process_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8606c8f-1999-449a-a445-ba69ca8ca79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import plot_functions\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "from statsmodels.stats.multitest import fdrcorrection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72a5ed26-e868-4853-a227-b74c2d4214a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'plot_functions' from '/Users/dlehotzk/Documents/tcga/dig/plot_functions.py'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(plot_functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28a10b3-da5e-4e9a-a013-a9e494d7be7f",
   "metadata": {},
   "source": [
    "# Plotly tables with hyperlink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8b1f767-c02a-4b7c-af27-8c10670ca6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# Define the table data\n",
    "header = ['Column 1', 'Column 2', 'Search Column']\n",
    "column_1 = ['Item A', 'Item B', 'Item C']\n",
    "column_2 = ['Data A', 'Data B', 'Data C']\n",
    "search_column = ['Search A', 'Search B', 'Search C']\n",
    "\n",
    "# Create clickable entries in the 'Search Column'\n",
    "clickable_search_column = [\n",
    "    f'<a href=\"https://www.google.com/search?q={entry}+age\" target=\"_blank\">{entry}</a>'\n",
    "    for entry in search_column\n",
    "]\n",
    "\n",
    "# Create a table\n",
    "fig = go.Figure(data=[go.Table(\n",
    "    header=dict(values=header, fill_color='paleturquoise', align='left'),\n",
    "    cells=dict(values=[column_1, clickable_search_column, column_2],\n",
    "               fill_color='lavender', align='left')\n",
    ")])\n",
    "\n",
    "\n",
    "# Save the figure as an HTML file\n",
    "pio.write_html(fig, file='clickable_table.html', auto_open=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0623bb05-47fb-4d2c-b333-d3647c0192df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the table data\n",
    "header = ['Column 1', 'Search Column']\n",
    "column_1 = ['Item A', 'Item B', 'Item C']\n",
    "search_column = ['Search A', 'Search B', 'Search C']\n",
    "\n",
    "# Create clickable entries in the 'Search Column'\n",
    "clickable_search_column = [\n",
    "    f'<a href=\"https://www.google.com/search?q={entry}\" target=\"_blank\">{entry}</a>'\n",
    "    for entry in search_column\n",
    "]\n",
    "\n",
    "# Create a table\n",
    "fig = go.Figure(data=[go.Table(\n",
    "    header=dict(values=header, fill_color='paleturquoise', align='left'),\n",
    "    cells=dict(values=[column_2, clickable_search_column],\n",
    "               fill_color='lavender', align='left')\n",
    ")])\n",
    "\n",
    "\n",
    "# Save the figure as an HTML file\n",
    "pio.write_html(fig, file='clickable_table.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490bf3c6-e6e9-483b-bd5a-3f5e81010c22",
   "metadata": {},
   "source": [
    "# PanCanAtlas Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5adb99d2-23c7-4f36-86cb-ca004959fea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/openpyxl/worksheet/_reader.py:329: UserWarning:\n",
      "\n",
      "Unknown extension is not supported and will be removed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene</th>\n",
       "      <th>Cancer</th>\n",
       "      <th>KEY</th>\n",
       "      <th>Tumor suppressor or oncogene prediction (by 20/20+)</th>\n",
       "      <th>Decision</th>\n",
       "      <th>Tissue Frequency</th>\n",
       "      <th>Pancan Frequency</th>\n",
       "      <th>Consensus Score</th>\n",
       "      <th>Correlation adusted score</th>\n",
       "      <th>Novel</th>\n",
       "      <th>Rescue Notes</th>\n",
       "      <th>Note about previous publication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABL1</td>\n",
       "      <td>PANCAN</td>\n",
       "      <td>ABL1_PANCAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rescued</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011675</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Evidence from OncoImpact/DriverNET overlap (SN...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACVR1</td>\n",
       "      <td>UCEC</td>\n",
       "      <td>ACVR1_UCEC</td>\n",
       "      <td>oncogene</td>\n",
       "      <td>official</td>\n",
       "      <td>0.05303</td>\n",
       "      <td>0.00749</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACVR1B</td>\n",
       "      <td>PANCAN</td>\n",
       "      <td>ACVR1B_PANCAN</td>\n",
       "      <td>possible tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010904</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found in 24132290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ACVR2A</td>\n",
       "      <td>COADREAD</td>\n",
       "      <td>ACVR2A_COADREAD</td>\n",
       "      <td>tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>0.028481</td>\n",
       "      <td>0.013988</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found in 22810696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ACVR2A</td>\n",
       "      <td>LIHC</td>\n",
       "      <td>ACVR2A_LIHC</td>\n",
       "      <td>possible tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>0.031073</td>\n",
       "      <td>0.013988</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found in private communication about integrati...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>ZMYM3</td>\n",
       "      <td>PRAD</td>\n",
       "      <td>ZMYM3_PRAD</td>\n",
       "      <td>tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>0.016771</td>\n",
       "      <td>0.018504</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found in 26544944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>ZNF133</td>\n",
       "      <td>OV</td>\n",
       "      <td>ZNF133_OV</td>\n",
       "      <td>oncogene</td>\n",
       "      <td>official</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.006058</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found in 21720365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>ZNF750</td>\n",
       "      <td>PANCAN</td>\n",
       "      <td>ZNF750_PANCAN</td>\n",
       "      <td>possible tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011455</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>ZNF750</td>\n",
       "      <td>ESCA</td>\n",
       "      <td>ZNF750_ESCA</td>\n",
       "      <td>possible tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.011455</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found in 28052061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>ZNF750</td>\n",
       "      <td>HNSC</td>\n",
       "      <td>ZNF750_HNSC</td>\n",
       "      <td>possible tsg</td>\n",
       "      <td>official</td>\n",
       "      <td>0.037849</td>\n",
       "      <td>0.011455</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>739 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gene    Cancer              KEY  \\\n",
       "3      ABL1    PANCAN      ABL1_PANCAN   \n",
       "4     ACVR1      UCEC       ACVR1_UCEC   \n",
       "5    ACVR1B    PANCAN    ACVR1B_PANCAN   \n",
       "6    ACVR2A  COADREAD  ACVR2A_COADREAD   \n",
       "7    ACVR2A      LIHC      ACVR2A_LIHC   \n",
       "..      ...       ...              ...   \n",
       "737   ZMYM3      PRAD       ZMYM3_PRAD   \n",
       "738  ZNF133        OV        ZNF133_OV   \n",
       "739  ZNF750    PANCAN    ZNF750_PANCAN   \n",
       "740  ZNF750      ESCA      ZNF750_ESCA   \n",
       "741  ZNF750      HNSC      ZNF750_HNSC   \n",
       "\n",
       "    Tumor suppressor or oncogene prediction (by 20/20+)  Decision  \\\n",
       "3                                                  NaN    rescued   \n",
       "4                                             oncogene   official   \n",
       "5                                         possible tsg   official   \n",
       "6                                                  tsg   official   \n",
       "7                                         possible tsg   official   \n",
       "..                                                 ...        ...   \n",
       "737                                                tsg   official   \n",
       "738                                           oncogene   official   \n",
       "739                                       possible tsg   official   \n",
       "740                                       possible tsg   official   \n",
       "741                                       possible tsg   official   \n",
       "\n",
       "    Tissue Frequency Pancan Frequency Consensus Score  \\\n",
       "3                NaN         0.011675               0   \n",
       "4            0.05303          0.00749             1.5   \n",
       "5                NaN         0.010904               1   \n",
       "6           0.028481         0.013988             1.5   \n",
       "7           0.031073         0.013988             1.5   \n",
       "..               ...              ...             ...   \n",
