10.gene-expression-signatures/3.apply-mycn-signature.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying MYCN Signature Learned in TARGET data to Cell Lines\n",
    "\n",
    "**Gregory Way, 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import ttest_ind\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sys.path.append(\"../8.gtex-interpret\")\n",
    "from scripts.utils import load_weight_matrix, apply_signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load NBL Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 18628)\n"
     ]
    },
    {
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       "      <th>16</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>...</th>\n",
       "      <th>105375787</th>\n",
       "      <th>106707243</th>\n",
       "      <th>106821730</th>\n",
       "      <th>106865373</th>\n",
       "      <th>107080638</th>\n",
       "      <th>107080644</th>\n",
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       "      <th>107984155</th>\n",
       "      <th>107984923</th>\n",
       "      <th>107987479</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHP134</th>\n",
       "      <td>0.496142</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031914</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.335754</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.123020</td>\n",
       "      <td>0.101682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.202001</td>\n",
       "      <td>0.350250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.359538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.398515</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>CHP212</th>\n",
       "      <td>0.130269</td>\n",
       "      <td>0.077607</td>\n",
       "      <td>0.384543</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.556493</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 18628 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1         2         9   12        13        14        15  \\\n",
       "CHP134  0.496142  0.000000  0.031914  0.0  0.000000  0.335754  1.000000   \n",
       "CHP212  0.130269  0.077607  0.384543  0.0  0.047631  0.483361  0.011138   \n",
       "\n",
       "              16        18        19    ...      105375787  106707243  \\\n",
       "CHP134  0.527104  0.123020  0.101682    ...            0.0   0.202001   \n",
       "CHP212  0.680574  0.002162  0.136316    ...            0.0   0.030310   \n",
       "\n",
       "        106821730  106865373  107080638  107080644  107181291  107984155  \\\n",
       "CHP134   0.350250        0.0   0.359538        0.0        0.0        0.0   \n",
       "CHP212   0.673424        0.0   0.086474        0.0        0.0        0.0   \n",
       "\n",
       "        107984923  107987479  \n",
       "CHP134   0.398515        0.0  \n",
       "CHP212   0.556493        0.0  \n",
       "\n",
       "[2 rows x 18628 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = os.path.join('data', 'nbl_celllines_processed_matrix.tsv.gz')\n",
    "nbl_df = pd.read_table(file, index_col=0)\n",
    "\n",
    "print(nbl_df.shape)\n",
    "nbl_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Apply Signature Learned by TARGET data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_stat</th>\n",
       "      <th>t_p</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>feature_num</th>\n",
       "      <th>z_dim</th>\n",
       "      <th>signal</th>\n",
       "      <th>seed</th>\n",
       "      <th>neg_log_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-18.539478</td>\n",
       "      <td>6.613869e-38</td>\n",
       "      <td>vae</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>signal</td>\n",
       "      <td>451283</td>\n",
       "      <td>37.179544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-17.652445</td>\n",
       "      <td>7.166732e-36</td>\n",
       "      <td>dae</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>signal</td>\n",
       "      <td>451283</td>\n",
       "      <td>35.144679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-17.324506</td>\n",
       "      <td>9.638913e-35</td>\n",
       "      <td>vae</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>signal</td>\n",
       "      <td>978124</td>\n",
       "      <td>34.015972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.472087</td>\n",
       "      <td>6.465500e-32</td>\n",
       "      <td>vae</td>\n",
       "      <td>95</td>\n",
       "      <td>150</td>\n",
       "      <td>signal</td>\n",
       "      <td>908341</td>\n",
       "      <td>31.189398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-16.650011</td>\n",
       "      <td>9.533733e-31</td>\n",
       "      <td>nmf</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>signal</td>\n",
       "      <td>486191</td>\n",
       "      <td>30.020737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      t_stat           t_p algorithm  feature_num  z_dim  signal    seed  \\\n",
       "0 -18.539478  6.613869e-38       vae           12     50  signal  451283   \n",
       "1 -17.652445  7.166732e-36       dae           88    100  signal  451283   \n",
       "2 -17.324506  9.638913e-35       vae            5     50  signal  978124   \n",
