05_coefficient_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of model coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import itertools as it\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pancancer_evaluation.config as cfg\n",
    "import pancancer_evaluation.utilities.data_utilities as du\n",
    "import pancancer_evaluation.utilities.analysis_utilities as au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pancancer_dir = os.path.join(cfg.results_dir, 'pancancer')\n",
    "single_cancer_dir = os.path.join(cfg.results_dir, 'single_cancer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the pan-cancer experiments, we added covariates (e.g. fixed effects, dummy/one-hot encoded variables) for the cancer types present in the dataset.\n",
    "\n",
    "Here, we want to look at the proportion of models in which the coefficient for these cancer type covariates. Are they mostly zero (since we're using elastic net regularization), or are they often informative? Does this differ between models that outperform the negative control and models that don't?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>fold</th>\n",
       "      <th>nz_cov_count</th>\n",
       "      <th>nz_covs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[BRCA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MLH1_UCEC</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[UCEC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MLH1_UCEC</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[UCEC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>CIC_SKCM</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[LGG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>CBL_UCEC</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[UCEC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>CBL_UCEC</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[UCEC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>JAK3_OV</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[UCEC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>H3F3A_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[BRCA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>H3F3A_LIHC</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[BRCA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>MAP2K1_SKCM</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[SKCM]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      identifier  fold  nz_cov_count nz_covs\n",
       "0    MAP3K1_BRCA     0             1  [BRCA]\n",
       "9      MLH1_UCEC     1             1  [UCEC]\n",
       "10     MLH1_UCEC     2             1  [UCEC]\n",
       "160     CIC_SKCM     0             1   [LGG]\n",
       "256     CBL_UCEC     0             1  [UCEC]\n",
       "258     CBL_UCEC     2             1  [UCEC]\n",
       "273      JAK3_OV     1             1  [UCEC]\n",
       "277   H3F3A_BRCA     1             1  [BRCA]\n",
       "280   H3F3A_LIHC     0             1  [BRCA]\n",
       "315  MAP2K1_SKCM     3             1  [SKCM]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_info_df = du.load_sample_info()\n",
    "tcga_cancer_types = list(np.unique(sample_info_df.cancer_type))\n",
    "\n",
    "identifier_coefs_info = []\n",
    "for identifier, coefs_list in au.generate_nonzero_coefficients(pancancer_dir):\n",
    "    for fold_no, coefs in enumerate(coefs_list):\n",
    "        cancer_covs = au.get_cancer_type_covariates(coefs, tcga_cancer_types)\n",
    "        identifier_coefs_info.append((identifier, fold_no, len(cancer_covs), cancer_covs))\n",
    "        \n",
    "# TODO: do we need a way to generate list of all features?\n",
    "ids_df = pd.DataFrame(identifier_coefs_info, columns=['identifier', 'fold', 'nz_cov_count', 'nz_covs'])\n",
    "ids_df[ids_df.nz_cov_count > 0].head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>fold</th>\n",
       "      <th>nz_cov_count</th>\n",
       "      <th>nz_covs</th>\n",
       "      <th>reject_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[BRCA]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    identifier  fold  nz_cov_count nz_covs  reject_null\n",
       "0  MAP3K1_BRCA     0             1  [BRCA]         True\n",
       "1  MAP3K1_BRCA     1             0      []         True\n",
       "2  MAP3K1_BRCA     2             0      []         True\n",
       "3  MAP3K1_BRCA     3             0      []         True\n",
       "4  MAP3K1_UCEC     0             0      []        False\n",
       "5  MAP3K1_UCEC     1             0      []        False\n",
       "6  MAP3K1_UCEC     2             0      []        False\n",
       "7  MAP3K1_UCEC     3             0      []        False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load significant identifiers and join into coefficients dataframe\n",
    "pancancer_comparison_df = pd.read_csv(os.path.join(cfg.results_dir, 'pancancer_pvals.tsv'),\n",
