWINT   ZXDC   ZYG11B  \\\n",
       "0   0.4120  ... -0.08770     NaN  0.02290  0.1090     NaN -0.332 -0.43300   \n",
       "1   1.3300  ... -0.03560     NaN  0.36300  1.0700  0.7370 -0.564 -0.00461   \n",
       "2   0.4190  ...  0.00112 -0.1450  0.01050 -0.1160     NaN  0.151 -0.07400   \n",
       "3   0.2290  ...  0.07250 -0.0552 -0.07140  0.0933  0.1560 -0.398 -0.07520   \n",
       "4   0.5650  ... -0.17600     NaN -1.22000 -0.5620  0.9370 -0.646  0.20700   \n",
       "..     ...  ...      ...     ...      ...     ...     ...    ...      ...   \n",
       "90  0.6820  ... -0.06990 -0.4010  0.55700  0.8270  0.3480 -0.187 -0.10700   \n",
       "91 -0.5360  ...  0.13100     NaN -0.00364  0.7630  0.0784 -0.203  0.01320   \n",
       "92 -0.0747  ... -0.17900 -0.5210  0.05230  0.5150  0.5850  0.138 -0.19000   \n",
       "93  0.3630  ... -0.14000     NaN -0.01220  0.2500  0.5530  0.387  0.06420   \n",
       "94 -0.1460  ... -0.02250  0.2980 -0.12400  0.1780  0.1470 -0.114  0.13700   \n",
       "\n",
       "      ZYX    ZZEF1    ZZZ3  \n",
       "0  -1.020 -0.12300 -0.0859  \n",
       "1  -1.130 -0.07570 -0.4730  \n",
       "2  -0.540  0.32000 -0.4190  \n",
       "3  -0.797 -0.03010 -0.4670  \n",
       "4  -1.850 -0.17600  0.0513  \n",
       "..    ...      ...     ...  \n",
       "90 -0.830  0.06200 -0.5280  \n",
       "91 -1.200 -0.35700  0.0299  \n",
       "92 -0.966 -0.00627 -0.2490  \n",
       "93 -0.437  0.10400 -0.4980  \n",
       "94  1.240  0.00682 -0.0233  \n",
       "\n",
       "[95 rows x 11000 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = './data/CPTAC/DISCOVERY_data_freeze_v_2.1/UCEC_discovery_Proteomics_PNNL_ratio_median_polishing_log2.txt'\n",
    "df_proteo = read_data(file_path)\n",
    "df_proteo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>A1BG</th>\n",
       "      <th>A1CF</th>\n",
       "      <th>A2M</th>\n",
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       "      <th>ZZZ3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S001</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>S099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>S100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>S101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>S102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>S103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 14860 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id  A1BG  A1CF  A2M  A2ML1  A3GALT2  A4GALT  A4GNT  AAAS  AACS  ...  \\\n",
       "0     S001     0     0    0      0        0       0      0     0     0  ...   \n",
       "1     S002     0     0    0      0        0       0      0     0     0  ...   \n",
       "2     S003     0     0    0      0        0       0      0     0     0  ...   \n",
       "3     S005     0     0    0      0        0       0      0     0     0  ...   \n",
       "4     S006     0     0    0      0        0       0      0     0     0  ...   \n",
       "..     ...   ...   ...  ...    ...      ...     ...    ...   ...   ...  ...   \n",
       "90    S099     0     0    0      0        0       0      1     0     0  ...   \n",
       "91    S100     0     0    0      0        0       0      0     0     0  ...   \n",
       "92    S101     0     0    0      0        0       0      0     0     0  ...   \n",
       "93    S102     0     0    0      0        0       0      0     0     0  ...   \n",
       "94    S103     0     0    0      0        0       0      0     0     0  ...   \n",
       "\n",
       "    ZUFSP  ZWILCH  ZXDA  ZXDB  ZXDC  ZYG11A  ZYG11B  ZYX  ZZEF1  ZZZ3  \n",
       "0       0       0     0     0     0       0       0    0      0     0  \n",
       "1       0       0     0     0     0       0       0    0      0     0  \n",
       "2       0       0     0     0     0       0       0    0      0     0  \n",
       "3       0       0     0     0     0       0       0    0      0     0  \n",
       "4       0       0     0     0     0       0       0    0      0     0  \n",
       "..    ...     ...   ...   ...   ...     ...     ...  ...    ...   ...  \n",
       "90      1       0     0     0     0       0       1    0      0     0  \n",
       "91      0       0     0     0     0       0       0    0      0     0  \n",
       "92      0       0     0     0     0       0       0    0      0     0  \n",
       "93      0       0     0     0     0       0       0    0      0     0  \n",
       "94      0       0     0     0     0       0       0    0      0     0  \n",
       "\n",
       "[95 rows x 14860 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sommut = read_data('./data/CPTAC/DISCOVERY_data_freeze_v_2.1/UCEC_discovery_somatic_mutation_gene_level.txt')\n",
    "df_sommut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>DDX11L1</th>\n",
       "      <th>FAM138A|chr1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S005</td>\n",
       "      <td>0</td>\n",
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       "      <td>S099</td>\n",
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       "      <td>S100</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>S101</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>S103</td>\n",
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       "</table>\n",
       "<p>95 rows × 25245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id  DDX11L1  FAM138A|chr1  FAM87B  LOC100133331|chr1  LOC100288069  \\\n",
       "0     S001        0             0       0                  0             0   \n",
       "1     S002        0             0       0                  0             0   \n",
       "2     S003        0             0       0                  0             0   \n",
       "3     S005        0             0       0                  0             0   \n",
       "4     S006        0             0       0                  0             0   \n",
       "..     ...      ...           ...     ...                ...           ...   \n",
       "90    S099        0             0       0                  0             0   \n",
       "91    S100       -1            -1      -1                 -1            -1   \n",
       "92    S101        0             0       0                  0             0   \n",
       "93    S102        0             0       0                  0             0   \n",
       "94    S103        0             0       0                  0             0   \n",
