notebooks/31_pMHCs_DeterminePBR.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><ul class=\"toc-item\"><li><span><a href=\"#Get-nonsyn-variants\" data-toc-modified-id=\"Get-nonsyn-variants-4.1\">Get nonsyn variants</a></span><ul class=\"toc-item\"><li><span><a href=\"#CBTTC\" data-toc-modified-id=\"CBTTC-4.1.1\">CBTTC</a></span></li><li><span><a href=\"#DKFZ\" data-toc-modified-id=\"DKFZ-4.1.2\">DKFZ</a></span></li><li><span><a href=\"#TARGET\" data-toc-modified-id=\"TARGET-4.1.3\">TARGET</a></span></li></ul></li><li><span><a href=\"#all-nonsyn-mutated-genes\" data-toc-modified-id=\"all-nonsyn-mutated-genes-4.2\">all nonsyn mutated genes</a></span></li></ul></li><li><span><a href=\"#Strong-binders-in-each-cohort\" data-toc-modified-id=\"Strong-binders-in-each-cohort-5\">Strong binders in each cohort</a></span></li><li><span><a href=\"#Presentation-score\" data-toc-modified-id=\"Presentation-score-6\">Presentation score</a></span></li><li><span><a href=\"#PBR\" data-toc-modified-id=\"PBR-7\">PBR</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Minimum-rank\" data-toc-modified-id=\"Minimum-rank-7.0.1\">Minimum rank</a></span></li><li><span><a href=\"#Count-number-of-rank-2\" data-toc-modified-id=\"Count-number-of-rank-2-7.0.2\">Count number of rank 2</a></span></li></ul></li></ul></li><li><span><a href=\"#Deprecated\" data-toc-modified-id=\"Deprecated-8\">Deprecated</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "library(ggbeeswarm)\n",
    "library(maftools)\n",
    "library(ggsignif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath,\"snv/total_mut_manifest_estimate.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mupexifiles <- list.files(paste0(datapath, \"Mupexi/\"), pattern = \"_all.mupexi\", recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "549"
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      "text/latex": [
       "549"
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      "text/markdown": [
       "549"
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      "text/plain": [
       "[1] 549"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(mupexifiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "all.mupexi <- do.call(\"rbind\", all.mupexifiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 34</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>HLA_allele</th><th scope=col>Norm_peptide</th><th scope=col>Norm_MHCrank_EL</th><th scope=col>Norm_MHCscore_EL</th><th scope=col>Norm_MHCaffinity</th><th scope=col>Norm_MHCrank_BA</th><th scope=col>Norm_MHCscore_BA</th><th scope=col>Mut_peptide</th><th scope=col>Mut_MHCrank_EL</th><th scope=col>Mut_MHCscore_EL</th><th scope=col>⋯</th><th scope=col>Cancer_Driver_Gene</th><th scope=col>Proteome_Peptide_Match</th><th scope=col>Expression_Level</th><th scope=col>Mutant_affinity_score</th><th scope=col>Normal_affinity_score</th><th scope=col>Expression_score</th><th scope=col>priority_Score</th><th scope=col>Norm_MHCbinder</th><th scope=col>Mut_MHCbinder</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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&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>⋯</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;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</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>HLA-A23:01</td><td>MFIFGVVGNL</td><td>3.1003</td><td>0.065651</td><td> 1530.4</td><td> 2.0909</td><td>0.322235</td><td>RFIFGVVGNL</td><td>1.3960</td><td>0.213971</td><td>⋯</td><td>No</td><td>No</td><td>-</td><td>0.9534695</td><td>0.0040640619</td><td>1</td><td>95</td><td>NA</td><td>WB</td><td>7316-10</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>HLA-C14:03</td><td>TIPAVMFIF </td><td>3.7203</td><td>0.174492</td><td> 3841.9</td><td> 6.4026</td><td>0.237164</td><td>TIPAVRFIF </td><td>1.4700</td><td>0.365306</td><td>⋯</td><td>No</td><td>No</td><td>-</td><td>0.9340110</td><td>0.0001837961</td><td>1</td><td>93</td><td>NA</td><td>NA</td><td>7316-10</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>HLA-A03:01</td><td>GPLPPPRPMR</td><td>1.9251</td><td>0.106137</td><td> 7744.2</td><td> 6.3183</td><td>0.172377</td><td>GPLPPPRPLR</td><td>1.6858</td><td>0.129295</td><td>⋯</td><td>No</td><td>No</td><td>-</td><td>0.8279261</td><td>0.5925458879</td><td>1</td><td>58</td><td>NA</td><td>NA</td><td>7316-10</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>HLA-C06:02</td><td>DVREPPALKY</td><td>2.1529</td><td>0.094989</td><td>17122.1</td><td> 8.5405</td><td>0.099046</td><td>DVREPPALQY</td><td>1.8401</td><td>0.107971</td><td>⋯</td><td>No</td><td>No</td><td>-</td><td>0.6898675</td><td>0.3176700658</td><td>1</td><td>58</td><td>NA</td><td>NA</td><td>7316-10</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>HLA-B44:03</td><td>REPPALKYGI</td><td>1.6853</td><td>0.060808</td><td> 9893.2</td><td> 4.1304</td><td>0.149742</td><td>REPPALQYGI</td><td>1.4515</td><td>0.079779</td><td>⋯</td><td>No</td><td>No</td><td>-</td><td>0.9394884</td><td>0.8282819887</td><td>1</td><td>55</td><td>NA</td><td>NA</td><td>7316-10</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>HLA-B58:02</td><td>VREPPALKY </td><td>1.3429</td><td>0.039176</td><td>36248.4</td><td>20.1432</td><td>0.029726</td><td>VREPPALQY </td><td>1.2016</td><td>0.044444</td><td>⋯</td><td>No</td><td>No</td><td>-</td><td>0.9818719</td><td>0.9639280118</td><td>1</td><td>51</td><td>NA</td><td>NA</td><td>7316-10</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 34\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & HLA\\_allele & Norm\\_peptide & Norm\\_MHCrank\\_EL & Norm\\_MHCscore\\_EL & Norm\\_MHCaffinity & Norm\\_MHCrank\\_BA & Norm\\_MHCscore\\_BA & Mut\\_peptide & Mut\\_MHCrank\\_EL & Mut\\_MHCscore\\_EL & ⋯ & Cancer\\_Driver\\_Gene & Proteome\\_Peptide\\_Match & Expression\\_Level & Mutant\\_affinity\\_score & Normal\\_affinity\\_score & Expression\\_score & priority\\_Score & Norm\\_MHCbinder & Mut\\_MHCbinder & sample\\_id\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & ⋯ & <chr> & <chr> & <chr> & <dbl> & <dbl> & <int> & <int> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & HLA-A23:01 & MFIFGVVGNL & 3.1003 & 0.065651 &  1530.4 &  2.0909 & 0.322235 & RFIFGVVGNL & 1.3960 & 0.213971 & ⋯ & No & No & - & 0.9534695 & 0.0040640619 & 1 & 95 & NA & WB & 7316-10\\\\\n",
       "\t2 & HLA-C14:03 & TIPAVMFIF  & 3.7203 & 0.174492 &  3841.9 &  6.4026 & 0.237164 & TIPAVRFIF  & 1.4700 & 0.365306 & ⋯ & No & No & - & 0.9340110 & 0.0001837961 & 1 & 93 & NA & NA & 7316-10\\\\\n",
       "\t3 & HLA-A03:01 & GPLPPPRPMR & 1.9251 & 0.106137 &  7744.2 &  6.3183 & 0.172377 & GPLPPPRPLR & 1.6858 & 0.129295 & ⋯ & No & No & - & 0.8279261 & 0.5925458879 & 1 & 58 & NA & NA & 7316-10\\\\\n",
       "\t4 & HLA-C06:02 & DVREPPALKY & 2.1529 & 0.094989 & 17122.1 &  8.5405 & 0.099046 & DVREPPALQY & 1.8401 & 0.107971 & ⋯ & No & No & - & 0.6898675 & 0.3176700658 & 1 & 58 & NA & NA & 7316-10\\\\\n",
       "\t5 & HLA-B44:03 & REPPALKYGI & 1.6853 & 0.060808 &  9893.2 &  4.1304 & 0.149742 & REPPALQYGI & 1.4515 & 0.079779 & ⋯ & No & No & - & 0.9394884 & 0.8282819887 & 1 & 55 & NA & NA & 7316-10\\\\\n",
       "\t6 & HLA-B58:02 & VREPPALKY  & 1.3429 & 0.039176 & 36248.4 & 20.1432 & 0.029726 & VREPPALQY  & 1.2016 & 0.044444 & ⋯ & No & No & - & 0.9818719 & 0.9639280118 & 1 & 51 & NA & NA & 7316-10\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 34\n",
       "\n",
       "| <!--/--> | HLA_allele &lt;chr&gt; | Norm_peptide &lt;chr&gt; | Norm_MHCrank_EL &lt;dbl&gt; | Norm_MHCscore_EL &lt;dbl&gt; | Norm_MHCaffinity &lt;dbl&gt; | Norm_MHCrank_BA &lt;dbl&gt; | Norm_MHCscore_BA &lt;dbl&gt; | Mut_peptide &lt;chr&gt; | Mut_MHCrank_EL &lt;dbl&gt; | Mut_MHCscore_EL &lt;dbl&gt; | ⋯ ⋯ | Cancer_Driver_Gene &lt;chr&gt; | Proteome_Peptide_Match &lt;chr&gt; | Expression_Level &lt;chr&gt; | Mutant_affinity_score &lt;dbl&gt; | Normal_affinity_score &lt;dbl&gt; | Expression_score &lt;int&gt; | priority_Score &lt;int&gt; | Norm_MHCbinder &lt;chr&gt; | Mut_MHCbinder &lt;chr&gt; | sample_id &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | HLA-A23:01 | MFIFGVVGNL | 3.1003 | 0.065651 |  1530.4 |  2.0909 | 0.322235 | RFIFGVVGNL | 1.3960 | 0.213971 | ⋯ | No | No | - | 0.9534695 | 0.0040640619 | 1 | 95 | NA | WB | 7316-10 |\n",
       "| 2 | HLA-C14:03 | TIPAVMFIF  | 3.7203 | 0.174492 |  3841.9 |  6.4026 | 0.237164 | TIPAVRFIF  | 1.4700 | 0.365306 | ⋯ | No | No | - | 0.9340110 | 0.0001837961 | 1 | 93 | NA | NA | 7316-10 |\n",
       "| 3 | HLA-A03:01 | GPLPPPRPMR | 1.9251 | 0.106137 |  7744.2 |  6.3183 | 0.172377 | GPLPPPRPLR | 1.6858 | 0.129295 | ⋯ | No | No | - | 0.8279261 | 0.5925458879 | 1 | 58 | NA | NA | 7316-10 |\n",
       "| 4 | HLA-C06:02 | DVREPPALKY | 2.1529 | 0.094989 | 17122.1 |  8.5405 | 0.099046 | DVREPPALQY | 1.8401 | 0.107971 | ⋯ | No | No | - | 0.6898675 | 0.3176700658 | 1 | 58 | NA | NA | 7316-10 |\n",
       "| 5 | HLA-B44:03 | REPPALKYGI | 1.6853 | 0.060808 |  9893.2 |  4.1304 | 0.149742 | REPPALQYGI | 1.4515 | 0.079779 | ⋯ | No | No | - | 0.9394884 | 0.8282819887 | 1 | 55 | NA | NA | 7316-10 |\n",
       "| 6 | HLA-B58:02 | VREPPALKY  | 1.3429 | 0.039176 | 36248.4 | 20.1432 | 0.029726 | VREPPALQY  | 1.2016 | 0.044444 | ⋯ | No | No | - | 0.9818719 | 0.9639280118 | 1 | 51 | NA | NA | 7316-10 |\n",
       "\n"
      ],
      "text/plain": [
       "  HLA_allele Norm_peptide Norm_MHCrank_EL Norm_MHCscore_EL Norm_MHCaffinity\n",
       "1 HLA-A23:01 MFIFGVVGNL   3.1003          0.065651          1530.4         \n",
