notebooks/29_HLA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Dependencies\" data-toc-modified-id=\"Dependencies-1\">Dependencies</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li><li><span><a href=\"#Load-all-HLA\" data-toc-modified-id=\"Load-all-HLA-5\">Load all HLA</a></span></li><li><span><a href=\"#Merge-with-metadata\" data-toc-modified-id=\"Merge-with-metadata-6\">Merge with metadata</a></span></li><li><span><a href=\"#Allele-frequencies-for-HLA-types\" data-toc-modified-id=\"Allele-frequencies-for-HLA-types-7\">Allele frequencies for HLA types</a></span></li><li><span><a href=\"#Most-common-HLAs-across-immune-clusters\" data-toc-modified-id=\"Most-common-HLAs-across-immune-clusters-8\">Most common HLAs across immune clusters</a></span></li><li><span><a href=\"#Stats\" data-toc-modified-id=\"Stats-9\">Stats</a></span></li><li><span><a href=\"#Stats-HLAs-across-cohorts\" data-toc-modified-id=\"Stats-HLAs-across-cohorts-10\">Stats HLAs across cohorts</a></span></li><li><span><a href=\"#HLA-vs-mutations\" data-toc-modified-id=\"HLA-vs-mutations-11\">HLA vs mutations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Get-nonsyn-variants\" data-toc-modified-id=\"Get-nonsyn-variants-11.1\">Get nonsyn variants</a></span><ul class=\"toc-item\"><li><span><a href=\"#CBTTC\" data-toc-modified-id=\"CBTTC-11.1.1\">CBTTC</a></span></li><li><span><a href=\"#DKFZ\" data-toc-modified-id=\"DKFZ-11.1.2\">DKFZ</a></span></li><li><span><a href=\"#TARGET\" data-toc-modified-id=\"TARGET-11.1.3\">TARGET</a></span></li></ul></li><li><span><a href=\"#All-nonsyn-mutated-genes\" data-toc-modified-id=\"All-nonsyn-mutated-genes-11.2\">All nonsyn mutated genes</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Loading required package: carData\n",
      "\n",
      "\n",
      "Attaching package: ‘car’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    recode\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(dplyr)\n",
    "library(car)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: grid\n",
      "\n",
      "========================================\n",
      "ComplexHeatmap version 2.6.2\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "  genomic data. Bioinformatics 2016.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
      "\n",
      "\n",
      "========================================\n",
      "circlize version 0.4.12\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: https://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization\n",
      "  in R. Bioinformatics 2014.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(circlize))\n",
      "========================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/color_schemes.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/Heatmap_functions.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/Oncoprint_functions.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_freq_plot <- function(metadata, hlatype1, hlatype2, title){\n",
    "    \n",
    "    myalleles <- c(metadata[[hlatype1]],metadata[[hlatype2]])\n",
    "    Tab_Freq <- as.data.frame(table(myalleles), stringsAsFactors = F)\n",
    "    Tab_Freq$AF <- Tab_Freq$Freq/(2*904)\n",
    "    Tab_Freq <- Tab_Freq[order(Tab_Freq$AF, decreasing = T),]\n",
    "    Tab_Freq$myalleles <- gsub(\".*[*]\", \"\", Tab_Freq$myalleles)\n",
    "    Tab_Freq$myalleles <- factor(Tab_Freq$myalleles,levels = Tab_Freq$myalleles)\n",
    "\n",
    "    AF_plot <- ggplot(Tab_Freq, aes(x = myalleles, y = AF)) +  \n",
    "    geom_bar(stat = \"identity\") + myplot + \n",
    "    theme(axis.title.y = element_text(size = 45),\n",
    "          axis.title.x = element_blank(),\n",
    "          axis.line = element_line(color = \"black\"),\n",
    "          axis.text.x = element_text(size = 30, angle = 45, hjust = 1, color = \"black\"),\n",
    "          axis.text.y = element_text(size = 30, color = \"black\")) +\n",
    "    labs(y = paste0(\"Allele frequency\", title))\n",
    "    \n",
    "    return(AF_plot)\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mountpath <- \"/Users/anabbi/Desktop/H4H/immpedcan/antigen_prediction/HLAtyping/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                90                279                393                163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC$immune_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "allfiles <- list.files(mountpath, pattern = \".tsv\", full.names = T, recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove abundance files\n",
    "allfiles <- allfiles[!grepl(\"abundance\", allfiles)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.files <- lapply(allfiles, \n",
    "                    function(f){read.table(f, header = TRUE, sep = \"\\t\", \n",
    "                                           stringsAsFactors = F, nrows = 1, row.names = 1)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfilesnames <- allfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "names(all.files) <- gsub(\".*HLAtyping//\", \"\", myfilesnames)\n",
    "names(all.files) <- gsub(\".result.tsv\", \"\", names(all.files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleanup filenames from ICGC\n",
    "names(all.files) <- gsub(\".*view-by-pid/\", \"\", names(all.files))\n",
    "\n",
    "names(all.files) <- gsub(\"/2020_0.*\", \"\", names(all.files))\n",
    "\n",
    "names(all.files) <- gsub(\"Optitype_ICGC/\", \"\", names(all.files))\n",
    "\n",
    "names(all.files) <- gsub(\"/\", \"\", names(all.files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "\n",
       "\n"
      ],
      "text/plain": [
       "named list()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(all.files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "replace PIDs to ICGC sample_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ICGC_map <- read.csv(file = paste0(datapath, \"HLA/MB_ICGCid_pid_Map4HLAtype_ICGC.csv\"),\n",
    "                        check.names = F, header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SampleID_ICGC</th><th scope=col>PID</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ICGC_MB1</td><td>179743</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ICGC_MB2</td><td>198274</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ICGC_MB3</td><td>MBBL3 </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ICGC_MB4</td><td>NA    </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ICGC_MB5</td><td>122372</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ICGC_MB6</td><td>163298</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & SampleID\\_ICGC & PID\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & ICGC\\_MB1 & 179743\\\\\n",
       "\t2 & ICGC\\_MB2 & 198274\\\\\n",
       "\t3 & ICGC\\_MB3 & MBBL3 \\\\\n",
       "\t4 & ICGC\\_MB4 & NA    \\\\\n",
       "\t5 & ICGC\\_MB5 & 122372\\\\\n",
       "\t6 & ICGC\\_MB6 & 163298\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | SampleID_ICGC &lt;chr&gt; | PID &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | ICGC_MB1 | 179743 |\n",
       "| 2 | ICGC_MB2 | 198274 |\n",
       "| 3 | ICGC_MB3 | MBBL3  |\n",
       "| 4 | ICGC_MB4 | NA     |\n",
       "| 5 | ICGC_MB5 | 122372 |\n",
       "| 6 | ICGC_MB6 | 163298 |\n",
       "\n"
      ],
      "text/plain": [
       "  SampleID_ICGC PID   \n",
       "1 ICGC_MB1      179743\n",
       "2 ICGC_MB2      198274\n",
       "3 ICGC_MB3      MBBL3 \n",
       "4 ICGC_MB4      NA    \n",
       "5 ICGC_MB5      122372\n",
       "6 ICGC_MB6      163298"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(ICGC_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pids <- c('104328','107430','108031','119591','127581',\n",
    "          '128398','138689','165133','167702','177904',\n",
    "          '181633','185985','186723','196865')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ICGC_map <- ICGC_map[ ICGC_map$PID %in% pids,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 14 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SampleID_ICGC</th><th scope=col>PID</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>19</th><td>ICGC_MB19</td><td>138689</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>ICGC_MB28</td><td>186723</td></tr>\n",
       "\t<tr><th scope=row>31</th><td>ICGC_MB31</td><td>128398</td></tr>\n",
       "\t<tr><th scope=row>34</th><td>ICGC_MB34</td><td>185985</td></tr>\n",
       "\t<tr><th scope=row>39</th><td>ICGC_MB39</td><td>107430</td></tr>\n",
       "\t<tr><th scope=row>40</th><td>ICGC_MB40</td><td>127581</td></tr>\n",
       "\t<tr><th scope=row>53</th><td>ICGC_MB53</td><td>108031</td></tr>\n",
       "\t<tr><th scope=row>57</th><td>ICGC_MB57</td><td>165133</td></tr>\n",