       "\n",
       "    LOC729737  MIR6723  MIR6859-1|chr1  OR4F16|chr1  ...  DAZ3  TTTY3  CDY1  \\\n",
       "0           0        0               0            0  ...     0      0     0   \n",
       "1           0        0               0            0  ...     0      0     0   \n",
       "2           0        0               0            0  ...     0      0     0   \n",
       "3           0        0               0            0  ...     0      0     0   \n",
       "4           0        0               0            0  ...     0      0     0   \n",
       "..        ...      ...             ...          ...  ...   ...    ...   ...   \n",
       "90          0        0               0            0  ...     0      0     0   \n",
       "91         -1       -1              -1           -1  ...     0      0     0   \n",
       "92          0        0               0            0  ...     0      0     0   \n",
       "93          0        0               0            0  ...     0      0     0   \n",
       "94          0        0               0            0  ...     0      0     0   \n",
       "\n",
       "    CSPG4P1Y  GOLGA2P2Y  TTTY3B  DDX11L16|chrY  IL9R|chrY  SPRY3|chrY  \\\n",
       "0          0          0       0              0          0           0   \n",
       "1          0          0       0              0          0           0   \n",
       "2          0          0       0              0          0           0   \n",
       "3          0          0       0              0          0           0   \n",
       "4          0          0       0              0          0           0   \n",
       "..       ...        ...     ...            ...        ...         ...   \n",
       "90         0          0       0              0          0           0   \n",
       "91         0          0       0              0          0           0   \n",
       "92         0          0       0              0          0           0   \n",
       "93         0          0       0              0          0           0   \n",
       "94         0          0       0              0          0           0   \n",
       "\n",
       "    VAMP7|chrY  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "..         ...  \n",
       "90           0  \n",
       "91           0  \n",
       "92           0  \n",
       "93           0  \n",
       "94           0  \n",
       "\n",
       "[95 rows x 25245 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna = read_data('./data/CPTAC/DISCOVERY_data_freeze_v_2.1/UCEC_discovery_WGS_CNA_gene_level_THRESHOLD.txt')\n",
    "df_cna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PTEN      75\n",
       "PIK3CA    47\n",
       "ARID1A    43\n",
       "PIK3R1    37\n",
       "KRAS      31\n",
       "CTNNB1    29\n",
       "CTCF      27\n",
       "KMT2B     23\n",
       "TP53      21\n",
       "ZFHX3     21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sommut.sum(axis=0, numeric_only=True).sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>A1BG_proteo</th>\n",
       "      <th>A2M_proteo</th>\n",
       "      <th>A2ML1_proteo</th>\n",
       "      <th>A4GALT_proteo</th>\n",
       "      <th>AAAS_proteo</th>\n",
       "      <th>AACS_proteo</th>\n",
       "      <th>AADAT_proteo</th>\n",
       "      <th>AAED1</th>\n",
       "      <th>AAGAB_proteo</th>\n",
       "      <th>...</th>\n",
       "      <th>ZUFSP</th>\n",
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       "      <th>ZXDA</th>\n",
       "      <th>ZXDB</th>\n",
       "      <th>ZXDC_sommut</th>\n",
       "      <th>ZYG11A</th>\n",
       "      <th>ZYG11B_sommut</th>\n",
       "      <th>ZYX_sommut</th>\n",
       "      <th>ZZEF1_sommut</th>\n",
       "      <th>ZZZ3_sommut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S001</td>\n",
       "      <td>-1.180</td>\n",
       "      <td>-0.8630</td>\n",
       "      <td>-0.8020</td>\n",
       "      <td>0.2220</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.6650</td>\n",
       "      <td>1.2800</td>\n",
       "      <td>-0.3390</td>\n",
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       "      <td>S002</td>\n",
       "      <td>-0.685</td>\n",
       "      <td>-1.0700</td>\n",
       "      <td>-0.6840</td>\n",
       "      <td>0.9840</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>0.3340</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>0.1390</td>\n",
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       "      <th>2</th>\n",
       "      <td>S003</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>-1.3200</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.2400</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>-0.0213</td>\n",
       "      <td>-0.0479</td>\n",
       "      <td>0.4190</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S005</td>\n",
       "      <td>-1.670</td>\n",
       "      <td>-1.1900</td>\n",
       "      <td>-0.4430</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>-0.0993</td>\n",
       "      <td>0.7570</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>-0.9290</td>\n",
       "      <td>0.2290</td>\n",
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       "      <th>4</th>\n",
       "      <td>S006</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>-0.0206</td>\n",
       "      <td>-0.5370</td>\n",
       "      <td>0.3110</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>-1.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5650</td>\n",
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       "      <td>0.0462</td>\n",
       "      <td>-0.0471</td>\n",
       "      <td>0.2270</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>1.2100</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.6820</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.7360</td>\n",
       "      <td>-0.5520</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.2730</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.5360</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>S101</td>\n",
       "      <td>-0.290</td>\n",
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       "      <td>3.1700</td>\n",
       "      <td>-0.9070</td>\n",
       "      <td>0.0317</td>\n",
       "      <td>-0.0425</td>\n",
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       "      <td>1.3900</td>\n",