       "3 -16.472087  6.465500e-32       vae           95    150  signal  908341   \n",
       "4 -16.650011  9.533733e-31       nmf            2     35  signal  486191   \n",
       "\n",
       "   neg_log_p  \n",
       "0  37.179544  \n",
       "1  35.144679  \n",
       "2  34.015972  \n",
       "3  31.189398  \n",
       "4  30.020737  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the top performing feature\n",
    "file = os.path.join(\"results\", \"nbl_mycn_separation_target_t_test.tsv\")\n",
    "target_full_results_df = pd.read_table(file).head(5)\n",
    "target_full_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "451283 50 vae_12\n"
     ]
    }
   ],
   "source": [
    "top_seed = str(target_full_results_df.seed.values[0])\n",
    "top_k = int(target_full_results_df.z_dim.values[0])\n",
    "top_feature = \"{}_{}\".format(target_full_results_df.algorithm.values[0],\n",
    "                             target_full_results_df.feature_num.values[0])\n",
    "\n",
    "print(top_seed, top_k, top_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pca_0</th>\n",
       "      <th>pca_1</th>\n",
       "      <th>pca_2</th>\n",
       "      <th>pca_3</th>\n",
       "      <th>pca_4</th>\n",
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       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
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       "      <th>8337</th>\n",
       "      <td>-0.004849</td>\n",
       "      <td>-0.023897</td>\n",
       "      <td>-0.016095</td>\n",
       "      <td>-0.018362</td>\n",
       "      <td>0.034889</td>\n",
       "      <td>0.008711</td>\n",
       "      <td>0.005634</td>\n",
       "      <td>-0.008216</td>\n",
       "      <td>-0.025272</td>\n",
       "      <td>0.026315</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000681</td>\n",
       "      <td>-0.033882</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>-0.059149</td>\n",
       "      <td>-0.009768</td>\n",
       "      <td>-0.058611</td>\n",
       "      <td>-0.022208</td>\n",
       "      <td>0.006569</td>\n",
       "      <td>-0.010747</td>\n",
       "      <td>-0.048158</td>\n",
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       "    <tr>\n",
       "      <th>56849</th>\n",
       "      <td>-0.026866</td>\n",
       "      <td>0.001740</td>\n",
       "      <td>-0.009147</td>\n",
       "      <td>-0.009055</td>\n",
       "      <td>-0.008393</td>\n",
       "      <td>-0.010301</td>\n",
       "      <td>0.009526</td>\n",
       "      <td>-0.006650</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.013793</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003355</td>\n",
       "      <td>-0.043707</td>\n",
       "      <td>-0.010430</td>\n",
       "      <td>-0.056132</td>\n",
       "      <td>0.051479</td>\n",
       "      <td>-0.019327</td>\n",
       "      <td>-0.236229</td>\n",
       "      <td>-0.036829</td>\n",
       "      <td>-0.329422</td>\n",
       "      <td>-0.090581</td>\n",
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       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>-0.025851</td>\n",
       "      <td>-0.000609</td>\n",
       "      <td>-0.004601</td>\n",
       "      <td>-0.013925</td>\n",
       "      <td>-0.003599</td>\n",
       "      <td>-0.007169</td>\n",
       "      <td>0.008380</td>\n",
       "      <td>-0.000327</td>\n",
       "      <td>-0.000511</td>\n",
       "      <td>0.008287</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>-0.042423</td>\n",
       "      <td>0.016851</td>\n",
       "      <td>-0.003597</td>\n",
       "      <td>0.003765</td>\n",
       "      <td>-0.110373</td>\n",
       "      <td>-0.216784</td>\n",
       "      <td>0.008079</td>\n",
       "      <td>-0.309065</td>\n",
       "      <td>-0.028705</td>\n",
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       "    <tr>\n",
       "      <th>6192</th>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>-0.002125</td>\n",
       "      <td>-0.006721</td>\n",
       "      <td>0.024305</td>\n",
       "      <td>-0.000472</td>\n",
       "      <td>-0.010771</td>\n",
       "      <td>-0.000680</td>\n",
       "      <td>-0.005298</td>\n",
       "      <td>-0.017055</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088823</td>\n",
       "      <td>0.012887</td>\n",
       "      <td>-0.060783</td>\n",
       "      <td>0.027842</td>\n",
       "      <td>-0.047115</td>\n",
       "      <td>-0.043765</td>\n",
       "      <td>0.034207</td>\n",
       "      <td>0.019005</td>\n",
       "      <td>-0.025002</td>\n",
       "      <td>0.084673</td>\n",
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       "      <th>3880</th>\n",
       "      <td>-0.021859</td>\n",
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       "      <td>-0.008843</td>\n",
       "      <td>-0.001078</td>\n",
       "      <td>-0.020019</td>\n",
       "      <td>-0.002469</td>\n",
       "      <td>0.016229</td>\n",
       "      <td>-0.005949</td>\n",
       "      <td>0.009196</td>\n",
       "      <td>0.020103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004139</td>\n",
       "      <td>-0.026892</td>\n",
       "      <td>0.034878</td>\n",
       "      <td>-0.002678</td>\n",