       "\t<tr><th scope=row>58</th><td>ICGC_MB58</td><td>167702</td></tr>\n",
       "\t<tr><th scope=row>60</th><td>ICGC_MB60</td><td>181633</td></tr>\n",
       "\t<tr><th scope=row>64</th><td>ICGC_MB64</td><td>177904</td></tr>\n",
       "\t<tr><th scope=row>65</th><td>ICGC_MB65</td><td>104328</td></tr>\n",
       "\t<tr><th scope=row>68</th><td>ICGC_MB68</td><td>119591</td></tr>\n",
       "\t<tr><th scope=row>71</th><td>ICGC_MB71</td><td>196865</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 14 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & SampleID\\_ICGC & PID\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t19 & ICGC\\_MB19 & 138689\\\\\n",
       "\t28 & ICGC\\_MB28 & 186723\\\\\n",
       "\t31 & ICGC\\_MB31 & 128398\\\\\n",
       "\t34 & ICGC\\_MB34 & 185985\\\\\n",
       "\t39 & ICGC\\_MB39 & 107430\\\\\n",
       "\t40 & ICGC\\_MB40 & 127581\\\\\n",
       "\t53 & ICGC\\_MB53 & 108031\\\\\n",
       "\t57 & ICGC\\_MB57 & 165133\\\\\n",
       "\t58 & ICGC\\_MB58 & 167702\\\\\n",
       "\t60 & ICGC\\_MB60 & 181633\\\\\n",
       "\t64 & ICGC\\_MB64 & 177904\\\\\n",
       "\t65 & ICGC\\_MB65 & 104328\\\\\n",
       "\t68 & ICGC\\_MB68 & 119591\\\\\n",
       "\t71 & ICGC\\_MB71 & 196865\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 14 × 2\n",
       "\n",
       "| <!--/--> | SampleID_ICGC &lt;chr&gt; | PID &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 19 | ICGC_MB19 | 138689 |\n",
       "| 28 | ICGC_MB28 | 186723 |\n",
       "| 31 | ICGC_MB31 | 128398 |\n",
       "| 34 | ICGC_MB34 | 185985 |\n",
       "| 39 | ICGC_MB39 | 107430 |\n",
       "| 40 | ICGC_MB40 | 127581 |\n",
       "| 53 | ICGC_MB53 | 108031 |\n",
       "| 57 | ICGC_MB57 | 165133 |\n",
       "| 58 | ICGC_MB58 | 167702 |\n",
       "| 60 | ICGC_MB60 | 181633 |\n",
       "| 64 | ICGC_MB64 | 177904 |\n",
       "| 65 | ICGC_MB65 | 104328 |\n",
       "| 68 | ICGC_MB68 | 119591 |\n",
       "| 71 | ICGC_MB71 | 196865 |\n",
       "\n"
      ],
      "text/plain": [
       "   SampleID_ICGC PID   \n",
       "19 ICGC_MB19     138689\n",
       "28 ICGC_MB28     186723\n",
       "31 ICGC_MB31     128398\n",
       "34 ICGC_MB34     185985\n",
       "39 ICGC_MB39     107430\n",
       "40 ICGC_MB40     127581\n",
       "53 ICGC_MB53     108031\n",
       "57 ICGC_MB57     165133\n",
       "58 ICGC_MB58     167702\n",
       "60 ICGC_MB60     181633\n",
       "64 ICGC_MB64     177904\n",
       "65 ICGC_MB65     104328\n",
       "68 ICGC_MB68     119591\n",
       "71 ICGC_MB71     196865"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ICGC_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "names(all.files)[match(ICGC_map$PID,names(all.files))] <- ICGC_map$SampleID_ICGC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bind all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.hla <- do.call(\"rbind\", all.files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.hla$sample_id <- rep(names(all.files), sapply(all.files, nrow))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>A1</th><th scope=col>A2</th><th scope=col>B1</th><th scope=col>B2</th><th scope=col>C1</th><th scope=col>C2</th><th scope=col>Reads</th><th scope=col>Objective</th><th scope=col>sample_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7316-10</th><td>A*03:01</td><td>A*23:01</td><td>B*44:03</td><td>B*58:02</td><td>C*06:02</td><td>C*14:03</td><td>24704</td><td>24036.992</td><td>7316-10  </td></tr>\n",
       "\t<tr><th scope=row>7316-100</th><td>A*33:03</td><td>A*03:01</td><td>B*56:01</td><td>B*18:01</td><td>C*02:02</td><td>C*01:02</td><td>16524</td><td>16077.852</td><td>7316-100 </td></tr>\n",
       "\t<tr><th scope=row>7316-101</th><td>A*29:02</td><td>A*33:03</td><td>B*35:01</td><td>B*15:03</td><td>C*02:10</td><td>C*07:01</td><td>11633</td><td>11318.909</td><td>7316-101 </td></tr>\n",
       "\t<tr><th scope=row>7316-1038</th><td>A*03:01</td><td>A*68:02</td><td>B*14:02</td><td>B*52:01</td><td>C*03:03</td><td>C*08:02</td><td> 9172</td><td> 8924.356</td><td>7316-1038</td></tr>\n",
       "\t<tr><th scope=row>7316-104</th><td>A*34:01</td><td>A*24:02</td><td>B*51:01</td><td>B*40:01</td><td>C*04:01</td><td>C*04:01</td><td>10682</td><td>10489.724</td><td>7316-104 </td></tr>\n",
       "\t<tr><th scope=row>7316-1052</th><td>A*11:01</td><td>A*02:02</td><td>B*15:01</td><td>B*41:01</td><td>C*17:01</td><td>C*03:03</td><td>21807</td><td>21218.211</td><td>7316-1052</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & A1 & A2 & B1 & B2 & C1 & C2 & Reads & Objective & sample\\_id\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t7316-10 & A*03:01 & A*23:01 & B*44:03 & B*58:02 & C*06:02 & C*14:03 & 24704 & 24036.992 & 7316-10  \\\\\n",
       "\t7316-100 & A*33:03 & A*03:01 & B*56:01 & B*18:01 & C*02:02 & C*01:02 & 16524 & 16077.852 & 7316-100 \\\\\n",
       "\t7316-101 & A*29:02 & A*33:03 & B*35:01 & B*15:03 & C*02:10 & C*07:01 & 11633 & 11318.909 & 7316-101 \\\\\n",
       "\t7316-1038 & A*03:01 & A*68:02 & B*14:02 & B*52:01 & C*03:03 & C*08:02 &  9172 &  8924.356 & 7316-1038\\\\\n",
       "\t7316-104 & A*34:01 & A*24:02 & B*51:01 & B*40:01 & C*04:01 & C*04:01 & 10682 & 10489.724 & 7316-104 \\\\\n",
       "\t7316-1052 & A*11:01 & A*02:02 & B*15:01 & B*41:01 & C*17:01 & C*03:03 & 21807 & 21218.211 & 7316-1052\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | A1 &lt;chr&gt; | A2 &lt;chr&gt; | B1 &lt;chr&gt; | B2 &lt;chr&gt; | C1 &lt;chr&gt; | C2 &lt;chr&gt; | Reads &lt;dbl&gt; | Objective &lt;dbl&gt; | sample_id &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 7316-10 | A*03:01 | A*23:01 | B*44:03 | B*58:02 | C*06:02 | C*14:03 | 24704 | 24036.992 | 7316-10   |\n",
       "| 7316-100 | A*33:03 | A*03:01 | B*56:01 | B*18:01 | C*02:02 | C*01:02 | 16524 | 16077.852 | 7316-100  |\n",
       "| 7316-101 | A*29:02 | A*33:03 | B*35:01 | B*15:03 | C*02:10 | C*07:01 | 11633 | 11318.909 | 7316-101  |\n",
       "| 7316-1038 | A*03:01 | A*68:02 | B*14:02 | B*52:01 | C*03:03 | C*08:02 |  9172 |  8924.356 | 7316-1038 |\n",
       "| 7316-104 | A*34:01 | A*24:02 | B*51:01 | B*40:01 | C*04:01 | C*04:01 | 10682 | 10489.724 | 7316-104  |\n",
       "| 7316-1052 | A*11:01 | A*02:02 | B*15:01 | B*41:01 | C*17:01 | C*03:03 | 21807 | 21218.211 | 7316-1052 |\n",
       "\n"
      ],
      "text/plain": [
       "          A1      A2      B1      B2      C1      C2      Reads Objective\n",
       "7316-10   A*03:01 A*23:01 B*44:03 B*58:02 C*06:02 C*14:03 24704 24036.992\n",
       "7316-100  A*33:03 A*03:01 B*56:01 B*18:01 C*02:02 C*01:02 16524 16077.852\n",
       "7316-101  A*29:02 A*33:03 B*35:01 B*15:03 C*02:10 C*07:01 11633 11318.909\n",
       "7316-1038 A*03:01 A*68:02 B*14:02 B*52:01 C*03:03 C*08:02  9172  8924.356\n",
       "7316-104  A*34:01 A*24:02 B*51:01 B*40:01 C*04:01 C*04:01 10682 10489.724\n",
       "7316-1052 A*11:01 A*02:02 B*15:01 B*41:01 C*17:01 C*03:03 21807 21218.211\n",
       "          sample_id\n",
       "7316-10   7316-10  \n",
       "7316-100  7316-100 \n",
       "7316-101  7316-101 \n",
       "7316-1038 7316-1038\n",
       "7316-104  7316-104 \n",
       "7316-1052 7316-1052"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(all.hla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(all.hla, file = paste0(datapath, \"HLA/all.hla.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load all HLA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge with metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"HLA/all.hla.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1405</li><li>9</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1405\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1405\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1405    9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(all.hla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_HLA <- merge(metadata_IC, all.hla, by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>904</li><li>32</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 904\n",
       "\\item 32\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 904\n",
       "2. 32\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 904  32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_HLA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(metadata_IC_HLA[,c(\"sample_id\", 'A1','A2','B1','B2','C1','C2')],\n",
    "         file = paste0(datapath, \"forGangesh/hla.csv\"), row.names = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Allele frequencies for HLA types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_A_plot <- HLA_freq_plot(metadata_IC_HLA, \"A1\", \"A2\", \" (HLA-A)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_B_plot <- HLA_freq_plot(metadata_IC_HLA, \"B1\", \"B2\", \" (HLA-B)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_C_plot <- HLA_freq_plot(metadata_IC_HLA, \"C1\", \"C2\", \" (HLA-C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"HLA_A_plot.pdf\"),\n",