       "737         0.016771         0.018504               1   \n",
       "738         0.029412         0.006058             1.5   \n",
       "739              NaN         0.011455             1.5   \n",
       "740         0.069767         0.011455             2.5   \n",
       "741         0.037849         0.011455             2.5   \n",
       "\n",
       "    Correlation adusted score Novel  \\\n",
       "3                         NaN     0   \n",
       "4                         1.5     0   \n",
       "5                           0     0   \n",
       "6                         1.5     0   \n",
       "7                         1.5     0   \n",
       "..                        ...   ...   \n",
       "737                         0     1   \n",
       "738                       1.5     1   \n",
       "739                       1.5     0   \n",
       "740                       2.5     0   \n",
       "741                       2.5     0   \n",
       "\n",
       "                                          Rescue Notes  \\\n",
       "3    Evidence from OncoImpact/DriverNET overlap (SN...   \n",
       "4                                                  NaN   \n",
       "5                                                  NaN   \n",
       "6                                                  NaN   \n",
       "7                                                  NaN   \n",
       "..                                                 ...   \n",
       "737                                                NaN   \n",
       "738                                                NaN   \n",
       "739                                                NaN   \n",
       "740                                                NaN   \n",
       "741                                                NaN   \n",
       "\n",
       "                       Note about previous publication  \n",
       "3                                                  NaN  \n",
       "4                                                    0  \n",
       "5                                    Found in 24132290  \n",
       "6                                    Found in 22810696  \n",
       "7    Found in private communication about integrati...  \n",
       "..                                                 ...  \n",
       "737                                  Found in 26544944  \n",
       "738                                  Found in 21720365  \n",
       "739                                                  0  \n",
       "740                                  Found in 28052061  \n",
       "741                                                  0  \n",
       "\n",
       "[739 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca = pd.read_excel('mmc1.xlsx', sheet_name='Table S1').iloc[2:]\n",
    "df_pca.columns = df_pca.iloc[0].tolist()\n",
    "df_pca = df_pca.iloc[1:]\n",
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96552af2-fb9f-4142-aa63-03a25ac660b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        TP53\n",
       "1      PIK3CA\n",
       "2       SPTA1\n",
       "3       KMT2D\n",
       "4       KMT2C\n",
       "        ...  \n",
       "294     RRAS2\n",
       "295      UNCX\n",
       "296     TCEB1\n",
       "297     CEBPA\n",
       "298     CBWD3\n",
       "Name: Gene, Length: 299, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_genes = df_pca.sort_values(['Pancan Frequency'], ascending=False)['Gene']\n",
    "pca_genes = pca_genes[~(pca_genes.duplicated())].copy().reset_index()['Gene']\n",
    "pca_genes.to_csv('pancanatlas_genes.tsv', sep='\\t', index=False)\n",
    "pca_genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cea884-2ba3-4160-89c0-189d7f1bc0bc",
   "metadata": {},
   "source": [
    "# Cancer Gene Census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f6d6694-76c7-4654-bf61-3bded0f39498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        A1CF\n",
       "1        ABI1\n",
       "2        ABL1\n",
       "3        ABL2\n",
       "4       ACKR3\n",
       "        ...  \n",
       "743    ZNF429\n",
       "744    ZNF479\n",
       "745    ZNF521\n",
       "746     ZNRF3\n",
       "747     ZRSR2\n",
       "Name: Gene Symbol, Length: 748, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cgc_new = pd.read_csv('gs://getzlab-workflows-reference_files-oa/Census_allThu_Jun_20_12_28_25_2024_hg38.tsv', sep='\\t', index_col=0)\n",
    "df_cgc_new = pd.Series(df_cgc_new.index)\n",
    "df_cgc_new.to_csv('cancer_gene_census_2024_06_20.tsv', sep='\\t', index=False, header=False)\n",
    "df_cgc_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5eeb3ec-5bde-4bd7-b48b-ac0be6aefe0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isin(pca_genes, df_cgc_new.tolist()).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba378b6b-3861-4692-af5d-64fe326229ea",
   "metadata": {},
   "source": [
    "# DIG input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa3e7fb2-d2f7-4c66-923f-c2503a33e4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res = pd.read_csv('out.tsv', sep='\\t', names=['CHROM', 'START', 'END', 'REF', 'ALT', 'SAMPLE', 'GENE', 'ANNOT', 'MUT_TYPE', 'CONTEXT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2507ea2-191b-4912-b40e-a39c8da1c2cb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHROM</th>\n",
       "      <th>START</th>\n",
       "      <th>END</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>SAMPLE</th>\n",
       "      <th>GENE</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>566107</td>\n",
       "      <td>566108</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>TCGA-WC-AA9A-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>A&gt;G</td>\n",
       "      <td>TAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>569341</td>\n",
       "      <td>569342</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>TCGA-V4-A9ES-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>T&gt;C</td>\n",
       "      <td>CTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>569517</td>\n",
       "      <td>569518</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>TCGA-VD-A8KF-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>G&gt;A</td>\n",
       "      <td>AGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>752696</td>\n",
       "      <td>752697</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>TCGA-YZ-A985-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>G&gt;A</td>\n",
       "      <td>CGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>872255</td>\n",
       "      <td>872256</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>TCGA-V4-A9F5-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>T&gt;G</td>\n",
       "      <td>TTG</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152009</th>\n",
       "      <td>22</td>\n",
       "      <td>51093797</td>\n",
       "      <td>51093798</td>\n",
       "      <td>C</td>\n",
       "      <td>-</td>\n",
       "      <td>TCGA-VD-AA8O-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>INDEL</td>\n",
       "      <td>Noncoding_INDEL</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152010</th>\n",
       "      <td>22</td>\n",
       "      <td>51140977</td>\n",
       "      <td>51140978</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>TCGA-YZ-A985-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>G&gt;A</td>\n",
       "      <td>CGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152011</th>\n",
       "      <td>22</td>\n",
       "      <td>51141986</td>\n",
       "      <td>51141987</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>TCGA-YZ-A985-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>G&gt;A</td>\n",
       "      <td>CGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152012</th>\n",
       "      <td>22</td>\n",
       "      <td>51169437</td>\n",
       "      <td>51169438</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>TCGA-YZ-A985-01A</td>\n",
       "      <td>SHANK3</td>\n",
       "      <td>Missense</td>\n",
       "      <td>G&gt;A</td>\n",
       "      <td>CGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152013</th>\n",
       "      <td>22</td>\n",
       "      <td>51169752</td>\n",
       "      <td>51169753</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>TCGA-VD-A8KE-01A</td>\n",
       "      <td>.</td>\n",
       "      <td>Noncoding</td>\n",
       "      <td>A&gt;C</td>\n",
       "      <td>CAC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152014 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CHROM     START       END REF ALT            SAMPLE    GENE  \\\n",
       "0           1    566107    566108   A   G  TCGA-WC-AA9A-01A       .   \n",
       "1           1    569341    569342   T   C  TCGA-V4-A9ES-01A       .   \n",
       "2           1    569517    569518   G   A  TCGA-VD-A8KF-01A       .   \n",
       "3           1    752696    752697   G   A  TCGA-YZ-A985-01A       .   \n",
       "4           1    872255    872256   T   G  TCGA-V4-A9F5-01A       .   \n",