       "      <td>-0.0655</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>0.3980</td>\n",
       "      <td>-0.1340</td>\n",
       "      <td>0.4610</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>S103</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.5890</td>\n",
       "      <td>0.5870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0835</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>-0.2150</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>-0.1460</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 25859 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id  A1BG_proteo  A2M_proteo  A2ML1_proteo  A4GALT_proteo  AAAS_proteo  \\\n",
       "0     S001       -1.180     -0.8630       -0.8020         0.2220       0.2560   \n",
       "1     S002       -0.685     -1.0700       -0.6840         0.9840       0.1350   \n",
       "2     S003       -0.528     -1.3200        0.4350            NaN      -0.2400   \n",
       "3     S005       -1.670     -1.1900       -0.4430         0.2430      -0.0993   \n",
       "4     S006       -0.374     -0.0206       -0.5370         0.3110       0.3750   \n",
       "..     ...          ...         ...           ...            ...          ...   \n",
       "90    S099       -1.070     -0.7120        0.0462        -0.0471       0.2270   \n",
       "91    S100       -1.280     -0.7360       -0.5520         0.1140       0.2730   \n",
       "92    S101       -0.290     -0.3200        3.1700        -0.9070       0.0317   \n",
       "93    S102        0.266      1.3900       -0.0655         0.4700       0.3980   \n",
       "94    S103        0.692      0.5890        0.5870            NaN      -0.0835   \n",
       "\n",
       "    AACS_proteo  AADAT_proteo   AAED1  AAGAB_proteo  ...  ZUFSP  \\\n",
       "0        0.6650        1.2800 -0.3390        0.4120  ...      0   \n",
       "1        0.3340        1.3000  0.1390        1.3300  ...      0   \n",
       "2        1.0400       -0.0213 -0.0479        0.4190  ...      0   \n",
       "3        0.7570        0.7400 -0.9290        0.2290  ...      0   \n",
       "4        0.0131       -1.1000     NaN        0.5650  ...      0   \n",
       "..          ...           ...     ...           ...  ...    ...   \n",
       "90       1.3500        1.2100  0.0048        0.6820  ...      1   \n",
       "91       1.0700        0.8500     NaN       -0.5360  ...      0   \n",
       "92      -0.0425           NaN -0.2730       -0.0747  ...      0   \n",
       "93      -0.1340        0.4610  1.0400        0.3630  ...      0   \n",
       "94       0.1720       -0.2150  0.0140       -0.1460  ...      0   \n",
       "\n",
       "    ZWILCH_sommut  ZXDA  ZXDB  ZXDC_sommut  ZYG11A  ZYG11B_sommut  ZYX_sommut  \\\n",
       "0               0     0     0            0       0              0           0   \n",
       "1               0     0     0            0       0              0           0   \n",
       "2               0     0     0            0       0              0           0   \n",
       "3               0     0     0            0       0              0           0   \n",
       "4               0     0     0            0       0              0           0   \n",
       "..            ...   ...   ...          ...     ...            ...         ...   \n",
       "90              0     0     0            0       0              1           0   \n",
       "91              0     0     0            0       0              0           0   \n",
       "92              0     0     0            0       0              0           0   \n",
       "93              0     0     0            0       0              0           0   \n",
       "94              0     0     0            0       0              0           0   \n",
       "\n",
       "    ZZEF1_sommut  ZZZ3_sommut  \n",
       "0              0            0  \n",
       "1              0            0  \n",
       "2              0            0  \n",
       "3              0            0  \n",
       "4              0            0  \n",
       "..           ...          ...  \n",
       "90             0            0  \n",
       "91             0            0  \n",
       "92             0            0  \n",
       "93             0            0  \n",
       "94             0            0  \n",
       "\n",
       "[95 rows x 25859 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = df_proteo.merge(df_sommut, on='case_id', suffixes=('_proteo', '_sommut'))\n",
    "merged_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A1BG_proteo</th>\n",
       "      <th>A2M_proteo</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.668930</td>\n",
       "      <td>0.129737</td>\n",
       "      <td>0.125693</td>\n",
       "      <td>-0.133788</td>\n",
       "      <td>-0.347029</td>\n",
       "      <td>-0.229781</td>\n",
       "      <td>0.042169</td>\n",
       "      <td>-0.048411</td>\n",
       "      <td>0.153237</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.159953</td>\n",
       "      <td>-0.029480</td>\n",
       "      <td>0.019870</td>\n",
       "      <td>-0.127285</td>\n",
       "      <td>0.013853</td>\n",
       "      <td>-0.079465</td>\n",
       "      <td>-0.157926</td>\n",
       "      <td>-0.124692</td>\n",
       "      <td>-0.029678</td>\n",
       "      <td>0.023839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M_proteo</th>\n",
       "      <td>0.668930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137645</td>\n",
       "      <td>0.094538</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>-0.251096</td>\n",
       "      <td>-0.072371</td>\n",
       "      <td>0.093278</td>\n",
       "      <td>0.031651</td>\n",
       "      <td>0.052590</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051961</td>\n",
       "      <td>-0.003025</td>\n",
       "      <td>0.050268</td>\n",
       "      <td>-0.016362</td>\n",
       "      <td>-0.033843</td>\n",
       "      <td>-0.003671</td>\n",
       "      <td>-0.051510</td>\n",
       "      <td>-0.097939</td>\n",
       "      <td>0.058407</td>\n",
       "      <td>0.048919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1_proteo</th>\n",
       "      <td>0.129737</td>\n",
       "      <td>0.137645</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103499</td>\n",
       "      <td>-0.206183</td>\n",