    "                                      index_col=False, sep='\\t')\n",
    "merge_df = (\n",
    "    ids_df.merge(pancancer_comparison_df, left_on='identifier', right_on='identifier')\n",
    "          .drop(columns=['delta_mean', 'p_value', 'corr_pval', 'nlog10_p'])\n",
    ")\n",
    "merge_df.head(n=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of models (fold/cancer type/gene)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0OpWamrHIvr62DE+L7MN68g6sJ+/BuvIOmVlPfn7Xv/FStm+JV6xYUb///rvefvttTZs2zXL61FSj06evvSTh9eTNmzPD0yL7sJ68A+vJe7CuvENm1lOBArmv+3y2HxPfvHmz7rvvPs2dO5f7NAMAkAlZWuJxcXGuK2o5nU7FxsaqdevW6tChg+vqT+fPn1dMTIxGjx6tqKiorIwDAMBtJcuOic+dO1dLly7VHXfcoY8++kjffPONvvvuO40fP16bNm3SnDlzXJcrzIzMHRP3UWpq+tctRvZjPXkH1pP3YF15h8ysJ48fEw8NDdWMGTNcd7TasGGDatSoIenyNZGvXGw/szgmfvthPXkH1pP3uJ3WVWqqQ6dOHZPDkWw9sZf5590aJclu91e+fAXk65u2nt0dE8+yEo+Kikpz04jExEQFBga6Hl+5O88/b5sHAMAVp04dU0BATuXKVei2u8nKP7fEjTE6f/6sTp06puDguzM0hsdObAsMDNT58+ddj6/cNxoAAHccjmTlypXntivw67HZbMqVK0+m9jp4rMQjIyO1evVqSdKmTZsUERHhqVkDALzYf6HAr8jssnqsxOvXry9/f39FR0dr3LhxGjJkiKdmDQD4j/rii8+1Zs0PmXrPrl07tWnT7zctw+OPX/7mVffuXRQfv/emjStl8cVeihQpoo8++kiS5OPjo5EjR2bl7AAASKNRoyaZfs+qVd8qf/78euCByCxIdHNxUBoA4LW++OJzLV++VE6nUy1bttZHHy2Qj4+PypV7QC+80ENvvTVH+fPnV7NmLTV79muKi/tdTqdR69btVKdOPf3xx1ZNmzZZxhgVKFBQffoM0JdfLpPd7qeIiFIqU+a+a+b5+++/af789+TnZ9ehQwdVp059dez4rMaMGaG6dR/VQw89rJ9/Xqdvv/1GQ4eOyNLlp8QBAF4td+7ciol5SS+++JzefHOeAgICNGrUcK1f/7Nrmp9+WqtDhw5o1qy3lZSUpOeff0aVKz+oiRPH6OWXx6po0XAtXvyxTp48qYYNGyt//vzXLfArjhw5pHfeWaCUlBQ1a9ZAHTs+64lFvQYlDgDwaqGhYdq/P0GnT59S//49JUkXLlzQgQMHXNPs3r1Tf/21Xd27d5EkORwOHT58SKdOnVTRouGSpObNn5SkDB1Dv/fe4rLb7bLb7cqRI+Ca1z11b7Hbu8Rt7r8gf0VSskNnz1z0UCAAwM1ms/no7rsLq2DBuzR16kzZ7XZ98cXnKlEiQqtXr5IkhYUVVYUKlTRo0FA5nU69886bKly4sIKDg5WQsE8hIaF6//13FBISJh8fHzmd6Zfw9U4i9/f314kTxyVJO3Zsv9mLeV23dYn72X01YFr6f1FN6lXTQ2kAAFklX758at26nbp376LU1FTdffc9qlOnvuv1atUe0caNG/Tii8/p4sULeuSR2sqZM5cGDIjRuHEj5ePjo/z586tVq7by8/PTzJnTVLRouCIjK2U4Q5MmzTRu3Eh9881XCgkJzYrFvEa23088s1JSUjN167aMlPixY+duRjTcoNvpEpG3M9aT97id1tXhw/EqVCjsht8/Z87rKly4iBo3bnoTU90c7q6dfr1l9vhlVwEAyE7Lly/VmjU/aOzYyTf0/v/9b642bFh/zfMxMS/pnnsK/9t4NwUlDgC4LT322ON67LHHb/j9zzzTWc880/kmJrr5PHbFNgAAcHNR4gAAeClKHAAAL0WJAwDgpTixDQDgNfLceYdy+N+86srIBb8OHTqojh3bKCKipOu5ihUrX/ekt6uvn+4JlDgAwGvk8LdbXv8jMzJ6wa+iRcP12mtv3LT53iyUOAAAmZSamqpJk8bq6NEjOnPmjB566GF17vyC6/V9++I1duzLstvt8vX11bBhL6tAgYJp7qTWpk171apV91/loMQBALCwd+8e181TJKlLlxdVtuz9Gjx4uJKSktS8eaM0Jb5+/S8qWbKUevToq7i4jTp37qx27vw7zZ3UunZ9RhUrVlHu3Onf4yM9lDgAABb+uTv9/PlEffXVcv3++2/KlSuXkpNT0kzfuHFTzZ//rvr166FcuQL1/PPd3N5JjRL/l9K701lqcrJOnknyYBoAwK3uiy+WKTAwtwYOHKr9+xO0dOmnaW4/umbNDypfvoI6deqiFSu+0vz57+qRR2qluZPae++9pcKF/93lWylxSfFvD3X7WlinMZIocQDA/6lYsbJGjIjR5s2bFBAQoCJFQnT8+DHX66VKldHIkcPl6+srHx8f9ejRVxERJdPcSa1mzTrKmTPXv8rBXcx61bQsce5ylrVupzsu3c5YT97jdlpX/7yjV3Z8xSyrcBczAMB/SnYV7q2KK7YBAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXoqz0wEAXiPozhzy9fe/aeNZXdBrxoxX9ddff+rkyRO6dOmS7rmnsPLmzafRoyfctAz/BiUOAPAavv7+6V7bI7OsLujVo0cfSdIXX3yu+Pi9euGFHjdt3jcDJQ4AQCaMGTNCZ86c0dmzZ9SmTQd99903evnlcZKkxx+P0tKlX+vIkcOaOHGskpOT5O+fQwMHxuiuuwrd9CwcEwcAIJMqVqyk2bPfdnvzktdfn6aWLVtrxow5atOmvWbPfi1LcrAlDgBAJoWGhl33+StXMt+9e6fmzfuf5s9/V5Jkt2dN3VLiAABkks12eUe2v38OnThxQpJ0+PAhnT17RpIUGlpUbdq01/33l1d8/F5t3LghS3JQ4gAA3KBSpUorMDBQnTt3VNGi4br77su3Fu3WrZemTBmv5ORkJSVdUq9e/bNk/pQ4AMBrpCYn//8zym/eeBnRqFET189Dh45w/Wy32zV+/CvXTF+4cBG98krWHAe/GiUOAPAal7/T7f4rYf81nJ0OAICXosQBAPBSlDgA4JZ25Wtb/wWZXVZKHABwy7Lb/XX+/Nn/RJEbY3T+/FnZ7Rm/NjwntgEAbln58hXQqVPHlJh4Oruj3HQ2m+2aP07sdn/ly1cgw2NQ4gCAW5avr13BwXdnd4wskTdvTp0+feFfjcHudAAAvBQlDgCAl6LEAQDwUpQ4AABeihIHAMBLUeIAAHgpShwAAC9FiQMA4KUocQAAvBQlDgCAl6LEAQDwUpQ4AABeihIHAMBLUeIAAHgpShwAAC9FiQMA4KUocQAAvBQlDgCAl8pQiScmJurAgQO6ePFiVucBAAAZZE/vxSVLluiDDz7Q6dOnFRQUpHPnzilPnjxq27atmjRp4qmMAADgOtyW+ODBgxUZGak333xTefLkcT1/7tw5ff755xowYIAmTZrkkZAAAOBabkv85ZdfVo4cOa55Pnfu3Grbtq1atGiRpcEAAED63Jb4lQJPTEzU3LlzdezYMdWqVUslS5ZUWFjYdQseAAB4juWJbTExMQoJCdHevXsVHBysoUOHeiIXAACwYFnip0+fVsuWLWW32xUZGSljjCdyAQAACxn6itmuXbskSYcPH5aPD18tBwDgVmDZyMOGDVNMTIy2bdumnj17avDgwZ7IBQAALKT7PXFJioiI0MKFCz2RBQAAZIJliS9ZskRvvPGGkpKSXM99++23WRoKAABYsyzxuXPnatasWbr77rs9kQcAAGSQZYmHhIQoLCzME1kAAEAmWJZ4QECAnnvuOZUuXVo2m02S1Ldv3ywPBgAA0mdZ4jVr1vREDgAAkEmWXzFr0qSJHA6HEhISdM8991DqAADcIixL/KWXXtLBgwe1du1anT9/XoMGDfJELgAAYMGyxPft26devXrJ399fderU0blz5zyRCwAAWLAs8dTUVJ08eVI2m02JiYlcdhUAgFuE5YltvXv3Vps2bXTs2DG1bt1aMTExnsgFAAAsWJZ4lSpV9PXXX+vkyZMKCgryRCYAAJABliX+6KOPKjU19f/eYLfr7rvv1oABA1S2bNksDQcAANyzLPGHHnpIDRo0UKVKlbRx40Z9/PHHatGihUaPHq0FCxZ4IiMAALgOy7PU9uzZo4cfflj+/v568MEHdezYMVWtWpUT3AAAyGaWW+L+/v5asGCBKlSooI0bN8rf319bt25Ns4sdAAB4nuXm9OTJk7V3715NnjxZCQkJmjhxok6cOKExY8Z4Ih8AAHDD7ZZ49+7dFRkZqVq1amnIkCFpXuPSqwAAZD+3JT59+nRt2LBBn3zyiXbv3q2wsDDVqlVLlStXlt1uuRceAABkMbdt7OPjo8qVK6ty5cqSpISEBH3//fdasGCBpk+f7rGAAADg+iw3qRMTE/Xmm2/q6NGjqlWrlvr16+eJXAAAwILliW0xMTEqUqSI9u7dq+DgYA0dOtQTuQAAgAXLEj99+rRatmwpu92uyMhIGWM8kQsAAFjI0BVbdu3aJUk6fPgwF3kBAOAWYdnIw4YNU0xMjLZt26aePXtq8ODBnsgFAAAsWJ7YFhERoVmzZmnfvn0qWrSo8ubN64FYAADAimWJz58/X++9956KFy