       "...       ...       ...       ...  ..  ..               ...     ...   \n",
       "152009     22  51093797  51093798   C   -  TCGA-VD-AA8O-01A       .   \n",
       "152010     22  51140977  51140978   G   A  TCGA-YZ-A985-01A       .   \n",
       "152011     22  51141986  51141987   G   A  TCGA-YZ-A985-01A       .   \n",
       "152012     22  51169437  51169438   G   A  TCGA-YZ-A985-01A  SHANK3   \n",
       "152013     22  51169752  51169753   A   C  TCGA-VD-A8KE-01A       .   \n",
       "\n",
       "            ANNOT         MUT_TYPE CONTEXT  \n",
       "0       Noncoding              A>G     TAA  \n",
       "1       Noncoding              T>C     CTC  \n",
       "2       Noncoding              G>A     AGC  \n",
       "3       Noncoding              G>A     CGT  \n",
       "4       Noncoding              T>G     TTG  \n",
       "...           ...              ...     ...  \n",
       "152009      INDEL  Noncoding_INDEL       .  \n",
       "152010  Noncoding              G>A     CGC  \n",
       "152011  Noncoding              G>A     CGC  \n",
       "152012   Missense              G>A     CGC  \n",
       "152013  Noncoding              A>C     CAC  \n",
       "\n",
       "[152014 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a79da1a9-97b9-4daf-b9f3-133735d9b645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res.SAMPLE.unique().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e095d7-ca02-42a0-86cb-04e1d0970699",
   "metadata": {},
   "source": [
    "# Mutation burden across samples within the cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc2fb9fd-1924-4221-af01-31e96355178a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TCGA-YZ-A985-01A: 25000 number of mutations\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "ax.hist(df_res.SAMPLE, bins=df_res.SAMPLE.unique().shape[0])\n",
    "ax.tick_params(axis='x', labelrotation=90)\n",
    "ids, cts = np.unique(df_res.SAMPLE, return_counts=True)\n",
    "idx_max = np.argsort(cts)[-1]\n",
    "print(ids[idx_max] + ': ' + str(cts[idx_max]) + ' number of mutations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c30f6a-c86c-4ed8-8c17-3916980a52da",
   "metadata": {},
   "source": [
    "## Excluding outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc8d2b1c-7617-4d1d-a420-aed199b6d1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//vGPaty4scLCwtS9e3dlZGRUxKkAAECQCXH6gN9//70GDBigIUOGaNWqVWratKn279+vRo0aOX0qAAAQhByPjyeeeEIxMTFatGiRf1vbtm2dPg0AAAhSjv/YZcWKFerbt69+//vfq1mzZurVq5deeumlUsf7fD55vd6AGwAAqL4cj48vv/xS8+fPV4cOHfTBBx/ojjvu0JQpU/TKK6+UOD4tLU2RkZH+W0xMjNNTAgAAVYjLGGOcPGBoaKj69u2rLVu2+LdNmTJF27dvV3p6erHxPp9PPp/Pf9/r9SomJkYej0cRERFOTk2S1GbGSsePCaD6OzhnRGVPAajSvF6vIiMjL+n7t+OvfLRs2VJdunQJ2Na5c2cdPny4xPFut1sREREBNwAAUH05Hh8DBgxQVlZWwLZ9+/apdevWTp8KAAAEIcfj495779XWrVv1+OOPKzs7W0uWLNE//vEPJScnO30qAAAQhByPj379+mnZsmV6/fXX1a1bNz3yyCOaO3euxo0b5/SpAABAEHL893xI0rXXXqtrr722Ig4NAACCHH/bBQAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCqwuNjzpw5crlcmjp1akWfCgAABIEKjY/t27frxRdfVI8ePSryNAAAIIhUWHzk5+dr3Lhxeumll9SoUaOKOg0AAAgyFRYfycnJGjFihBISEi46zufzyev1BtwAAED1FVIRB126dKl27typ7du3lzk2LS1Ns2fProhpAIBj2sxYWdlTKLeDc0ZU9hSAEjn+ykdubq7uuecevfbaa6pbt26Z41NTU+XxePy33Nxcp6cEAACqEMdf+dixY4eOHTum3r17+7edP39emzZt0vPPPy+fz6fatWv797ndbrndbqenAQAAqijH42Po0KHas2dPwLaJEyeqU6dOmj59ekB4AACAmsfx+AgPD1e3bt0CttWvX1+NGzcuth0AANQ8/IZTAABgVYV82uVCGzZssHEaAAAQBHjlAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFY5Hh9paWnq16+fwsPD1axZM1133XXKyspy+jQAACBIOR4fGzduVHJysrZu3ao1a9bo7Nmzuvrqq1VQUOD0qQAAQBAKcfqAq1evDri/ePFiNWvWTDt27NCgQYOcPh0AAAgyjsfHhTwejyQpKiqqxP0+n08+n89/3+v1VvSUAABAJarQ+CgsLNTUqVM1YMAAdevWrcQxaWlpmj17dkVOAwBqpDYzVlb2FMrt4JwRlT0FWFChn3ZJTk7W3r17tXTp0lLHpKamyuPx+G+5ubkVOSUAAFDJKuyVj7vuukvvvfeeNm3apFatWpU6zu12y+12V9Q0AABAFeN4fBhjdPfdd2vZsmXasGGD2rZt6/QpAABAEHM8PpKTk7VkyRK9++67Cg8PV15eniQpMjJSYWFhTp8OAAAEGcff8zF//nx5PB4NHjxYLVu29N/eeOMNp08FAACCUIX82AUAAKA0/G0XAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwKqQyp4AAABF2sxYWdlTqBEOzhlRqefnlQ8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYVWHxMW/ePLVp00Z169ZV//799cknn1TUqQAAQBCpkPh44403lJKSor/97W/auXOn4uLiNGzYMB07dqwiTgcAAIJIhcTH008/rVtvvVUTJ05Uly5dtGDBAtWrV08LFy6siNMBAIAgEuL0AX/44Qft2LFDqamp/m21atVSQkKC0tPTi433+Xzy+Xz++x6PR5Lk9XqdnpokqdB3qkKOCwBAsKiI77FFxzTGlDnW8fj49ttvdf78eTVv3jxge/PmzfXFF18UG5+WlqbZs2cX2x4TE+P01AAAgKTIuRV37JMnTyoyMvKiYxyPj/JKTU1VSkqK/35hYaGOHz+uxo0by+VyVeLMivN6vYqJiVFubq4iIiIqezrW1NR1SzV37TV13VLNXXtNXbdUc9fu9LqNMTp58qSio6PLHOt4fDRp0kS1a9fW0aNHA7YfPXpULVq0KDbe7XbL7XYHbGvYsKHT03JUREREjfoCLVJT1y3V3LXX1HVLNXftNXXdUs1du5PrLusVjyKOv+E0NDRUffr00dq1a/3bCgsLtXbtWsXHxzt9OgAAEGQq5McuKSkpSkpKUt++fXX55Zdr7ty5Kigo0MSJEyvidAAAIIhUSHzcdNNN+t///qdZs2YpLy9PPXv21OrVq4u9CTXYuN1u/e1vfyv2Y6LqrqauW6q5a6+p65Zq7tpr6rqlmrv2yly3y1zKZ2IAAAAcwt92AQAAVhEfAADAKuIDAABYRXwAAACralx8bNq0SSNHjlR0dLRcLpeWL1/u33f27FlNnz5d3bt3V/369RUdHa0JEybom2++CThGmzZt5HK5Am5z5swJGLN792799re/Vd26dRUTE6Mnn3zSxvJKdbF1S9Kf/vSnYmsaPnx4wJjjx49r3LhxioiIUMOGDXXLLbcoPz8/YExVW7dU9tovXHfR7amnnvKPCcZrnpaWpn79+ik8PFzNmjXTddddp6ysrIAxZ86cUXJysho3bqwGDRrohhtuKPYLAg8fPqwRI0aoXr16atasmaZNm6Zz584FjNmwYYN69+4tt9ut9u3ba/HixRW9vFKVte7jx4/r7rvvVseOHRUWFqbY2FhNmTLF/3elipT0NbF06dKAMVVp3dKlXfPBgwcXW9fkyZMDxlS3a37w4MFSn+dvvfWWf1wwXvP58+erR48e/l8UFh8fr1WrVvn3V9nnuKlh3n//ffPggw+ad955x0gyy5Yt8+87ceKESUhIMG+88Yb54osvTHp6urn88stNnz59Ao7RunVr8/DDD5sjR474b/n5+f79Ho/HNG/e3IwbN87s3bvXvP766yYsLMy8+OKLtpZZzMXWbYwxSUlJZvjw4QFrOn78eMCY4cOHm7i4OLN161bz8ccfm/bt25uxY8f691fFdRtT9tp/uuYjR46YhQsXGpfLZQ4cOOAfE4zXfNiwYWbRokVm7969JjMz01xzzTUmNjY2YN6TJ082MTExZu3atSYjI8P85je/MVdccYV//7lz50y3bt1MQkKC2bVrl3n//fdNkyZNTGpqqn/Ml19+aerVq2dSUlLMZ599Zp577jlTu3Zts3r1aqvrLVLWuvfs2WPGjBljVqxYYbKzs83atWtNhw4dzA033BBwHElm0aJFAdf89OnT/v1Vbd3GXNo1v/LKK82tt94asC6Px+PfXx2v+blz54o9z2fPnm0aNGhgTp486T9OMF7zFStWmJUrV5p9+/aZrKws88ADD5g6deqYvXv3GmOq7nO8xsXHT5X0jehCn3zyiZFkDh065N/WunVr88wzz5T6mBdeeME0atTI+Hw+/7bp06ebjh07/tIpO6K0+Bg9enSpj/nss8+MJLN9+3b/tlWrVhmXy2W+/vprY0zVX7cxl3bNR48eba666qqAbcF+zY0x5tixY0aS2bhxozHmx9iuU6eOeeutt/xjPv/8cyPJpKenG2N+DLdatWqZvLw8/5j58+ebiIgI/1rvv/9+07Vr14Bz3XTTTWbYsGEVvaRLcuG6S/Lmm2+a0NBQc/bsWf+2sr5Wqvq6jSl57VdeeaW55557Sn1MTbnmPXv2NJMmTQrYVh2uuTHGNGrUyPzzn/+s0s/xGvdjl/LyeDxyuVzF/t7MnDlz1LhxY/Xq1UtPPfVUwEtU6enpGjRokEJDQ/3bhg0bpqysLH3//fe2pl5uGzZsULNmzdSxY0fdcccd+u677/z70tPT1bBhQ/Xt29e/LSEhQbVq1dK2bdv8Y4Jx3T919OhRrVy5UrfcckuxfcF+zYt+rBAVFSVJ2rFjh86ePauEhAT/mE6dOik2Nlbp6emSflxX9+7dA35B4LBhw+T1evXf//7XP+anxygaU3SMynbhuksbExERoZCQwN+7mJycrCZNmujyyy/XwoULA/5UeFVft1T62l977TU1adJE3bp1U2pqqk6dOuXfVxOu+Y4dO5SZmVni8zyYr/n58+e1dOlSFRQUKD4+vko/xyv9r9pWZWfOnNH06dM1duzYgD+6M2XKFPXu3VtRUVHasmWLUlNTdeTIET399NOSpLy8PLVt2zbgWEUXNi8vT40aNbK3iEs0fPhwjRkzRm3bttWBAwf0wAMPKDExUenp6apdu7by8vLUrFmzgMeEhIQoKipKeXl5koJz3Rd65ZVXFB4erjFjxgRsD/ZrXlhYqKlTp2rAgAHq1q2bf16hoaHFwrp58+YB1/TC30z803VdbIzX69Xp06cVFhZWEUu6JCWt+0LffvutHnnkEd12220B2x9++GFdddVVqlevnj788EPdeeedys/P15QpUyRV7XVLpa/9D3/4g1q3bq3o6Gjt3r1b06dPV1ZWlt555x1JNeOav/zyy+rcubOuuOKKgO3Bes337Nmj+Ph4nTlzRg0aNNCyZcvUpUsXZWZmVtnnOPFRirNnz+rGG2+UMUbz588P2JeSkuL/d48ePRQaGqrbb79daWlpQfvreW+++Wb/v7t3764ePXqoXbt22rBhg4YOHVqJM7Nr4cKFGjdunOrWrRuwPdiveXJysvbu3avNmzdX9lSsKmvdXq9XI0aMUJcuXfTQQw8F7Js5c6b/37169VJBQYGeeuop/zeiqq60tf80srp3766WLVtq6NChOnDggNq1a2d7mo4r65qfPn1aS5YsCbi+RYL1mnfs2FGZmZnyeDx6++23lZSUpI0bN1b2tC6KH7uUoCg8Dh06pDVr1pT5p4b79++vc+fO6eDBg5KkFi1aFHs3cdH9Fi1aVMicnXbZZZepSZMmys7OlvTjvI8dOxYw5ty5czp+/Lh/TcG+7o8//lhZWVn685//XObYYLrmd911l9577z2tX79erVq18m9v0aKFfvjhB504cSJg/NGjR8t1TUsbExERUan/D7i0dRc5efKkhg8frvDwcC1btkx16tS56PH69++vr776Sj6fT1LVXbdU9tp/qn///pIU8Fyvrtdckt5++22dOnVKEyZMKPN4wXLNQ0ND1b59e/Xp00dpaWmKi4vTs88+W6Wf48THBYrCY//+/froo4/UuHHjMh+TmZmpWrVq+X8sER8fr02bNuns2bP+MWvWrFHHjh0r/eX3S/XVV1/pu+++U8uWLSX9uKYTJ05ox44d/jHr1q1TYWGh/3+8gn3dL7/8svr06aO4uLgyxwbDNTfG6K677tKyZcu0bt26Yj8W6tOnj+rUqaO1a9f6t2VlZenw4cOKj4+X9OO69uzZExCeRUHepUsX/5ifHqNoTNExbCtr3dKPr3hcffXVCg0N1YoVK4q90lWSzMxMNWrUyP9KV1Vbt3Rpa79QZmamJAU816vjNS/y8ssva9SoUWratGmZxw2Ga16SwsJC+Xy+qv0c/9lvVQ1SJ0+eNLt27TK7du0ykszTTz9tdu3aZQ4dOmR++OEHM2rUKNOqVSuTmZkZ8HGronf9btmyxTzzzDMmMzPTHDhwwLz66qumadOmZsKECf5znDhxwjRv3tyMHz/e7N271yxdutTUq1evUj92ebF1nzx50vzlL38x6enpJicnx3z00Uemd+/epkOHDubMmTP+YwwfPtz06tXLbNu2zWzevNl06NAh4KO2VXHdxlx87UU8Ho+pV6+emT9/frHHB+s1v+OOO0xkZKTZsGFDwNfyqVOn/GMmT55sYmNjzbp160xGRoaJj4838fHx/v1FH8O7+uqrTWZmplm9erVp2rRpiR/DmzZtmvn888/NvHnzKvXjh2Wt2+PxmP79+5vu3bub7OzsgDHnzp0zxvz48cWXXnrJ7Nmzx+zfv9+88MILpl69embWrFlVdt3GlL327Oxs8/DDD5uMjAyTk5Nj3n33XXPZZZeZQYMG+Y9RHa95kf379xuXy2VWrVpV7BjBes1nzJhhNm7caHJycszu3bvNjBkzjMvlMh9++KExpuo+x2tcfKxfv95IKnZLSkoyOTk5Je6TZNavX2+MMWbHjh2mf//+JjIy0tStW9d07tzZPP744wHfpI0x5tNPPzUDBw40brfb/OpXvzJz5syphNX+n4ut+9SpU+bqq682TZs2NXXq1DGtW7c2t956a8BHr4wx5rvvvjNjx441DRo0MBEREWbixIkBn5E3puqt25iLr73Iiy++aMLCwsyJEyeKPT5Yr3lpX8uLFi3yjzl9+rS58847TaNGjUy9evXM9ddfb44cORJwnIMHD5rExEQTFhZmmjRpYu67776Aj6Qa8+N/4549e5rQ0FBz2WWXBZzDtrLWXdrXgySTk5NjjPnxY+Q9e/Y0DRo0MPXr1zdxcXFmwYIF5vz58wHnqkrrNqbstR8+fNgMGjTIREVFGbfbbdq3b2+mTZsW8Hs+jKl+17xIamqqiYmJKXYdjQneaz5p0iTTunVrExoaapo2bWqGDh3qDw9jqu5z3GXMTz5HBAAAUMF4zwcAALCK+AAAAFYRHwAAwCriAwAAWEV8AAAAq4gPAABgFfEBAACsIj4AAIBVxAcAALCK+AAAAFYRHwAAwCriAwAAWPX/AKLSYJxsZ0UbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outliers = ['TCGA-YZ-A985-01A']\n",