       "      <td>0.041496</td>\n",
       "      <td>-0.076818</td>\n",
       "      <td>-0.083662</td>\n",
       "      <td>0.133702</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097727</td>\n",
       "      <td>-0.148260</td>\n",
       "      <td>-0.117542</td>\n",
       "      <td>-0.084775</td>\n",
       "      <td>-0.023204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.043936</td>\n",
       "      <td>-0.107738</td>\n",
       "      <td>0.051249</td>\n",
       "      <td>-0.156473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4GALT_proteo</th>\n",
       "      <td>0.125693</td>\n",
       "      <td>0.094538</td>\n",
       "      <td>0.103499</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.185359</td>\n",
       "      <td>0.213150</td>\n",
       "      <td>-0.126206</td>\n",
       "      <td>-0.114542</td>\n",
       "      <td>0.021880</td>\n",
       "      <td>-0.338177</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309268</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>-0.248702</td>\n",
       "      <td>-0.058406</td>\n",
       "      <td>-0.003371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030953</td>\n",
       "      <td>0.056977</td>\n",
       "      <td>-0.043212</td>\n",
       "      <td>-0.107470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAAS_proteo</th>\n",
       "      <td>-0.133788</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>-0.206183</td>\n",
       "      <td>-0.185359</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.108713</td>\n",
       "      <td>-0.069045</td>\n",
       "      <td>0.056345</td>\n",
       "      <td>-0.112788</td>\n",
       "      <td>0.096138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068744</td>\n",
       "      <td>-0.077652</td>\n",
       "      <td>0.017033</td>\n",
       "      <td>-0.064985</td>\n",
       "      <td>0.041987</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.042336</td>\n",
       "      <td>0.176711</td>\n",
       "      <td>0.070072</td>\n",
       "      <td>0.042435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYG11A</th>\n",
       "      <td>-0.079465</td>\n",
       "      <td>-0.003671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>-0.043763</td>\n",
       "      <td>-0.077952</td>\n",
       "      <td>-0.178135</td>\n",
       "      <td>0.049388</td>\n",
       "      <td>0.057847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.334358</td>\n",
       "      <td>0.392807</td>\n",
       "      <td>0.263361</td>\n",
       "      <td>0.239298</td>\n",
       "      <td>0.293803</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489247</td>\n",
       "      <td>-0.015125</td>\n",
       "      <td>0.392807</td>\n",
       "      <td>-0.015125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYG11B_sommut</th>\n",
       "      <td>-0.157926</td>\n",
       "      <td>-0.051510</td>\n",
       "      <td>-0.043936</td>\n",
       "      <td>-0.030953</td>\n",
       "      <td>0.042336</td>\n",
       "      <td>0.107896</td>\n",
       "      <td>0.096594</td>\n",
       "      <td>0.040558</td>\n",
       "      <td>0.146915</td>\n",
       "      <td>0.004989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.699462</td>\n",
       "      <td>0.392807</td>\n",
       "      <td>0.263361</td>\n",
       "      <td>0.239298</td>\n",
       "      <td>-0.034565</td>\n",
       "      <td>0.489247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015125</td>\n",
       "      <td>0.392807</td>\n",
       "      <td>-0.015125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZYX_sommut</th>\n",
       "      <td>-0.124692</td>\n",
       "      <td>-0.097939</td>\n",
       "      <td>-0.107738</td>\n",
       "      <td>0.056977</td>\n",
       "      <td>0.176711</td>\n",
       "      <td>-0.018708</td>\n",
       "      <td>0.035532</td>\n",
       "      <td>-0.117483</td>\n",
       "      <td>-0.025104</td>\n",
       "      <td>-0.031500</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021624</td>\n",
       "      <td>-0.018625</td>\n",
       "      <td>-0.026780</td>\n",
       "      <td>-0.029090</td>\n",
       "      <td>-0.024311</td>\n",
       "      <td>-0.015125</td>\n",
       "      <td>-0.015125</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018625</td>\n",
       "      <td>-0.010638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1_sommut</th>\n",
       "      <td>-0.029678</td>\n",
       "      <td>0.058407</td>\n",
       "      <td>0.051249</td>\n",
       "      <td>-0.043212</td>\n",
       "      <td>0.070072</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.034641</td>\n",
       "      <td>0.103176</td>\n",
       "      <td>0.106458</td>\n",
       "      <td>0.029150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261862</td>\n",
       "      <td>0.311594</td>\n",
       "      <td>0.200570</td>\n",
       "      <td>0.179468</td>\n",
       "      <td>0.227002</td>\n",
       "      <td>0.392807</td>\n",
       "      <td>0.392807</td>\n",
       "      <td>-0.018625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3_sommut</th>\n",
       "      <td>0.023839</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>-0.156473</td>\n",
       "      <td>-0.107470</td>\n",
       "      <td>0.042435</td>\n",
       "      <td>-0.045870</td>\n",
       "      <td>0.177354</td>\n",
       "      <td>0.025093</td>\n",
       "      <td>-0.132443</td>\n",
       "      <td>0.048633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021624</td>\n",
       "      <td>-0.018625</td>\n",
       "      <td>-0.026780</td>\n",
       "      <td>0.365703</td>\n",
       "      <td>-0.024311</td>\n",
       "      <td>-0.015125</td>\n",
       "      <td>-0.015125</td>\n",
       "      <td>-0.010638</td>\n",
       "      <td>-0.018625</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25858 rows × 25858 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               A1BG_proteo  A2M_proteo  A2ML1_proteo  A4GALT_proteo  \\\n",
       "A1BG_proteo       1.000000    0.668930      0.129737       0.125693   \n",
       "A2M_proteo        0.668930    1.000000      0.137645       0.094538   \n",
       "A2ML1_proteo      0.129737    0.137645      1.000000       0.103499   \n",
       "A4GALT_proteo     0.125693    0.094538      0.103499       1.000000   \n",