+unTt36sUXX1TTpk09kQ0AAKTDssQ//vhjLV26VDly5NDFixfVvn17ShwAgFuA5THx/Pnzy9fXV5IUEBDA7nQAAG4Rllvixhg1a9ZMFSpU0LZt2+RwOFwXfJkyZUqWBwQAANdnWeJdu3Z1/dykSZMsDQMAADLOssTHjx+vxx9/XM2aNWNXOgAAtxDLY+LvvPOO/Pz81LVrV/Xp00fr1q3zRC4AAGDBssTz5Mmjdu3aacyYMfLx8VG/fv305JNPatWqVR6IBwAA3MnQ98Q/++wzBQYG6sknn9T48ePlcDjUqlUr1apVywMRAQDA9ViW+NGjRzVlyhSFhIS4nvPz89PIkSOzNBgAAEif293pqampSk5O1q5du1SoUCElJycrKSlJTz31lCSpQoUKHgsJAACu5XZLfNGiRZo9e7aOHz+uBg0ayBgjX19fVaxY0ZP5AACAG25LvFWrVmrVqpU++eQTtWzZ0pOZAABABlienU6BAwBwa7IscQAAcGuyLPGlS5d6IgcAAMgkyxL/6KOPsjTATz/9pIEDB6pnz57avn17ls4LAIDbieX3xJOTk9WsWTOFh4fLx+dy59/Mu5ddvHhREyZM0J9//qk1a9aoVKlSN21sAABuZ5Yl3r9//ywNUKdOHV24cEHz5s3L8nkBAHA7sdydXqZMGa1du1ZLlizR6dOnddddd93UAKdOndKYMWPUs2dP5c+f/6aODQDA7cyyxGNiYhQSEqK9e/cqODhYQ4cOzfDgcXFx6tChgyTJ6XQqNjZWrVu3VocOHRQfHy9JGjdunI4cOaIpU6boq6++usHFAADgv8dyd/rp06fVsmVLLV26VJGRkTLGZGjguXPnaunSpbrjjjskSStXrlRycrIWLlyoTZs2afz48Zo1a5YmTpyYqcC+vjblzZszw9Pb7b7/eprMzA+Z5+vrw2fsBVhP3oN15R1uxnqyLHFJ2rVrlyTp8OHDrpPbrISGhmrGjBkaOHCgJGnDhg2qUaOGJOmBBx7Q1q1bbySvUlONTp++kKFpCxTILYcj1XI6q2kyOj/cmLx5c/IZewHWk/dgXXmHzKynAgVyX/d5y0YeNmyYYmJitG3bNvXs2VODBw/O0AyjoqJkt//f3wiJiYkKDAx0Pfb19ZXD4cjQWAAA4FqWW+JFixbVSy+9pDJlymjlypWKiIi4oRkFBgbq/PnzrsdOpzNNyQMAgMyx3BLv37+/4uLiJEl79uzJ8Jb4P0VGRmr16tWSpE2bNt3wHwMAAOAyyxI/cuSI2rRpI0nq3Lmzjh49ekMzql+/vvz9/RUdHa1x48ZpyJAhNzQOAAC4LEP7s/fs2aPw8HDt27dPTqczw4MXKVLEddlWHx8fjRw58sZSAgCAa1iWeExMjHr37q0TJ06oYMGCevnllz2RCwAAWLAs8fLly+uzzz7zRBYAAJAJliW+ZMkSvfHGG0pKSnI99+2332ZpKAAAYM2yxOfOnatZs2bp7rvv9kQeAACQQZYlHhISorCwME9kAQAAmWBZ4gEBAXruuedUunRp2Ww2SVLfvn2zPBgAAEifZYnXrFnTEzkAAEAmWZZ4kyZNtGXLFjkcDhljbvhiLwAA4OayLPHu3bsrJSVFR48eVWpqqgoWLKjGjRt7IhsAAEiH5WVXExMT9dZbb6lcuXJavHhxmq+aAQCA7GNZ4lfuNHbx4kUFBAQoJSUly0MBAABrliVev359vf766ypVqpRatWqV5p7gAAAg+1geE2/Xrp3Onz+vXLlyqUKFCrr//vs9kQsAAFiw3BJ/7bXXNGPGDEnSvHnz9N5772V5KAAAYM2yxL/77jsNHjxYkjR9+nR99913WR4KAABYsyxxm82m5ORkSVJKSoqMMVkeCgAAWLM8Jh4dHa0mTZooIiJCu3fvVufOnT2RCwAAWHBb4r/++qsqVqyoJ598UnXr1lVCQoJCQkIUFBTkyXwAAMANtyX+xx9/6L333lOuXLlUrVo1PfLII8qbN68HowEAgPS4LfFnnnlGzzzzjBITE7V69WpNmDBBZ8+eVfny5dWlSxdPZgQAANdheUw8MDBQjRo1UqNGjWSMUVxcnCdyAQAAC25LvE6dOq77h0uXL7/qcDjk7++vL7/80iPhAACAe25L/KuvvpIxRi+//LKio6NVrlw5bdu2TQsWLPBkPgAA4IbbEvf395ckJSQkqFy5cpKkMmXKaPfu3Z5JBgAA0mV5TDx37tyaOnWqypUrp40bN6pw4cKeyAUAACxYXrFt8uTJKlCggFavXq2CBQtq3LhxnsgFAAAsuN0SX7NmjevnsLAwhYWFSZJ++uknVa9ePeuTAQCAdLkt8eXLl7t9EyUOAED2c1viV+8237Fjh3bu3Knw8HCVLl3aI8EAAED6LE9smzdvnpYtW6Zy5crp7bffVsOGDfXss896IhsAAEiHZYkvW7ZM8+fPl91uV0pKiqKjoylxAABuAZZnpxtjZLdf7no/Pz/5+flleSgAAGDNcks8MjJSPXv2VMWKFbVhwwZVqFDBE7kAAIAFt1vi69evlyT16dNHzZs3l8PhUPPmzTVo0CCPhQMAAO65LfEJEybowoULeu6551StWjV16NBBDz/8sJKTkz2ZDwAAuOF2d3q1atXUrFkzHT58WA0aNJB0+fi4zWbTt99+67GAAADg+tyWeJ8+fdSnTx+9/vrr6tatmyczAQCADHC7O/3VV1/VmTNnrlvgJ0+e1JQpU7I0GAAASJ/bLfHmzZtryJAhMsaoZMmSCg4O1tmzZxUXFycfHx8NGDDAkzkBAMA/uC3xsLAwzZw5U3v27NH69et16tQpFShQQEOHDlVoaKgnMwIAgOuw/J54eHi4wsPDPZEFAABkguUV2wAAwK2JEgcAwEtZlvjRo0e1c+dO7dmzRzExMfrzzz89kQsAAFiwLPFBgwbp+PHjevXVV1WtWjWNHTvWE7kAAIAFyxJ3OByqXLmyzp49q8cee0xOp9MTuQAAgAXLEk9JSdG4ceNUqVIl/fzzz0pNTfVELgAAYMGyxMePH6/w8HB16dJFJ0+e1KRJkzyRCwAAWHD7PfE1a9a4fg4LC9Ovv/6qPHnyKD4+XiEhIR4JBwAA3HNb4suXL3f7purVq2dJGAAAkHFuS3zcuHGun/fs2aN9+/apZMmSKliwoEeCAQCA9FledvX999/XihUrdObMGT3xxBOKj49XbGysJ7IBAIB0WJ7Ytnz5cr3zzjvKnTu3OnbsqLi4OE/kAgAAFixL3BgjSbLZbJIkf3//rE0EAAAyxHJ3euPGjdWuXTsdPHhQnTt3Vr169TyRCwAAWLAs8fbt26tq1arasWOHwsPDVapUKU/kAgAAFtyW+GuvvXbNc7t27dLKlSvVvXv3LA0FAACsuT0mHhwcrODgYG3atEnHjx9XaGiozpw5o+3bt3syHwAAcMPtlnh0dLQkacWKFRoxYoQk6fHHH9czzzzjkWAAACB9lmennzp1Svv27ZMk7d69W4mJiVkeCgAAWLM8sS0mJkZ9+/bV0aNHFRwczA1QAAC4RViWeKVKlTR37lwlJCSoSJEiCgoK8kQuAABgwXJ3+hdffKHo6GjNmTNHrVu31meffeaJXAAAwILllvi7776rxYsXK1euXEpMTFTHjh3VtGlTT2QDAADpsNwSt9lsypUrlyQpMDBQOXLkyPJQAADAmuWWeGhoqMaPH69KlSrpt99+U2hoqCdyAQAAC5Zb4mPHjlVISIjWrVunkJAQjR492hO5AACABcsSv3jxogoVKqRKlSopX758+uabbzyRCwAAWLDcnd6pUycVL15cuXPnlnT5GHmjRo2yPBgAAEifZYnnzp1b48aN80QWAACQCZYlXr16dS1YsEDFixd3PVe5cuUsDQUAAKxZlvhvv/2m5ORkrV+/XtLl3emUOAAA2c+yxC9cuKB33nnHA1EAAEBmWJZ4iRIltHz5cpUuXVo2m02SFB4enuXBAABA+ixLfPv27dq+fbvrsc1m03vvvZeloQAAgDXLEp83b54ncgAAgEyyvNgLAAC4Nbkt8XPnznkyBwAAyCS3Jd61a1dJ0ksvveSxMPiPskkFCuRO9788d96R3SkB4Jbj9ph4QECAWrRoofj4eP3111+SJGOMbDabPvzwQ48FxO3Pz+6rAdN+SHeaSb1qeigNAHgPtyU+d+5cHT16VLGxsRoxYoSMMZ7MBQAALLgtcR8fHxUqVEgzZ87UwoULtXPnThUtWlRt2rTxZD4AAOCG5dnpsbGx2rdvn6pVq6YDBw5o2LBhnsgFAAAsWH5PPD4+XvPnz5ck1atXT9HR0VkeCgAAWLPcEk9KStLFixclSZcuXVJqamqWhwIAANYst8SfeuopNW3aVCVKlNDOnTvVs2dPT+QCAAAWLEv88ccf1yOPPKKEhAQVKVJE+fLl80QuAABgwbLEJSlv3rzKmzdvFkcBAACZwbXTAQDwUpYl/tZbb3kiBwAAyCTLEv/hhx84Ix0AgFuQ5THxU6dOqUaNGipSpIhsNhvXTgcA4BZhWeKzZ8/2RA4AAJBJliVut9s1adIknTp1SlFRUSpZsqQKFy7siWwAACAdlsfEhw8frhYtWig5OVmVKlXSmDFjPJELAABYyNBlV6tWrSqbzaZ7771XOXLk8EQuAABgwbLE/f399eOPP8rpdGrTpk3y9/f3RC4AAGDBssRHjRqlxYsX69SpU3r77bc1YsQID8QCAABWLE9sK1SokJ5//nnt3btXJUqUUEhIiCdyAQAAC5YlPnPmTP3444+6//779c4776hBgwZ6+umnPRANAACkx7LEV69erQ8++EA+Pj5yOBxq27YtJQ4AwC3AssSDgoJ08eJF5cqVSykpKQoKCvJELuAaBQrkTvf11ORknTyT5KE0AJD93JZ469atZbPZdOLECddFXnbt2sUtSZFt4t8emu7rYZ3GSKLEAfx3uC3xV155