       "      <td>-0.014309</td>\n",
       "      <td>-0.114518</td>\n",
       "      <td>-0.147565</td>\n",
       "      <td>-0.087444</td>\n",
       "      <td>-0.254048</td>\n",
       "      <td>-0.044970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pca_0     pca_1     pca_2     pca_3     pca_4     pca_5     pca_6  \\\n",
       "gene_id                                                                         \n",
       "8337    -0.004849 -0.023897 -0.016095 -0.018362  0.034889  0.008711  0.005634   \n",
       "56849   -0.026866  0.001740 -0.009147 -0.009055 -0.008393 -0.010301  0.009526   \n",
       "5999    -0.025851 -0.000609 -0.004601 -0.013925 -0.003599 -0.007169  0.008380   \n",
       "6192     0.000613  0.000113 -0.002125 -0.006721  0.024305 -0.000472 -0.010771   \n",
       "3880    -0.021859  0.000071 -0.008843 -0.001078 -0.020019 -0.002469  0.016229   \n",
       "\n",
       "            pca_7     pca_8     pca_9    ...       dae_40    dae_41    dae_42  \\\n",
       "gene_id                                  ...                                    \n",
       "8337    -0.008216 -0.025272  0.026315    ...    -0.000681 -0.033882  0.003584   \n",
       "56849   -0.006650  0.000642  0.013793    ...    -0.003355 -0.043707 -0.010430   \n",
       "5999    -0.000327 -0.000511  0.008287    ...     0.001536 -0.042423  0.016851   \n",
       "6192    -0.000680 -0.005298 -0.017055    ...     0.088823  0.012887 -0.060783   \n",
       "3880    -0.005949  0.009196  0.020103    ...     0.004139 -0.026892  0.034878   \n",
       "\n",
       "           dae_43    dae_44    dae_45    dae_46    dae_47    dae_48    dae_49  \n",
       "gene_id                                                                        \n",
       "8337    -0.059149 -0.009768 -0.058611 -0.022208  0.006569 -0.010747 -0.048158  \n",
       "56849   -0.056132  0.051479 -0.019327 -0.236229 -0.036829 -0.329422 -0.090581  \n",
       "5999    -0.003597  0.003765 -0.110373 -0.216784  0.008079 -0.309065 -0.028705  \n",
       "6192     0.027842 -0.047115 -0.043765  0.034207  0.019005 -0.025002  0.084673  \n",
       "3880    -0.002678 -0.014309 -0.114518 -0.147565 -0.087444 -0.254048 -0.044970  \n",
       "\n",
       "[5 rows x 250 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df = load_weight_matrix(dataset='TARGET',\n",
    "                               z_dim=top_k,\n",
    "                               seed=top_seed)\n",
    "weight_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7653 (95.6625%) of genes are used\n"
     ]
    }
   ],
   "source": [
    "result_mycn_df, nbl_missing_genes = (\n",
    "    apply_signature(weight_df=weight_df,\n",
    "                    other_df=nbl_df,\n",
    "                    feature=top_feature,\n",
    "                    align=True)\n",
    ")\n",
    "\n",
    "use_genes = weight_df.shape[0] - len(nbl_missing_genes)\n",
    "print('{} ({}%) of genes are used'.format(use_genes, use_genes / weight_df.shape[0] * 100 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>vae_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHP134</th>\n",
       "      <td>27.227119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHP212</th>\n",
       "      <td>25.432603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COGN415</th>\n",
       "      <td>22.036502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COGN440</th>\n",
       "      <td>24.639280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COGN453</th>\n",
       "      <td>23.804501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            vae_12\n",
       "CHP134   27.227119\n",
       "CHP212   25.432603\n",
       "COGN415  22.036502\n",
       "COGN440  24.639280\n",
       "COGN453  23.804501"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_mycn_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Align with Phenotype Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell Line</th>\n",
       "      <th>MYCN status</th>\n",
       "      <th>1p36 del</th>\n",
       "      <th>3p26 del</th>\n",
       "      <th>11q23 del</th>\n",
       "      <th>17q21-qter unbal gain</th>\n",
       "      <th>ALK mutation</th>\n",
       "      <th>p53 mutation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHP134</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22...</td>\n",
       "      <td>Gain/AI p26.3</td>\n",
       "      <td>None</td>\n",
       "      <td>Gain q12-qter</td>\n",
       "      <td>WT</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHP212</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Loss p13.2-pter</td>\n",
       "      <td>Gain/AI p26.3</td>\n",
       "      <td>cnLOH 23.3</td>\n",
       "      <td>Gain q12-qter</td>\n",
       "      <td>WT</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COGN415</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>F1174L</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COGN440</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>WT</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COGN453</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>F1174L</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cell Line MYCN status                                           1p36 del  \\\n",