       "AAAS_proteo      -0.133788    0.034448     -0.206183      -0.185359   \n",
       "...                    ...         ...           ...            ...   \n",
       "ZYG11A           -0.079465   -0.003671           NaN            NaN   \n",
       "ZYG11B_sommut    -0.157926   -0.051510     -0.043936      -0.030953   \n",
       "ZYX_sommut       -0.124692   -0.097939     -0.107738       0.056977   \n",
       "ZZEF1_sommut     -0.029678    0.058407      0.051249      -0.043212   \n",
       "ZZZ3_sommut       0.023839    0.048919     -0.156473      -0.107470   \n",
       "\n",
       "               AAAS_proteo  AACS_proteo  AADAT_proteo     AAED1  AAGAB_proteo  \\\n",
       "A1BG_proteo      -0.133788    -0.347029     -0.229781  0.042169     -0.048411   \n",
       "A2M_proteo        0.034448    -0.251096     -0.072371  0.093278      0.031651   \n",
       "A2ML1_proteo     -0.206183     0.041496     -0.076818 -0.083662      0.133702   \n",
       "A4GALT_proteo    -0.185359     0.213150     -0.126206 -0.114542      0.021880   \n",
       "AAAS_proteo       1.000000    -0.108713     -0.069045  0.056345     -0.112788   \n",
       "...                    ...          ...           ...       ...           ...   \n",
       "ZYG11A            0.051497    -0.043763     -0.077952 -0.178135      0.049388   \n",
       "ZYG11B_sommut     0.042336     0.107896      0.096594  0.040558      0.146915   \n",
       "ZYX_sommut        0.176711    -0.018708      0.035532 -0.117483     -0.025104   \n",
       "ZZEF1_sommut      0.070072     0.000094      0.034641  0.103176      0.106458   \n",
       "ZZZ3_sommut       0.042435    -0.045870      0.177354  0.025093     -0.132443   \n",
       "\n",
       "               AAK1_proteo  ...     ZUFSP  ZWILCH_sommut      ZXDA      ZXDB  \\\n",
       "A1BG_proteo       0.153237  ... -0.159953      -0.029480  0.019870 -0.127285   \n",
       "A2M_proteo        0.052590  ... -0.051961      -0.003025  0.050268 -0.016362   \n",
       "A2ML1_proteo      0.008633  ... -0.097727      -0.148260 -0.117542 -0.084775   \n",
       "A4GALT_proteo    -0.338177  ... -0.309268       0.025826 -0.248702 -0.058406   \n",
       "AAAS_proteo       0.096138  ...  0.068744      -0.077652  0.017033 -0.064985   \n",
       "...                    ...  ...       ...            ...       ...       ...   \n",
       "ZYG11A            0.057847  ...  0.334358       0.392807  0.263361  0.239298   \n",
       "ZYG11B_sommut     0.004989  ...  0.699462       0.392807  0.263361  0.239298   \n",
       "ZYX_sommut       -0.031500  ... -0.021624      -0.018625 -0.026780 -0.029090   \n",
       "ZZEF1_sommut      0.029150  ...  0.261862       0.311594  0.200570  0.179468   \n",
       "ZZZ3_sommut       0.048633  ... -0.021624      -0.018625 -0.026780  0.365703   \n",
       "\n",
       "               ZXDC_sommut    ZYG11A  ZYG11B_sommut  ZYX_sommut  ZZEF1_sommut  \\\n",
       "A1BG_proteo       0.013853 -0.079465      -0.157926   -0.124692     -0.029678   \n",
       "A2M_proteo       -0.033843 -0.003671      -0.051510   -0.097939      0.058407   \n",
       "A2ML1_proteo     -0.023204       NaN      -0.043936   -0.107738      0.051249   \n",
       "A4GALT_proteo    -0.003371       NaN      -0.030953    0.056977     -0.043212   \n",
       "AAAS_proteo       0.041987  0.051497       0.042336    0.176711      0.070072   \n",
       "...                    ...       ...            ...         ...           ...   \n",
       "ZYG11A            0.293803  1.000000       0.489247   -0.015125      0.392807   \n",
       "ZYG11B_sommut    -0.034565  0.489247       1.000000   -0.015125      0.392807   \n",
       "ZYX_sommut       -0.024311 -0.015125      -0.015125    1.000000     -0.018625   \n",
       "ZZEF1_sommut      0.227002  0.392807       0.392807   -0.018625      1.000000   \n",
       "ZZZ3_sommut      -0.024311 -0.015125      -0.015125   -0.010638     -0.018625   \n",
       "\n",
       "               ZZZ3_sommut  \n",
       "A1BG_proteo       0.023839  \n",
       "A2M_proteo        0.048919  \n",
       "A2ML1_proteo     -0.156473  \n",
       "A4GALT_proteo    -0.107470  \n",
       "AAAS_proteo       0.042435  \n",
       "...                    ...  \n",
       "ZYG11A           -0.015125  \n",
       "ZYG11B_sommut    -0.015125  \n",
       "ZYX_sommut       -0.010638  \n",
       "ZZEF1_sommut     -0.018625  \n",
       "ZZZ3_sommut       1.000000  \n",
       "\n",
       "[25858 rows x 25858 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations = merged_df.corr(numeric_only=True)\n",
    "correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations.columns.str.contains('CTNNB1').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LEF1_proteo</th>\n",
       "      <th>LEF1_sommut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CTNNB1_proteo</th>\n",
       "      <td>0.540913</td>\n",
       "      <td>0.103803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTNNB1_sommut</th>\n",
       "      <td>0.512732</td>\n",
       "      <td>0.141714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               LEF1_proteo  LEF1_sommut\n",
       "CTNNB1_proteo     0.540913     0.103803\n",
       "CTNNB1_sommut     0.512732     0.141714"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations[correlations.columns.str.contains('CTNNB1')][['LEF1_proteo', 'LEF1_sommut']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>gene</th>\n",
       "      <th>cna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S001</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S002</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S003</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S005</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S006</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case_id     gene  cna\n",
       "0    S001  DDX11L1    0\n",
       "1    S002  DDX11L1    0\n",
       "2    S003  DDX11L1    0\n",
       "3    S005  DDX11L1    0\n",
       "4    S006  DDX11L1    0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long = df_cna.melt(id_vars=['case_id']).rename(columns={'variable': 'gene', 'value': 'cna'})\n",
    "df_cna_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split multi-values field 'gene' into two separate columns for the gene and chromosome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>case_id</th>\n",