xZM5AABAJrkt8SuXVt28ebOWL1+upKT/28Lha2YAAGQ/y2PigwYNUufOnZUnTx5P5AEAABlkWeJhYWFq3ry5J7IAAIBMsCzxqKgo9enTR8WKFXM917179ywNBQAArFmW+AcffKD69euzOx0AgFuMZYnfeeed6tKlS5YH+emnn7Rs2TJudQoAQAZZlni+fPkUGxurMmXKyGazSbr8HfKbKT4+Xtu2bUtzBjwAAEif5V3MwsLCVLBgQR0/flzHjh3TsWPHbnqIsLAwPfvsszd9XAAAbmeWW+KcmQ4AwK3JssT79Okjm80mp9Op/fv3KywsTAsWLMjwDOLi4jR58mTNmzdPTqdTI0aM0F9//SV/f3+NHj1aYWFh/2oBAAD4r7Is8YULF7p+Pnv2rGJjYzM8+Ny5c7V06VLdcccdkqSVK1cqOTlZCxcu1KZNmzR+/HjNmjXLNf3kyZMtx/T1tSlv3pwZzmC3+/7raTIzP9yYm7GeJNZVVvL19eHz9RKsK+9wM9aTZYlfLXfu3Nq3b1+Gpw8NDdWMGTM0cOBASdKGDRtUo0YNSdIDDzygrVu3Zmb2kqTUVKPTpy9kaNoCBXLL4Ui1nM5qmozODzfmZq0niXWVlfLmzcnn6yVYV94hM+vJ3V0cLUv8yt3MjDE6efKkqlatmuGAUVFR2r9/v+txYmKiAgMDXY99fX3lcDhkt2fqbwkAAKAMlPjVdzPLkSOHgoODb3hmgYGBOn/+vOux0+mkwAEAuEFuG3TJkiVu39SsWbMbmllkZKS+//57NWrUSJs2bVJERMQNjQMAANIp8V27dqV5bIzR4sWLFRAQcMMlXr9+fa1du1bR0dEyxmjs2LE3NA4AAEinxPv16+f6OT4+XoMHD1atWrUUExOTqRkUKVJEH330kSTJx8dHI0eOvMGoAADgapYHpOfPn693331XQ4YMUe3atT2RCQAAZIDbEj9y5IiGDBmiO++8Ux9//LHuvPNOT+YCAAAW3JZ448aN5efnp4ceeuiaXeBTpkzJ8mAAACB9bkv89ddf92QOAACQSW5LvEqVKp7MAQAAMsnyVqQAAODWRIkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeyZ3cAAFkvz513KIe/+1/3pGSHzp656MFEAG4GShz4D8jhb9eAaT+4fX1Sr5oeTAPgZmF3OgAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClKHEAALwUJQ4AgJeixAEA8FKUOAAAXooSBwDAS1HiAAB4KUocAAAvRYkDAOClbMYYk90hAABA5rElDgCAl6LEAQDwUpQ4AABeihIHAMBLUeIAAHgpShwAAC9125W40+lUbGysWrdurQ4dOig+Pj67IyEdcXFx6tChQ3bHQDpSUlI0YMAAtW3bVi1bttS3336b3ZFwHampqRoyZIiio6PVrl077du3L7sjIR0nTpxQzZo1tWvXrn81zm1X4itXrlRycrIWLlyofv36afz48dkdCW7MnTtXw4YNU1JSUnZHQTqWLl2qvHnz6oMPPtDcuXM1atSo7I6E6/j+++8lSR9++KF69uypcePGZXMiuJOSkqLY2FgFBAT867FuuxLfsGGDatSoIUl64IEHtHXr1mxOBHdCQ0M1Y8aM7I4BCw0aNFCvXr1cj319fbMxDdypV6+e6w+sgwcPKjg4OJsTwZ0JEyYoOjpaBQsW/Ndj3XYlnpiYqMDAQNdjX19fORyObEwEd6KiomS327M7BizkypVLgYGBSkxMVM+ePdW7d+/sjgQ37Ha7Bg0apFGjRikqKiq74+A6Fi9erKCgINfG5r9125V4YGCgzp8/73rsdDopCuBfOnTokJ566ik1bdpUTZo0ye44SMeECRP09ddfa/jw4bpw4UJ2x8E/LFq0SOvWrVOHDh30559/atCgQTp27NgNj3fbtVtkZKS+//57NWrUSJs2bVJERER2RwK82vHjx9WpUyfFxsaqatWq2R0HbixZskRHjhzR888/rzvuuEM2m41DH7eg+fPnu37u0KGDRowYoQIFCtzweLddidevX19r165VdHS0jDEaO3ZsdkcCvNrs2bN19uxZzZw5UzNnzpR0+aTEm3FSDm6eRx99VEOGDFG7du3kcDgUExOjHDlyZHcsZDHuYgYAgJe67Y6JAwDwX0GJAwDgpShxAAC8FCUOAICXosQBAPBSlDhwk/3yyy+qVKmSDh065Hpu8uTJWrx4cTam8rzu3bun+/rChQuVkpLioTS3bgbg36DEgSzg5+enIUOG6L/8Dc7XXnst3dfnzJkjp9PpoTS3bgbg37jtLvYC3AoeeughOZ1OzZ8/X+3bt0/z2ttvv63ly5fLbrerUqVKGjBggGbMmKH9+/frxIkTOnjwoIYMGaLKlSurc+fOkiSHw6G4uDh99dVXOnz4sF599VX5+voqJCREI0eO1Oeff65FixbJ6XSqZ8+eOnbsmN599135+/uraNGiGjlypPz8/FwZ9u7dq2HDhiklJUUBAQF69dVXdeHCBQ0dOlQOh0M2m03Dhg3TgQMHtHLlStcdsZo1a6a33npLX375pb755hs5HA7lzp1bM2bM0LJly9Jk6N+/v9auXatff/3VVeiXLl3ShAkT9Ntvv+nYsWPq06ePZs6cqSlTpmj9+vUyxujpp59Ww4YN03xmH3/8sRYsWCCn06m6deuqR48eWrp06TXL+Pnnn2v37t3q37+/kpKS1LBhQ3333Xfq0KGDSpUqpb///luJiYmaNm2a1q1blyYD4JUMgJvq559/Nr179zYnT540devWNXv27DGTJk0yixYtMtu3bzctW7Y0ycnJxul0mm7dupnvvvvOTJ8+3QwbNswYY8yaNWtMp06dXOM5nU7Tr18/8+GHHxqn02keffRRc/z4cWOMMa+++qpZuHChWbRokenatasxxpiTJ0+aevXqmXPnzhljjBkzZoyZN29emoxdu3Y1P/zwgzHGmOXLl5sff/zR9OjRw6xYscIYY8y2bdvME088YRwOh4mKijLnz583cXFxpkePHiY1NdXMmDHDpKamGmOM6dSpk/ntt9/SZDDGmIcfftgYY8z7779vDh8+bIwxZtasWWbmzJnGGGNq165tLl26ZFatWmV69+5tjDHm0qVL5vHHHzdnzpxxjXP8+HFTv359c/HiRZOammrGjBlj9u/ff91lXLRokZk0aZJrrNq1axtjjGnfvr1ZunSpMcaYV155xcyZMydNBsBbsSUOZJF8+fIpJiZGgwcPVmRkpCRp9+7dKl++vGuruFKlSvr7778lSaVLl5YkFSpUSMnJya5xRo0apfDwcLVu3VonTpzQ0aNHXXcSu3TpkqpVq6bQ0FCFh4dLkhISElS8eHHX3fwqV66sNWvWpMm2Z88eVahQQZLUqFEjSdK4ceNUuXJlV5bDhw/L19dXUVFR+uabb7Rp0yY9+eST8vHxkZ+fn/r27aucOXPq8OHDrjsFXslwtbvuuktjxoxRzpw5deTIEddnccWOHTv0xx9/qEOHDpIu73U4ePCg8uTJ41qeEiVKuC7zGhMTo82bN193GcuXL+8a1/zjUEaZMmVcn+/x48evs8YA78MxcSAL1alTR+Hh4fr0008lSffee682b94sh8MhY4zWr1/vKj6bzXbN+6dOnSpjjLp16ybp8h8GhQoV0syZMzVv3jx17dpVDz74oCTJx+fyr3ORIkW0a9cu1x2sfv3112vKtVixYtqyZYskaenSpZo3b56KFSum3377TZL0559/uu5H3bJlSy1dulRxcXGqVq2atm/frpUrV2rq1KkaPny4nE6nqzCvZLjasGHDNHbsWI0fP14FCxZ0TWuz2eR0OnXvvffqwQcf1Lx58/Tuu++qYcOGKlKkiOv9oaGh2r17t+sPm549eyp//vzXXcYcOXK47gj1xx9/WK6fKxkAb8WWOJDFhg4dqp9//lmSVLJkSTVs2FBt2rSR0+lUxYoVVa9ePW3fvv2a923evFlvvPGGqlSp4tpKffHFFzV06FB16dJFxhjlypVLEydOTHMmfFBQkHr06KGnnnpKPj4+Cg0NVf/+/dOMPXDgQMXGxmrWrFkKCAjQpEmTVLt2bQ0fPlxvv/22HA6HxowZI0kKCQmRJNWtW1c+Pj4KCwvTHXfcoebNm8vf318FChTQ0aNH3S5/06ZN1apVK+XJk0fBwcGuaStVqqQuXbrovffe06+//qq2bdvqwoULqlevnmsL+8rydO7cWe3bt5fNZlPt2rVVuHDh6y5jUlKSFixYoDZt2qhs2bLKlStXuuvm6gzX+yMKuNVxAxQAALwUu9MBAPBSlDgAAF6KEgcAwEtR4gAAeClKHAAAL0WJAwDgpShxAAC8FCUOAICX+n8wCZrxPJd/NwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set({'figure.figsize': (8, 6)})\n",
    "sns.histplot(data=merge_df, x='nz_cov_count', hue='reject_null', log_scale=(False, True),\n",
    "             kde=False, multiple='dodge')\n",
    "plt.xticks(range(0, 5))\n",
    "plt.xlim(-0.1, 4.1)\n",
    "plt.title('Distribution of nonzero cancer type covariate count')\n",
    "plt.xlabel('Nonzero covariate count')\n",