       "2 HLA-C14:03 TIPAVMFIF    3.7203          0.174492          3841.9         \n",
       "3 HLA-A03:01 GPLPPPRPMR   1.9251          0.106137          7744.2         \n",
       "4 HLA-C06:02 DVREPPALKY   2.1529          0.094989         17122.1         \n",
       "5 HLA-B44:03 REPPALKYGI   1.6853          0.060808          9893.2         \n",
       "6 HLA-B58:02 VREPPALKY    1.3429          0.039176         36248.4         \n",
       "  Norm_MHCrank_BA Norm_MHCscore_BA Mut_peptide Mut_MHCrank_EL Mut_MHCscore_EL ⋯\n",
       "1  2.0909         0.322235         RFIFGVVGNL  1.3960         0.213971        ⋯\n",
       "2  6.4026         0.237164         TIPAVRFIF   1.4700         0.365306        ⋯\n",
       "3  6.3183         0.172377         GPLPPPRPLR  1.6858         0.129295        ⋯\n",
       "4  8.5405         0.099046         DVREPPALQY  1.8401         0.107971        ⋯\n",
       "5  4.1304         0.149742         REPPALQYGI  1.4515         0.079779        ⋯\n",
       "6 20.1432         0.029726         VREPPALQY   1.2016         0.044444        ⋯\n",
       "  Cancer_Driver_Gene Proteome_Peptide_Match Expression_Level\n",
       "1 No                 No                     -               \n",
       "2 No                 No                     -               \n",
       "3 No                 No                     -               \n",
       "4 No                 No                     -               \n",
       "5 No                 No                     -               \n",
       "6 No                 No                     -               \n",
       "  Mutant_affinity_score Normal_affinity_score Expression_score priority_Score\n",
       "1 0.9534695             0.0040640619          1                95            \n",
       "2 0.9340110             0.0001837961          1                93            \n",
       "3 0.8279261             0.5925458879          1                58            \n",
       "4 0.6898675             0.3176700658          1                58            \n",
       "5 0.9394884             0.8282819887          1                55            \n",
       "6 0.9818719             0.9639280118          1                51            \n",
       "  Norm_MHCbinder Mut_MHCbinder sample_id\n",
       "1 NA             WB            7316-10  \n",
       "2 NA             NA            7316-10  \n",
       "3 NA             NA            7316-10  \n",
       "4 NA             NA            7316-10  \n",
       "5 NA             NA            7316-10  \n",
       "6 NA             NA            7316-10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(all.mupexi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_tmb <- total_mut_manifest_estimate[ total_mut_manifest_estimate$group != \"TCGA\",]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a new variable percentage reads for total_mut_manifest_estimate_quanti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_tmb$percread <- 8.0947988*exp(ped_tmb$ImmuneScore*0.0006267)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Initial CNS Tumor           Primary     Primary Tumor \n",
       "              470               196                98 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(ped_tmb$sample_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ped_tmb_IC <- merge(ped_tmb, metadata_IC[,c(\"sample_id\", \"immune_cluster\")], by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<ol class=list-inline><li>764</li><li>33</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 764\n",
       "\\item 33\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 764\n",
       "2. 33\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 764  33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(ped_tmb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get nonsyn variants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CBTTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf <- mymaf_CBTTC@data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculate tumour VAF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf$t_VAF <- CBTTC_data_maf$t_alt_count / CBTTC_data_maf$t_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "0.03125 0.23913 0.32468 0.31363 0.39216 1.00000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(CBTTC_data_maf$t_VAF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf$Tumor_Sample_Barcode <- as.character(CBTTC_data_maf$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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$Variant_Classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf_sampleid <- merge(CBTTC_mafsummary[,c(\"sample_id\", \"Tumor_Sample_Barcode\")], \n",
    "                                 CBTTC_data_maf, by = \"Tumor_Sample_Barcode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "796"
      ],
      "text/latex": [
       "796"
      ],
      "text/markdown": [
       "796"
      ],
      "text/plain": [
       "[1] 796"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(unique(CBTTC_data_maf_sampleid$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset  snv dfs to include only samples in estimate df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTTC_data_maf_kb <- CBTTC_data_maf[grepl(\"oncogenic\", CBTTC_data_maf$oncogenic,ignore.case = T),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DKFZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "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$variant_classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "DKFZ_maf_nsyn <- DKFZ_maf[!DKFZ_maf$variant_classification %in% c(\"splicing\", \"unknown\", \"synonymous SNV\"),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(DKFZ_maf_nsyn)[colnames(DKFZ_maf_nsyn) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(DKFZ_maf)[colnames(DKFZ_maf) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "DKFZ_maf_kb <- DKFZ_maf[grepl(\"oncogenic\", DKFZ_maf$oncogenic,\n",
    "                                                ignore.case = T),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_gdc_data<- mymaf_gdc@data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_gdc_data$Tumor_Sample_Barcode <- as.character(mymaf_gdc_data$Tumor_Sample_Barcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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$Variant_Classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "subset to TARGET data and match with total_mut_manifest_estimate_quanti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_target_data <- mymaf_gdc_data[grepl(\"TARGET\", mymaf_gdc_data$Tumor_Sample_Barcode),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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$Tumor_Sample_Barcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_target_data <- mymaf_target_data[mymaf_target_data$Tumor_Sample_Barcode %in% \n",
    "                                       ped_tmb_IC$sample_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mymaf_target_data)[colnames(mymaf_target_data) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymaf_target_data_kb <- mymaf_target_data[ grepl(\"oncogenic\", mymaf_target_data$oncogenic,\n",
    "                                                ignore.case = T),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mymaf_target_data_kb)[colnames(mymaf_target_data_kb) == \"Tumor_Sample_Barcode\"] <- \"sample_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "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": 57,
   "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$Hugo_Symbol), stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "mutated_genes_all <- c(dkfz_allmut$Var1,cbttc_allmut$Var1,target_allmut$Var1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "allmutated_genes <- mutated_genes_all[!duplicated(mutated_genes_all)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "12100"
      ],
      "text/latex": [
       "12100"
      ],
      "text/markdown": [
       "12100"
      ],
      "text/plain": [
       "[1] 12100"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(allmutated_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "   SB    WB \n",
       " 7591 21680 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(all.mupexi$Mut_MHCbinder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strong binders in each cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     SB      WB    <NA> \n",
       "   7591   21680 1546095 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(all.mupexi$Mut_MHCbinder, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "bindersamples <- all.mupexi[all.mupexi$Mut_MHCbinder == \"SB\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_SB <- metadata_IC[ metadata_IC$sample_id %in% bindersamples$sample_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>459</li><li>24</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 459\n",
       "\\item 24\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 459\n",
       "2. 24\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 459  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_SB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp <- as.data.frame(table(bindersamples$sample_id), stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_SB$binders <- NA\n",
    "\n",
    "for(i in 1:nrow(tmp)){\n",
    "    metadata_IC_SB$binders[match(tmp$Var1[i],metadata_IC_SB$sample_id)] <- tmp$Freq[i]   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymed <- lapply(unique(metadata_IC_SB$cohort), function(x){\n",