       "      <th>gene</th>\n",
       "      <th>cna</th>\n",
       "      <th>chr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S001</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S002</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S003</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S005</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S006</td>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case_id     gene  cna   chr\n",
       "0    S001  DDX11L1    0  None\n",
       "1    S002  DDX11L1    0  None\n",
       "2    S003  DDX11L1    0  None\n",
       "3    S005  DDX11L1    0  None\n",
       "4    S006  DDX11L1    0  None"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long[['gene', 'chr']] = df_cna_long['gene'].str.split('|', n=2, expand=True).rename(columns={0:'gene', 1:'chr'})\n",
    "df_cna_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='chr'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cna_long['chr'].value_counts().plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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></th>\n",
       "      <th>gene</th>\n",
       "      <th>chr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DDX11L1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FAM138A</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FAM87B</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOC100133331</td>\n",
       "      <td>chr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LOC100288069</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25239</th>\n",
       "      <td>TTTY3B</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25240</th>\n",
       "      <td>DDX11L16</td>\n",
       "      <td>chrY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25241</th>\n",
       "      <td>IL9R</td>\n",
       "      <td>chrY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25242</th>\n",
       "      <td>SPRY3</td>\n",
       "      <td>chrY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25243</th>\n",
       "      <td>VAMP7</td>\n",
       "      <td>chrY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25244 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               gene   chr\n",
       "0           DDX11L1  None\n",
       "1           FAM138A  chr1\n",
       "2            FAM87B  None\n",
       "3      LOC100133331  chr1\n",
       "4      LOC100288069  None\n",
       "...             ...   ...\n",
       "25239        TTTY3B  None\n",
       "25240      DDX11L16  chrY\n",
       "25241          IL9R  chrY\n",
       "25242         SPRY3  chrY\n",
       "25243         VAMP7  chrY\n",
       "\n",
       "[25244 rows x 2 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long[['gene', 'chr']].drop_duplicates(subset=['gene', 'chr']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>13</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>S002</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>S002</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>30</th>\n",
       "      <td>S002</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>S002</td>\n",
       "      <td>chr4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>S002</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>S002</td>\n",
       "      <td>chr9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>S002</td>\n",
       "      <td>chrX</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
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       "      <td>chrY</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr1</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr10</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr11</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>S003</td>\n",
       "      <td>chr9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>S003</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>S003</td>\n",
       "      <td>chrY</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id    chr  min  max  sum  mean  median\n",
       "0     S001   chr1    0    0    0   0.0     0.0\n",
       "1     S001  chr10    0    0    0   0.0     0.0\n",
       "2     S001  chr11    0    0    0   0.0     0.0\n",
       "3     S001  chr12    0    0    0   0.0     0.0\n",
       "4     S001  chr14    0    0    0   0.0     0.0\n",
       "5     S001  chr15    0    0    0   0.0     0.0\n",
       "6     S001  chr16    0    0    0   0.0     0.0\n",
       "7     S001  chr17    0    0    0   0.0     0.0\n",
       "8     S001  chr19    0    0    0   0.0     0.0\n",
       "9     S001   chr2    0    0    0   0.0     0.0\n",
       "10    S001  chr22    0    0    0   0.0     0.0\n",
       "11    S001   chr3    0    0    0   0.0     0.0\n",
       "12    S001   chr4    0    0    0   0.0     0.0\n",
       "13    S001   chr5   -2    0   -8  -1.0    -1.0\n",
       "14    S001   chr6    0    0    0   0.0     0.0\n",
       "15    S001   chr7    0    0    0   0.0     0.0\n",
       "16    S001   chr8    0    0    0   0.0     0.0\n",
       "17    S001   chr9    0    0    0   0.0     0.0\n",
       "18    S001   chrX    0    0    0   0.0     0.0\n",
       "19    S001   chrY    0    0    0   0.0     0.0\n",
       "20    S002   chr1    0    0    0   0.0     0.0\n",
       "21    S002  chr10    0    0    0   0.0     0.0\n",
       "22    S002  chr11    0    0    0   0.0     0.0\n",
       "23    S002  chr12    0    0    0   0.0     0.0\n",
       "24    S002  chr14    0    0    0   0.0     0.0\n",
       "25    S002  chr15    0    0    0   0.0     0.0\n",
       "26    S002  chr16    0    0    0   0.0     0.0\n",
       "27    S002  chr17    0    0    0   0.0     0.0\n",
       "28    S002  chr19    0    0    0   0.0     0.0\n",
       "29    S002   chr2    0    0    0   0.0     0.0\n",
       "30    S002  chr22    0    0    0   0.0     0.0\n",
       "31    S002   chr3    0    0    0   0.0     0.0\n",
       "32    S002   chr4    0    0    0   0.0     0.0\n",
       "33    S002   chr5    0    0    0   0.0     0.0\n",
       "34    S002   chr6    0    0    0   0.0     0.0\n",
       "35    S002   chr7    0    0    0   0.0     0.0\n",
       "36    S002   chr8    0    0    0   0.0     0.0\n",
       "37    S002   chr9    0    0    0   0.0     0.0\n",
       "38    S002   chrX    0    0    0   0.0     0.0\n",
       "39    S002   chrY    0    0    0   0.0     0.0\n",