    "plt.ylabel('Number of models (fold/cancer type/gene)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it looks like the cancer type covariates are largely unused by the models, and the proportion of models using them doesn't seem to vary much between well-performing and poorly-performing models.\n",
    "\n",
    "Now we want to do something similar for the sample mutation burden covariate (\"log10_mut\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>fold</th>\n",
       "      <th>mut_cov</th>\n",
       "      <th>reject_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    identifier  fold  mut_cov  reject_null\n",
       "0  MAP3K1_BRCA     0     True         True\n",
       "1  MAP3K1_BRCA     1     True         True\n",
       "2  MAP3K1_BRCA     2    False         True\n",
       "3  MAP3K1_BRCA     3     True         True\n",
       "4  MAP3K1_UCEC     0    False        False\n",
       "5  MAP3K1_UCEC     1     True        False\n",
       "6  MAP3K1_UCEC     2     True        False\n",
       "7  MAP3K1_UCEC     3    False        False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identifier_mut_coefs = []\n",
    "for identifier, coefs_list in au.generate_nonzero_coefficients(pancancer_dir):\n",
    "    for fold_no, coefs in enumerate(coefs_list):\n",
    "        mut_cov = au.get_mutation_covariate(coefs)\n",
    "        identifier_mut_coefs.append((identifier, fold_no, mut_cov))\n",
    "        \n",
    "mut_ids_df = pd.DataFrame(identifier_mut_coefs, columns=['identifier', 'fold', 'mut_cov'])\n",
    "mut_ids_df.head(n=10)\n",
    "\n",
    "mut_merge_df = (\n",
    "    mut_ids_df.merge(pancancer_comparison_df, left_on='identifier', right_on='identifier')\n",
    "              .drop(columns=['delta_mean', 'p_value', 'corr_pval', 'nlog10_p'])\n",
    ")\n",
    "mut_merge_df.head(n=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jake/anaconda3/envs/pancancer-evaluation/lib/python3.6/site-packages/numpy/lib/histograms.py:778: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "  a, weights = _ravel_and_check_weights(a, weights)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of models (fold/cancer type/gene)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set({'figure.figsize': (8, 6)})\n",
    "sns.histplot(data=mut_merge_df, x='mut_cov', hue='reject_null',\n",
    "             kde=False, discrete=True, multiple='dodge', shrink=.8)\n",
    "plt.xticks((0, 1))\n",
    "plt.xlabel('Mutation covariate included')\n",
    "plt.ylabel('Number of models (fold/cancer type/gene)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the mutation burden covariate is included in our models fairly frequently, more than half the time. It also seems to be zeroed out more often in well-performing models than poorly-performing models. This makes sense: in the case where a mutation doesn't result in a gene expression perturbation, there should be a bit of predictive signal in mutation burden, but in the case where a mutation perturbs gene expression noticeably, signal in the mutation burden should be at least somewhat redundant to the signal in the expression data (at least in some cases).\n",
    "\n",
    "Last, we want to look at the genes selected by elastic net. The underlying question is: are the genes selected primarily the most variable ones (i.e. genes with a high mean absolute deviation), or is the model selecting less variable genes in some cases? Again, we'll segment by well-performing/poorly-performing and see if we notice a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading gene expression data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['213' '7038' '1277' '2335' '3852']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>mean_absolute_deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>213</td>\n",
       "      <td>154357.230103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7038</td>\n",
       "      <td>77954.882427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1277</td>\n",
       "      <td>74338.643636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2335</td>\n",
       "      <td>66635.420552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3852</td>\n",
       "      <td>62003.720145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_id  mean_absolute_deviation\n",
       "0     213            154357.230103\n",
       "1    7038             77954.882427\n",
       "2    1277             74338.643636\n",
       "3    2335             66635.420552\n",
       "4    3852             62003.720145"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get top n genes in terms of MAD\n",
    "# note the actual feature selection may be different, since it does this selection\n",
    "# independently for each training set (subset of all data)\n",
    "rnaseq_df = du.load_expression_data(verbose=True)\n",
    "mad_genes_df = (\n",
    "    rnaseq_df.mad(axis=0)\n",
    "             .sort_values(ascending=False)\n",
    "             .reset_index()\n",
    ")\n",