    "   width = 45, height = 15)\n",
    "HLA_A_plot\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"HLA_B_plot.pdf\"),\n",
    "   width = 45, height = 15)\n",
    "HLA_B_plot\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"HLA_C_plot.pdf\"),\n",
    "   width = 45, height = 15)\n",
    "HLA_C_plot\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most common HLAs across immune clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_tab.fx <- function(metadata, HLA1, HLA2, HLA){\n",
    "\n",
    "myalleles <- c(metadata[[HLA1]], metadata[[HLA2]])\n",
    "Tab_Freq <- as.data.frame(table(myalleles), stringsAsFactors = F)\n",
    "Tab_Freq$AF <- Tab_Freq$Freq/(2*904)\n",
    "Tab_Freq <- Tab_Freq[order(Tab_Freq$AF, decreasing = T),]\n",
    "\n",
    "common_hlas <- Tab_Freq$myalleles[ Tab_Freq$AF > 0.02]\n",
    "    \n",
    " \n",
    "tmp1 <- metadata[,c(\"immune_cluster\", HLA1)]\n",
    "tmp2 <- metadata[,c(\"immune_cluster\", HLA2)]\n",
    "colnames(tmp1)[2] <- HLA  \n",
    "colnames(tmp2)[2] <- HLA     \n",
    "    \n",
    "tmp <- rbind(tmp1, tmp2)    \n",
    "    \n",
    "tmp <- tmp[ tmp[[HLA]] %in% common_hlas,]\n",
    "    \n",
    "myhla_tab <- as.data.frame(prop.table(table(tmp[[\"immune_cluster\"]], tmp[[HLA]]), margin = 2),\n",
    "                       stringsAsFactors = F)\n",
    "  \n",
    "    \n",
    " return(myhla_tab)   \n",
    "    \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "HLA_A_tab <- HLA_tab.fx(metadata_IC_HLA, \"A1\", \"A2\", \"A\")\n",
    "\n",
    "HLA_B_tab <- HLA_tab.fx(metadata_IC_HLA, \"B1\", \"B2\", \"B\")\n",
    "\n",
    "HLA_C_tab <- HLA_tab.fx(metadata_IC_HLA, \"C1\", \"C2\", \"C\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "HLA_B_tab <- rbind(c(\"EMPTY\",\"EMPTY\", 0.00), HLA_B_tab)\n",
    "\n",
    "HLA_C_tab <- rbind(c(\"EMPTY\",\"EMPTY1\", 0.00), HLA_C_tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_tab <- rbind(HLA_A_tab, HLA_B_tab, HLA_C_tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_tab$Var1 <- factor(HLA_tab$Var1, levels = c(\"EMPTY\",\"Pediatric inflamed\", \"Myeloid-driven\",\n",
    "                                                \"Pediatric cold\", \"Immune excluded\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_tab$Freq <- as.numeric(HLA_tab$Freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "HLA_tab$Var2 <- factor(HLA_tab$Var2, levels = unique(HLA_tab$Var2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmEMPTY <- rep(\"black\",length(levels(HLA_tab$Var2)))\n",
    "rmEMPTY[c(which(levels(HLA_tab$Var2) == \"EMPTY\"), \n",
    "          which(levels(HLA_tab$Var2) == \"EMPTY1\"))] <- \"white\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Vectorized input to `element_text()` is not officially supported.\n",
      "Results may be unexpected or may change in future versions of ggplot2.”\n"
     ]
    }
   ],
   "source": [
    "HLA_plot_IC <- ggplot() +  \n",
    "    geom_bar(data = HLA_tab, aes(x = Var2, y = Freq, fill = Var1), stat = \"identity\") + \n",
    "    scale_fill_manual(values = c(\"EMPTY\" = \"white\", cluster_col)) + \n",
    "    myplot + myaxis +\n",
    "    theme(axis.title.x = element_blank(), \n",
    "          axis.text.x = element_text(size = 35, color = rmEMPTY),\n",
    "          axis.text.y = element_text(size = 35),\n",
    "          axis.title.y = element_blank(),\n",
    "          legend.position = \"none\",\n",
    "          plot.title = element_text(hjust = 0.5, size = 35)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add significance asterisks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.df <- data.frame(x = c(\"A*02:01\", \"C*03:04\"), y = c(1,1))\n",
    "HLA_plot_IC <- HLA_plot_IC + \n",
    "geom_text(data = label.df, aes(x = x, y = y), label = \"*\", size = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QgAgCa89g6g/Fg0BAAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQ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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HLA_plot_IC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"HLA_plot_IC.pdf\"),\n",
    "   width = 20, height = 10)\n",
    "HLA_plot_IC  + ggtitle(~underline(\"HLAs > 2%\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp <- HLA_tab[1:4,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp$Var1 <- factor(tmp$Var1, levels = c(\"Pediatric inflamed\", \"Myeloid-driven\",\n",
    "                                        \"Pediatric cold\", \"Immune excluded\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "IC_legend_plot <- ggplot(data = tmp) + \n",
    "    geom_bar(aes(x = Var2, y = Freq, fill = Var1), stat = \"identity\") +\n",
    "    scale_fill_manual(values =  cluster_col) + theme(legend.title = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend <- cowplot::get_legend(IC_legend_plot)\n",
    "\n",
    "grid.newpage()\n",
    "grid.draw(legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"IC_legend.pdf\"),\n",
    "   width = 5, height = 5)\n",
    "#grid.newpage()\n",
    "grid.draw(legend)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.fx <- function(metadata, HLA1, HLA2){\n",
    "\n",
    "myalleles <- c(metadata[[HLA1]], metadata[[HLA2]])\n",
    "Tab_Freq <- as.data.frame(table(myalleles), stringsAsFactors = F)\n",
    "Tab_Freq$AF <- Tab_Freq$Freq/(2*904)\n",
    "Tab_Freq <- Tab_Freq[order(Tab_Freq$AF, decreasing = T),]\n",
    "\n",
    "common_hlas <- Tab_Freq$myalleles[ Tab_Freq$AF > 0.02]\n",
    "    \n",
    "for(hla in common_hlas){\n",
    " \n",
    "    tmp <- metadata\n",
    "    tmp$myHLA <- NA\n",
    "tmp$myHLA[ tmp[[HLA1]] == hla] <- 1\n",
    "tmp$myHLA[ tmp[[HLA2]] == hla] <- 1\n",
    "tmp$myHLA[ is.na(tmp$myHLA)] <- 0\n",
    "    \n",
    "#   print(table(tmp$myHLA)) \n",
    "#    message(\"lrm\")\n",
    "#    print(rms::lrm(myHLA ~ immune_cluster + cohort, data = tmp))\n",
    "    \n",
    "if(chisq.test(table(tmp$immune_cluster, tmp$myHLA))$p.value < 0.05){\n",
    "    message(paste0(hla))  }  \n",
    "}\n",
    "    } \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C*03:04\n",
      "\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    }
   ],
   "source": [
    "stats.fx(metadata_IC_HLA, \"C1\", \"C2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    }
   ],
   "source": [
    "stats.fx(metadata_IC_HLA, \"B1\", \"B2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A*02:01\n",
      "\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$immune_cluster, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    }
   ],
   "source": [
    "stats.fx(metadata_IC_HLA, \"A1\", \"A2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stats HLAs across cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "myalleles <- c(metadata_IC_HLA[[\"B1\"]], metadata_IC_HLA[[\"B2\"]])\n",
    "Tab_Freq <- as.data.frame(table(myalleles), stringsAsFactors = F)\n",
    "Tab_Freq$AF <- Tab_Freq$Freq/(2*904)\n",
    "Tab_Freq <- Tab_Freq[order(Tab_Freq$AF, decreasing = T),]\n",
    "\n",
    "common_hlas <- Tab_Freq$myalleles[ Tab_Freq$AF > 0.02]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "ETMRB*08:01\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "NBLB*35:01\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "pedLGGB*15:01\n",
      "\n",
      "MBB*15:01\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "NFBB*44:03\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "EPNB*13:02\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "ATRTB*57:01\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "MBB*52:01\n",
      "\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n",
      "Warning message in chisq.test(table(tmp$mycancer, tmp$myHLA)):\n",
      "“Chi-squared approximation may be incorrect”\n"
     ]
    }
   ],
   "source": [
    "for(hla in common_hlas){\n",
    "\n",
    "    tmp <- metadata_IC_HLA\n",
    "    tmp$myHLA <- NA\n",
    "    tmp$myHLA[ tmp[[\"B1\"]] == hla] <- 1\n",
    "    tmp$myHLA[ tmp[[\"B2\"]] == hla] <- 1\n",
    "    tmp$myHLA[ is.na(tmp$myHLA)] <- 0\n",
    "     \n",
    "    for(c in unique(tmp$cohort)){\n",
    "\n",
    "        tmp$mycancer <- NA\n",
    "        tmp$mycancer[ tmp$cohort == c] <- 1\n",
    "        tmp$mycancer[ is.na(tmp$mycancer)] <- 0      \n",
    "        if(chisq.test(table(tmp$mycancer, tmp$myHLA))$p.value < 0.05)\n",
    "            message(c(c,hla))\n",