       "0    CHP134   Amplified  LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22...   \n",
       "1    CHP212   Amplified                                    Loss p13.2-pter   \n",
       "2   COGN415   Amplified                                            Unknown   \n",
       "3   COGN440   Amplified                                            Unknown   \n",
       "4   COGN453   Amplified                                            Unknown   \n",
       "\n",
       "        3p26 del   11q23 del 17q21-qter unbal gain ALK mutation p53 mutation  \n",
       "0  Gain/AI p26.3        None         Gain q12-qter           WT           WT  \n",
       "1  Gain/AI p26.3  cnLOH 23.3         Gain q12-qter           WT           WT  \n",
       "2        Unknown     Unknown               Unknown       F1174L           WT  \n",
       "3        Unknown     Unknown               Unknown           WT           WT  \n",
       "4        Unknown     Unknown               Unknown       F1174L           WT  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = os.path.join(\"download\", \"nbl_cellline_phenotype.txt\")\n",
    "pheno_df = pd.read_table(file)\n",
    "\n",
    "pheno_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vae_12</th>\n",
       "      <th>Cell Line</th>\n",
       "      <th>MYCN status</th>\n",
       "      <th>1p36 del</th>\n",
       "      <th>3p26 del</th>\n",
       "      <th>11q23 del</th>\n",
       "      <th>17q21-qter unbal gain</th>\n",
       "      <th>ALK mutation</th>\n",
       "      <th>p53 mutation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.227119</td>\n",
       "      <td>CHP134</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22...</td>\n",
       "      <td>Gain/AI p26.3</td>\n",
       "      <td>None</td>\n",
       "      <td>Gain q12-qter</td>\n",
       "      <td>WT</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.432603</td>\n",
       "      <td>CHP212</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Loss p13.2-pter</td>\n",
       "      <td>Gain/AI p26.3</td>\n",
       "      <td>cnLOH 23.3</td>\n",
       "      <td>Gain q12-qter</td>\n",
       "      <td>WT</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.036502</td>\n",
       "      <td>COGN415</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>F1174L</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.639280</td>\n",
       "      <td>COGN440</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>WT</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.804501</td>\n",
       "      <td>COGN453</td>\n",
       "      <td>Amplified</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>F1174L</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      vae_12 Cell Line MYCN status  \\\n",
       "0  27.227119    CHP134   Amplified   \n",
       "1  25.432603    CHP212   Amplified   \n",
       "2  22.036502   COGN415   Amplified   \n",
       "3  24.639280   COGN440   Amplified   \n",
       "4  23.804501   COGN453   Amplified   \n",
       "\n",
       "                                            1p36 del       3p26 del  \\\n",
       "0  LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22...  Gain/AI p26.3   \n",
       "1                                    Loss p13.2-pter  Gain/AI p26.3   \n",
       "2                                            Unknown        Unknown   \n",
       "3                                            Unknown        Unknown   \n",
       "4                                            Unknown        Unknown   \n",
       "\n",
       "    11q23 del 17q21-qter unbal gain ALK mutation p53 mutation  \n",
       "0        None         Gain q12-qter           WT           WT  \n",
       "1  cnLOH 23.3         Gain q12-qter           WT           WT  \n",
       "2     Unknown               Unknown       F1174L           WT  \n",
       "3     Unknown               Unknown           WT           WT  \n",
       "4     Unknown               Unknown       F1174L           WT  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = (\n",
    "    result_mycn_df\n",
    "    .merge(pheno_df,\n",
    "           left_index=True,\n",
    "           right_on=\"Cell Line\")\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "file = os.path.join(\"results\", \"mycn_nbl_scores.tsv\")\n",
    "merged_df.to_csv(file, sep='\\t')\n",
    "\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amplified        20\n",
       "Non-amplified    10\n",
       "Name: MYCN status, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['MYCN status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"MYCN status\", y=top_feature, data=merged_df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.165192715953426, pvalue=0.004294088628841832)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform t-test on the result\n",
    "amplified_scores = merged_df.loc[merged_df['MYCN status'] == \"Amplified\", top_feature]\n",
    "notamplified_scores = merged_df.loc[merged_df['MYCN status'] != \"Amplified\", top_feature]\n",
    "\n",
    "ttest_ind(amplified_scores, notamplified_scores, equal_var=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:biobombe]",
   "language": "python",
   "name": "conda-env-biobombe-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}