    "mad_genes_df.columns = ['gene_id', 'mean_absolute_deviation']\n",
    "mad_genes = mad_genes_df.gene_id.astype(str).values\n",
    "print(mad_genes[:5])\n",
    "mad_genes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>identifier</th>\n",
       "      <th>fold</th>\n",
       "      <th>n_mad_genes</th>\n",
       "      <th>mad_proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.457516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.422287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>3</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.456633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.452722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>1</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.397849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>2</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.442308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MAP3K1_UCEC</td>\n",
       "      <td>3</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MLH1_UCEC</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MLH1_UCEC</td>\n",
       "      <td>1</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.490826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    identifier  fold  n_mad_genes  mad_proportion\n",
       "0  MAP3K1_BRCA     0         4000        0.457516\n",
       "1  MAP3K1_BRCA     1         4000        0.422287\n",
       "2  MAP3K1_BRCA     2         4000        0.500000\n",
       "3  MAP3K1_BRCA     3         4000        0.456633\n",
       "4  MAP3K1_UCEC     0         4000        0.452722\n",
       "5  MAP3K1_UCEC     1         4000        0.397849\n",
       "6  MAP3K1_UCEC     2         4000        0.442308\n",
       "7  MAP3K1_UCEC     3         4000        0.583333\n",
       "8    MLH1_UCEC     0         4000        0.583333\n",
       "9    MLH1_UCEC     1         4000        0.490826"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many of nonzero coefficients are in top n MAD genes?\n",
    "n_mad_genes = 4000\n",
    "\n",
    "identifier_mad_coefs = []\n",
    "for identifier, coefs_list in au.generate_nonzero_coefficients(pancancer_dir):\n",
    "    for fold_no, coefs in enumerate(coefs_list):\n",
    "        mad_proportion = au.get_mad_proportion(coefs, mad_genes[:n_mad_genes])\n",
    "        identifier_mad_coefs.append((identifier, fold_no, n_mad_genes, mad_proportion))\n",
    "        \n",
    "# TODO: do we need a way to generate list of all features?\n",
    "mad_ids_df = pd.DataFrame(identifier_mad_coefs, columns=['identifier', 'fold', 'n_mad_genes', 'mad_proportion'])\n",
    "mad_ids_df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of models (fold/cancer type/gene)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mad_merge_df = (\n",
    "    mad_ids_df.merge(pancancer_comparison_df, left_on='identifier', right_on='identifier')\n",
    "              .drop(columns=['delta_mean', 'p_value', 'corr_pval', 'nlog10_p'])\n",
    ")\n",
    "sns.set({'figure.figsize': (8, 6)})\n",
    "sns.histplot(data=mad_merge_df, x='mad_proportion', hue='reject_null', kde=False)\n",
    "plt.xlabel('Proportion of selected/nonzero genes in top {} MAD genes'.format(n_mad_genes))\n",
    "plt.ylabel('Number of models (fold/cancer type/gene)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to ask: how stable are models between folds (for the same gene and cancer type)?\n",
    "\n",
    "We can quantify this using Jaccard similarity: treat coefficients as binary vectors (either 0 or 1=nonzero) and evaluate overlap between nonzero coefficients.\n",
    "\n",
    "$$J(\\text{model A}, \\text{model B}) = \\frac{|\\{\\beta \\mid \\beta_A \\neq 0 \\text{ and } \\beta_B \\neq 0\\}|}{|\\{\\beta \\mid \\beta_A \\neq 0 \\text{ or } \\beta_B \\neq 0\\}|}$$"
   ]
  },
  {
   "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>identifier</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>train_set</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>reject_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.150555</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.063291</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.078431</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.175238</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.058981</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>pancancer</td>\n",
       "      <td>0.193237</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>pancancer</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>pancancer</td>\n",
       "      <td>0.162047</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MAP3K1_BRCA</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>pancancer</td>\n",
       "      <td>0.008721</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    identifier  f1  f2      train_set   jaccard  reject_null\n",
       "0  MAP3K1_BRCA   0   1  single cancer  0.150555         True\n",