    "    }\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HLA vs mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"HLA/all.hla.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1405</li><li>9</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1405\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1405\n",
       "2. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1405    9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(all.hla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_HLA <- merge(metadata_IC, all.hla, by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath,\"snv/total_mut_manifest_estimate.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_tmb <- total_mut_manifest_estimate[ total_mut_manifest_estimate$group != \"TCGA\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_tmb_IC <- merge(ped_tmb, metadata_IC[,c(\"sample_id\", \"immune_cluster\")], by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get nonsyn variants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CBTTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_mafsummary <- read.table(file = paste0(datapath, \"snv/CBTTC_mafsummary_overlapped_3VAF.txt\"),\n",
    "                        sep = \"\\t\", check.names = F,\n",
    "                        header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>796</li><li>10</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 796\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 796\n",
       "2. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 796  10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTTC_mafsummary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(maftools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_CBTTC <- read.maf(maf =  paste0(datapath, \"snv/CBTTC_maf_overlapped_3VAF.maf\"),\n",
    "                      useAll = TRUE, verbose = FALSE,\n",
    "                      vc_nonSyn = c(\"Frame_Shift_Del\", \"Frame_Shift_Ins\", \n",
    "                                    \"Nonsense_Mutation\",\"Nonstop_Mutation\", \n",
    "                                    \"In_Frame_Del\",\"In_Frame_Ins\", \n",
    "                                    \"Missense_Mutation\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 12 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ID</th><th scope=col>summary</th><th scope=col>Mean</th><th scope=col>Median</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NCBI_Build       </td><td>GRCh38 </td><td>    NA</td><td>NA</td></tr>\n",
       "\t<tr><td>Center           </td><td>Mutect2</td><td>    NA</td><td>NA</td></tr>\n",
       "\t<tr><td>Samples          </td><td>842    </td><td>    NA</td><td>NA</td></tr>\n",
       "\t<tr><td>nGenes           </td><td>13511  </td><td>    NA</td><td>NA</td></tr>\n",
       "\t<tr><td>Frame_Shift_Del  </td><td>845    </td><td> 1.004</td><td> 0</td></tr>\n",
       "\t<tr><td>Frame_Shift_Ins  </td><td>769    </td><td> 0.913</td><td> 0</td></tr>\n",
       "\t<tr><td>In_Frame_Del     </td><td>160    </td><td> 0.190</td><td> 0</td></tr>\n",
       "\t<tr><td>In_Frame_Ins     </td><td>30     </td><td> 0.036</td><td> 0</td></tr>\n",
       "\t<tr><td>Missense_Mutation</td><td>50771  </td><td>60.298</td><td> 4</td></tr>\n",
       "\t<tr><td>Nonsense_Mutation</td><td>2840   </td><td> 3.373</td><td> 0</td></tr>\n",
       "\t<tr><td>Nonstop_Mutation </td><td>25     </td><td> 0.030</td><td> 0</td></tr>\n",
       "\t<tr><td>total            </td><td>55440  </td><td>65.843</td><td> 5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 12 × 4\n",
       "\\begin{tabular}{llll}\n",
       " ID & summary & Mean & Median\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t NCBI\\_Build        & GRCh38  &     NA & NA\\\\\n",
       "\t Center            & Mutect2 &     NA & NA\\\\\n",
       "\t Samples           & 842     &     NA & NA\\\\\n",
       "\t nGenes            & 13511   &     NA & NA\\\\\n",
       "\t Frame\\_Shift\\_Del   & 845     &  1.004 &  0\\\\\n",
       "\t Frame\\_Shift\\_Ins   & 769     &  0.913 &  0\\\\\n",
       "\t In\\_Frame\\_Del      & 160     &  0.190 &  0\\\\\n",
       "\t In\\_Frame\\_Ins      & 30      &  0.036 &  0\\\\\n",
       "\t Missense\\_Mutation & 50771   & 60.298 &  4\\\\\n",
       "\t Nonsense\\_Mutation & 2840    &  3.373 &  0\\\\\n",
       "\t Nonstop\\_Mutation  & 25      &  0.030 &  0\\\\\n",
       "\t total             & 55440   & 65.843 &  5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 12 × 4\n",
       "\n",
       "| ID &lt;chr&gt; | summary &lt;chr&gt; | Mean &lt;dbl&gt; | Median &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| NCBI_Build        | GRCh38  |     NA | NA |\n",
       "| Center            | Mutect2 |     NA | NA |\n",
       "| Samples           | 842     |     NA | NA |\n",
       "| nGenes            | 13511   |     NA | NA |\n",
       "| Frame_Shift_Del   | 845     |  1.004 |  0 |\n",
       "| Frame_Shift_Ins   | 769     |  0.913 |  0 |\n",
       "| In_Frame_Del      | 160     |  0.190 |  0 |\n",
       "| In_Frame_Ins      | 30      |  0.036 |  0 |\n",
       "| Missense_Mutation | 50771   | 60.298 |  4 |\n",
       "| Nonsense_Mutation | 2840    |  3.373 |  0 |\n",
       "| Nonstop_Mutation  | 25      |  0.030 |  0 |\n",
       "| total             | 55440   | 65.843 |  5 |\n",
       "\n"
      ],
      "text/plain": [
       "   ID                summary Mean   Median\n",
       "1  NCBI_Build        GRCh38      NA NA    \n",
       "2  Center            Mutect2     NA NA    \n",
       "3  Samples           842         NA NA    \n",
       "4  nGenes            13511       NA NA    \n",
       "5  Frame_Shift_Del   845      1.004  0    \n",
       "6  Frame_Shift_Ins   769      0.913  0    \n",
       "7  In_Frame_Del      160      0.190  0    \n",
       "8  In_Frame_Ins      30       0.036  0    \n",
       "9  Missense_Mutation 50771   60.298  4    \n",
       "10 Nonsense_Mutation 2840     3.373  0    \n",
       "11 Nonstop_Mutation  25       0.030  0    \n",
       "12 total             55440   65.843  5    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mymaf_CBTTC@summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf <- mymaf_CBTTC@data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate tumour VAF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf$t_VAF <- CBTTC_data_maf$t_alt_count / CBTTC_data_maf$t_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "0.03125 0.23913 0.32468 0.31363 0.39216 1.00000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(CBTTC_data_maf$t_VAF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf$Tumor_Sample_Barcode <- as.character(CBTTC_data_maf$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  Frame_Shift_Del   Frame_Shift_Ins      In_Frame_Del      In_Frame_Ins \n",
       "              845               769               160                30 \n",
       "Missense_Mutation Nonsense_Mutation  Nonstop_Mutation \n",
       "            50771              2840                25 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(CBTTC_data_maf$Variant_Classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf_sampleid <- merge(CBTTC_mafsummary[,c(\"sample_id\", \"Tumor_Sample_Barcode\")], \n",
    "                                 CBTTC_data_maf, by = \"Tumor_Sample_Barcode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "796"
      ],
      "text/latex": [
       "796"
      ],
      "text/markdown": [
       "796"
      ],
      "text/plain": [
       "[1] 796"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(unique(CBTTC_data_maf_sampleid$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset  snv dfs to include only samples in estimate df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf <- CBTTC_data_maf_sampleid[CBTTC_data_maf_sampleid$sample_id %in% ped_tmb_IC$sample_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "470"
      ],
      "text/latex": [
       "470"
      ],
      "text/markdown": [
       "470"
      ],
      "text/plain": [
       "[1] 470"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(unique(CBTTC_data_maf$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf_kb <- CBTTC_data_maf[grepl(\"oncogenic\", CBTTC_data_maf$oncogenic,ignore.case = T),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DKFZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "DKFZ_maf <- read.table(file = paste0(datapath, \"snv/dkfz_mafKB.maf\"),\n",
    "                        sep = \"\\t\", check.names = F,\n",
    "                        header = TRUE, comment.char = \"\",stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    frameshift deletion    frameshift insertion  nonframeshift deletion \n",
       "                    600                     330                     150 \n",