    "ids, cts = np.unique(df_res.loc[~df_res.SAMPLE.isin(outliers), 'SAMPLE'], return_counts=True)\n",
    "plt.hist(cts)\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "ind_sorted = np.argsort(cts)\n",
    "plt.bar(ids[ind_sorted], cts[ind_sorted])\n",
    "ax.tick_params(axis='x', labelrotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a840a738-40a9-4050-97f6-46812045ca78",
   "metadata": {},
   "source": [
    "# DIG output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b5bc09-ec27-4b6e-a6c2-f9fa76996bd6",
   "metadata": {},
   "source": [
    "## Coding genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "205c98f0-b7ae-4657-9059-fc30c7e33e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('genes.results.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8482af70-1b47-488c-8e54-446bfec90fa9",
   "metadata": {},
   "source": [
    "### Output columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b1ec0c0-9176-4d1c-b718-c78618882adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GENE',\n",
       " 'CHROM',\n",
       " 'GENE_LENGTH',\n",
       " 'R_SIZE',\n",
       " 'R_OBS',\n",
       " 'R_INDEL',\n",
       " 'MU',\n",
       " 'SIGMA',\n",
       " 'ALPHA',\n",
       " 'THETA',\n",
       " 'MU_INDEL',\n",
       " 'SIGMA_INDEL',\n",
       " 'ALPHA_INDEL',\n",
       " 'THETA_INDEL',\n",
       " 'FLAG',\n",
       " 'Pi_SYN',\n",
       " 'Pi_MIS',\n",
       " 'Pi_NONS',\n",
       " 'Pi_SPL',\n",
       " 'Pi_TRUNC',\n",
       " 'Pi_NONSYN',\n",
       " 'Pi_INDEL',\n",
       " 'OBS_SYN',\n",
       " 'OBS_MIS',\n",
       " 'OBS_NONS',\n",
       " 'OBS_SPL',\n",
       " 'OBS_INDEL',\n",
       " 'OBS_TRUNC',\n",
       " 'OBS_NONSYN',\n",
       " 'N_SAMP_SYN',\n",
       " 'N_SAMP_MIS',\n",
       " 'N_SAMP_NONS',\n",
       " 'N_SAMP_SPL',\n",
       " 'N_SAMP_TRUNC',\n",
       " 'N_SAMP_NONSYN',\n",
       " 'N_SAMP_INDEL',\n",
       " 'EXP_SYN',\n",
       " 'EXP_MIS',\n",
       " 'EXP_NONS',\n",
       " 'EXP_SPL',\n",
       " 'EXP_TRUNC',\n",
       " 'EXP_NONSYN',\n",
       " 'PVAL_SYN_BURDEN',\n",
       " 'PVAL_MIS_BURDEN',\n",
       " 'PVAL_NONS_BURDEN',\n",
       " 'PVAL_SPL_BURDEN',\n",
       " 'PVAL_TRUNC_BURDEN',\n",
       " 'PVAL_NONSYN_BURDEN',\n",
       " 'PVAL_SYN_BURDEN_SAMPLE',\n",
       " 'PVAL_MIS_BURDEN_SAMPLE',\n",
       " 'PVAL_NONS_BURDEN_SAMPLE',\n",
       " 'PVAL_SPL_BURDEN_SAMPLE',\n",
       " 'PVAL_TRUNC_BURDEN_SAMPLE',\n",
       " 'PVAL_NONSYN_BURDEN_SAMPLE',\n",
       " 'EXP_INDEL',\n",
       " 'PVAL_INDEL_BURDEN',\n",
       " 'PVAL_MUT_BURDEN']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2562342a-5668-46d4-8c3d-83c581bef8d9",
   "metadata": {},
   "source": [
    "### Results for given mutation burden type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81d5d258-204e-49fa-a1cc-84e4b0aad535",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log2\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtype = 'SYN'\n",
    "d = np.log2(df['OBS_' + mtype]) - np.log2(df['EXP_' + mtype])\n",
    "p = df['PVAL_' + mtype + '_BURDEN']\n",
    "l = df.GENE\n",
    "plot_functions.plot_volcano(p, d, l)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386611e6-00e7-4066-9341-93d37fc6dbef",
   "metadata": {},
   "source": [
    "### Distribution of GP parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3fd493bf-b2d3-46cb-b551-2193725dbd11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.SIGMA, bins=100, log=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4538e236-e163-4184-a6df-24a318bfa1fa",
   "metadata": {},
   "source": [
    "### Recomputing p-values from model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4bc0fd77-9aa1-4f18-b7d1-cce1144c623a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nb_pvalue_greater_midp(k, alpha, p):\n",
    "    \"\"\" Calculate an UPPER TAIL p-value for a negative binomial distribution with a midp correction\n",
    "    \"\"\"\n",
    "    return 0.5 * sp.stats.nbinom.pmf(k, alpha, p) + sp.special.betainc(k+1, alpha, 1-p)\n",
    "\n",
    "def nb_pvalue_lower(k, alpha, p):\n",
    "    return sp.special.betainc(k+1, alpha, 1-p)\n",
    "\n",
    "def nb_pvalue_upper(k, alpha, p):\n",
    "    ind_0 = k==0\n",
    "    pvals = np.zeros_like(alpha)\n",
    "    pvals[ind_0] = sp.stats.nbinom.pmf(k[ind_0], alpha[ind_0], p[ind_0])\n",
    "    pvals[~ind_0] = sp.special.betainc(k[~ind_0], alpha[~ind_0], 1-p[~ind_0])\n",
    "    return pvals\n",
    "\n",
    "def nb_pvalue_uniform_midp(k, alpha, p):\n",
    "    \"\"\" Calculate the upper tail p-value for negative binomial distribution using uniform approximation and a random draw.\n",
    "    \"\"\"\n",
    "    return np.random.uniform(size=k.shape) * sp.stats.nbinom.pmf(k, alpha, p) + sp.special.betainc(k+1, alpha, 1-p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ee655d-6a1e-4f14-8294-031e31d6ccca",
   "metadata": {},
   "source": [
    "#### Total burden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cda7d87b-5212-42a2-b486-8772bd5262f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum and maximum differnces between original and recomputed p-values:\n",
      "\n",
      "SYN -1.170175067954915e-13 7.416289804496046e-14\n",
      "MIS -2.674527266322002e-13 1.6220358389773537e-13\n",
      "NONS -3.863576125695545e-14 2.0761170560490427e-14\n",
      "TRUNC -5.484501741648273e-14 2.6867397195928788e-14\n",
      "SPL -5.3734794391857577e-14 9.2148511043888e-15\n",
      "NONSYN -1.2922996006636822e-13 1.6575629757653587e-13\n",
      "MUT -0.6148743092078043 0.3762246949086101\n",
      "INDEL -1.5765166949677223e-14 1.432187701766452e-14\n"
     ]
    }
   ],
   "source": [
    "muts = ['SYN', 'MIS', 'NONS', 'TRUNC', 'SPL', 'NONSYN', 'MUT']\n",
    "print('Minimum and maximum differnces between original and recomputed p-values:\\n')\n",
    "for mut in muts:\n",
    "    if mut=='MUT':\n",
    "        col_i = 'PVAL_' + mut + '_BURDEN'\n",
    "        df[col_i + '_recomp'] = np.nan\n",
    "        df[col_i + '_uniform'] = np.nan\n",
    "        for idx in df.index:\n",
    "            # combining p-values using Fisher's method\n",
    "            df.at[idx, col_i + '_recomp'] = sp.stats.combine_pvalues([df.at[idx, 'PVAL_NONSYN_BURDEN_recomp'], df.at[idx, 'PVAL_INDEL_BURDEN']], method='fisher').pvalue\n",
    "            df.at[idx, col_i + '_uniform'] = sp.stats.combine_pvalues([df.at[idx, 'PVAL_NONSYN_BURDEN_uniform'], df.at[idx, 'PVAL_INDEL_BURDEN']], method='fisher').pvalue\n",
    "    else:\n",
    "        # recomputing p-values using DIG's model parameters\n",
    "        df['PVAL_' + mut + '_BURDEN_recomp'] = nb_pvalue_greater_midp(\n",
    "            df['OBS_' + mut],\n",
    "            df.ALPHA,\n",
    "            1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "        )\n",
    "        df['PVAL_' + mut + '_BURDEN_uniform'] = nb_pvalue_uniform_midp(\n",
    "                df['OBS_' + mut],\n",
    "                df.ALPHA,\n",
    "                1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "            )\n",
    "    print(mut, np.min(df['PVAL_' + mut + '_BURDEN_recomp'] - df['PVAL_' + mut + '_BURDEN']), np.max(df['PVAL_' + mut + '_BURDEN_recomp'] - df['PVAL_' + mut + '_BURDEN']))\n",
    "    \n",
    "df['PVAL_INDEL_BURDEN_recomp'] = nb_pvalue_greater_midp(df.OBS_INDEL, df.ALPHA_INDEL, 1 / (df.THETA_INDEL*df.Pi_INDEL + 1))\n",
    "df['PVAL_INDEL_BURDEN_uniform'] = nb_pvalue_uniform_midp(df.OBS_INDEL, df.ALPHA_INDEL, 1 / (df.THETA_INDEL*df.Pi_INDEL + 1))\n",
    "print('INDEL', np.min(df['PVAL_INDEL_BURDEN_recomp'] - df['PVAL_INDEL_BURDEN']), np.max(df['PVAL_INDEL_BURDEN_recomp'] - df['PVAL_INDEL_BURDEN']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e086a17-2169-4ea4-9ff0-d24faaa04613",
   "metadata": {},
   "source": [
    "#### Sample-wise burden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50676272-971a-4595-a2b7-1cf3eaeb8faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum and maximum differnces between original and recomputed p-values:\n",
      "\n",
      "SYN -1.170175067954915e-13 7.416289804496046e-14\n",
      "MIS -2.674527266322002e-13 1.6220358389773537e-13\n",
      "NONS -3.863576125695545e-14 2.0761170560490427e-14\n",
      "TRUNC -5.484501741648273e-14 2.6867397195928788e-14\n",
      "SPL -5.3734794391857577e-14 9.2148511043888e-15\n",
      "NONSYN -1.2922996006636822e-13 1.6575629757653587e-13\n",
      "MUT -0.6137014422602022 0.5246556547050266\n"
     ]
    }
   ],
   "source": [
    "print('Minimum and maximum differnces between original and recomputed p-values:\\n')\n",
    "for mut in muts:\n",
    "    if mut=='MUT':\n",
    "        # racomputing p-values by aggregating mutation burdens\n",
    "        df['PVAL_' + mut + '_BURDEN_SAMPLE_recomp'] = nb_pvalue_greater_midp(\n",
    "            df['N_SAMP_NONSYN'] + df['N_SAMP_INDEL'],\n",
    "            df.ALPHA,\n",
    "            1 / (df.THETA * (df['Pi_NONS'] + df['Pi_INDEL']) + 1)\n",
    "        )\n",