       "1  MAP3K1_BRCA   0   2  single cancer  0.063291         True\n",
       "2  MAP3K1_BRCA   0   3  single cancer  0.156433         True\n",
       "3  MAP3K1_BRCA   1   2  single cancer  0.078431         True\n",
       "4  MAP3K1_BRCA   1   3  single cancer  0.175238         True\n",
       "5  MAP3K1_BRCA   2   3  single cancer  0.058981         True\n",
       "6  MAP3K1_BRCA   0   1      pancancer  0.193237         True\n",
       "7  MAP3K1_BRCA   0   2      pancancer  0.012739         True\n",
       "8  MAP3K1_BRCA   0   3      pancancer  0.162047         True\n",
       "9  MAP3K1_BRCA   1   2      pancancer  0.008721         True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_fold_jaccard = []\n",
    "for jaccard_analysis_dir in (single_cancer_dir, pancancer_dir):\n",
    "    train_set = ('single cancer' if jaccard_analysis_dir == single_cancer_dir\n",
    "                 else 'pancancer')\n",
    "    for identifier, coefs_list in au.generate_nonzero_coefficients(jaccard_analysis_dir):\n",
    "        # for each pairwise fold comparison, calculate jaccard index\n",
    "        num_folds = len(coefs_list)\n",
    "        for f1, f2 in it.combinations(range(num_folds), 2):\n",
    "            try:\n",
    "                nonzero_coefs_f1 = list(zip(*coefs_list[f1]))[0]\n",
    "                nonzero_coefs_f2 = list(zip(*coefs_list[f2]))[0]\n",
    "                inter_fold_jaccard, _, __ = au.compute_jaccard(nonzero_coefs_f1, nonzero_coefs_f2)\n",
    "            except IndexError:\n",
    "                # this can occur if all coefficients for a given fold were zero\n",
    "                # (i.e. model predicts the mean/fits only an intercept)\n",
    "                # if so, we call it a jaccard index of 0\n",
    "                inter_fold_jaccard = 0.0\n",
    "            per_fold_jaccard.append((identifier, f1, f2, train_set, inter_fold_jaccard))\n",
    "\n",
    "intra_jaccard_df = pd.DataFrame(per_fold_jaccard, columns=['identifier', 'f1', 'f2', 'train_set', 'jaccard'])\n",
    "intra_jaccard_merge_df = (\n",
    "    intra_jaccard_df.merge(pancancer_comparison_df, left_on='identifier', right_on='identifier')\n",
    "                    .drop(columns=['delta_mean', 'p_value', 'corr_pval', 'nlog10_p'])\n",
    ")\n",
    "intra_jaccard_merge_df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set({'figure.figsize': (15, 6)})\n",
    "fig, axarr = plt.subplots(1, 2)\n",
    "\n",
    "sns.histplot(data=intra_jaccard_merge_df[intra_jaccard_merge_df.train_set == 'single cancer'], \n",
    "             x='jaccard', stat='count', hue='reject_null', element='step', kde=False, ax=axarr[0])\n",
    "axarr[0].set_xlabel('Inter-fold Jaccard index')\n",
    "axarr[0].set_ylabel('Bin count')\n",
    "axarr[0].set_title('Jaccard similarity between folds of same model, single cancer')\n",
    "sns.histplot(data=intra_jaccard_merge_df[intra_jaccard_merge_df.train_set == 'pancancer'], \n",
    "             x='jaccard', stat='count', hue='reject_null', element='step', kde=False, ax=axarr[1])\n",
    "axarr[1].set_xlabel('Inter-fold Jaccard index')\n",
    "axarr[1].set_ylabel('Bin count')\n",
    "axarr[1].set_title('Jaccard similarity between folds of same model, pancancer')\n",
    "plt.savefig('./coefficient_similarity.png', dpi=300, bbox_inches='tight', pad_inches=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2750, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>train_set</th>\n",
       "      <th>mean_jaccard</th>\n",
       "      <th>reject_null</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALK_SKCM</td>\n",
       "      <td>ALK_LUAD</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.004860</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALK_SKCM</td>\n",
       "      <td>ALK_COAD</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.005658</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALK_SKCM</td>\n",
       "      <td>ALK_UCEC</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.004947</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALK_LUAD</td>\n",
       "      <td>ALK_COAD</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALK_LUAD</td>\n",
       "      <td>ALK_UCEC</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.002542</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALK_COAD</td>\n",
       "      <td>ALK_UCEC</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APC_UCEC</td>\n",
       "      <td>APC_STAD</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.006181</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>APC_UCEC</td>\n",
       "      <td>APC_LUAD</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.006634</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APC_UCEC</td>\n",
       "      <td>APC_BLCA</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APC_UCEC</td>\n",