       "nonframeshift insertion       nonsynonymous SNV                splicing \n",
       "                     42                   18871                      59 \n",
       "               stopgain            stopgain SNV            stoploss SNV \n",
       "                     16                    1222                       4 \n",
       "         synonymous SNV \n",
       "                    424 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(DKFZ_maf$variant_classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "DKFZ_maf_nsyn <- DKFZ_maf[!DKFZ_maf$variant_classification %in% c(\"splicing\", \"unknown\", \"synonymous SNV\"),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(DKFZ_maf_nsyn)[colnames(DKFZ_maf_nsyn) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>21718</li><li>17</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 21718\n",
       "\\item 17\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 21718\n",
       "2. 17\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 21718    17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>21235</li><li>17</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 21235\n",
       "\\item 17\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 21235\n",
       "2. 17\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 21235    17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(DKFZ_maf)\n",
    "dim(DKFZ_maf_nsyn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(DKFZ_maf)[colnames(DKFZ_maf) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "DKFZ_maf_kb <- DKFZ_maf[grepl(\"oncogenic\", DKFZ_maf$oncogenic, ignore.case = T),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>21718</li><li>17</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 21718\n",
       "\\item 17\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 21718\n",
       "2. 17\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 21718    17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>481</li><li>17</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 481\n",
       "\\item 17\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 481\n",
       "2. 17\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 481  17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(DKFZ_maf)\n",
    "dim(DKFZ_maf_kb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_gdc <- read.maf(maf =  paste0(datapath, \"snv/GDC_allmaf_3VAF_fl_dedup_sampleid_KB.maf\"),\n",
    "                      useAll = TRUE, verbose = FALSE,\n",
    "                      vc_nonSyn = c(\"Frame_Shift_Del\", \"Frame_Shift_Ins\", \n",
    "                                    \"Nonsense_Mutation\",\"Nonstop_Mutation\", \n",
    "                                    \"In_Frame_Del\",\"In_Frame_Ins\", \n",
    "                                    \"Missense_Mutation\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_gdc_data<- mymaf_gdc@data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2749"
      ],
      "text/latex": [
       "2749"
      ],
      "text/markdown": [
       "2749"
      ],
      "text/plain": [
       "[1] 2749"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(unique(mymaf_gdc_data$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_gdc_data$Tumor_Sample_Barcode <- as.character(mymaf_gdc_data$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  Frame_Shift_Del   Frame_Shift_Ins      In_Frame_Del      In_Frame_Ins \n",
       "            17243             10161              1306              1752 \n",
       "Missense_Mutation Nonsense_Mutation  Nonstop_Mutation \n",
       "           288342             21973               264 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mymaf_gdc_data$Variant_Classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "subset to TARGET data and match with total_mut_manifest_estimate_quanti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_target_data <- mymaf_gdc_data[grepl(\"TARGET\", mymaf_gdc_data$Tumor_Sample_Barcode),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "131"
      ],
      "text/latex": [
       "131"
      ],
      "text/markdown": [
       "131"
      ],
      "text/plain": [
       "[1] 131"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(unique(mymaf_target_data$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_target_data <- mymaf_target_data[mymaf_target_data$Tumor_Sample_Barcode %in% \n",
    "                                       ped_tmb_IC$sample_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mymaf_target_data)[colnames(mymaf_target_data) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_target_data_kb <- mymaf_target_data[ grepl(\"oncogenic\", mymaf_target_data$oncogenic, ignore.case = T),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mymaf_target_data_kb)[colnames(mymaf_target_data_kb) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1888</li><li>156</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1888\n",
       "\\item 156\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1888\n",
       "2. 156\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1888  156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>19</li><li>156</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 19\n",
       "\\item 156\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 19\n",
       "2. 156\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  19 156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(mymaf_target_data)\n",
    "dim(mymaf_target_data_kb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>20087</li><li>163</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20087\n",
       "\\item 163\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20087\n",
       "2. 163\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20087   163"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>324</li><li>163</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 324\n",
       "\\item 163\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 324\n",
       "2. 163\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 324 163"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTTC_data_maf)\n",
    "dim(CBTTC_data_maf_kb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All nonsyn mutated genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dkfz_allmut <- as.data.frame(table(DKFZ_maf$Hugo_Symbol), stringsAsFactors = F)\n",
    "cbttc_allmut <- as.data.frame(table(CBTTC_data_maf$Hugo_Symbol), stringsAsFactors = F)\n",
    "target_allmut <- as.data.frame(table(mymaf_target_data$Hugo_Symbol), stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "mutated_genes_all <- c(dkfz_allmut$Var1,cbttc_allmut$Var1,target_allmut$Var1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "allmutated_genes <- mutated_genes_all[!duplicated(mutated_genes_all)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "10540"
      ],
      "text/latex": [
       "10540"
      ],
      "text/markdown": [
       "10540"
      ],
      "text/plain": [
       "[1] 10540"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(allmutated_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create a hla x mutation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "allHLAs <- c(metadata_IC_HLA$A1, metadata_IC_HLA$A2, metadata_IC_HLA$B1, \n",
    "             metadata_IC_HLA$B2, metadata_IC_HLA$C1, metadata_IC_HLA$C2)\n",
    "\n",
    "uniqueHLAs <- unique(allHLAs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'A*03:01'</li><li>'A*33:03'</li><li>'A*29:02'</li><li>'A*11:01'</li><li>'A*02:01'</li><li>'A*68:01'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'A*03:01'\n",
       "\\item 'A*33:03'\n",
       "\\item 'A*29:02'\n",
       "\\item 'A*11:01'\n",
       "\\item 'A*02:01'\n",
       "\\item 'A*68:01'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'A*03:01'\n",
       "2. 'A*33:03'\n",
       "3. 'A*29:02'\n",
       "4. 'A*11:01'\n",
       "5. 'A*02:01'\n",
       "6. 'A*68:01'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"A*03:01\" \"A*33:03\" \"A*29:02\" \"A*11:01\" \"A*02:01\" \"A*68:01\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(uniqueHLAs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "hla_mut_mat <- matrix(nrow = length(uniqueHLAs), ncol = length(allmutated_genes),\n",