    "    median(metadata_IC_SB$binders[ metadata_IC_SB$cohort == x])})\n",
    "\n",
    "names(mymed) <- unique(metadata_IC_SB$cohort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_SB$cohort <- factor(metadata_IC_SB$cohort,\n",
    "                             levels = names(mymed[order(unlist(mymed))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "SB_plot <- ggplot(metadata_IC_SB, aes(x = cohort, y = binders, fill = cohort)) +  \n",
    "geom_boxplot() + myplot + \n",
    "theme(axis.title.y = element_text(size = 25),\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",
    "     legend.position = \"none\") +\n",
    "scale_fill_manual(values = cohort_col) +\n",
    "scale_y_continuous(trans = \"log10\") +annotation_logticks(sides = \"l\")+\n",
    "labs(y = paste0(\"Number of strong binding peptides\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BIRaT3yw8F1bl2/dPLIwcMnbiTnTaFu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AqCQQ7AAAAAAqCYYDFCunnvuE7vbKvgkA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\nVim7MTFjY0f5AYQwc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0lqK3MZun9HC2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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SB_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "aov(formula = binders ~ immune_cluster + cohort, data = metadata_IC_SB)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       "-59.71 -10.02  -2.02   4.12 844.12 \n",
       "\n",
       "Coefficients:\n",
       "                              Estimate Std. Error t value Pr(>|t|)   \n",
       "(Intercept)                    -3.2172    19.8087  -0.162  0.87106   \n",
       "immune_clusterMyeloid-driven   13.1010     8.0425   1.629  0.10403   \n",
       "immune_clusterPediatric cold    2.9302     8.2007   0.357  0.72103   \n",
       "immune_clusterImmune excluded  -0.7832     8.8795  -0.088  0.92975   \n",
       "cohortpedLGG                    2.1344    20.0371   0.107  0.91521   \n",
       "cohortEPN                       4.9734    21.2268   0.234  0.81486   \n",
       "cohortCP                        6.0126    25.0711   0.240  0.81058   \n",
       "cohortETMR                      7.6437    39.1952   0.195  0.84547   \n",
       "cohortNFB                       3.1595    26.4388   0.120  0.90493   \n",
       "cohortMNG                       4.9656    25.0867   0.198  0.84319   \n",
       "cohortATRT                     15.4064    22.8724   0.674  0.50093   \n",
       "cohortSCHW                      7.7768    28.7804   0.270  0.78712   \n",
       "cohortMB                       18.3625    20.8798   0.879  0.37964   \n",
       "cohortpedHGG                   60.9964    21.4601   2.842  0.00469 **\n",
       "cohortNBL                      24.8963    20.2581   1.229  0.21974   \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 47.51 on 444 degrees of freedom\n",
       "Multiple R-squared:  0.1049,\tAdjusted R-squared:  0.07671 \n",
       "F-statistic: 3.718 on 14 and 444 DF,  p-value: 0.000006402\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binders_fit <- aov(binders ~ immune_cluster + cohort, metadata_IC_SB)\n",
    "summary.lm(binders_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AGAOVLHuGYV2+4VMA2YFQvmSVhXrNvgyZNv6bIag910WRoAQBfULXbK1vleCHYA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2fPIED4fQ6XR02yJPLS/sDEEEuGh1aHJ/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\nwvNsPtXvNes4jlm+bf30jnU5SXAE1AplmyWgEIQvF+PSo7TMPbfH4CuS8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gGAEhA7pVPwIWxR94Z0zaGqWfScdyZResV7pkC9OgWR+r8O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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "binderplot <- ggplot(data = metadata_IC_SB, aes(x = immune_cluster, y = binders, fill = immune_cluster)) + \n",
    "geom_violin() + geom_boxplot(width = 0.1, outlier.colour = NA) + myplot +\n",
    "theme(axis.title.y = element_text(size = 30),\n",
    "      axis.title.x = element_blank(),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text.x = element_text(angle = 45, hjust = 1, size = 30, color = \"black\"),\n",
    "      axis.text.y = element_text(size = 30, color = \"black\"),\n",
    "      plot.title = element_text( hjust = 0.5, size = 30),\n",
    "     legend.position = \"none\") +\n",
    "scale_fill_manual(values = cluster_col) +\n",
    "scale_y_continuous(trans = \"log10\") +annotation_logticks(sides = \"l\")+\n",
    "labs(y = paste0(\"Number of strong binding peptides\"))\n",
    "\n",
    "binderplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>459</li><li>25</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 459\n",
       "\\item 25\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 459\n",
       "2. 25\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 459  25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_SB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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(file = paste0(plotpath,\"Immune_clusters_SB.pdf\"),\n",
    "    width = 10, \n",
    "    height = 10,\n",
    "    useDingbats = FALSE)\n",
    "binderplot + ggtitle(~underline(\"pedNST (n = 459)\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgg <- metadata_IC_SB[ metadata_IC_SB$cohort == \"pedHGG\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_SB$binders[metadata_IC_SB$cohort == \"pedHGG\"] and metadata_IC_SB$immune_cluster[metadata_IC_SB$cohort == \"pedHGG\"] \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  0.115              -              -             \n",
       "Pediatric cold  0.806              0.020          -             \n",
       "Immune excluded 0.976              0.026          0.720         \n",
       "\n",
       "P value adjustment method: none "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_SB$binders[metadata_IC_SB$cohort == \"pedHGG\"],\n",
    "metadata_IC_SB$immune_cluster[metadata_IC_SB$cohort == \"pedHGG\"],\n",
    "                p.adjust = \"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>35</li><li>25</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 35\n",
       "\\item 25\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 35\n",
       "2. 25\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 35 25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(hgg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(c(1.32221929473392, 1.68124123737559, 1.34242268082221, :\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(c(1.32221929473392, 1.68124123737559, 1.34242268082221, :\n",
      "“cannot compute exact p-value with ties”\n"
     ]
    },
    {
     "data": {
      "image/png": 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htGtZoO9f03JDI+PbcgN+3zm/Bgv7mDm2mXe0S1jW94vuL3ycVL7BISEoT+bQOQ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A24i66ursT+Wfoss/RVdnfyLqqlksWFeS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tOP0kopb4jk/Q8zp9WHnyN2rXJKPr19eDlo\n+9+zvHeDQQGqgLBTNrhCKwc79fZV8u08Am3XzPmftxQaT+PBaTu4bNQNus5XNG65Cd9+Gm7B\ntAVZzv4+iDCg7WTBz8TQVcMtCZrAaZpNOPqO9PaoDtruyrlnMD0ZoiL3aZCPm6k2wXRCEBVd\n6zGbb6dTWl2fLjHgH6i/IIrpEwcaECDslJzqyiYiQ7WEtR2rjcfRj7Sz56u1Hag6aUDP+coN\nn62PuXx080+m+37vVnesFnPauGsSeF4ZpfjFgfEWjQlFAI8mPUVoawra7sfzLRO8L2SJa0IG\nSINfCcGTLQjT0nBo/T4hMF7sEvnztDvGtN23fJTFJwAaBiDslBlcrXOa2k7FcFRwsgTa7nAQ\nqDopQcf5WnZrmi7GevqjzlLej+dRFDiQd6jpoqM7HKtfCzQ6k3Byrs+1FiXqqhHpNyXUdqOP\nJZejKMrJj/DtqYMgiGqHiaDtGIadeWVedwK3K5m46zzzSoZo5+qHdVb88apuwTATAWkBwk55\nEaxXJyNtBzCEaOcrN35lR6zx9EaczqXz6oUBA6qvIBOuoKVxOxx1dUDVMQg358KE3wQSHBsb\n9hg2ZY73tBF2BgK1LuhqO0TrN3sXx47N+IGTqh0mXsyGllRMkXdtWgdBExg7eM76e8uOrX6+\nCye5WjYXbvrbdtjhBBFpUZxrkzDO8+Ze4WA+QEqAsFNmCOLnQNspJxScr7lBg3HG07L1e06h\nQRwPbpR3q+rjRp2tQFG0NDcXVB1jlDxZ0VUTa6wmnaYdfJpf68EpSznjZYWfirS1HR5wvjIH\n5+M+Z5yvrmn32Udi8vD+uJLk635DTTQFzKI/cG886XqctRsb+NrR7x3jnwRoIICwU3JA29UH\nqDlfOUnbeuIvHPqDDiRQipD7eXNqTcETndnhDHwCAMOPCG8jjJXUjEYf+yBkJm7OyeH6OGtK\nru2a9l7/FFQdU2QEOGI21NWMRx9NIFtffzxebIVPP9e22/SCNPk5akFLzFCXQ18Y+gRAQwOE\nnfID2q4eQM2I7IStNgItxnUdNjwpFPPi3x/9ZalWPZ7V/zC0I2CUqtilpvyLO8tg1NlMkvC3\nf5e3F5RoorXdp2tzrbUEjmDpD9j2HFQdY5SHzcTU99YbdCSFNHKu9NVOJ12cNe1WRonKOv8e\nPAgjA5tMvl7FwAcAGiAg7OoFoO3qAZSMWJV63KMVfhCibuS+5UEe2TWhMGaTM98zpDYgII/5\nj9KQ+XZmJD9ySsXKN5bUpcqJWtBaUNiJ03bor4+hqz2seAFdLG1TtzU302DaMkjpxVF8d13T\nYSGks4e2qkNRFI1fiak8jnRY8UrKZw80UEDY1RdA29UDKBnxV+wGe0G3DcLSsRq17tzLfNyu\nT2nK7e0jzbC7twazw6HHFKN8O+TC3y81mHGXXNal7q8J3FLXEHDDiq0vyC5Mf//242coVMM0\nVTen8mW6sU8sSX6DeFVX8iL43GshXx/74p/Y+e4Mm7GAVABhV48AbVcPoGTEwkd+/XQRAlR0\nTByGjfeau3DeFI++FnoCIVmGEy7DNixNyt8HTBxDpZ8vj5/nhvNFmv7cSLIaJL+er+pWM1DV\nPfDp/sECflioHa0QJGJauhr89YhwDBVVt6lvc0Sz74EMwWNTt/eoOQrSXwCpAcKufgHarh5A\nyYjlicfHGgsUzRCJls0qaEdAk9quviInEZY3a835X3mXrSR1pAuuT2lXG1yl7XG2CEXLEsRo\nu8L8ArE9JwBp82Ae3yh9934SHkBV1SEIgiCdhBNf789pDqoOkDYg7OodUtB2lmtfQxSvXKFk\nRG5hXNBMOz2hJrDCqBgM2hIjLsUCwFOr6mq+/52vRBQlqyZhvSX/a9ef94DAY1eVfLA/v/+7\napd1r6oFW9m7nU7NEBzaPf5+WIii6K83/o66lPqJAVIlwpsfnyos7KjE1cX4YDKkrdZ9EHiJ\nb4f7g6oDpA0Iu/pInbQdrDKKAUUjcgqiAxe5WekKlUetEQ76NhP97+eCUKdLRexqa8z3T7Gr\nb9nlMZjSGPoTrwpOpdJnyzrzN2tZZotjsGox/8LIFggejXZ9PFwseG2aqfWKBaTHq1UWtZZo\nt+gJ9imK2RKVkXP5Tj+W6zHBEQnnoZcPIG1A2NVTJNR2oOoUCRpGLMuKPr9/g88MT9feNt1t\nbO3sHVzHeK/ec+pBGiRLSEzt909R1aEoipZeGovxurFM5kdi51N+6Pi2GNFt4fNYsMR0wnpr\nEonOo/X8h6DsZMf340P5TluLlfE1yRM0cmArL4zkx7q2W/ZcdicPNFhA2NVf6Gu7PQGg6him\nqjg56uLhPVtWL/GePs1rzkLfbYEXH6cUkTrU6BoRkC4F4fPtbCmrOhRF0aq3G7vUXMhV2o0M\nSakNWOWmBbo25duxca9tbwiCWWNXGCGk6LoF50jjYwFUSd7aBTP1xl4qRGlXNnm2iC/mm866\nJ7tzBxosIOzqNSALFAbOt7hgn2GdWwoUtuCh2bb35G03UojtAkaUL