       "40    S003   chr1    0    0    0   0.0     0.0\n",
       "41    S003  chr10    0    0    0   0.0     0.0\n",
       "42    S003  chr11    0    0    0   0.0     0.0\n",
       "43    S003  chr12    0    0    0   0.0     0.0\n",
       "44    S003  chr14    0    0    0   0.0     0.0\n",
       "45    S003  chr15    0    0    0   0.0     0.0\n",
       "46    S003  chr16    0    0    0   0.0     0.0\n",
       "47    S003  chr17    0    0    0   0.0     0.0\n",
       "48    S003  chr19    0    0    0   0.0     0.0\n",
       "49    S003   chr2    0    0    0   0.0     0.0\n",
       "50    S003  chr22    0    0    0   0.0     0.0\n",
       "51    S003   chr3    0    0    0   0.0     0.0\n",
       "52    S003   chr4    0    0    0   0.0     0.0\n",
       "53    S003   chr5    0    0    0   0.0     0.0\n",
       "54    S003   chr6    0    0    0   0.0     0.0\n",
       "55    S003   chr7    0    0    0   0.0     0.0\n",
       "56    S003   chr8    0    0    0   0.0     0.0\n",
       "57    S003   chr9    0    0    0   0.0     0.0\n",
       "58    S003   chrX    0    0    0   0.0     0.0\n",
       "59    S003   chrY    0    0    0   0.0     0.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long_agg = df_cna_long.groupby(by=['case_id','chr'])['cna'].agg(['min', 'max', 'sum', 'mean', 'median'])\n",
    "df_cna_long_agg.reset_index().head(60)\n",
    "# .max().sort_values(ascending=False) #.describe().head(50) #['cna'].sum().sort_values().plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">max</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr</th>\n",
       "      <th>chr1</th>\n",
       "      <th>chr10</th>\n",
       "      <th>chr11</th>\n",
       "      <th>chr12</th>\n",
       "      <th>chr14</th>\n",
       "      <th>chr15</th>\n",
       "      <th>chr16</th>\n",
       "      <th>chr17</th>\n",
       "      <th>chr19</th>\n",
       "      <th>chr2</th>\n",
       "      <th>...</th>\n",
       "      <th>chr22</th>\n",
       "      <th>chr3</th>\n",
       "      <th>chr4</th>\n",
       "      <th>chr5</th>\n",
       "      <th>chr6</th>\n",
       "      <th>chr7</th>\n",
       "      <th>chr8</th>\n",
       "      <th>chr9</th>\n",
       "      <th>chrX</th>\n",
       "      <th>chrY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_id</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>S001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S005</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         max                                                       ...   sum  \\\n",
       "chr     chr1 chr10 chr11 chr12 chr14 chr15 chr16 chr17 chr19 chr2  ... chr22   \n",
       "case_id                                                            ...         \n",
       "S001     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  ...   0.0   \n",
       "S002     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  ...   0.0   \n",
       "S003     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  ...   0.0   \n",
       "S005     0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  0.0  ...   0.0   \n",
       "S006     1.0   2.0  -1.0  -1.0   1.0  -1.0   1.0   0.0  -1.0  0.0  ...   2.0   \n",
       "\n",
       "                                                       \n",
       "chr     chr3 chr4 chr5 chr6 chr7 chr8 chr9  chrX chrY  \n",
       "case_id                                                \n",
       "S001     0.0  0.0 -8.0  0.0  0.0  0.0  0.0   0.0  0.0  \n",
       "S002     0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  \n",
       "S003     0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  \n",
       "S005     0.0  0.0  0.0  0.0  0.0  2.0  0.0   0.0  0.0  \n",
       "S006     1.0 -5.0 -7.0  0.0 -1.0  0.0 -1.0  13.0  0.0  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long_agg_pivot = df_cna_long_agg.reset_index().pivot_table(index='case_id', columns='chr', values=['min', 'max', 'sum', 'mean', 'median'])\n",
    "df_cna_long_agg_pivot.head()\n",
    "# .columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['max(chr1)',\n",
       " 'max(chr10)',\n",
       " 'max(chr11)',\n",
       " 'max(chr12)',\n",
       " 'max(chr14)',\n",
       " 'max(chr15)',\n",
       " 'max(chr16)',\n",
       " 'max(chr17)',\n",
       " 'max(chr19)',\n",
       " 'max(chr2)',\n",
       " 'max(chr22)',\n",
       " 'max(chr3)',\n",
       " 'max(chr4)',\n",
       " 'max(chr5)',\n",
       " 'max(chr6)',\n",
       " 'max(chr7)',\n",
       " 'max(chr8)',\n",
       " 'max(chr9)',\n",
       " 'max(chrX)',\n",
       " 'max(chrY)',\n",
       " 'mean(chr1)',\n",
       " 'mean(chr10)',\n",
       " 'mean(chr11)',\n",
       " 'mean(chr12)',\n",
       " 'mean(chr14)',\n",
       " 'mean(chr15)',\n",
       " 'mean(chr16)',\n",
       " 'mean(chr17)',\n",
       " 'mean(chr19)',\n",
       " 'mean(chr2)',\n",
       " 'mean(chr22)',\n",
       " 'mean(chr3)',\n",
       " 'mean(chr4)',\n",
       " 'mean(chr5)',\n",
       " 'mean(chr6)',\n",
       " 'mean(chr7)',\n",
       " 'mean(chr8)',\n",
       " 'mean(chr9)',\n",
       " 'mean(chrX)',\n",
       " 'mean(chrY)',\n",
       " 'median(chr1)',\n",
       " 'median(chr10)',\n",
       " 'median(chr11)',\n",
       " 'median(chr12)',\n",
       " 'median(chr14)',\n",
       " 'median(chr15)',\n",
       " 'median(chr16)',\n",
       " 'median(chr17)',\n",
       " 'median(chr19)',\n",
       " 'median(chr2)',\n",
       " 'median(chr22)',\n",
       " 'median(chr3)',\n",
       " 'median(chr4)',\n",
       " 'median(chr5)',\n",
       " 'median(chr6)',\n",
       " 'median(chr7)',\n",
       " 'median(chr8)',\n",
       " 'median(chr9)',\n",
       " 'median(chrX)',\n",
       " 'median(chrY)',\n",
       " 'min(chr1)',\n",
       " 'min(chr10)',\n",
       " 'min(chr11)',\n",
       " 'min(chr12)',\n",
       " 'min(chr14)',\n",
       " 'min(chr15)',\n",
       " 'min(chr16)',\n",
       " 'min(chr17)',\n",
       " 'min(chr19)',\n",
       " 'min(chr2)',\n",
       " 'min(chr22)',\n",
       " 'min(chr3)',\n",
       " 'min(chr4)',\n",
       " 'min(chr5)',\n",
       " 'min(chr6)',\n",
       " 'min(chr7)',\n",
       " 'min(chr8)',\n",
       " 'min(chr9)',\n",
       " 'min(chrX)',\n",
       " 'min(chrY)',\n",
       " 'sum(chr1)',\n",
       " 'sum(chr10)',\n",
       " 'sum(chr11)',\n",
       " 'sum(chr12)',\n",
       " 'sum(chr14)',\n",