    "        df['PVAL_' + mut + '_BURDEN_SAMPLE_uniform'] = nb_pvalue_uniform_midp(\n",
    "            df['N_SAMP_NONSYN'] + df['N_SAMP_INDEL'],\n",
    "            df.ALPHA,\n",
    "            1 / (df.THETA * (df['Pi_NONS'] + df['Pi_INDEL']) + 1)\n",
    "            )    \n",
    "        print(mut, np.min(df['PVAL_' + mut + '_BURDEN_SAMPLE_recomp'] - df['PVAL_' + mut + '_BURDEN']), np.max(df['PVAL_' + mut + '_BURDEN_SAMPLE_recomp'] - df['PVAL_' + mut + '_BURDEN']))\n",
    "    else:\n",
    "        df['PVAL_' + mut + '_BURDEN_SAMPLE_recomp'] = nb_pvalue_greater_midp(\n",
    "                df['N_SAMP_' + mut],\n",
    "                df.ALPHA,\n",
    "                1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "            )\n",
    "        df['PVAL_' + mut + '_BURDEN_SAMPLE_uniform'] = nb_pvalue_uniform_midp(\n",
    "                df['N_SAMP_' + mut],\n",
    "                df.ALPHA,\n",
    "                1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "            )\n",
    "        print(mut, np.min(df['PVAL_' + mut + '_BURDEN_SAMPLE_recomp'] - df['PVAL_' + mut + '_BURDEN_SAMPLE']), np.max(df['PVAL_' + mut + '_BURDEN_SAMPLE_recomp'] - df['PVAL_' + mut + '_BURDEN_SAMPLE']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574168bd-ff67-4b62-b729-30af7cb093dc",
   "metadata": {},
   "source": [
    "### Computing lower and upper bounds for p-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aae1da25-0073-4c9e-8e18-ec234a689132",
   "metadata": {},
   "outputs": [],
   "source": [
    "muts = ['SYN', 'MIS', 'NONS', 'TRUNC', 'SPL', 'NONSYN']\n",
    "for mut in muts:\n",
    "    # total burden\n",
    "    df['PVAL_' + mut + '_BURDEN_lower'] = nb_pvalue_lower(\n",
    "        df['OBS_' + mut],\n",
    "        df.ALPHA,\n",
    "        1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "    )\n",
    "    df['PVAL_' + mut + '_BURDEN_upper'] = nb_pvalue_upper(\n",
    "        df['OBS_' + mut],\n",
    "        df.ALPHA,\n",
    "        1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "    )\n",
    "    # sample-wise burden\n",
    "    df['PVAL_' + mut + '_BURDEN_SAMPLE_lower'] = nb_pvalue_lower(\n",
    "            df['N_SAMP_' + mut],\n",
    "            df.ALPHA,\n",
    "            1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "        )\n",
    "    df['PVAL_' + mut + '_BURDEN_SAMPLE_upper'] = nb_pvalue_upper(\n",
    "            df['N_SAMP_' + mut],\n",
    "            df.ALPHA,\n",
    "            1 / (df.THETA * df['Pi_' + mut] + 1)\n",
    "        )\n",
    "# total indel burden\n",
    "df['PVAL_INDEL_BURDEN_lower'] = nb_pvalue_lower(\n",
    "    df.OBS_INDEL, \n",
    "    df.ALPHA, \n",
    "    1 / (df.THETA_INDEL * df.Pi_INDEL + 1)\n",
    ")\n",
    "df['PVAL_INDEL_BURDEN_upper'] = nb_pvalue_upper(\n",
    "    df.OBS_INDEL, \n",
    "    df.ALPHA, \n",
    "    1 / (df.THETA_INDEL * df.Pi_INDEL + 1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c7697946-840f-45ad-8692-6a1b70550503",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7d/ccyblkpj6wvbl5n45y_l75180000gq/T/ipykernel_23966/2892428053.py:2: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log2\n",
      "\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/matplotlib/transforms.py:365: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtype = 'NONSYN'\n",
    "d = np.log2(df['OBS_' + mtype].to_numpy()) - np.log2(df['EXP_' + mtype].to_numpy())\n",
    "p_pmid = df['PVAL_' + mtype + '_BURDEN' + '_recomp'].to_numpy()\n",
    "p_upmid = df['PVAL_' + mtype + '_BURDEN' + '_uniform'].to_numpy()\n",
    "l = df.GENE.to_numpy()\n",
    "p_bounds = df[['PVAL_' + mtype + '_BURDEN' + '_lower', 'PVAL_' + mtype + '_BURDEN' + '_upper']].to_numpy()\n",
    "plot_functions.plot_volcano(p, d, l)\n",
    "plot_functions.plot_volcano(p_pmid, d, l, ymax_vol=16, ymax_qq=16)\n",
    "plot_functions.plot_volcano(p_upmid, d, l, ymax_vol=16, ymax_qq=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946fe012-9326-49d2-ae05-e5e3d9749263",
   "metadata": {},
   "source": [
    "### Q-Q plot for Fisher's combined probability test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "593302f4-469c-4edc-8573-390c27d56f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = df.shape[0]\n",
    "X = sp.stats.chi2.ppf((np.arange(n-1) + 1) / n, df=4)\n",
    "Y = np.sort(-2 * np.log(df[['PVAL_NONSYN_BURDEN_recomp', 'PVAL_INDEL_BURDEN_recomp']].to_numpy()).sum(axis=1))\n",
    "plt.scatter(X, Y[:-1])\n",
    "plt.plot([0, X.max()], [0, X.max()], 'k--')\n",
    "plt.xlim([0, X.max()])\n",
    "plt.ylim([0, 1.5 * X.max()])\n",
    "plt.title('Q-Q: Fisher\\'s combined probability test under the null')\n",
    "plt.xlabel('Theoretical quantiles')\n",
    "plt.ylabel('Sample quantiles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a5f2d6c6-c483-4e43-92fe-da84e69c3d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = df.shape[0]\n",
    "X = sp.stats.chi2.ppf((np.arange(n-1) + 1) / n, df=4)\n",
    "Y = np.sort(-2 * np.log(df[['PVAL_NONSYN_BURDEN_uniform', 'PVAL_INDEL_BURDEN_uniform']].to_numpy()).sum(axis=1))\n",
    "plt.scatter(X, Y[:-1])\n",
    "plt.plot([0, X.max()], [0, X.max()], 'k--')\n",
    "plt.xlim([0, X.max()])\n",
    "plt.ylim([0, 1.5 * X.max()])\n",
    "plt.title('Q-Q: Fisher\\'s combined probability test under the null')\n",
    "plt.xlabel('Theoretical quantiles')\n",
    "plt.ylabel('Sample quantiles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887e12ef-f2f6-40d8-8f95-4e56bb0304b8",
   "metadata": {},
   "source": [
    "## Promoters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "968f20aa-5107-49a1-b911-5e825177b049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ELT',\n",
       " 'ELT_SIZE',\n",
       " 'FLAG',\n",
       " 'R_SIZE',\n",
       " 'R_OBS',\n",
       " 'R_INDEL',\n",
       " 'MU',\n",
       " 'SIGMA',\n",
       " 'ALPHA',\n",
       " 'THETA',\n",
       " 'MU_INDEL',\n",
       " 'SIGMA_INDEL',\n",
       " 'ALPHA_INDEL',\n",
       " 'THETA_INDEL',\n",
       " 'Pi_SUM',\n",
       " 'Pi_INDEL',\n",
       " 'OBS_SAMPLES',\n",
       " 'OBS_SNV',\n",
       " 'OBS_INDEL',\n",
       " 'EXP_SNV',\n",
       " 'PVAL_SNV_BURDEN',\n",
       " 'PVAL_SAMPLE_BURDEN',\n",
       " 'EXP_INDEL',\n",
       " 'PVAL_INDEL_BURDEN',\n",
       " 'PVAL_MUT_BURDEN']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prom = pd.read_csv('promoters.results.txt', sep='\\t')\n",
    "df_prom.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "74330027-dc07-410a-bdb4-5459a8dbf74c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prom['OBS_MUT'] = df_prom['OBS_SNV'] + df_prom['OBS_INDEL']\n",
    "df_prom['EXP_MUT'] = df_prom['EXP_SNV'] + df_prom['EXP_INDEL']\n",
    "df_prom['Pi_MUT'] = df_prom['Pi_SUM'] + df_prom['Pi_INDEL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8e4b21b0-ddf7-4dc4-ac6a-a751724fdbfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNV -2.4480417692984702e-14 3.197442310920451e-14\n",
      "INDEL -1.1435297153639112e-14 2.383510055992133e-15\n",
      "SAMPLES -2.4480417692984702e-14 3.197442310920451e-14\n",
      "MUT -2.2370993946196904e-14 1.1712852909795402e-14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/scipy/stats/_stats_py.py:9694: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pfxs_obs = ['SNV', 'INDEL', 'SAMPLES', 'MUT']\n",
    "pfxs_pval = ['SNV', 'INDEL', 'SAMPLE', 'MUT']\n",
    "pfxs_pi = ['SUM', 'INDEL', 'SUM', 'MUT']\n",
    "pfxs_at = ['', '_INDEL', '', '']\n",
    "\n",
    "for i in range(len(pfxs_obs)):\n",
    "    if pfxs_pval[i]=='MUT':\n",
    "        col_i = 'PVAL_' + pfxs_pval[i] + '_BURDEN'\n",
    "        df_prom[col_i + '_recomp'] = np.nan\n",
    "        df_prom[col_i + '_uniform'] = np.nan\n",
    "        df_prom[col_i + '_lower'] = np.nan\n",
    "        df_prom[col_i + '_upper'] = np.nan\n",
    "        # combining p-values with Fisher's method\n",
    "        for idx in df_prom.index:\n",
    "            df_prom.at[idx, col_i + '_recomp'] = sp.stats.combine_pvalues([df_prom.at[idx, 'PVAL_SNV_BURDEN_recomp'], df_prom.at[idx, 'PVAL_INDEL_BURDEN_recomp']], method='fisher').pvalue\n",
    "            df_prom.at[idx, col_i + '_uniform'] = sp.stats.combine_pvalues([df_prom.at[idx, 'PVAL_SNV_BURDEN_uniform'], df_prom.at[idx, 'PVAL_INDEL_BURDEN_uniform']], method='fisher').pvalue\n",
    "            df_prom.at[idx, col_i + '_lower'] = sp.stats.combine_pvalues([df_prom.at[idx, 'PVAL_SNV_BURDEN_lower'], df_prom.at[idx, 'PVAL_INDEL_BURDEN_lower']], method='fisher').pvalue\n",
    "            df_prom.at[idx, col_i + '_upper'] = sp.stats.combine_pvalues([df_prom.at[idx, 'PVAL_SNV_BURDEN_upper'], df_prom.at[idx, 'PVAL_INDEL_BURDEN_upper']], method='fisher').pvalue\n",
    "    else:\n",
    "        df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN_recomp'] = nb_pvalue_greater_midp(\n",
    "                df_prom['OBS_' + pfxs_obs[i]],\n",
    "                df_prom['ALPHA' + pfxs_at[i]],\n",
    "                1 / (df_prom['THETA' + pfxs_at[i]] * df_prom['Pi_' + pfxs_pi[i]] + 1)\n",
    "            ) \n",