1yhEhfs1NtXRXz/FXHr\nbTQEVR2Kph10xLQG1R1yJJOwdkbGLjsEQRBEy9y6A77PiFbXJQ8hUFLWfFhnhbFB65FHb9Kt\nV3dvOl/Ot8Zv5wIAI4Cwq++ALJA/P98c9eqmKzbJQb394HX3sokyksGIikN1V1+R3z8n68wC\nb5yqQ0uvTcK0j9L1OEtW6bamHYX21JuliaF+ExxM9BprtbAYODfoNbjT5UHmvn5qCBkUqhD/\n59+NP77P/lzZnDXQoAFh1wAAWSBHqnJuLrJtglCFpd9vXWQ+gSsHjMgs5e8D/hwqtnRctaqj\n//1/DnDkB1ppOO5PJxuYVlPYTA96+ioIPyNmGEqq6lDuOz9M0KS57xuZnDLQwAFh1zAAWSAX\nuDkXJxoL5KuytNvZDBw+dsK4UYN7mekSVaLTMJt6MZ0g6g6MyBS1lU1ElwVmP19hoSLR958X\nMIB/oJZnKNkxVbE+ptWjum1OkuizANLn85mReoKzVLPbMrGqDkU/HRuEuauz3vBeBmcLACDs\nFIyK4tzsT5/zi8pIetdIDoEsMFocXSH+QEBCyl+us8FFSWlbTdh1N7UUY9rKgrgLG0ZbCnn0\n1Exn3ykgeEnQdtJHoF6dSG0n6fd/d0Zz/hG9dhEUuuW9/An3mmZxOl53oESN4pB/cayBwBzV\n7eP7sEDkMv0rzq8nJqxSx+MsdAwDZAIIO8WgNC3i8ArP3uYtNKuvMBr6vw8Yv+JIdI40m0BA\nvToZUv78b6x7R814zPFEkt6elZ/urHLUF4jBa2K/JY5oPGg7KfMjZn3vprjvU+rarvyEO2a4\n9YaPRIO4n86PalMzpvHIC6V1/WCANCmN+burgN0R9fZDNoZlES7R3C+P1vbF1iTUdtqfLPW7\ndQAgBISd3Cn779qqgW3JmkM1MnZdFpoivuI9VaBenYzI+Kc3xqZ6g4NSRTtgqjJCZ3TEZ0Ei\nhlNuE+oLcL5Kme9P/ZjVdlcmYLOhrdbGC4mBstcb7bX4LzfpmvTmPCAduJ9vepkLL9RNLNyX\nHolMLq4tL1j+OebYEmf8kt5iaHAWyDpAVoCwkyvcvLs+dk2FlgpBtDtNP/ZGsJKpxEC9Oubh\nPl+GqUZmMO0WpX7uX8IXWOLLodzSqn8AACAASURBVLSbfvs74VBwvkoZZrVdfsAArENWzcLr\nejZf6P/675pPb0wJDRXrje8IepEBcoebe3uedWOECNWm7TrZ9LC3+71VI8H8dy3bVc+ktnwD\ngHhA2MkPTubZ8cbkifT4a8a2F7AzIz/K3wd4DvK5/5XyAc+W/sa/ivf+J4PqcZzMM574WJ52\n8x+QbOCC81XKMKntvp4fgW8Dy9KxdJu1fM1qHy/3rvq4VUCl45LHsLGuuPx4tW+kMdkOixCs\nls7rn1BfOABAGoCwkxelUUuscKuDSgu7CasDLt6+H37rbODGue7Weqqg6hSB2uD6xt2WUNR2\nubv78A3baxdlXYeiKFoSMc8Ue8uv6XqYKI0CRVFwvkod5rQdN/l/zlSq3jTtvTW+nMFPCEiD\n76+PzemhryrGlqot+ywKzZBmlDQAUAKEnXz4cW92W+wKYOi2+99vAjEYJR+vrhnUxQVUnTwR\nSJmkqO1iFvILXzWedovme5Y9mW+MvT5YQZEEGcKctvt6a5a5aA+9To/lD78x+NkAKcItfn95\nq5dTh6YqQnZU1THpO3HjtSTYfwXkAwg7ecB9u8aKvxqw2o05nwmlDRQSbvoJDwN8yAwVbfdg\nNr/sFWv0OdppDV/OjNTBKrt1byU7fUAiGNN23IKI1Y4thIUAgiCsZl2mHU0g2XUHFJiKgsTo\nG6cP793pv2P3gSMnzt14klTIFn8YADAHCDs5UBk2E3PdN138BCJqFJiKj4eH0dV2L5cZ102W\ncZ4twiRfsJwPQAEs2cLgnmzR2/N+U1w6VcfYqzVpYzVw6toTz/Ph3g4AAKkAwk72cO/P0a9d\n/hv9cUyyyNqvWVmgB2UEbW1XGDgQM5akbplo3vl1wrzbxJtQK0HWMF4Dpar8x/dfIOcAAJAy\nhLsCAF0qPgRO+nN73C9Kg9NjY7/WPujt7t5CxFgSvj1YPtBqwNqnZfQPBeijYTE7NOoQTtuV\nxe/xcPGJ+EZ8gG6vXh35j94dC3xaSfc9Ow8YwG8aX/b5czHdFwBow/n2+sL2vzz723U2N2pn\nZDPzWqE2rvrMzxfb3AatekxsipauBx7cwGu74kcrXdzIlwVVzaY6jcUF4AN1g1OYGB6yfanX\naNe+PW1sevR1Hem1fOeZx+k/UXmfGQAwh7yVZT2gNr6eYgOAh3P4Uk598nXa7/c1cllXLQRB\nEJWOy2Mh5Upm0PHbcWNX/IYZ18j5QDbdt/uw0Yr/Ai5Hi+v+AQARFL04ONmKStoqMz3HAKlT\nkfs0yMfNVJvQiCq61mM2306H/GOgXgLCro4IZE02d9r5SlzJ+LtTMWuNx0l68dLcd/42/AL1\njYeFQPCVDKGh7T5u7oZ1h+sMPpxOazeVe38Ov72ozvR7sBXLHOyMC9MsBbp+gLZTZn4lBE+2\nEGwAJozW7xMC46FcEFDvAGFXNypiV1tj1g9NqwVh+eKOiV7Qhn9E24WP6b3jz8hZmEIprG6b\nkyU9d0ASKGu7osvj9bCjtHtte01dxLOjFrSrPVTV9TAUwWCMvKtTO+A3RLXNh3hvOHDuZvj9\nyPDrIXvWzRpsLuTLA22nqLAzr8zrTsn3yhN3nWdeyYAsVqBeAcKuztQu4pRUHYqin/c4YNaV\ndguiaa4qydtsMcKiz/4ciU4bkBiq2i7zYH/cPhCrzfCQNErG5qYf6MdvXKQxOIjK7wqQAE7K\n//pjRYCqwaDtj4UTVDlFCReWObbGhySDtlNA8q5NE5DpiJaxg+esv7fs2Ornu3CSq2VzocBy\nlbbDDidAb16g/gDCThoUhM+3s6Wm6lAURf9dboK9PIw4TdMd859/N/7hGuNCIc5O5lDTdtyM\nIBd8IykVQ7cDb8QVnP71an1P/nY70mZmBFx0GOLz8cFYWdfSPSSDPE2V8+XJtsGtcVanq+2M\nFkfTLmoIUIbzcR++vUfT7rOPxOThb6ZKkq/7DTUR3HnXH7g3HlQ3UE8AYScluEIxUOzU21eJ\nb9C5MT7tsGuK0ZyH9JpL3JuOKcMwBOLq5QI1bffl1gxjAQ9B0y4zjpEH9nyL3uDcEjO62fBT\nn5n+KA2WOF8LzFdtODNMbLwVO/X8NHNcqix1bQddfRkmI8CR7+dG1IxHH00g02o/Hi+2wk9e\nbbtNL0B0A/UDEHbMwE4N8TRSId18SdrYFXu1VzGdc4+OOsNF6f1x7Lu0zhqgByVtV5l4yK0l\nIoB6O6e5/7uf9hN3O1D68fqW4R1wqsFgzIU8mX6kBsWrFR0wOsBhL8XM5dxrU41xu31UtB2o\nOqYpD5uJWRb1Bh1JIQ17KH2100kXr+pWRpFaEACUDRB2DFCt6ngQa7uiKxPw5ev0hwWncSi+\nfslJN/51xdAnRrpnD9CBkrYrSzgyqj1RwbLGBt2GjJ81b/HShV7D+1roCgzR7rH2OXSbZI6C\nAEfMt91jeyrlI9lJgYP1BGwlUtvtCQBVxzClF0fx3XVNh4WQ3hCBqgPqPSDspA77+QoL/OYb\nobbLPuSCL2uvZjbrJqVNty8n3PmRO9p/XoH+kvKFkrbj5NxZ3I16qh6CNLFZGilZTxKAIvFr\nzPjft8aUG7QO/nJpXBu8xURqO4Bhqm5iqkgZ+8SSlAcSr+pKXgSfew1ZsoBSA8KOASgmxGUd\nH6KLH6VhPvW8uLTJorBp7flHtJp1D+JC5A+1eLufHy8v72+oJl7UaZiOPvAaPDxMUydhh6KF\nl8cINo0BbSc3Ejd1rTWDwV+PCMdQUXWb+jZHNPseyJDJSQMAM4CwYwZq2u7r3TmCqfmsVs6+\nt7LI8lxLXvs7Y1YmdZvtiVC4VjGgWgPle/xp31HdW6mTaLomZn/8ff4DvVwaQDK+BDphvnn7\nnek0j+e+W9+FJWhA0Hby4cG8VrU26Lv3k/AAqqoOQRAE6eT3TlYnDgDSB4SdNGCnhd18I1iS\ngpK2q0w64t4KEUSn05+bLr//jtNsnML4kBnW2LpoqtZrXkKlE+ao+vbh3gl/n+mjXBx6dO9u\n5+AyYvqyHacfpZWSaWkafSkq815eObh52ayxQxxsunS27t6r/7DJizYH30ssohpoCdSdOGzd\nIQ23YNqCLGd/HwJxDtpODkR464sQdlTi6mJ8jPjPW637ILNTBwBpA8KuznA/nRtrpIK0cN0r\nmbYrexvg0ZYosr6RgY3HDJ81m3dsW7twsmvHpnjJoDtwXyLIOmaQvMsknX6ygLzhhs/Wxxi2\nmz/tLi7vVnesFgrauF+LgXcEhL7KlFer+JVr2i16gn2KYrZEZeRc/j02y/UYaHNAaQFhV0fY\nrzfa1NS6lFjbcbJvLOhCLCOI0eq67DFUr2OEOneZBG2nPJTdmoa94uuPOkuzxUdR4EDeoaaL\nju5wrH4tqGwiB74fH8qfdBYr42vc6jRyYCsvjOTfYLdb9lx2Jw8A0gWEXd3IDnTClDBXN1sY\nJVzahFq8XWni+SWObYg8dwKoGLhueQZ3k0wgrS6ToO2UBW78yo5YM+mNOJ1L5/jCgAG8A1Un\nXEFL43Y46uqAqpMPyVu7YBbYsZcKUdqVTZ4t4rfhbjrrnuzOHQCkCwi7OpG81aZ2JWAZep7P\nJomQopgnyy1+fcrX04Y0sl61he30/U8LIAyLEaTaZRK0nZKQGzQYNzO1bP2oFw/kRnnX7N6N\nOluBomhpbi6oOjnxYZ0Vxo6tRx69SbdeHbajT2v8di4AKBMg7OpCabBbzaVetfOqFyKjamg0\nBa/Me3nt0NYVMz1delp3NDM172TTb+ikxdtPPcqEuB2mYKDLJGg7pYCTtK0n3qT6gw4kUJpp\nP29OrSl4ojM7nOkTBcSQua8feS0hClWIcT24++yn5boFAEUChB192GnPX/JCcfj3iG287ojv\nIE1D2wGyhaEuk6Dt5Af1pGZ2wlYbXCM3BNF12PCkUMwbfH/0l2WNkGD1PwxGlTs/I2YYSqrq\nUO47P2v+Eea+b2RyygDABCDsaMJrF9ao019h+SgaW5Mg32JuJLX9UdB2igiTXSYJtF3rmWFg\ncAahn9RclXrcQ7DqkLqR+5YHeVUk71EYs8mZn1GrNiAAWvoyCftbcvSNM0cDDx07e/NpSiHp\n9Px8ZqSegB0RzW7LKHQM+3RsEMZjb73hvVTPHwBkCgg7OmCbwDbq9FdYhG91ir1jQAHlFwFt\np2gw3WUSp+0gZZJRJE5q/hW7wV5LcBhLx2rUunMv83H+2NKU29tHmmF3bw1mh8P0ZQh27qN9\nXr0NcZvlmm37ztr/NJ9QdOdfHGsgYEXdPr4PC0QWcv8V59cTcx+g43H2CzOfBgBkAgg7GqTs\ntMcF1zcyNa3Ooup3kFaZBNB2ioQsukxWaztQdUxS16Tmwkd+/XSJRqromDgMG+81d+G8KR59\nLfQEMmwMJ1yGbVhG4OTeWdFHT6i5Bw+Wbs+lN7MIxF1pzN9dBaW9evshG8OIG/pwvzxa2xfb\nG07baX8y9PMBlBoQdhi4lZVkOy88CLbVeOjPf0jzvUDbKQwy6jJZ8fHKuWeg6hhDKknN5YnH\nxxqTJaUToWWzCozKCJyMM+NMxDRWZrV03flCOIeZ+/mml7mwFZtYuC89EplcXCvmyz/HHFvi\n3BY3ssXQ4CyQdYCSA8KuBm7+rbnW5mNOppHGb6AoSqrtjBZH014NQNspBtBlUvmRYlIztzAu\naKYdmZ8Ii4rBoC0x4lIsAIn4EbnQUoyqq7G0/SqCBm7c3NvzrBsTHqDatF0nmx72dr+3aiRo\nZC3bVc8o17oBAIUFhB2KojxVZ6WJIAiiYiSs7djl5djUCEJt12zYGZpF61EUtJ1CAF0mlR6p\nJzVzCqIDF7lZ6Qr5+GrEgb7NRP/7uaId/ICkfL/jhQuUU9O3GbN40679ezYtmehsIhQKSdKc\n98erfSOpu19ZLZ3XP4E9daBeAMIOq+p44LUdOzXE08R84oUscdqu9YhT2RL48Am0ndHiaOLq\nGQATQJdJJYe5pOayrOjz+zf4zPB07W3T3cbWzt7BdYz36j2nHqTBvRdzcN+stuQvrmpGHv+L\n+455mp3zcLu7kYBgI9F26PfXx+b00BfX0Ee1ZZ9FoRnQehuoL4CwQ1FO5vkJJvipX6Pt+Gmw\nqh3EaztN64USlSjDaTuIr2cMduqL+G/C/4Yuk8oN00nNgGxhY1zoLIMRp9MJ/KLcgshltk0R\nHGTaDuUWv7+81cupQ1Nh/6uqjknfiRuvJcH+K1CvAGGHoiiKVmWeI9B2/v61xU0QBEFUO66I\nwd3USbH8bLW2A1XHGOzUEE8jtS4bEoWfgi6TyowskpoBGfJs6W81ss5kYVQp6bjip6t7UtV2\nKIqiaEVBYvSN04f37vTfsfvAkRPnbjxJIq+IBwBKDAi7Ggi0HQ51sylXhFvBSlXb7QkAVccM\nfM+r/vTbwrto0GVSiZFRUjPAKOyslAxe/ElJ0OAaI404XSz6KNraDgAaBCDsMJBrO2JVxwPa\nRik4VWknMZ5X9b57s4SGQJdJ5QWSmpUenje9zdCgpAoUTdtqwzOF2vBTFHZIQdsBgBAg7PAQ\najvNfv9LF9kxDLSdIsN5uwaXB9lu4WOh3BToMqm0QFKzcoNp58NqMzQo6cOW7jxLGK98Se0V\nQNsBAB4QdniwTcP4ENVAEQC0nQJTeGFMM4xhtIYcFe4AB10m5Q/727vws4E7/Xzmz1+8Yu3m\nfacffiwSW1EEkpqVGfbzFRbY9ZbVxqF3B96fZmviKb8MaDsAwADCDgOxqgNtp/xw3/tZYw1r\nvipOWDBAl0n58Svlyrox3VtpCM079ZZ2U/c9yhUx8yCpWbkhqPfEo9HEq3RSG0DbAUANIOz4\nCLSCVW/UiLgGighA2ykqxaHjmmOs0mz0OYK4bOgyKQ++Pt72h7FgPwg8TbrMuZhONvUgqVnZ\nIdF2Jkuf0ZtNoO0AAEVREHY4MLJM3WzKlYw0ohoooO2UE27ixq4Ypx3LevMHgqsGdJmULRXJ\npyebCfvpCGC1GugfS5w0DknNyg+httMdd41umQDQdgAAwk6Qio+Hhxlo1ObAEte3A22nlHy/\nOhEbRac/5QZh+wDoMikryt8HDG1FoScr32R/HE4lirmDpOb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JEkD1xqpNUQAAFaRJREFUKH+wIxWW7fL+9bQW2HKah2GPFVGfmR4CJMOQ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\nD7Gjvq8ioVklAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hggplot <- ggplot(data = hgg, aes(x = immune_cluster, y = binders, fill = immune_cluster)) + \n",
    "geom_boxplot(outlier.colour = NA) + geom_beeswarm(cex = 3, size = 5) + myplot +\n",
    "theme(axis.title.y = element_text(size = 30),\n",
    "      axis.title.x = element_blank(),\n",
    "      plot.title = element_text(size = 30, hjust = 0.5),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text.x = element_text(angle = 45, hjust = 1,size = 30, color = \"black\"),\n",
    "      axis.text.y = element_text(size = 30, color = \"black\"),\n",
    "     legend.position = \"none\") +\n",
    "scale_fill_manual(values = cluster_col) +\n",
    "scale_y_continuous(trans = \"log10\") +annotation_logticks(sides = \"l\")+\n",
    "labs(y = paste0(\"Number of strong binding peptides\")) +\n",
    "geom_signif(comparisons = list(c(\"Myeloid-driven\", \"Pediatric cold\")), y_position = 3.1,\n",
    "                     map_signif_level=TRUE, textsize = 20, test = \"wilcox.test\", vjust = 0.5) +\n",
    "geom_signif(comparisons = list(c(\"Myeloid-driven\", \"Immune excluded\")), y_position = 3.5,\n",
    "                     map_signif_level=TRUE, textsize = 20, test = \"wilcox.test\", vjust = 0.5)\n",
    "\n",
    "hggplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(c(1.32221929473392, 1.68124123737559, 1.34242268082221, :\n",
      "“cannot compute exact p-value with ties”\n",
      "Warning message in wilcox.test.default(c(1.32221929473392, 1.68124123737559, 1.34242268082221, :\n",
      "“cannot compute exact p-value with ties”\n"
     ]
    },
    {
     "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(file = paste0(plotpath,\"Immune_clusters_SB_pedHGG.pdf\"),\n",
    "    width = 10, \n",
    "    height = 10,\n",
    "    useDingbats = FALSE)\n",
    "hggplot + ggtitle(~underline(\"pedHGG (n = 35)\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Presentation score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only one mismatch\n",