    "                     dimnames = list(uniqueHLAs, allmutated_genes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean rownames to match mupexi files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(hla_mut_mat) <- paste0(\"HLA-\", rownames(hla_mut_mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(hla_mut_mat) <- gsub(\"[*]\", \"\", rownames(hla_mut_mat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load mupexi files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "mupexifiles <- list.files(paste0(datapath, \"Mupexi/\"), pattern = \"overlappedvar_all.mupexi\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.mupexifiles <- lapply(mupexifiles, function(f){read.table(paste0(datapath,\"Mupexi/\",f), header = TRUE, sep = \"\\t\", stringsAsFactors = F)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.mupexi <- do.call(\"rbind\", all.mupexifiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.mupexi <- all.mupexi[all.mupexi$Gene_Symbol %in% allmutated_genes,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HLA-A03:01\n",
      "\n",
      "HLA-A33:03\n",
      "\n",
      "HLA-A29:02\n",
      "\n",
      "HLA-A11:01\n",
      "\n",
      "HLA-A02:01\n",
      "\n",
      "HLA-A68:01\n",
      "\n",
      "HLA-A02:05\n",
      "\n",
      "HLA-A74:03\n",
      "\n",
      "HLA-A30:01\n",
      "\n",
      "HLA-A23:01\n",
      "\n",
      "HLA-A31:01\n",
      "\n",
      "HLA-A24:02\n",
      "\n",
      "HLA-A24:03\n",
      "\n",
      "HLA-A01:01\n",
      "\n",
      "HLA-A66:01\n",
      "\n",
      "HLA-A02:06\n",
      "\n",
      "HLA-A68:05\n",
      "\n",
      "HLA-A34:02\n",
      "\n",
      "HLA-A32:01\n",
      "\n",
      "HLA-A36:01\n",
      "\n",
      "HLA-A30:02\n",
      "\n",
      "HLA-A02:07\n",
      "\n",
      "HLA-A29:01\n",
      "\n",
      "HLA-A80:01\n",
      "\n",
      "HLA-A68:02\n",
      "\n",
      "HLA-A24:07\n",
      "\n",
      "HLA-A26:01\n",
      "\n",
      "HLA-A30:04\n",
      "\n",
      "HLA-A03:02\n",
      "\n",
      "HLA-A33:01\n",
      "\n",
      "HLA-A74:01\n",
      "\n",
      "HLA-A02:11\n",
      "\n",
      "HLA-A02:02\n",
      "\n",
      "HLA-A25:01\n",
      "\n",
      "HLA-A68:03\n",
      "\n",
      "HLA-A34:01\n",
      "\n",
      "HLA-A02:03\n",
      "\n",
      "HLA-A66:02\n",
      "\n",
      "HLA-A24:23\n",
      "\n",
      "HLA-A69:01\n",
      "\n",
      "HLA-A26:16\n",
      "\n",
      "HLA-A11:02\n",
      "\n",
      "HLA-A02:85\n",
      "\n",
      "HLA-A30:03\n",
      "\n",
      "HLA-A02:08\n",
      "\n",
      "HLA-A29:10\n",
      "\n",
      "HLA-A01:02\n",
      "\n",
      "HLA-A30:10\n",
      "\n",
      "HLA-A01:03\n",
      "\n",
      "HLA-A24:25\n",
      "\n",
      "HLA-A02:20\n",
      "\n",
      "HLA-A02:22\n",
      "\n",
      "HLA-A23:04\n",
      "\n",
      "HLA-A66:03\n",
      "\n",
      "HLA-A24:05\n",
      "\n",
      "HLA-A68:24\n",
      "\n",
      "HLA-A03:22\n",
      "\n",
      "HLA-A24:35\n",
      "\n",
      "HLA-A11:03\n",
      "\n",
      "HLA-B44:03\n",
      "\n",
      "HLA-B56:01\n",
      "\n",
      "HLA-B35:01\n",
      "\n",
      "HLA-B14:02\n",
      "\n",
      "HLA-B15:01\n",
      "\n",
      "HLA-B08:01\n",
      "\n",
      "HLA-B37:01\n",
      "\n",
      "HLA-B51:01\n",
      "\n",
      "HLA-B55:01\n",
      "\n",
      "HLA-B15:02\n",
      "\n",
      "HLA-B50:01\n",
      "\n",
      "HLA-B27:02\n",
      "\n",
      "HLA-B35:03\n",
      "\n",
      "HLA-B13:02\n",
      "\n",
      "HLA-B35:08\n",
      "\n",
      "HLA-B44:02\n",
      "\n",
      "HLA-B49:01\n",
      "\n",
      "HLA-B52:01\n",
      "\n",
      "HLA-B07:02\n",
      "\n",
      "HLA-B57:01\n",
      "\n",
      "HLA-B48:01\n",
      "\n",
      "HLA-B27:05\n",
      "\n",
      "HLA-B42:01\n",
      "\n",
      "HLA-B40:01\n",
      "\n",
      "HLA-B40:02\n",
      "\n",
      "HLA-B39:01\n",
      "\n",
      "HLA-B57:03\n",
      "\n",
      "HLA-B07:10\n",
      "\n",
      "HLA-B14:03\n",
      "\n",
      "HLA-B54:01\n",
      "\n",
      "HLA-B44:06\n",
      "\n",
      "HLA-B41:01\n",
      "\n",
      "HLA-B18:03\n",
      "\n",
      "HLA-B39:34\n",
      "\n",
      "HLA-B07:05\n",
      "\n",
      "HLA-B14:01\n",
      "\n",
      "HLA-B18:01\n",
      "\n",
      "HLA-B47:01\n",
      "\n",
      "HLA-B58:01\n",
      "\n",
      "HLA-B39:06\n",
      "\n",
      "HLA-B38:01\n",
      "\n",
      "HLA-B53:01\n",
      "\n",
      "HLA-B15:10\n",
      "\n",
      "HLA-B35:02\n",
      "\n",
      "HLA-B44:05\n",
      "\n",
      "HLA-B15:18\n",
      "\n",
      "HLA-B45:01\n",
      "\n",
      "HLA-B39:10\n",
      "\n",
      "HLA-B40:06\n",
      "\n",
      "HLA-B15:03\n",
      "\n",
      "HLA-B35:05\n",
      "\n",
      "HLA-B81:01\n",
      "\n",
      "HLA-B35:12\n",
      "\n",
      "HLA-B15:35\n",
      "\n",
      "HLA-B46:01\n",
      "\n",
      "HLA-B51:08\n",
      "\n",
      "HLA-B41:02\n",
      "\n",
      "HLA-B27:12\n",
      "\n",
      "HLA-B58:02\n",
      "\n",
      "HLA-B51:21\n",
      "\n",
      "HLA-B78:01\n",
      "\n",
      "HLA-B18:05\n",
      "\n",
      "HLA-B35:10\n",
      "\n",
      "HLA-B51:09\n",
      "\n",
      "HLA-B15:27\n",
      "\n",
      "HLA-B13:01\n",
      "\n",
      "HLA-B39:05\n",
      "\n",
      "HLA-B15:21\n",
      "\n",
      "HLA-B15:31\n",
      "\n",
      "HLA-B15:16\n",
      "\n",
      "HLA-B35:04\n",
      "\n",
      "HLA-B35:43\n",
      "\n",
      "HLA-B15:09\n",
      "\n",
      "HLA-B50:02\n",
      "\n",
      "HLA-B15:17\n",
      "\n",
      "HLA-B27:03\n",
      "\n",
      "HLA-B27:06\n",
      "\n",
      "HLA-B57:02\n",
      "\n",
      "HLA-B73:01\n",
      "\n",
      "HLA-B35:17\n",
      "\n",
      "HLA-B15:47\n",
      "\n",
      "HLA-B51:14\n",
      "\n",
      "HLA-B39:02\n",
      "\n",
      "HLA-B51:22\n",
      "\n",
      "HLA-B15:15\n",
      "\n",
      "HLA-B51:06\n",
      "\n",
      "HLA-B15:11\n",
      "\n",
      "HLA-B15:08\n",
      "\n",
      "HLA-B47:03\n",
      "\n",
      "HLA-B15:13\n",
      "\n",
      "HLA-B55:02\n",
      "\n",
      "HLA-C06:02\n",
      "\n",
      "HLA-C02:02\n",
      "\n",
      "HLA-C02:10\n",
      "\n",
      "HLA-C03:03\n",
      "\n",
      "HLA-C17:01\n",
      "\n",
      "HLA-C07:01\n",
      "\n",
      "HLA-C07:02\n",
      "\n",
      "HLA-C04:01\n",
      "\n",
      "HLA-C12:03\n",
      "\n",
      "HLA-C01:02\n",
      "\n",
      "HLA-C16:01\n",
      "\n",
      "HLA-C03:04\n",
      "\n",
      "HLA-C05:01\n",
      "\n",
      "HLA-C08:03\n",
      "\n",
      "HLA-C07:04\n",
      "\n",
      "HLA-C08:02\n",
      "\n",
      "HLA-C16:02\n",
      "\n",
      "HLA-C15:05\n",
      "\n",
      "HLA-C12:02\n",
      "\n",
      "HLA-C16:04\n",
      "\n",
      "HLA-C14:02\n",
      "\n",
      "HLA-C15:02\n",
      "\n",
      "HLA-C03:02\n",
      "\n",
      "HLA-C18:01\n",
      "\n",
      "HLA-C08:15\n",
      "\n",
      "HLA-C14:03\n",
      "\n",
      "HLA-C15:09\n",
      "\n",
      "HLA-C08:01\n",
      "\n",
      "HLA-C15:06\n",
      "\n",
      "HLA-C08:04\n",
      "\n",
      "HLA-C04:04\n",
      "\n",
      "HLA-C05:09\n",
      "\n",
      "HLA-C15:04\n",
      "\n",
      "HLA-C04:10\n",
      "\n",
      "HLA-C03:06\n",
      "\n",
      "HLA-C04:07\n",
      "\n",
      "HLA-C03:67\n",
      "\n",
      "HLA-C02:06\n",
      "\n",
      "HLA-C04:03\n",
      "\n",
      "HLA-C04:06\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for(h in 1:nrow(hla_mut_mat)){\n",
    "    myhla <- rownames(hla_mut_mat)[h]\n",
    "    message(myhla)\n",
    "    mymupexi <- all.mupexi[ all.mupexi$HLA_allele == myhla,]\n",
    "   if(nrow(mymupexi)>0){ \n",
    "       for(g in unique(mymupexi$Gene_Symbol)){\n",
    "           hla_mut_mat[myhla, g] <- min(mymupexi$Mut_MHCrank_BA[ mymupexi$Gene_Symbol == g])\n",
    "       }\n",
    "   }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>HLA-B13:02</dt><dd>0.041</dd><dt>HLA-A03:01</dt><dd>0.2851</dd><dt>HLA-B58:01</dt><dd>0.3706</dd><dt>HLA-B57:01</dt><dd>0.4398</dd><dt>HLA-B58:02</dt><dd>0.5674</dd><dt>HLA-A11:01</dt><dd>0.6595</dd><dt>HLA-A29:02</dt><dd>0.768</dd><dt>HLA-C16:01</dt><dd>0.7775</dd><dt>HLA-C05:01</dt><dd>0.9473</dd><dt>HLA-B07:02</dt><dd>0.9728</dd><dt>HLA-B27:12</dt><dd>1.0098</dd><dt>HLA-B27:05</dt><dd>1.0327</dd><dt>HLA-A02:05</dt><dd>1.1447</dd><dt>HLA-B40:01</dt><dd>1.1637</dd><dt>HLA-A33:03</dt><dd>1.1876</dd><dt>HLA-A74:01</dt><dd>1.2684</dd><dt>HLA-B50:01</dt><dd>1.5264</dd><dt>HLA-C06:02</dt><dd>2.211</dd><dt>HLA-A02:01</dt><dd>2.2193</dd><dt>HLA-B45:01</dt><dd>2.48</dd><dt>HLA-A31:01</dt><dd>2.5699</dd><dt>HLA-A30:01</dt><dd>2.9787</dd><dt>HLA-A66:01</dt><dd>3.0129</dd><dt>HLA-B44:03</dt><dd>4.1825</dd><dt>HLA-A68:01</dt><dd>4.2325</dd><dt>HLA-B44:02</dt><dd>4.7088</dd><dt>HLA-A32:01</dt><dd>4.9329</dd><dt>HLA-C07:01</dt><dd>5.8338</dd><dt>HLA-C03:04</dt><dd>6.0788</dd><dt>HLA-C07:02</dt><dd>6.1335</dd><dt>HLA-B39:01</dt><dd>6.5845</dd><dt>HLA-B14:02</dt><dd>6.69</dd><dt>HLA-A01:01</dt><dd>7.0888</dd><dt>HLA-A30:02</dt><dd>7.8713</dd><dt>HLA-B18:01</dt><dd>7.9391</dd><dt>HLA-B40:06</dt><dd>8.0204</dd><dt>HLA-A23:01</dt><dd>8.6577</dd><dt>HLA-A80:01</dt><dd>8.8069</dd><dt>HLA-B41:02</dt><dd>8.8073</dd><dt>HLA-B46:01</dt><dd>8.9496</dd><dt>HLA-C08:02</dt><dd>9.1435</dd><dt>HLA-B51:01</dt><dd>9.3142</dd><dt>HLA-A24:07</dt><dd>9.4074</dd><dt>HLA-A24:02</dt><dd>9.5366</dd><dt>HLA-C03:02</dt><dd>9.8001</dd><dt>HLA-B52:01</dt><dd>9.9084</dd><dt>HLA-C14:02</dt><dd>11.0083</dd><dt>HLA-C02:02</dt><dd>11.4616</dd><dt>HLA-B35:01</dt><dd>12.4842</dd><dt>HLA-B40:02</dt><dd>13.9009</dd><dt>HLA-C16:02</dt><dd>14.0219</dd><dt>HLA-C12:02</dt><dd>15.0647</dd><dt>HLA-B08:01</dt><dd>15.1122</dd><dt>HLA-A26:01</dt><dd>15.7462</dd><dt>HLA-C04:01</dt><dd>17.2873</dd><dt>HLA-C01:02</dt><dd>17.8662</dd><dt>HLA-C12:03</dt><dd>19.7214</dd><dt>HLA-C07:04</dt><dd>20.9814</dd><dt>HLA-C17:01</dt><dd>22.6899</dd><dt>HLA-C15:02</dt><dd>23.1787</dd><dt>HLA-B35:03</dt><dd>23.5018</dd><dt>HLA-A02:11</dt><dd>25.1339</dd><dt>HLA-B35:02</dt><dd>27.022</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[HLA-B13:02] 0.041\n",