    "        df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN_uniform'] = nb_pvalue_uniform_midp(\n",
    "                df_prom['OBS_' + pfxs_obs[i]],\n",
    "                df_prom['ALPHA' + pfxs_at[i]],\n",
    "                1 / (df_prom['THETA' + pfxs_at[i]] * df_prom['Pi_' + pfxs_pi[i]] + 1)\n",
    "            ) \n",
    "        df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN_lower'] = nb_pvalue_lower(\n",
    "            df_prom['OBS_' + pfxs_obs[i]],\n",
    "            df_prom['ALPHA' + pfxs_at[i]],\n",
    "            1 / (df_prom['THETA' + pfxs_at[i]] * df_prom['Pi_' + pfxs_pi[i]] + 1)\n",
    "        )\n",
    "        df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN_upper'] = nb_pvalue_upper(\n",
    "            df_prom['OBS_' + pfxs_obs[i]],\n",
    "            df_prom['ALPHA' + pfxs_at[i]],\n",
    "            1 / (df_prom['THETA' + pfxs_at[i]] * df_prom['Pi_' + pfxs_pi[i]] + 1)\n",
    "        )\n",
    "    print(pfxs_obs[i], np.min(df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN_recomp'] - df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN']), np.max(df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN_recomp'] - df_prom['PVAL_' + pfxs_pval[i] + '_BURDEN']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32a6ef7d-abc4-4fc4-904f-777b019d653b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7d/ccyblkpj6wvbl5n45y_l75180000gq/T/ipykernel_23966/4184195437.py:1: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log2\n",
      "\n",
      "/var/folders/7d/ccyblkpj6wvbl5n45y_l75180000gq/T/ipykernel_23966/4184195437.py:1: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in subtract\n",
      "\n",
      "/Users/dlehotzk/Documents/tcga/dig/plot_functions.py:117: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log10\n",
      "\n",
      "/Users/dlehotzk/Documents/tcga/dig/plot_functions.py:168: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log10\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = np.log2(df_prom.OBS_MUT.to_numpy()) - np.log2(df_prom.EXP_MUT.to_numpy())\n",
    "p = df_prom.PVAL_MUT_BURDEN_recomp.to_numpy()\n",
    "l = np.array([g.split('::')[-2] for g in df_prom.ELT])\n",
    "plot_functions.plot_volcano(p, d, l, pval_bounds=df_prom[['PVAL_MUT_BURDEN_upper', 'PVAL_MUT_BURDEN_lower']].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08b0e447-e61e-4b0c-9e90-797d85ae43f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7d/ccyblkpj6wvbl5n45y_l75180000gq/T/ipykernel_23966/1981111821.py:1: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log2\n",
      "\n",
      "/var/folders/7d/ccyblkpj6wvbl5n45y_l75180000gq/T/ipykernel_23966/1981111821.py:1: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in subtract\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = np.log2(df_prom.OBS_MUT.to_numpy()) - np.log2(df_prom.EXP_MUT.to_numpy())\n",
    "p = df_prom.PVAL_MUT_BURDEN_uniform.to_numpy()\n",
    "l = np.array([g.split('::')[-2] for g in df_prom.ELT])\n",
    "plot_functions.plot_volcano(p, d, l)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcaa8c1-1f01-4646-b7a4-4146bdec459d",
   "metadata": {},
   "source": [
    "### Q-Q plot for Fisher's combined probability test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f0e974e2-5144-427a-a831-5435181a2bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = df_prom.shape[0]\n",
    "X = sp.stats.chi2.ppf((np.arange(n-1) + 1) / n, df=4)\n",
    "Y = np.sort(-2 * np.log(df_prom[['PVAL_SNV_BURDEN', 'PVAL_INDEL_BURDEN']].to_numpy()).sum(axis=1))\n",
    "plt.scatter(X, Y[:-1])\n",
    "plt.plot([0, X.max()], [0, X.max()], 'k--')\n",
    "plt.xlim([0, X.max()])\n",
    "plt.ylim([0, 1.5 * X.max()])\n",
    "plt.title('Q-Q: Fisher\\'s combined probability test under the null')\n",
    "plt.xlabel('Theoretical quantiles')\n",
    "plt.ylabel('Sample quantiles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7be08047-5b61-4b2f-98d0-19d8648ea965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = df_prom.shape[0]\n",
    "X = sp.stats.chi2.ppf((np.arange(n-1) + 1) / n, df=4)\n",
    "Y = np.sort(-2 * np.log(df_prom[['PVAL_SNV_BURDEN_uniform', 'PVAL_INDEL_BURDEN_uniform']].to_numpy()).sum(axis=1))\n",
    "plt.scatter(X, Y[:-1])\n",
    "plt.plot([0, X.max()], [0, X.max()], 'k--')\n",
    "plt.xlim([0, X.max()])\n",
    "plt.ylim([0, 1.5 * X.max()])\n",
    "plt.title('Q-Q: Fisher\\'s combined probability test under the null')\n",
    "plt.xlabel('Theoretical quantiles')\n",
    "plt.ylabel('Sample quantiles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f319cc4c-9ae9-48f0-8522-619f6bae7937",
   "metadata": {},
   "source": [
    "# Combining evidence from different regions for each gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "682d5c47-ceec-48ac-813f-7b6c4af35db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/scipy/stats/_stats_py.py:9694: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n",
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/scipy/stats/_stats_py.py:9694: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n",
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/scipy/stats/_stats_py.py:9694: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n",
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/scipy/stats/_stats_py.py:9694: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n",
      "/Users/dlehotzk/anaconda3/lib/python3.10/site-packages/scipy/stats/_stats_py.py:9694: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dig_outputs = {\n",
    "    'coding': 'genes.results.txt',\n",
    "    'promoter': 'promoters.results.txt',\n",
    "    '3utr': '3utr.results.txt',\n",
    "    '5utr': '5utr.results.txt'\n",
    "}\n",
    "\n",
    "result_types = list(dig_outputs.keys())\n",
    "for result_type in result_types:\n",
    "    \n",
    "    dfi = pd.read_csv(dig_outputs[result_type], sep='\\t')\n",
    "    \n",
    "    if result_type == 'coding':\n",
    "        dfi = dfi.set_index('GENE')\n",
    "        df_comb = pd.DataFrame(index = dfi.index.copy())\n",
    "        col_obs = ['OBS_NONSYN', 'N_SAMP_NONSYN', 'OBS_INDEL']\n",
    "        col_pi = ['Pi_NONSYN', 'Pi_NONSYN', 'Pi_INDEL']\n",
    "        col_size = 'GENE_LENGTH'\n",
    "    else:\n",
    "        dfi['GENE'] = dfi.ELT.str.split('::', expand=True)[2]\n",
    "        dfi = dfi.sort_values(['GENE', 'OBS_SNV'])\n",
    "        dfi = dfi.set_index('GENE')\n",
    "        dfi = dfi.loc[~dfi.index.duplicated()]\n",
    "        col_obs = ['OBS_SNV', 'OBS_SAMPLES', 'OBS_INDEL']\n",
    "        col_pi = ['Pi_SUM', 'Pi_SUM', 'Pi_INDEL']\n",
    "        col_size = 'ELT_SIZE'\n",
    "\n",
    "    genes_in_coding = dfi.index[dfi.index.isin(df_comb.index)]\n",
    "    dfi_comp = dfi.loc[genes_in_coding]\n",
    "    df_comb.loc[genes_in_coding, 'SIZE_' + result_type] = dfi_comp[col_size].to_numpy().copy()\n",
    "        \n",
    "    for j, mt in enumerate(['SNV', 'SNV_SAMPLE', 'INDEL']):\n",
    "    \n",
    "        if mt == 'INDEL':\n",
    "            pfx_at = '_INDEL'\n",
    "        else:\n",
    "            pfx_at = ''\n",
    "        df_comb.loc[genes_in_coding, 'PVAL_' + result_type + '_' + mt + '_recalc'] = nb_pvalue_greater_midp(\n",
    "            dfi_comp[col_obs[j]],\n",
    "            dfi_comp['ALPHA' + pfx_at],\n",
    "            1 / (dfi_comp['THETA' + pfx_at] * dfi_comp[col_pi[j]] + 1)\n",
    "        )\n",
    "        df_comb.loc[genes_in_coding, 'PVAL_' + result_type + '_' + mt + '_unif'] = nb_pvalue_uniform_midp(\n",
    "            dfi_comp[col_obs[j]],\n",
    "            dfi_comp['ALPHA' + pfx_at],\n",
    "            1 / (dfi_comp['THETA' + pfx_at] * dfi_comp[col_pi[j]] + 1)\n",
    "        )\n",
    "        df_comb.loc[genes_in_coding, 'PVAL_' + result_type + '_' + mt + '_lower'] = nb_pvalue_lower(\n",
    "            dfi_comp[col_obs[j]],\n",
    "            dfi_comp['ALPHA' + pfx_at],\n",
    "            1 / (dfi_comp['THETA' + pfx_at] * dfi_comp[col_pi[j]] + 1)\n",
    "        )\n",
    "        df_comb.loc[genes_in_coding, 'PVAL_' + result_type + '_' + mt + '_upper'] = nb_pvalue_upper(\n",
    "            dfi_comp[col_obs[j]],\n",
    "            dfi_comp['ALPHA' + pfx_at],\n",
    "            1 / (dfi_comp['THETA' + pfx_at] * dfi_comp[col_pi[j]] + 1)\n",
    "        )\n",
    "    \n",
    "    # combining p-values with Fisher's method\n",
    "    col_i = 'PVAL_' + result_type + '_' + 'MUT'\n",
    "    df_comb[col_i + '_recalc'] = np.nan\n",
    "    df_comb[col_i + '_unif'] = np.nan\n",
    "    df_comb[col_i + '_lower'] = np.nan\n",
    "    df_comb[col_i + '_upper'] = np.nan\n",
    "    for idx in df_comb.index:\n",
    "        df_comb.at[idx, col_i + '_recalc'] = sp.stats.combine_pvalues([df_comb.at[idx, 'PVAL_' + result_type + '_SNV_recalc'], df_comb.at[idx, 'PVAL_' + result_type + '_INDEL_recalc']], method='fisher')[1]\n",
    "        df_comb.at[idx, col_i + '_unif'] = sp.stats.combine_pvalues([df_comb.at[idx, 'PVAL_' + result_type + '_SNV_unif'], df_comb.at[idx, 'PVAL_' + result_type + '_INDEL_unif']], method='fisher')[1]\n",