    "onemis_mupexi <- all.mupexi[ all.mupexi$Mismatches == 1,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "      0       1       2       3       4       5 \n",
       " 101667 1141300  137512  126593   65213    3081 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(all.mupexi$Mismatches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "?write.table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 100 × 25</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>HLA_allele</th><th scope=col>Norm_peptide</th><th scope=col>Norm_MHCrank_EL</th><th scope=col>Norm_MHCscore_EL</th><th scope=col>Norm_MHCaffinity</th><th scope=col>Norm_MHCrank_BA</th><th scope=col>Norm_MHCscore_BA</th><th scope=col>Mut_peptide</th><th scope=col>Mut_MHCrank_EL</th><th scope=col>Mut_MHCscore_EL</th><th scope=col>⋯</th><th scope=col>Amino_Acid_Change</th><th scope=col>Allele_Frequency</th><th scope=col>Mismatches</th><th scope=col>peptide_position</th><th scope=col>Chr</th><th scope=col>Genomic_Position</th><th scope=col>Protein_position</th><th scope=col>Mutation_Consequence</th><th scope=col>Gene_Symbol</th><th scope=col>Cancer_Driver_Gene</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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&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>⋯</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7381</th><td>HLA-B15:02</td><td>APPTRGPPP  </td><td>32.4167</td><td>0.000276</td><td>37281.8</td><td>62.7439</td><td>0.027128</td><td>APPTRGPPP  </td><td>32.4167</td><td>0.000276</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>9:9  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7382</th><td>HLA-B15:01</td><td>APPTRGPPP  </td><td>67.0000</td><td>0.000047</td><td>41264.6</td><td>86.4839</td><td>0.017747</td><td>APPTRGPPP  </td><td>67.0000</td><td>0.000047</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>9:9  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7383</th><td>HLA-A11:01</td><td>APPTRGPPP  </td><td>57.0833</td><td>0.000046</td><td>42153.6</td><td>80.1349</td><td>0.015777</td><td>APPTRGPPP  </td><td>57.0833</td><td>0.000046</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>9:9  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7384</th><td>HLA-C03:03</td><td>APPTRGPPP  </td><td>34.2667</td><td>0.000160</td><td>37735.9</td><td>40.4554</td><td>0.026009</td><td>APPTRGPPP  </td><td>34.2667</td><td>0.000160</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>9:9  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7385</th><td>HLA-A02:01</td><td>APPTRGPPP  </td><td>65.0000</td><td>0.000036</td><td>42610.8</td><td>81.4870</td><td>0.014780</td><td>APPTRGPPP  </td><td>65.0000</td><td>0.000036</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>9:9  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7386</th><td>HLA-C08:01</td><td>SAPPTRGPPP </td><td>53.4615</td><td>0.000031</td><td>41049.5</td><td>44.3978</td><td>0.018230</td><td>SAPPTRGPPP </td><td>53.4615</td><td>0.000031</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>10:10</td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7387</th><td>HLA-A11:01</td><td>AENDKPHDVEI</td><td>44.2000</td><td>0.000096</td><td>41386.6</td><td>76.2431</td><td>0.017474</td><td>AENDKPHDVEI</td><td>44.2000</td><td>0.000096</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>11:11</td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7388</th><td>HLA-C08:01</td><td>APPTRGPPP  </td><td>44.1667</td><td>0.000065</td><td>41920.2</td><td>48.1296</td><td>0.016290</td><td>APPTRGPPP  </td><td>44.1667</td><td>0.000065</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>9:9  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7389</th><td>HLA-B15:02</td><td>PPTRGPPP   </td><td>92.5000</td><td>0.000003</td><td>46658.9</td><td>96.5796</td><td>0.006392</td><td>PPTRGPPP   </td><td>92.5000</td><td>0.000003</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>8:8  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7390</th><td>HLA-A11:01</td><td>PPTRGPPP   </td><td>98.3333</td><td>0.000001</td><td>47700.7</td><td>97.4988</td><td>0.004351</td><td>PPTRGPPP   </td><td>98.3333</td><td>0.000001</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>8:8  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7391</th><td>HLA-B15:01</td><td>PPTRGPPP   </td><td>96.6667</td><td>0.000002</td><td>47344.4</td><td>97.7438</td><td>0.005044</td><td>PPTRGPPP   </td><td>96.6667</td><td>0.000002</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>8:8  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7392</th><td>HLA-C03:03</td><td>PPTRGPPP   </td><td>90.0000</td><td>0.000001</td><td>48403.6</td><td>95.2994</td><td>0.002999</td><td>PPTRGPPP   </td><td>90.0000</td><td>0.000001</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>8:8  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7393</th><td>HLA-B15:02</td><td>NDKPHDVEI  </td><td> 9.3652</td><td>0.004685</td><td>32303.8</td><td>44.7744</td><td>0.040374</td><td>NDKPHDVEI  </td><td> 9.3652</td><td>0.004685</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>9:9  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7394</th><td>HLA-B15:01</td><td>DKPHDVEI   </td><td>62.7500</td><td>0.000062</td><td>43607.5</td><td>93.2134</td><td>0.012643</td><td>DKPHDVEI   </td><td>62.7500</td><td>0.000062</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7395</th><td>HLA-C03:03</td><td>DKPHDVEI   </td><td>44.1667</td><td>0.000062</td><td>45618.5</td><td>74.7301</td><td>0.008476</td><td>DKPHDVEI   </td><td>44.1667</td><td>0.000062</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7396</th><td>HLA-A02:01</td><td>DKPHDVEI   </td><td>61.7857</td><td>0.000045</td><td>44319.1</td><td>88.4493</td><td>0.011147</td><td>DKPHDVEI   </td><td>61.7857</td><td>0.000045</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7397</th><td>HLA-C08:01</td><td>DKPHDVEI   </td><td>38.6000</td><td>0.000107</td><td>45951.5</td><td>73.6098</td><td>0.007804</td><td>DKPHDVEI   </td><td>38.6000</td><td>0.000107</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7398</th><td>HLA-C08:01</td><td>NDKPHDVEI  </td><td>16.5369</td><td>0.001357</td><td>39801.3</td><td>39.7903</td><td>0.021084</td><td>NDKPHDVEI  </td><td>16.5369</td><td>0.001357</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>9:9  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7399</th><td>HLA-A02:01</td><td>NDKPHDVEI  </td><td>34.5385</td><td>0.000296</td><td>39576.3</td><td>69.3201</td><td>0.021608</td><td>NDKPHDVEI  </td><td>34.5385</td><td>0.000296</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>9:9  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7400</th><td>HLA-C03:03</td><td>NDKPHDVEI  </td><td>18.7625</td><td>0.001063</td><td>39753.9</td><td>46.4034</td><td>0.021194</td><td>NDKPHDVEI  </td><td>18.7625</td><td>0.001063</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>9:9  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7401</th><td>HLA-B15:01</td><td>NDKPHDVEI  </td><td>19.9500</td><td>0.001588</td><td>34641.0</td><td>64.4962</td><td>0.033918</td><td>NDKPHDVEI  </td><td>19.9500</td><td>0.001588</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>9:9  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7402</th><td>HLA-A11:01</td><td>NDKPHDVEI  </td><td>37.4545</td><td>0.000149</td><td>40984.7</td><td>74.1964</td><td>0.018376</td><td>NDKPHDVEI  </td><td>37.4545</td><td>0.000149</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>9:9  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7403</th><td>HLA-C08:01</td><td>PPTRGPPP   </td><td>95.0000</td><td>0.000001</td><td>48617.8</td><td>95.8047</td><td>0.002591</td><td>PPTRGPPP   </td><td>95.0000</td><td>0.000001</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>8:8  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7404</th><td>HLA-A02:01</td><td>PPTRGPPP   </td><td>98.3333</td><td>0.000001</td><td>47580.6</td><td>96.9123</td><td>0.004584</td><td>PPTRGPPP   </td><td>98.3333</td><td>0.000001</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>8:8  </td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7405</th><td>HLA-B15:02</td><td>DKPHDVEI   </td><td>59.7059</td><td>0.000042</td><td>44008.5</td><td>91.0435</td><td>0.011797</td><td>DKPHDVEI   </td><td>59.7059</td><td>0.000042</td><td>⋯</td><td>INK/IX</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>58440921-58440925  </td><td>137    </td><td>F</td><td>VAPB</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7406</th><td>HLA-A11:01</td><td>RSAPPTRGPPP</td><td>44.6000</td><td>0.000094</td><td>34852.7</td><td>47.0194</td><td>0.033355</td><td>RSAPPTRGPPP</td><td>44.6000</td><td>0.000094</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>11:11</td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7407</th><td>HLA-B15:02</td><td>RSAPPTRGPPP</td><td>67.7778</td><td>0.000025</td><td>40459.1</td><td>76.3904</td><td>0.019569</td><td>RSAPPTRGPPP</td><td>67.7778</td><td>0.000025</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>11:11</td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7408</th><td>HLA-C08:01</td><td>RSAPPTRGPPP</td><td>57.2222</td><td>0.000023</td><td>41704.9</td><td>47.1520</td><td>0.016766</td><td>RSAPPTRGPPP</td><td>57.2222</td><td>0.000023</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>11:11</td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7409</th><td>HLA-A02:01</td><td>RSAPPTRGPPP</td><td>67.0000</td><td>0.000032</td><td>38570.6</td><td>65.5582</td><td>0.023987</td><td>RSAPPTRGPPP</td><td>67.0000</td><td>0.000032</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>11:11</td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>7410</th><td>HLA-C03:03</td><td>RSAPPTRGPPP</td><td>55.0000</td><td>0.000024</td><td>40282.2</td><td>48.2192</td><td>0.019974</td><td>RSAPPTRGPPP</td><td>55.0000</td><td>0.000024</td><td>⋯</td><td>P/PX  </td><td>-</td><td>0</td><td>11:11</td><td>X </td><td>136874412-136874413</td><td>302,174</td><td>F</td><td>RBMX</td><td>No</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>8730</th><td>HLA-A03:01</td><td>ALPEEYRD   </td><td> 81.6667</td><td>0.000018</td><td>45655.6</td><td>95.9159</td><td>0.008401</td><td>ALPEEYRD   </td><td> 81.6667</td><td>0.000018</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>8:8  </td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8731</th><td>HLA-C17:01</td><td>ALPEEYRD   </td><td> 83.8889</td><td>0.000013</td><td>47918.0</td><td>91.7732</td><td>0.003931</td><td>ALPEEYRD   </td><td> 83.8889</td><td>0.000013</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>8:8  </td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8737</th><td>HLA-B41:01</td><td>ALPEEYRD   </td><td> 80.7143</td><td>0.000019</td><td>45060.3</td><td>95.8047</td><td>0.009614</td><td>ALPEEYRD   </td><td> 80.7143</td><td>0.000019</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>8:8  </td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8738</th><td>HLA-C17:01</td><td>MALPEEYRD  </td><td> 57.0000</td><td>0.000159</td><td>44248.1</td><td>65.1053</td><td>0.011295</td><td>MALPEEYRD  </td><td> 57.0000</td><td>0.000159</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>9:9  </td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8739</th><td>HLA-A02:05</td><td>FMALPEEYRD </td><td> 33.3578</td><td>0.001381</td><td>10283.4</td><td>21.7312</td><td>0.146167</td><td>FMALPEEYRD </td><td> 33.3578</td><td>0.001381</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>10:10</td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8740</th><td>HLA-A03:01</td><td>MALPEEYRD  </td><td> 60.7143</td><td>0.000058</td><td>42602.0</td><td>86.7146</td><td>0.014799</td><td>MALPEEYRD  </td><td> 60.7143</td><td>0.000058</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>9:9  </td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8741</th><td>HLA-C16:02</td><td>FMALPEEYRD </td><td> 59.5763</td><td>0.000131</td><td>35916.5</td><td>52.6243</td><td>0.030576</td><td>FMALPEEYRD </td><td> 59.5763</td><td>0.000131</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>10:10</td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>8742</th><td>HLA-B41:01</td><td>MALPEEYRD  </td><td> 67.3333</td><td>0.000048</td><td>40328.4</td><td>78.7388</td><td>0.019868</td><td>MALPEEYRD  </td><td> 67.3333</td><td>0.000048</td><td>⋯</td><td>D/X</td><td>-</td><td>0</td><td>9:9  </td><td>8 </td><td>28113105           </td><td>183,91,169,169,111,64                                           </td><td>F</td><td>ELP3 </td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38921</th><td>HLA-A02:01</td><td>AATTAIGG   </td><td> 85.0000</td><td>0.000008</td><td>43745.5</td><td>86.1357</td><td>0.012351</td><td>AATTAIGG   </td><td> 85.0000</td><td>0.000008</td><td>⋯</td><td>G/X</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>32434639           </td><td>638,643,643,643,582                                             </td><td>F</td><td>ASXL1</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38922</th><td>HLA-C07:01</td><td>AATTAIGG   </td><td> 85.0000</td><td>0.000012</td><td>46677.0</td><td>93.0558</td><td>0.006356</td><td>AATTAIGG   </td><td> 85.0000</td><td>0.000012</td><td>⋯</td><td>G/X</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>32434639           </td><td>638,643,643,643,582                                             </td><td>F</td><td>ASXL1</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38923</th><td>HLA-B15:09</td><td>AATTAIGG   </td><td> 68.3333</td><td>0.000007</td><td>45417.6</td><td>74.8935</td><td>0.008884</td><td>AATTAIGG   </td><td> 68.3333</td><td>0.000007</td><td>⋯</td><td>G/X</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>32434639           </td><td>638,643,643,643,582                                             </td><td>F</td><td>ASXL1</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38924</th><td>HLA-C06:02</td><td>AATTAIGG   </td><td> 72.6316</td><td>0.000051</td><td>45921.1</td><td>88.8202</td><td>0.007865</td><td>AATTAIGG   </td><td> 72.6316</td><td>0.000051</td><td>⋯</td><td>G/X</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>32434639           </td><td>638,643,643,643,582                                             </td><td>F</td><td>ASXL1</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38925</th><td>HLA-B49:01</td><td>AATTAIGG   </td><td> 55.5263</td><td>0.000066</td><td>42744.2</td><td>72.9057</td><td>0.014491</td><td>AATTAIGG   </td><td> 55.5263</td><td>0.000066</td><td>⋯</td><td>G/X</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>32434639           </td><td>638,643,643,643,582                                             </td><td>F</td><td>ASXL1</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38926</th><td>HLA-A01:01</td><td>AATTAIGG   </td><td> 71.8571</td><td>0.000129</td><td>43823.2</td><td>86.5010</td><td>0.012187</td><td>AATTAIGG   </td><td> 71.8571</td><td>0.000129</td><td>⋯</td><td>G/X</td><td>-</td><td>0</td><td>8:8  </td><td>20</td><td>32434639           </td><td>638,643,643,643,582                                             </td><td>F</td><td>ASXL1</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38927</th><td>HLA-B49:01</td><td>CVICMDTISNK</td><td> 93.3333</td><td>0.000003</td><td>42450.6</td><td>71.2276</td><td>0.015128</td><td>CVICMDTISNK</td><td> 93.3333</td><td>0.000003</td><td>⋯</td><td>-/X</td><td>-</td><td>0</td><td>10:11</td><td>3 </td><td>122569799-122569800</td><td>570                                                             </td><td>F</td><td>DTX3L</td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38928</th><td>HLA-B15:09</td><td>CVICMDTISNK</td><td>100.0000</td><td>0.000000</td><td>46830.8</td><td>88.4402</td><td>0.006052</td><td>CVICMDTISNK</td><td>100.0000</td><td>0.000000</td><td>⋯</td><td>-/X</td><td>-</td><td>0</td><td>10:11</td><td>3 </td><td>122569799-122569800</td><td>570                                                             </td><td>F</td><td>DTX3L</td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38932</th><td>HLA-C07:01</td><td>CVICMDTISNK</td><td> 62.6000</td><td>0.000070</td><td>35885.1</td><td>42.4715</td><td>0.030657</td><td>CVICMDTISNK</td><td> 62.6000</td><td>0.000070</td><td>⋯</td><td>-/X</td><td>-</td><td>0</td><td>10:11</td><td>3 </td><td>122569799-122569800</td><td>570                                                             </td><td>F</td><td>DTX3L</td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38935</th><td>HLA-A01:01</td><td>CVICMDTISNK</td><td> 53.0978</td><td>0.000348</td><td>30357.6</td><td>24.5774</td><td>0.046117</td><td>CVICMDTISNK</td><td> 53.0978</td><td>0.000348</td><td>⋯</td><td>-/X</td><td>-</td><td>0</td><td>10:11</td><td>3 </td><td>122569799-122569800</td><td>570                                                             </td><td>F</td><td>DTX3L</td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38937</th><td>HLA-C06:02</td><td>CVICMDTISNK</td><td> 76.5385</td><td>0.000038</td><td>39034.9</td><td>51.3369</td><td>0.022881</td><td>CVICMDTISNK</td><td> 76.5385</td><td>0.000038</td><td>⋯</td><td>-/X</td><td>-</td><td>0</td><td>10:11</td><td>3 </td><td>122569799-122569800</td><td>570                                                             </td><td>F</td><td>DTX3L</td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38938</th><td>HLA-A01:01</td><td>NKPILLPNES </td><td> 84.3750</td><td>0.000057</td><td>44271.6</td><td>88.8395</td><td>0.011246</td><td>NKPILLPNES </td><td> 84.3750</td><td>0.000057</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>10:10</td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38939</th><td>HLA-C07:01</td><td>NKPILLPNES </td><td> 74.6154</td><td>0.000029</td><td>43911.4</td><td>77.1029</td><td>0.012001</td><td>NKPILLPNES </td><td> 74.6154</td><td>0.000029</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>10:10</td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38940</th><td>HLA-B15:09</td><td>NKPILLPNES </td><td> 39.4000</td><td>0.000060</td><td>40305.3</td><td>39.2211</td><td>0.019921</td><td>NKPILLPNES </td><td> 39.4000</td><td>0.000060</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>10:10</td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38941</th><td>HLA-C06:02</td><td>NKPILLPNES </td><td> 68.2692</td><td>0.000070</td><td>43931.4</td><td>76.2076</td><td>0.011959</td><td>NKPILLPNES </td><td> 68.2692</td><td>0.000070</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>10:10</td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38942</th><td>HLA-A02:01</td><td>NKPILLPNES </td><td> 73.7500</td><td>0.000020</td><td>42300.7</td><td>80.1969</td><td>0.015455</td><td>NKPILLPNES </td><td> 73.7500</td><td>0.000020</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>10:10</td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38943</th><td>HLA-A02:01</td><td>CVICMDTISNK</td><td> 54.6296</td><td>0.000071</td><td>26168.5</td><td>34.0837</td><td>0.059841</td><td>CVICMDTISNK</td><td> 54.6296</td><td>0.000071</td><td>⋯</td><td>-/X</td><td>-</td><td>0</td><td>10:11</td><td>3 </td><td>122569799-122569800</td><td>570                                                             </td><td>F</td><td>DTX3L</td><td>No </td></tr>\n",