       "      <td>APC_HNSC</td>\n",
       "      <td>single cancer</td>\n",
       "      <td>0.007406</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id1       id2      train_set  mean_jaccard reject_null\n",
       "0  ALK_SKCM  ALK_LUAD  single cancer      0.004860         one\n",
       "1  ALK_SKCM  ALK_COAD  single cancer      0.005658         one\n",
       "2  ALK_SKCM  ALK_UCEC  single cancer      0.004947         one\n",
       "3  ALK_LUAD  ALK_COAD  single cancer      0.002685        none\n",
       "4  ALK_LUAD  ALK_UCEC  single cancer      0.002542        none\n",
       "5  ALK_COAD  ALK_UCEC  single cancer      0.003938        none\n",
       "0  APC_UCEC  APC_STAD  single cancer      0.006181        none\n",
       "1  APC_UCEC  APC_LUAD  single cancer      0.006634        none\n",
       "2  APC_UCEC  APC_BLCA  single cancer      0.012322        none\n",
       "3  APC_UCEC  APC_HNSC  single cancer      0.007406        none"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how stable are models between cancer types for the same gene?\n",
    "genes_df = du.load_vogelstein()\n",
    "\n",
    "inter_cancer_jaccard_df = None\n",
    "for jaccard_analysis_dir in (single_cancer_dir, pancancer_dir):\n",
    "    train_set = ('single cancer' if jaccard_analysis_dir == single_cancer_dir\n",
    "                 else 'pancancer')\n",
    "    for _, gene_series in genes_df.iterrows():\n",
    "        gene = gene_series.gene\n",
    "        per_gene_jaccard = {}\n",
    "        for identifier, coefs_list in au.generate_nonzero_coefficients_for_gene(\n",
    "                                                        jaccard_analysis_dir, gene):\n",
    "            per_gene_jaccard[identifier] = coefs_list\n",
    "        results_df = au.compare_inter_cancer_coefs(gene, per_gene_jaccard,\n",
    "                                                   pancancer_comparison_df,\n",
    "                                                   train_set=train_set)\n",
    "        if inter_cancer_jaccard_df is None:\n",
    "            inter_cancer_jaccard_df = results_df\n",
    "        else:\n",
    "            inter_cancer_jaccard_df = pd.concat((inter_cancer_jaccard_df, results_df))\n",
    "            \n",
    "print(inter_cancer_jaccard_df.shape)\n",
    "inter_cancer_jaccard_df.head(n=10)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Same gene/different cancer types, pancancer')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set({'figure.figsize': (16, 6)})\n",
    "fig, axarr = plt.subplots(1, 2)\n",
    "\n",
    "sns.histplot(data=inter_cancer_jaccard_df[inter_cancer_jaccard_df.train_set == 'single cancer'],\n",
    "             x='mean_jaccard', stat='count', hue='reject_null', element='step',\n",
    "             kde=False, ax=axarr[0])\n",
    "axarr[0].set_xlabel('Inter-cancer type, same gene Jaccard index')\n",
    "axarr[0].set_ylabel('Bin count')\n",
    "axarr[0].set_title('Same gene/different cancer types, single cancer')\n",
    "sns.histplot(data=inter_cancer_jaccard_df[inter_cancer_jaccard_df.train_set == 'pancancer'],\n",
    "             x='mean_jaccard', stat='count', hue='reject_null', element='step',\n",
    "             kde=False, ax=axarr[1])\n",
    "axarr[1].set_xlabel('Inter-cancer type, same gene Jaccard index')\n",
    "axarr[1].set_ylabel('Bin count')\n",
    "axarr[1].set_title('Same gene/different cancer types, pancancer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that for the pancancer coefficient comparisons, the model overlaps (coefficient Jaccard similarities) are fairly high, and there is a lot of separation between well-performing and poor-performing models (with well-performing models having higher overlap). This is expected since the pan-cancer training set is mostly consistent between folds or between experiments (with the exception of the cancer type under consideration), so it makes sense that the resulting model is consistent as well.\n",
    "\n",
    "In the single-cancer case, model overlaps are much lower in general, and there is not nearly as much separation between well-performing and poorly performing models. This is a bit surprising since we're only considering models that predict mutations in the same gene, so we'd expect them to use some of the same predictors, but this doesn't really seem to be the case often. This may be due to the high level of correlation/collinearity in gene expression data (there are multiple choices of predictors that can predict the same labels, since many of the predictors convey redundant information).\n",
    "\n",
    "Maybe looking at this from a pathway or network perspective would help to elucidate this; that is, even if the model selects different genes between folds, we'd expect them to be in the same pathway, or to be close together in a gene interaction network."
   ]
  }
 ],
 "metadata": {
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