    "        df_comb.at[idx, col_i + '_lower'] = sp.stats.combine_pvalues([df_comb.at[idx, 'PVAL_' + result_type + '_SNV_lower'], df_comb.at[idx, 'PVAL_' + result_type + '_INDEL_lower']], method='fisher')[1]\n",
    "        df_comb.at[idx, col_i + '_upper'] = sp.stats.combine_pvalues([df_comb.at[idx, 'PVAL_' + result_type + '_SNV_upper'], df_comb.at[idx, 'PVAL_' + result_type + '_INDEL_upper']], method='fisher')[1]\n",
    "\n",
    "# combining p-values across region types\n",
    "for mt in ['SNV', 'SNV_SAMPLE', 'INDEL', 'MUT']:\n",
    "    for typ in ['recalc', 'unif', 'lower', 'upper']:\n",
    "        for idx in df_comb.index:\n",
    "            df_comb.at[idx, 'PVAL_' + mt + '_' + typ] = sp.stats.combine_pvalues(\n",
    "            [df_comb.at[idx, 'PVAL_' + rt + '_' + mt + '_' + typ] for rt in result_types if ~np.isnan(df_comb.at[idx, 'PVAL_' + rt + '_' + mt + '_' + typ])], \n",
    "            method='fisher')[1]\n",
    "        isna = df_comb['PVAL_' + mt + '_' + typ].isna()\n",
    "        df_comb.loc[~isna, 'FDR_' + mt + '_' + typ] = fdrcorrection(df_comb.loc[~isna, 'PVAL_' + mt + '_' + typ])[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "55b7101f-bae1-422b-bc9f-2c7c49fc6656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>SIZE_coding</th>\n",
       "      <th>PVAL_coding_SNV_recalc</th>\n",
       "      <th>PVAL_coding_SNV_unif</th>\n",
       "      <th>PVAL_coding_SNV_lower</th>\n",
       "      <th>PVAL_coding_SNV_upper</th>\n",
       "      <th>PVAL_coding_SNV_SAMPLE_recalc</th>\n",
       "      <th>PVAL_coding_SNV_SAMPLE_unif</th>\n",
       "      <th>PVAL_coding_SNV_SAMPLE_lower</th>\n",
       "      <th>PVAL_coding_SNV_SAMPLE_upper</th>\n",
       "      <th>PVAL_coding_INDEL_recalc</th>\n",
       "      <th>...</th>\n",
       "      <th>PVAL_INDEL_upper</th>\n",
       "      <th>FDR_INDEL_upper</th>\n",
       "      <th>PVAL_MUT_recalc</th>\n",
       "      <th>FDR_MUT_recalc</th>\n",
       "      <th>PVAL_MUT_unif</th>\n",
       "      <th>FDR_MUT_unif</th>\n",
       "      <th>PVAL_MUT_lower</th>\n",
       "      <th>FDR_MUT_lower</th>\n",
       "      <th>PVAL_MUT_upper</th>\n",
       "      <th>FDR_MUT_upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENE</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>A1BG</th>\n",
       "      <td>1488.0</td>\n",
       "      <td>0.539230</td>\n",
       "      <td>0.888620</td>\n",
       "      <td>0.078459</td>\n",
       "      <td>0.921541</td>\n",
       "      <td>0.539230</td>\n",
       "      <td>0.257851</td>\n",
       "      <td>0.078459</td>\n",
       "      <td>0.921541</td>\n",
       "      <td>0.501861</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.858069</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.981912</td>\n",
       "      <td>0.999923</td>\n",
       "      <td>1.107975e-11</td>\n",
       "      <td>1.009687e-10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>1785.0</td>\n",
       "      <td>0.546922</td>\n",
       "      <td>0.238456</td>\n",
       "      <td>0.093844</td>\n",
       "      <td>0.906156</td>\n",
       "      <td>0.546922</td>\n",
       "      <td>0.656277</td>\n",
       "      <td>0.093844</td>\n",
       "      <td>0.906156</td>\n",
       "      <td>0.502198</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.894464</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.335311</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>2.922788e-07</td>\n",
       "      <td>3.504792e-07</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>4425.0</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.677546</td>\n",
       "      <td>0.254459</td>\n",
       "      <td>0.745541</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.374661</td>\n",
       "      <td>0.254459</td>\n",
       "      <td>0.745541</td>\n",
       "      <td>0.506623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.847140</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.324363</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>7.296373e-06</td>\n",
       "      <td>7.582954e-06</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1</th>\n",
       "      <td>4365.0</td>\n",
       "      <td>0.598584</td>\n",
       "      <td>0.827728</td>\n",
       "      <td>0.197168</td>\n",
       "      <td>0.802832</td>\n",
       "      <td>0.598584</td>\n",
       "      <td>0.528897</td>\n",
       "      <td>0.197168</td>\n",
       "      <td>0.802832</td>\n",
       "      <td>0.505130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.889448</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.794263</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>3.202935e-07</td>\n",
       "      <td>3.815477e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2</th>\n",
       "      <td>1023.0</td>\n",
       "      <td>0.529251</td>\n",
       "      <td>0.340636</td>\n",
       "      <td>0.058503</td>\n",
       "      <td>0.941497</td>\n",
       "      <td>0.529251</td>\n",
       "      <td>0.293646</td>\n",
       "      <td>0.058503</td>\n",
       "      <td>0.941497</td>\n",
       "      <td>0.501217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999955</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.762110</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.638011</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>1.231459e-04</td>\n",
       "      <td>1.242193e-04</td>\n",
       "      <td>0.999942</td>\n",
       "      <td>1.0</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>ZYX</th>\n",
       "      <td>1719.0</td>\n",
       "      <td>0.547539</td>\n",
       "      <td>0.355534</td>\n",
       "      <td>0.095079</td>\n",
       "      <td>0.904921</td>\n",
       "      <td>0.547539</td>\n",
       "      <td>0.301790</td>\n",
       "      <td>0.095079</td>\n",
       "      <td>0.904921</td>\n",
       "      <td>0.502365</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.872100</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.579512</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>2.596297e-08</td>\n",
       "      <td>3.971246e-08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZEF1</th>\n",
       "      <td>8886.0</td>\n",
       "      <td>0.697706</td>\n",
       "      <td>0.637650</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>0.604589</td>\n",
       "      <td>0.697706</td>\n",
       "      <td>0.722043</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>0.604589</td>\n",
       "      <td>0.511440</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.657883</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.555044</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>1.357497e-07</td>\n",
       "      <td>1.730082e-07</td>\n",
       "      <td>0.985464</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ3</th>\n",
       "      <td>2712.0</td>\n",
       "      <td>0.570808</td>\n",
       "      <td>0.785013</td>\n",
       "      <td>0.141616</td>\n",
       "      <td>0.858384</td>\n",
       "      <td>0.570808</td>\n",
       "      <td>0.596077</td>\n",
       "      <td>0.141616</td>\n",
       "      <td>0.858384</td>\n",
       "      <td>0.503479</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.613934</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.643294</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>7.550878e-08</td>\n",
       "      <td>1.020346e-07</td>\n",
       "      <td>0.988930</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hsa-mir-1199</th>\n",
       "      <td>1086.0</td>\n",
       "      <td>0.527879</td>\n",
       "      <td>0.161315</td>\n",
       "      <td>0.055758</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>0.527879</td>\n",
       "      <td>0.736672</td>\n",
       "      <td>0.055758</td>\n",
       "      <td>0.944242</td>\n",
       "      <td>0.501191</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.816681</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.168135</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>1.437654e-07</td>\n",
       "      <td>1.823890e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hsa-mir-150</th>\n",
       "      <td>834.0</td>\n",
       "      <td>0.521693</td>\n",
       "      <td>0.415846</td>\n",
       "      <td>0.043386</td>\n",
       "      <td>0.956614</td>\n",
       "      <td>0.521693</td>\n",
       "      <td>0.555882</td>\n",
       "      <td>0.043386</td>\n",
       "      <td>0.956614</td>\n",
       "      <td>0.501053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.747486</td>\n",
       "      <td>0.921369</td>\n",
       "      <td>0.590798</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>3.026832e-05</td>\n",
       "      <td>3.078922e-05</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19210 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              SIZE_coding  PVAL_coding_SNV_recalc  PVAL_coding_SNV_unif  \\\n",
       "GENE                                                                      \n",