       "\t<tr><th scope=row>38950</th><td>HLA-B49:01</td><td>PILLPNES   </td><td> 88.3333</td><td>0.000006</td><td>47854.8</td><td>96.7679</td><td>0.004053</td><td>PILLPNES   </td><td> 88.3333</td><td>0.000006</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>8:8  </td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38951</th><td>HLA-A01:01</td><td>PILLPNES   </td><td> 87.3684</td><td>0.000045</td><td>45819.9</td><td>95.1856</td><td>0.008069</td><td>PILLPNES   </td><td> 87.3684</td><td>0.000045</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>8:8  </td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38952</th><td>HLA-C07:01</td><td>PILLPNES   </td><td> 93.7500</td><td>0.000004</td><td>47918.5</td><td>96.5023</td><td>0.003930</td><td>PILLPNES   </td><td> 93.7500</td><td>0.000004</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>8:8  </td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38953</th><td>HLA-B15:09</td><td>PILLPNES   </td><td> 70.0000</td><td>0.000006</td><td>46971.9</td><td>89.7703</td><td>0.005774</td><td>PILLPNES   </td><td> 70.0000</td><td>0.000006</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>8:8  </td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>38954</th><td>HLA-C06:02</td><td>PILLPNES   </td><td> 88.1250</td><td>0.000014</td><td>48041.5</td><td>96.7353</td><td>0.003693</td><td>PILLPNES   </td><td> 88.1250</td><td>0.000014</td><td>⋯</td><td>S/X</td><td>-</td><td>0</td><td>8:8  </td><td>16</td><td>2085231-2085232    </td><td>1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498</td><td>F</td><td>TSC2 </td><td>Yes</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 100 × 25\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & HLA\\_allele & Norm\\_peptide & Norm\\_MHCrank\\_EL & Norm\\_MHCscore\\_EL & Norm\\_MHCaffinity & Norm\\_MHCrank\\_BA & Norm\\_MHCscore\\_BA & Mut\\_peptide & Mut\\_MHCrank\\_EL & Mut\\_MHCscore\\_EL & ⋯ & Amino\\_Acid\\_Change & Allele\\_Frequency & Mismatches & peptide\\_position & Chr & Genomic\\_Position & Protein\\_position & Mutation\\_Consequence & Gene\\_Symbol & Cancer\\_Driver\\_Gene\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <dbl> & ⋯ & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t7381 & HLA-B15:02 & APPTRGPPP   & 32.4167 & 0.000276 & 37281.8 & 62.7439 & 0.027128 & APPTRGPPP   & 32.4167 & 0.000276 & ⋯ & P/PX   & - & 0 & 9:9   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7382 & HLA-B15:01 & APPTRGPPP   & 67.0000 & 0.000047 & 41264.6 & 86.4839 & 0.017747 & APPTRGPPP   & 67.0000 & 0.000047 & ⋯ & P/PX   & - & 0 & 9:9   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7383 & HLA-A11:01 & APPTRGPPP   & 57.0833 & 0.000046 & 42153.6 & 80.1349 & 0.015777 & APPTRGPPP   & 57.0833 & 0.000046 & ⋯ & P/PX   & - & 0 & 9:9   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7384 & HLA-C03:03 & APPTRGPPP   & 34.2667 & 0.000160 & 37735.9 & 40.4554 & 0.026009 & APPTRGPPP   & 34.2667 & 0.000160 & ⋯ & P/PX   & - & 0 & 9:9   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7385 & HLA-A02:01 & APPTRGPPP   & 65.0000 & 0.000036 & 42610.8 & 81.4870 & 0.014780 & APPTRGPPP   & 65.0000 & 0.000036 & ⋯ & P/PX   & - & 0 & 9:9   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7386 & HLA-C08:01 & SAPPTRGPPP  & 53.4615 & 0.000031 & 41049.5 & 44.3978 & 0.018230 & SAPPTRGPPP  & 53.4615 & 0.000031 & ⋯ & P/PX   & - & 0 & 10:10 & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7387 & HLA-A11:01 & AENDKPHDVEI & 44.2000 & 0.000096 & 41386.6 & 76.2431 & 0.017474 & AENDKPHDVEI & 44.2000 & 0.000096 & ⋯ & INK/IX & - & 0 & 11:11 & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7388 & HLA-C08:01 & APPTRGPPP   & 44.1667 & 0.000065 & 41920.2 & 48.1296 & 0.016290 & APPTRGPPP   & 44.1667 & 0.000065 & ⋯ & P/PX   & - & 0 & 9:9   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7389 & HLA-B15:02 & PPTRGPPP    & 92.5000 & 0.000003 & 46658.9 & 96.5796 & 0.006392 & PPTRGPPP    & 92.5000 & 0.000003 & ⋯ & P/PX   & - & 0 & 8:8   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7390 & HLA-A11:01 & PPTRGPPP    & 98.3333 & 0.000001 & 47700.7 & 97.4988 & 0.004351 & PPTRGPPP    & 98.3333 & 0.000001 & ⋯ & P/PX   & - & 0 & 8:8   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7391 & HLA-B15:01 & PPTRGPPP    & 96.6667 & 0.000002 & 47344.4 & 97.7438 & 0.005044 & PPTRGPPP    & 96.6667 & 0.000002 & ⋯ & P/PX   & - & 0 & 8:8   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7392 & HLA-C03:03 & PPTRGPPP    & 90.0000 & 0.000001 & 48403.6 & 95.2994 & 0.002999 & PPTRGPPP    & 90.0000 & 0.000001 & ⋯ & P/PX   & - & 0 & 8:8   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7393 & HLA-B15:02 & NDKPHDVEI   &  9.3652 & 0.004685 & 32303.8 & 44.7744 & 0.040374 & NDKPHDVEI   &  9.3652 & 0.004685 & ⋯ & INK/IX & - & 0 & 9:9   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7394 & HLA-B15:01 & DKPHDVEI    & 62.7500 & 0.000062 & 43607.5 & 93.2134 & 0.012643 & DKPHDVEI    & 62.7500 & 0.000062 & ⋯ & INK/IX & - & 0 & 8:8   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7395 & HLA-C03:03 & DKPHDVEI    & 44.1667 & 0.000062 & 45618.5 & 74.7301 & 0.008476 & DKPHDVEI    & 44.1667 & 0.000062 & ⋯ & INK/IX & - & 0 & 8:8   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7396 & HLA-A02:01 & DKPHDVEI    & 61.7857 & 0.000045 & 44319.1 & 88.4493 & 0.011147 & DKPHDVEI    & 61.7857 & 0.000045 & ⋯ & INK/IX & - & 0 & 8:8   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7397 & HLA-C08:01 & DKPHDVEI    & 38.6000 & 0.000107 & 45951.5 & 73.6098 & 0.007804 & DKPHDVEI    & 38.6000 & 0.000107 & ⋯ & INK/IX & - & 0 & 8:8   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7398 & HLA-C08:01 & NDKPHDVEI   & 16.5369 & 0.001357 & 39801.3 & 39.7903 & 0.021084 & NDKPHDVEI   & 16.5369 & 0.001357 & ⋯ & INK/IX & - & 0 & 9:9   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7399 & HLA-A02:01 & NDKPHDVEI   & 34.5385 & 0.000296 & 39576.3 & 69.3201 & 0.021608 & NDKPHDVEI   & 34.5385 & 0.000296 & ⋯ & INK/IX & - & 0 & 9:9   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7400 & HLA-C03:03 & NDKPHDVEI   & 18.7625 & 0.001063 & 39753.9 & 46.4034 & 0.021194 & NDKPHDVEI   & 18.7625 & 0.001063 & ⋯ & INK/IX & - & 0 & 9:9   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7401 & HLA-B15:01 & NDKPHDVEI   & 19.9500 & 0.001588 & 34641.0 & 64.4962 & 0.033918 & NDKPHDVEI   & 19.9500 & 0.001588 & ⋯ & INK/IX & - & 0 & 9:9   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7402 & HLA-A11:01 & NDKPHDVEI   & 37.4545 & 0.000149 & 40984.7 & 74.1964 & 0.018376 & NDKPHDVEI   & 37.4545 & 0.000149 & ⋯ & INK/IX & - & 0 & 9:9   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7403 & HLA-C08:01 & PPTRGPPP    & 95.0000 & 0.000001 & 48617.8 & 95.8047 & 0.002591 & PPTRGPPP    & 95.0000 & 0.000001 & ⋯ & P/PX   & - & 0 & 8:8   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7404 & HLA-A02:01 & PPTRGPPP    & 98.3333 & 0.000001 & 47580.6 & 96.9123 & 0.004584 & PPTRGPPP    & 98.3333 & 0.000001 & ⋯ & P/PX   & - & 0 & 8:8   & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7405 & HLA-B15:02 & DKPHDVEI    & 59.7059 & 0.000042 & 44008.5 & 91.0435 & 0.011797 & DKPHDVEI    & 59.7059 & 0.000042 & ⋯ & INK/IX & - & 0 & 8:8   & 20 & 58440921-58440925   & 137     & F & VAPB & No\\\\\n",
       "\t7406 & HLA-A11:01 & RSAPPTRGPPP & 44.6000 & 0.000094 & 34852.7 & 47.0194 & 0.033355 & RSAPPTRGPPP & 44.6000 & 0.000094 & ⋯ & P/PX   & - & 0 & 11:11 & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7407 & HLA-B15:02 & RSAPPTRGPPP & 67.7778 & 0.000025 & 40459.1 & 76.3904 & 0.019569 & RSAPPTRGPPP & 67.7778 & 0.000025 & ⋯ & P/PX   & - & 0 & 11:11 & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7408 & HLA-C08:01 & RSAPPTRGPPP & 57.2222 & 0.000023 & 41704.9 & 47.1520 & 0.016766 & RSAPPTRGPPP & 57.2222 & 0.000023 & ⋯ & P/PX   & - & 0 & 11:11 & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7409 & HLA-A02:01 & RSAPPTRGPPP & 67.0000 & 0.000032 & 38570.6 & 65.5582 & 0.023987 & RSAPPTRGPPP & 67.0000 & 0.000032 & ⋯ & P/PX   & - & 0 & 11:11 & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t7410 & HLA-C03:03 & RSAPPTRGPPP & 55.0000 & 0.000024 & 40282.2 & 48.2192 & 0.019974 & RSAPPTRGPPP & 55.0000 & 0.000024 & ⋯ & P/PX   & - & 0 & 11:11 & X  & 136874412-136874413 & 302,174 & F & RBMX & No\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t8730 & HLA-A03:01 & ALPEEYRD    &  81.6667 & 0.000018 & 45655.6 & 95.9159 & 0.008401 & ALPEEYRD    &  81.6667 & 0.000018 & ⋯ & D/X & - & 0 & 8:8   & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8731 & HLA-C17:01 & ALPEEYRD    &  83.8889 & 0.000013 & 47918.0 & 91.7732 & 0.003931 & ALPEEYRD    &  83.8889 & 0.000013 & ⋯ & D/X & - & 0 & 8:8   & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8737 & HLA-B41:01 & ALPEEYRD    &  80.7143 & 0.000019 & 45060.3 & 95.8047 & 0.009614 & ALPEEYRD    &  80.7143 & 0.000019 & ⋯ & D/X & - & 0 & 8:8   & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8738 & HLA-C17:01 & MALPEEYRD   &  57.0000 & 0.000159 & 44248.1 & 65.1053 & 0.011295 & MALPEEYRD   &  57.0000 & 0.000159 & ⋯ & D/X & - & 0 & 9:9   & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8739 & HLA-A02:05 & FMALPEEYRD  &  33.3578 & 0.001381 & 10283.4 & 21.7312 & 0.146167 & FMALPEEYRD  &  33.3578 & 0.001381 & ⋯ & D/X & - & 0 & 10:10 & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8740 & HLA-A03:01 & MALPEEYRD   &  60.7143 & 0.000058 & 42602.0 & 86.7146 & 0.014799 & MALPEEYRD   &  60.7143 & 0.000058 & ⋯ & D/X & - & 0 & 9:9   & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8741 & HLA-C16:02 & FMALPEEYRD  &  59.5763 & 0.000131 & 35916.5 & 52.6243 & 0.030576 & FMALPEEYRD  &  59.5763 & 0.000131 & ⋯ & D/X & - & 0 & 10:10 & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t8742 & HLA-B41:01 & MALPEEYRD   &  67.3333 & 0.000048 & 40328.4 & 78.7388 & 0.019868 & MALPEEYRD   &  67.3333 & 0.000048 & ⋯ & D/X & - & 0 & 9:9   & 8  & 28113105            & 183,91,169,169,111,64                                            & F & ELP3  & No \\\\\n",