       " 'sum(chr15)',\n",
       " 'sum(chr16)',\n",
       " 'sum(chr17)',\n",
       " 'sum(chr19)',\n",
       " 'sum(chr2)',\n",
       " 'sum(chr22)',\n",
       " 'sum(chr3)',\n",
       " 'sum(chr4)',\n",
       " 'sum(chr5)',\n",
       " 'sum(chr6)',\n",
       " 'sum(chr7)',\n",
       " 'sum(chr8)',\n",
       " 'sum(chr9)',\n",
       " 'sum(chrX)',\n",
       " 'sum(chrY)']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = [f'{pair[0]}({pair[1]})' for pair in df_cna_long_agg_pivot.columns]\n",
    "column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "         max(chr1)  max(chr10)  max(chr11)  max(chr12)  max(chr14)  \\\n",
       "case_id                                                              \n",
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       "\n",
       "         max(chr15)  max(chr16)  max(chr17)  max(chr19)  max(chr2)  ...  \\\n",
       "case_id                                                             ...   \n",
       "S001            0.0         0.0         0.0         0.0        0.0  ...   \n",
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       "S006           -1.0         1.0         0.0        -1.0        0.0  ...   \n",
       "\n",
       "         sum(chr22)  sum(chr3)  sum(chr4)  sum(chr5)  sum(chr6)  sum(chr7)  \\\n",
       "case_id                                                                      \n",
       "S001            0.0        0.0        0.0       -8.0        0.0        0.0   \n",
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       "\n",
       "         sum(chr8)  sum(chr9)  sum(chrX)  sum(chrY)  \n",
       "case_id                                              \n",
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       "\n",
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     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long_agg_pivot.columns = column_names\n",
    "df_cna_long_agg_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>S100</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S101</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S102</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S103</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 25187 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gene     A1BG  A1BG-AS1  A1CF  A2M  A2M-AS1  A2ML1  A2MP1  A3GALT2  A4GALT  \\\n",
       "case_id                                                                      \n",
       "S001      0.0       0.0   0.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "S002      0.0       0.0   0.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "S003      0.0       0.0   0.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "S005      0.0       0.0   1.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "S006      2.0       2.0   1.0 -1.0     -1.0   -1.0   -1.0      0.0     1.0   \n",
       "...       ...       ...   ...  ...      ...    ...    ...      ...     ...   \n",
       "S099      0.0       0.0   0.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "S100     -1.0      -1.0   0.0  0.0      0.0    0.0    0.0      1.0    -1.0   \n",
       "S101      0.0       0.0   0.0  0.0      0.0    0.0    0.0     -1.0    -1.0   \n",
       "S102      0.0       0.0   0.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "S103      0.0       0.0   0.0  0.0      0.0    0.0    0.0      0.0     0.0   \n",
       "\n",
       "gene     A4GNT  ...  hsa-mir-664  hsa-mir-720  hsa-mir-886  hsa-mir-887  \\\n",
       "case_id         ...                                                       \n",
       "S001       0.0  ...          0.0          0.0          0.0          0.0   \n",
       "S002       0.0  ...          0.0          0.0          0.0          0.0   \n",
       "S003       0.0  ...          0.0          0.0          0.0          0.0   \n",
       "S005       0.0  ...          0.0          0.0          0.0          0.0   \n",
       "S006       1.0  ...          1.0          1.0         -1.0          0.0   \n",
       "...        ...  ...          ...          ...          ...          ...   \n",
       "S099       0.0  ...          0.0          0.0          0.0          0.0   \n",
       "S100       1.0  ...          1.0          0.0         -1.0          0.0   \n",
       "S101       0.0  ...          2.0          0.0          0.0          0.0   \n",
       "S102       0.0  ...         -1.0          2.0          0.0          0.0   \n",
       "S103       0.0  ...          0.0          0.0          0.0          0.0   \n",
       "\n",
       "gene     hsa-mir-924  hsa-mir-941-1  hsa-mir-941-2  hsa-mir-941-3  hsa-mir-95  \\\n",
       "case_id                                                                         \n",
       "S001             0.0            1.0            1.0            1.0         0.0   \n",
       "S002             0.0            0.0            0.0            0.0         0.0   \n",
       "S003             0.0            0.0            0.0            0.0         0.0   \n",
       "S005             0.0            0.0            0.0            0.0         0.0   \n",
       "S006             1.0            2.0            2.0            2.0         1.0   \n",
       "...              ...            ...            ...            ...         ...   \n",
       "S099             0.0            0.0            0.0            0.0         0.0   \n",
       "S100            -1.0            1.0            1.0            1.0         0.0   \n",
       "S101             0.0            0.0            0.0            0.0         0.0   \n",
       "S102             0.0            0.0            0.0            0.0         0.0   \n",
       "S103             0.0            0.0            0.0            0.0         0.0   \n",
       "\n",
       "gene     hsa-mir-96  \n",
       "case_id              \n",
       "S001            0.0  \n",
       "S002            0.0  \n",
       "S003            0.0  \n",
       "S005            0.0  \n",
       "S006            0.0  \n",
       "...             ...  \n",
       "S099            1.0  \n",
       "S100            1.0  \n",
       "S101            0.0  \n",
       "S102            2.0  \n",
       "S103            0.0  \n",
       "\n",
       "[95 rows x 25187 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cna_long.pivot_table(index='case_id', columns='gene', values='cna')"
   ]
  }
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