       "A1BG               1488.0                0.539230              0.888620   \n",
       "A1CF               1785.0                0.546922              0.238456   \n",
       "A2M                4425.0                0.627229              0.677546   \n",
       "A2ML1              4365.0                0.598584              0.827728   \n",
       "A3GALT2            1023.0                0.529251              0.340636   \n",
       "...                   ...                     ...                   ...   \n",
       "ZYX                1719.0                0.547539              0.355534   \n",
       "ZZEF1              8886.0                0.697706              0.637650   \n",
       "ZZZ3               2712.0                0.570808              0.785013   \n",
       "hsa-mir-1199       1086.0                0.527879              0.161315   \n",
       "hsa-mir-150         834.0                0.521693              0.415846   \n",
       "\n",
       "              PVAL_coding_SNV_lower  PVAL_coding_SNV_upper  \\\n",
       "GENE                                                         \n",
       "A1BG                       0.078459               0.921541   \n",
       "A1CF                       0.093844               0.906156   \n",
       "A2M                        0.254459               0.745541   \n",
       "A2ML1                      0.197168               0.802832   \n",
       "A3GALT2                    0.058503               0.941497   \n",
       "...                             ...                    ...   \n",
       "ZYX                        0.095079               0.904921   \n",
       "ZZEF1                      0.395411               0.604589   \n",
       "ZZZ3                       0.141616               0.858384   \n",
       "hsa-mir-1199               0.055758               0.944242   \n",
       "hsa-mir-150                0.043386               0.956614   \n",
       "\n",
       "              PVAL_coding_SNV_SAMPLE_recalc  PVAL_coding_SNV_SAMPLE_unif  \\\n",
       "GENE                                                                       \n",
       "A1BG                               0.539230                     0.257851   \n",
       "A1CF                               0.546922                     0.656277   \n",
       "A2M                                0.627229                     0.374661   \n",
       "A2ML1                              0.598584                     0.528897   \n",
       "A3GALT2                            0.529251                     0.293646   \n",
       "...                                     ...                          ...   \n",
       "ZYX                                0.547539                     0.301790   \n",
       "ZZEF1                              0.697706                     0.722043   \n",
       "ZZZ3                               0.570808                     0.596077   \n",
       "hsa-mir-1199                       0.527879                     0.736672   \n",
       "hsa-mir-150                        0.521693                     0.555882   \n",
       "\n",
       "              PVAL_coding_SNV_SAMPLE_lower  PVAL_coding_SNV_SAMPLE_upper  \\\n",
       "GENE                                                                       \n",
       "A1BG                              0.078459                      0.921541   \n",
       "A1CF                              0.093844                      0.906156   \n",
       "A2M                               0.254459                      0.745541   \n",
       "A2ML1                             0.197168                      0.802832   \n",
       "A3GALT2                           0.058503                      0.941497   \n",
       "...                                    ...                           ...   \n",
       "ZYX                               0.095079                      0.904921   \n",
       "ZZEF1                             0.395411                      0.604589   \n",
       "ZZZ3                              0.141616                      0.858384   \n",
       "hsa-mir-1199                      0.055758                      0.944242   \n",
       "hsa-mir-150                       0.043386                      0.956614   \n",
       "\n",
       "              PVAL_coding_INDEL_recalc  ...  PVAL_INDEL_upper  \\\n",
       "GENE                                    ...                     \n",
       "A1BG                          0.501861  ...          1.000000   \n",
       "A1CF                          0.502198  ...          1.000000   \n",
       "A2M                           0.506623  ...          0.999999   \n",
       "A2ML1                         0.505130  ...          1.000000   \n",
       "A3GALT2                       0.501217  ...          0.999955   \n",
       "...                                ...  ...               ...   \n",
       "ZYX                           0.502365  ...          1.000000   \n",
       "ZZEF1                         0.511440  ...          1.000000   \n",
       "ZZZ3                          0.503479  ...          1.000000   \n",
       "hsa-mir-1199                  0.501191  ...          1.000000   \n",
       "hsa-mir-150                   0.501053  ...          0.999983   \n",
       "\n",
       "              FDR_INDEL_upper  PVAL_MUT_recalc  FDR_MUT_recalc  PVAL_MUT_unif  \\\n",
       "GENE                                                                            \n",
       "A1BG                      1.0         0.858069        0.921369       0.981912   \n",
       "A1CF                      1.0         0.894464        0.921369       0.335311   \n",
       "A2M                       1.0         0.847140        0.921369       0.324363   \n",
       "A2ML1                     1.0         0.889448        0.921369       0.794263   \n",
       "A3GALT2                   1.0         0.762110        0.921369       0.638011   \n",
       "...                       ...              ...             ...            ...   \n",
       "ZYX                       1.0         0.872100        0.921369       0.579512   \n",
       "ZZEF1                     1.0         0.657883        0.921369       0.555044   \n",
       "ZZZ3                      1.0         0.613934        0.921369       0.643294   \n",
       "hsa-mir-1199              1.0         0.816681        0.921369       0.168135   \n",
       "hsa-mir-150               1.0         0.747486        0.921369       0.590798   \n",
       "\n",
       "              FDR_MUT_unif  PVAL_MUT_lower  FDR_MUT_lower  PVAL_MUT_upper  \\\n",
       "GENE                                                                        \n",
       "A1BG              0.999923    1.107975e-11   1.009687e-10        1.000000   \n",
       "A1CF              0.999841    2.922788e-07   3.504792e-07        0.999998   \n",
       "A2M               0.999841    7.296373e-06   7.582954e-06        0.999985   \n",
       "A2ML1             0.999841    3.202935e-07   3.815477e-07        1.000000   \n",
       "A3GALT2           0.999841    1.231459e-04   1.242193e-04        0.999942   \n",
       "...                    ...             ...            ...             ...   \n",
       "ZYX               0.999841    2.596297e-08   3.971246e-08        1.000000   \n",
       "ZZEF1             0.999841    1.357497e-07   1.730082e-07        0.985464   \n",
       "ZZZ3              0.999841    7.550878e-08   1.020346e-07        0.988930   \n",
       "hsa-mir-1199      0.999841    1.437654e-07   1.823890e-07        1.000000   \n",
       "hsa-mir-150       0.999841    3.026832e-05   3.078922e-05        0.999994   \n",
       "\n",
       "              FDR_MUT_upper  \n",
       "GENE                         \n",
       "A1BG                    1.0  \n",
       "A1CF                    1.0  \n",
       "A2M                     1.0  \n",
       "A2ML1                   1.0  \n",
       "A3GALT2                 1.0  \n",
       "...                     ...  \n",
       "ZYX                     1.0  \n",
       "ZZEF1                   1.0  \n",
       "ZZZ3                    1.0  \n",
       "hsa-mir-1199            1.0  \n",
       "hsa-mir-150             1.0  \n",
       "\n",
       "[19210 rows x 100 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32770134-3ac5-482e-afa8-f9c2588f528d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = np.sort(-2 * np.log(df_comb[['PVAL_' + rt + '_SNV_unif' for rt in result_types]].to_numpy()).sum(axis=1))\n",
    "Y = Y[~np.isnan(Y)]\n",
    "n = Y.shape[0]\n",
    "X = sp.stats.chi2.ppf((np.arange(n-1) + 1) / n, df=8)\n",
    "plt.scatter(X, Y[:-1])\n",
    "plt.plot([0, X.max()], [0, X.max()], 'k--')\n",
    "plt.xlim([0, X.max()])\n",
    "plt.ylim([0, 5*X.max()])\n",
    "plt.title('Q-Q: Fisher\\'s combined probability test under the null')\n",
    "plt.xlabel('Theoretical quantiles')\n",
    "plt.ylabel('Sample quantiles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2770bd85-6148-4ea1-8c59-c73df3cf4576",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}