       "\\item[HLA-A03:01] 0.2851\n",
       "\\item[HLA-B58:01] 0.3706\n",
       "\\item[HLA-B57:01] 0.4398\n",
       "\\item[HLA-B58:02] 0.5674\n",
       "\\item[HLA-A11:01] 0.6595\n",
       "\\item[HLA-A29:02] 0.768\n",
       "\\item[HLA-C16:01] 0.7775\n",
       "\\item[HLA-C05:01] 0.9473\n",
       "\\item[HLA-B07:02] 0.9728\n",
       "\\item[HLA-B27:12] 1.0098\n",
       "\\item[HLA-B27:05] 1.0327\n",
       "\\item[HLA-A02:05] 1.1447\n",
       "\\item[HLA-B40:01] 1.1637\n",
       "\\item[HLA-A33:03] 1.1876\n",
       "\\item[HLA-A74:01] 1.2684\n",
       "\\item[HLA-B50:01] 1.5264\n",
       "\\item[HLA-C06:02] 2.211\n",
       "\\item[HLA-A02:01] 2.2193\n",
       "\\item[HLA-B45:01] 2.48\n",
       "\\item[HLA-A31:01] 2.5699\n",
       "\\item[HLA-A30:01] 2.9787\n",
       "\\item[HLA-A66:01] 3.0129\n",
       "\\item[HLA-B44:03] 4.1825\n",
       "\\item[HLA-A68:01] 4.2325\n",
       "\\item[HLA-B44:02] 4.7088\n",
       "\\item[HLA-A32:01] 4.9329\n",
       "\\item[HLA-C07:01] 5.8338\n",
       "\\item[HLA-C03:04] 6.0788\n",
       "\\item[HLA-C07:02] 6.1335\n",
       "\\item[HLA-B39:01] 6.5845\n",
       "\\item[HLA-B14:02] 6.69\n",
       "\\item[HLA-A01:01] 7.0888\n",
       "\\item[HLA-A30:02] 7.8713\n",
       "\\item[HLA-B18:01] 7.9391\n",
       "\\item[HLA-B40:06] 8.0204\n",
       "\\item[HLA-A23:01] 8.6577\n",
       "\\item[HLA-A80:01] 8.8069\n",
       "\\item[HLA-B41:02] 8.8073\n",
       "\\item[HLA-B46:01] 8.9496\n",
       "\\item[HLA-C08:02] 9.1435\n",
       "\\item[HLA-B51:01] 9.3142\n",
       "\\item[HLA-A24:07] 9.4074\n",
       "\\item[HLA-A24:02] 9.5366\n",
       "\\item[HLA-C03:02] 9.8001\n",
       "\\item[HLA-B52:01] 9.9084\n",
       "\\item[HLA-C14:02] 11.0083\n",
       "\\item[HLA-C02:02] 11.4616\n",
       "\\item[HLA-B35:01] 12.4842\n",
       "\\item[HLA-B40:02] 13.9009\n",
       "\\item[HLA-C16:02] 14.0219\n",
       "\\item[HLA-C12:02] 15.0647\n",
       "\\item[HLA-B08:01] 15.1122\n",
       "\\item[HLA-A26:01] 15.7462\n",
       "\\item[HLA-C04:01] 17.2873\n",
       "\\item[HLA-C01:02] 17.8662\n",
       "\\item[HLA-C12:03] 19.7214\n",
       "\\item[HLA-C07:04] 20.9814\n",
       "\\item[HLA-C17:01] 22.6899\n",
       "\\item[HLA-C15:02] 23.1787\n",
       "\\item[HLA-B35:03] 23.5018\n",
       "\\item[HLA-A02:11] 25.1339\n",
       "\\item[HLA-B35:02] 27.022\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "HLA-B13:02\n",
       ":   0.041HLA-A03:01\n",
       ":   0.2851HLA-B58:01\n",
       ":   0.3706HLA-B57:01\n",
       ":   0.4398HLA-B58:02\n",
       ":   0.5674HLA-A11:01\n",
       ":   0.6595HLA-A29:02\n",
       ":   0.768HLA-C16:01\n",
       ":   0.7775HLA-C05:01\n",
       ":   0.9473HLA-B07:02\n",
       ":   0.9728HLA-B27:12\n",
       ":   1.0098HLA-B27:05\n",
       ":   1.0327HLA-A02:05\n",
       ":   1.1447HLA-B40:01\n",
       ":   1.1637HLA-A33:03\n",
       ":   1.1876HLA-A74:01\n",
       ":   1.2684HLA-B50:01\n",
       ":   1.5264HLA-C06:02\n",
       ":   2.211HLA-A02:01\n",
       ":   2.2193HLA-B45:01\n",
       ":   2.48HLA-A31:01\n",
       ":   2.5699HLA-A30:01\n",
       ":   2.9787HLA-A66:01\n",
       ":   3.0129HLA-B44:03\n",
       ":   4.1825HLA-A68:01\n",
       ":   4.2325HLA-B44:02\n",
       ":   4.7088HLA-A32:01\n",
       ":   4.9329HLA-C07:01\n",
       ":   5.8338HLA-C03:04\n",
       ":   6.0788HLA-C07:02\n",
       ":   6.1335HLA-B39:01\n",
       ":   6.5845HLA-B14:02\n",
       ":   6.69HLA-A01:01\n",
       ":   7.0888HLA-A30:02\n",
       ":   7.8713HLA-B18:01\n",
       ":   7.9391HLA-B40:06\n",
       ":   8.0204HLA-A23:01\n",
       ":   8.6577HLA-A80:01\n",
       ":   8.8069HLA-B41:02\n",
       ":   8.8073HLA-B46:01\n",
       ":   8.9496HLA-C08:02\n",
       ":   9.1435HLA-B51:01\n",
       ":   9.3142HLA-A24:07\n",
       ":   9.4074HLA-A24:02\n",
       ":   9.5366HLA-C03:02\n",
       ":   9.8001HLA-B52:01\n",
       ":   9.9084HLA-C14:02\n",
       ":   11.0083HLA-C02:02\n",
       ":   11.4616HLA-B35:01\n",
       ":   12.4842HLA-B40:02\n",
       ":   13.9009HLA-C16:02\n",
       ":   14.0219HLA-C12:02\n",
       ":   15.0647HLA-B08:01\n",
       ":   15.1122HLA-A26:01\n",
       ":   15.7462HLA-C04:01\n",
       ":   17.2873HLA-C01:02\n",
       ":   17.8662HLA-C12:03\n",
       ":   19.7214HLA-C07:04\n",
       ":   20.9814HLA-C17:01\n",
       ":   22.6899HLA-C15:02\n",
       ":   23.1787HLA-B35:03\n",
       ":   23.5018HLA-A02:11\n",
       ":   25.1339HLA-B35:02\n",
       ":   27.022\n",
       "\n"
      ],
      "text/plain": [
       "HLA-B13:02 HLA-A03:01 HLA-B58:01 HLA-B57:01 HLA-B58:02 HLA-A11:01 HLA-A29:02 \n",
       "    0.0410     0.2851     0.3706     0.4398     0.5674     0.6595     0.7680 \n",
       "HLA-C16:01 HLA-C05:01 HLA-B07:02 HLA-B27:12 HLA-B27:05 HLA-A02:05 HLA-B40:01 \n",
       "    0.7775     0.9473     0.9728     1.0098     1.0327     1.1447     1.1637 \n",
       "HLA-A33:03 HLA-A74:01 HLA-B50:01 HLA-C06:02 HLA-A02:01 HLA-B45:01 HLA-A31:01 \n",
       "    1.1876     1.2684     1.5264     2.2110     2.2193     2.4800     2.5699 \n",
       "HLA-A30:01 HLA-A66:01 HLA-B44:03 HLA-A68:01 HLA-B44:02 HLA-A32:01 HLA-C07:01 \n",
       "    2.9787     3.0129     4.1825     4.2325     4.7088     4.9329     5.8338 \n",
       "HLA-C03:04 HLA-C07:02 HLA-B39:01 HLA-B14:02 HLA-A01:01 HLA-A30:02 HLA-B18:01 \n",
       "    6.0788     6.1335     6.5845     6.6900     7.0888     7.8713     7.9391 \n",
       "HLA-B40:06 HLA-A23:01 HLA-A80:01 HLA-B41:02 HLA-B46:01 HLA-C08:02 HLA-B51:01 \n",
       "    8.0204     8.6577     8.8069     8.8073     8.9496     9.1435     9.3142 \n",
       "HLA-A24:07 HLA-A24:02 HLA-C03:02 HLA-B52:01 HLA-C14:02 HLA-C02:02 HLA-B35:01 \n",
       "    9.4074     9.5366     9.8001     9.9084    11.0083    11.4616    12.4842 \n",
       "HLA-B40:02 HLA-C16:02 HLA-C12:02 HLA-B08:01 HLA-A26:01 HLA-C04:01 HLA-C01:02 \n",
       "   13.9009    14.0219    15.0647    15.1122    15.7462    17.2873    17.8662 \n",
       "HLA-C12:03 HLA-C07:04 HLA-C17:01 HLA-C15:02 HLA-B35:03 HLA-A02:11 HLA-B35:02 \n",
       "   19.7214    20.9814    22.6899    23.1787    23.5018    25.1339    27.0220 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort(hla_mut_mat[,\"BRAF\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 15 × 10540 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>A1BG</th><th scope=col>A1CF</th><th scope=col>A2M</th><th scope=col>A2ML1</th><th scope=col>A4GNT</th><th scope=col>AACS</th><th scope=col>AADAC</th><th scope=col>AADACL3</th><th scope=col>AADACL4</th><th scope=col>AADAT</th><th scope=col>⋯</th><th scope=col>ZNF283</th><th scope=col>ZNF479</th><th scope=col>ZNF529</th><th scope=col>ZNF543</th><th scope=col>ZNF575</th><th scope=col>ZNF595</th><th scope=col>ZNF613</th><th scope=col>ZNF652</th><th scope=col>ZNF682</th><th scope=col>ZRANB1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>HLA-A03:01</th><td>NA</td><td>     NA</td><td>10.5800</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>18.2336</td><td> 2.8489</td><td>    NA</td><td>    NA</td><td>NA</td><td>13.4889</td></tr>\n",
       "\t<tr><th scope=row>HLA-A33:03</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A29:02</th><td>NA</td><td>     NA</td><td>     NA</td><td>13.1534</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A11:01</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td> 3.1732</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A02:01</th><td>NA</td><td>     NA</td><td> 0.1228</td><td> 0.3138</td><td>1.2569</td><td>NA</td><td>NA</td><td> 7.4787</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>4.3014</td><td>     NA</td><td>     NA</td><td>    NA</td><td>17.202</td><td>NA</td><td> 2.1849</td></tr>\n",
       "\t<tr><th scope=row>HLA-A68:01</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>6.1595</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A02:05</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A74:03</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A30:01</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>14.7619</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A23:01</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A31:01</th><td>NA</td><td>24.0729</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>0.8689</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A24:02</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A24:03</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A01:01</th><td>NA</td><td> 0.1558</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>12.1352</td><td>0.8019</td><td>NA</td><td>⋯</td><td>NA</td><td>11.2051</td><td>1.7053</td><td>    NA</td><td>     NA</td><td>     NA</td><td>12.473</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>HLA-A66:01</th><td>NA</td><td>     NA</td><td>     NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>NA</td><td>     NA</td><td>    NA</td><td>NA</td><td>⋯</td><td>NA</td><td>     NA</td><td>    NA</td><td>    NA</td><td>42.3998</td><td>     NA</td><td>    NA</td><td>    NA</td><td>NA</td><td>     NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 15 × 10540 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & A1BG & A1CF & A2M & A2ML1 & A4GNT & AACS & AADAC & AADACL3 & AADACL4 & AADAT & ⋯ & ZNF283 & ZNF479 & ZNF529 & ZNF543 & ZNF575 & ZNF595 & ZNF613 & ZNF652 & ZNF682 & ZRANB1\\\\\n",
       "\\hline\n",
       "\tHLA-A03:01 & NA &      NA & 10.5800 &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA & 18.2336 &  2.8489 &     NA &     NA & NA & 13.4889\\\\\n",
       "\tHLA-A33:03 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A29:02 & NA &      NA &      NA & 13.1534 &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A11:01 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &  3.1732 &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A02:01 & NA &      NA &  0.1228 &  0.3138 & 1.2569 & NA & NA &  7.4787 &     NA & NA & ⋯ & NA &      NA &     NA & 4.3014 &      NA &      NA &     NA & 17.202 & NA &  2.1849\\\\\n",
       "\tHLA-A68:01 & NA &      NA &      NA &      NA & 6.1595 & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A02:05 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A74:03 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A30:01 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA & 14.7619 &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A23:01 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A31:01 & NA & 24.0729 &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA & 0.8689 &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A24:02 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A24:03 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA &      NA &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\tHLA-A01:01 & NA &  0.1558 &      NA &      NA &     NA & NA & NA & 12.1352 & 0.8019 & NA & ⋯ & NA & 11.2051 & 1.7053 &     NA &      NA &      NA & 12.473 &     NA & NA &      NA\\\\\n",
       "\tHLA-A66:01 & NA &      NA &      NA &      NA &     NA & NA & NA &      NA &     NA & NA & ⋯ & NA &      NA &     NA &     NA & 42.3998 &      NA &     NA &     NA & NA &      NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 15 × 10540 of type dbl\n",
       "\n",
       "| <!--/--> | A1BG | A1CF | A2M | A2ML1 | A4GNT | AACS | AADAC | AADACL3 | AADACL4 | AADAT | ⋯ | ZNF283 | ZNF479 | ZNF529 | ZNF543 | ZNF575 | ZNF595 | ZNF613 | ZNF652 | ZNF682 | ZRANB1 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| HLA-A03:01 | NA |      NA | 10.5800 |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA | 18.2336 |  2.8489 |     NA |     NA | NA | 13.4889 |\n",
       "| HLA-A33:03 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A29:02 | NA |      NA |      NA | 13.1534 |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A11:01 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |  3.1732 |     NA |     NA | NA |      NA |\n",
       "| HLA-A02:01 | NA |      NA |  0.1228 |  0.3138 | 1.2569 | NA | NA |  7.4787 |     NA | NA | ⋯ | NA |      NA |     NA | 4.3014 |      NA |      NA |     NA | 17.202 | NA |  2.1849 |\n",
       "| HLA-A68:01 | NA |      NA |      NA |      NA | 6.1595 | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A02:05 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A74:03 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A30:01 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA | 14.7619 |     NA |     NA | NA |      NA |\n",
       "| HLA-A23:01 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A31:01 | NA | 24.0729 |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA | 0.8689 |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A24:02 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A24:03 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA |      NA |      NA |     NA |     NA | NA |      NA |\n",
       "| HLA-A01:01 | NA |  0.1558 |      NA |      NA |     NA | NA | NA | 12.1352 | 0.8019 | NA | ⋯ | NA | 11.2051 | 1.7053 |     NA |      NA |      NA | 12.473 |     NA | NA |      NA |\n",
       "| HLA-A66:01 | NA |      NA |      NA |      NA |     NA | NA | NA |      NA |     NA | NA | ⋯ | NA |      NA |     NA |     NA | 42.3998 |      NA |     NA |     NA | NA |      NA |\n",
       "\n"
      ],
      "text/plain": [
       "           A1BG A1CF    A2M     A2ML1   A4GNT  AACS AADAC AADACL3 AADACL4 AADAT\n",
       "HLA-A03:01 NA        NA 10.5800      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A33:03 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A29:02 NA        NA      NA 13.1534     NA NA   NA         NA     NA  NA   \n",
       "HLA-A11:01 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A02:01 NA        NA  0.1228  0.3138 1.2569 NA   NA     7.4787     NA  NA   \n",
       "HLA-A68:01 NA        NA      NA      NA 6.1595 NA   NA         NA     NA  NA   \n",
       "HLA-A02:05 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A74:03 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A30:01 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A23:01 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A31:01 NA   24.0729      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A24:02 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A24:03 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "HLA-A01:01 NA    0.1558      NA      NA     NA NA   NA    12.1352 0.8019  NA   \n",
       "HLA-A66:01 NA        NA      NA      NA     NA NA   NA         NA     NA  NA   \n",
       "           ⋯ ZNF283 ZNF479  ZNF529 ZNF543 ZNF575  ZNF595  ZNF613 ZNF652 ZNF682\n",
       "HLA-A03:01 ⋯ NA          NA     NA     NA 18.2336  2.8489     NA     NA NA    \n",
       "HLA-A33:03 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A29:02 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A11:01 ⋯ NA          NA     NA     NA      NA  3.1732     NA     NA NA    \n",
       "HLA-A02:01 ⋯ NA          NA     NA 4.3014      NA      NA     NA 17.202 NA    \n",
       "HLA-A68:01 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A02:05 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A74:03 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A30:01 ⋯ NA          NA     NA     NA      NA 14.7619     NA     NA NA    \n",
       "HLA-A23:01 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A31:01 ⋯ NA          NA 0.8689     NA      NA      NA     NA     NA NA    \n",
       "HLA-A24:02 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A24:03 ⋯ NA          NA     NA     NA      NA      NA     NA     NA NA    \n",
       "HLA-A01:01 ⋯ NA     11.2051 1.7053     NA      NA      NA 12.473     NA NA    \n",
       "HLA-A66:01 ⋯ NA          NA     NA     NA 42.3998      NA     NA     NA NA    \n",
       "           ZRANB1 \n",
       "HLA-A03:01 13.4889\n",
       "HLA-A33:03      NA\n",
       "HLA-A29:02      NA\n",
       "HLA-A11:01      NA\n",
       "HLA-A02:01  2.1849\n",
       "HLA-A68:01      NA\n",
       "HLA-A02:05      NA\n",
       "HLA-A74:03      NA\n",
       "HLA-A30:01      NA\n",
       "HLA-A23:01      NA\n",
       "HLA-A31:01      NA\n",
       "HLA-A24:02      NA\n",
       "HLA-A24:03      NA\n",
       "HLA-A01:01      NA\n",
       "HLA-A66:01      NA"
      ]
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   "source": [
    "head(hla_mut_mat,15)"
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