       "\t38921 & HLA-A02:01 & AATTAIGG    &  85.0000 & 0.000008 & 43745.5 & 86.1357 & 0.012351 & AATTAIGG    &  85.0000 & 0.000008 & ⋯ & G/X & - & 0 & 8:8   & 20 & 32434639            & 638,643,643,643,582                                              & F & ASXL1 & Yes\\\\\n",
       "\t38922 & HLA-C07:01 & AATTAIGG    &  85.0000 & 0.000012 & 46677.0 & 93.0558 & 0.006356 & AATTAIGG    &  85.0000 & 0.000012 & ⋯ & G/X & - & 0 & 8:8   & 20 & 32434639            & 638,643,643,643,582                                              & F & ASXL1 & Yes\\\\\n",
       "\t38923 & HLA-B15:09 & AATTAIGG    &  68.3333 & 0.000007 & 45417.6 & 74.8935 & 0.008884 & AATTAIGG    &  68.3333 & 0.000007 & ⋯ & G/X & - & 0 & 8:8   & 20 & 32434639            & 638,643,643,643,582                                              & F & ASXL1 & Yes\\\\\n",
       "\t38924 & HLA-C06:02 & AATTAIGG    &  72.6316 & 0.000051 & 45921.1 & 88.8202 & 0.007865 & AATTAIGG    &  72.6316 & 0.000051 & ⋯ & G/X & - & 0 & 8:8   & 20 & 32434639            & 638,643,643,643,582                                              & F & ASXL1 & Yes\\\\\n",
       "\t38925 & HLA-B49:01 & AATTAIGG    &  55.5263 & 0.000066 & 42744.2 & 72.9057 & 0.014491 & AATTAIGG    &  55.5263 & 0.000066 & ⋯ & G/X & - & 0 & 8:8   & 20 & 32434639            & 638,643,643,643,582                                              & F & ASXL1 & Yes\\\\\n",
       "\t38926 & HLA-A01:01 & AATTAIGG    &  71.8571 & 0.000129 & 43823.2 & 86.5010 & 0.012187 & AATTAIGG    &  71.8571 & 0.000129 & ⋯ & G/X & - & 0 & 8:8   & 20 & 32434639            & 638,643,643,643,582                                              & F & ASXL1 & Yes\\\\\n",
       "\t38927 & HLA-B49:01 & CVICMDTISNK &  93.3333 & 0.000003 & 42450.6 & 71.2276 & 0.015128 & CVICMDTISNK &  93.3333 & 0.000003 & ⋯ & -/X & - & 0 & 10:11 & 3  & 122569799-122569800 & 570                                                              & F & DTX3L & No \\\\\n",
       "\t38928 & HLA-B15:09 & CVICMDTISNK & 100.0000 & 0.000000 & 46830.8 & 88.4402 & 0.006052 & CVICMDTISNK & 100.0000 & 0.000000 & ⋯ & -/X & - & 0 & 10:11 & 3  & 122569799-122569800 & 570                                                              & F & DTX3L & No \\\\\n",
       "\t38932 & HLA-C07:01 & CVICMDTISNK &  62.6000 & 0.000070 & 35885.1 & 42.4715 & 0.030657 & CVICMDTISNK &  62.6000 & 0.000070 & ⋯ & -/X & - & 0 & 10:11 & 3  & 122569799-122569800 & 570                                                              & F & DTX3L & No \\\\\n",
       "\t38935 & HLA-A01:01 & CVICMDTISNK &  53.0978 & 0.000348 & 30357.6 & 24.5774 & 0.046117 & CVICMDTISNK &  53.0978 & 0.000348 & ⋯ & -/X & - & 0 & 10:11 & 3  & 122569799-122569800 & 570                                                              & F & DTX3L & No \\\\\n",
       "\t38937 & HLA-C06:02 & CVICMDTISNK &  76.5385 & 0.000038 & 39034.9 & 51.3369 & 0.022881 & CVICMDTISNK &  76.5385 & 0.000038 & ⋯ & -/X & - & 0 & 10:11 & 3  & 122569799-122569800 & 570                                                              & F & DTX3L & No \\\\\n",
       "\t38938 & HLA-A01:01 & NKPILLPNES  &  84.3750 & 0.000057 & 44271.6 & 88.8395 & 0.011246 & NKPILLPNES  &  84.3750 & 0.000057 & ⋯ & S/X & - & 0 & 10:10 & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38939 & HLA-C07:01 & NKPILLPNES  &  74.6154 & 0.000029 & 43911.4 & 77.1029 & 0.012001 & NKPILLPNES  &  74.6154 & 0.000029 & ⋯ & S/X & - & 0 & 10:10 & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38940 & HLA-B15:09 & NKPILLPNES  &  39.4000 & 0.000060 & 40305.3 & 39.2211 & 0.019921 & NKPILLPNES  &  39.4000 & 0.000060 & ⋯ & S/X & - & 0 & 10:10 & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38941 & HLA-C06:02 & NKPILLPNES  &  68.2692 & 0.000070 & 43931.4 & 76.2076 & 0.011959 & NKPILLPNES  &  68.2692 & 0.000070 & ⋯ & S/X & - & 0 & 10:10 & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38942 & HLA-A02:01 & NKPILLPNES  &  73.7500 & 0.000020 & 42300.7 & 80.1969 & 0.015455 & NKPILLPNES  &  73.7500 & 0.000020 & ⋯ & S/X & - & 0 & 10:10 & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38943 & HLA-A02:01 & CVICMDTISNK &  54.6296 & 0.000071 & 26168.5 & 34.0837 & 0.059841 & CVICMDTISNK &  54.6296 & 0.000071 & ⋯ & -/X & - & 0 & 10:11 & 3  & 122569799-122569800 & 570                                                              & F & DTX3L & No \\\\\n",
       "\t38950 & HLA-B49:01 & PILLPNES    &  88.3333 & 0.000006 & 47854.8 & 96.7679 & 0.004053 & PILLPNES    &  88.3333 & 0.000006 & ⋯ & S/X & - & 0 & 8:8   & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38951 & HLA-A01:01 & PILLPNES    &  87.3684 & 0.000045 & 45819.9 & 95.1856 & 0.008069 & PILLPNES    &  87.3684 & 0.000045 & ⋯ & S/X & - & 0 & 8:8   & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38952 & HLA-C07:01 & PILLPNES    &  93.7500 & 0.000004 & 47918.5 & 96.5023 & 0.003930 & PILLPNES    &  93.7500 & 0.000004 & ⋯ & S/X & - & 0 & 8:8   & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38953 & HLA-B15:09 & PILLPNES    &  70.0000 & 0.000006 & 46971.9 & 89.7703 & 0.005774 & PILLPNES    &  70.0000 & 0.000006 & ⋯ & S/X & - & 0 & 8:8   & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\t38954 & HLA-C06:02 & PILLPNES    &  88.1250 & 0.000014 & 48041.5 & 96.7353 & 0.003693 & PILLPNES    &  88.1250 & 0.000014 & ⋯ & S/X & - & 0 & 8:8   & 16 & 2085231-2085232     & 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 & F & TSC2  & Yes\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 100 × 25\n",
       "\n",
       "| <!--/--> | HLA_allele &lt;chr&gt; | Norm_peptide &lt;chr&gt; | Norm_MHCrank_EL &lt;dbl&gt; | Norm_MHCscore_EL &lt;dbl&gt; | Norm_MHCaffinity &lt;dbl&gt; | Norm_MHCrank_BA &lt;dbl&gt; | Norm_MHCscore_BA &lt;dbl&gt; | Mut_peptide &lt;chr&gt; | Mut_MHCrank_EL &lt;dbl&gt; | Mut_MHCscore_EL &lt;dbl&gt; | ⋯ ⋯ | Amino_Acid_Change &lt;chr&gt; | Allele_Frequency &lt;chr&gt; | Mismatches &lt;int&gt; | peptide_position &lt;chr&gt; | Chr &lt;chr&gt; | Genomic_Position &lt;chr&gt; | Protein_position &lt;chr&gt; | Mutation_Consequence &lt;chr&gt; | Gene_Symbol &lt;chr&gt; | Cancer_Driver_Gene &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 7381 | HLA-B15:02 | APPTRGPPP   | 32.4167 | 0.000276 | 37281.8 | 62.7439 | 0.027128 | APPTRGPPP   | 32.4167 | 0.000276 | ⋯ | P/PX   | - | 0 | 9:9   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7382 | HLA-B15:01 | APPTRGPPP   | 67.0000 | 0.000047 | 41264.6 | 86.4839 | 0.017747 | APPTRGPPP   | 67.0000 | 0.000047 | ⋯ | P/PX   | - | 0 | 9:9   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7383 | HLA-A11:01 | APPTRGPPP   | 57.0833 | 0.000046 | 42153.6 | 80.1349 | 0.015777 | APPTRGPPP   | 57.0833 | 0.000046 | ⋯ | P/PX   | - | 0 | 9:9   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7384 | HLA-C03:03 | APPTRGPPP   | 34.2667 | 0.000160 | 37735.9 | 40.4554 | 0.026009 | APPTRGPPP   | 34.2667 | 0.000160 | ⋯ | P/PX   | - | 0 | 9:9   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7385 | HLA-A02:01 | APPTRGPPP   | 65.0000 | 0.000036 | 42610.8 | 81.4870 | 0.014780 | APPTRGPPP   | 65.0000 | 0.000036 | ⋯ | P/PX   | - | 0 | 9:9   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7386 | HLA-C08:01 | SAPPTRGPPP  | 53.4615 | 0.000031 | 41049.5 | 44.3978 | 0.018230 | SAPPTRGPPP  | 53.4615 | 0.000031 | ⋯ | P/PX   | - | 0 | 10:10 | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7387 | HLA-A11:01 | AENDKPHDVEI | 44.2000 | 0.000096 | 41386.6 | 76.2431 | 0.017474 | AENDKPHDVEI | 44.2000 | 0.000096 | ⋯ | INK/IX | - | 0 | 11:11 | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7388 | HLA-C08:01 | APPTRGPPP   | 44.1667 | 0.000065 | 41920.2 | 48.1296 | 0.016290 | APPTRGPPP   | 44.1667 | 0.000065 | ⋯ | P/PX   | - | 0 | 9:9   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7389 | HLA-B15:02 | PPTRGPPP    | 92.5000 | 0.000003 | 46658.9 | 96.5796 | 0.006392 | PPTRGPPP    | 92.5000 | 0.000003 | ⋯ | P/PX   | - | 0 | 8:8   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7390 | HLA-A11:01 | PPTRGPPP    | 98.3333 | 0.000001 | 47700.7 | 97.4988 | 0.004351 | PPTRGPPP    | 98.3333 | 0.000001 | ⋯ | P/PX   | - | 0 | 8:8   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7391 | HLA-B15:01 | PPTRGPPP    | 96.6667 | 0.000002 | 47344.4 | 97.7438 | 0.005044 | PPTRGPPP    | 96.6667 | 0.000002 | ⋯ | P/PX   | - | 0 | 8:8   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7392 | HLA-C03:03 | PPTRGPPP    | 90.0000 | 0.000001 | 48403.6 | 95.2994 | 0.002999 | PPTRGPPP    | 90.0000 | 0.000001 | ⋯ | P/PX   | - | 0 | 8:8   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7393 | HLA-B15:02 | NDKPHDVEI   |  9.3652 | 0.004685 | 32303.8 | 44.7744 | 0.040374 | NDKPHDVEI   |  9.3652 | 0.004685 | ⋯ | INK/IX | - | 0 | 9:9   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7394 | HLA-B15:01 | DKPHDVEI    | 62.7500 | 0.000062 | 43607.5 | 93.2134 | 0.012643 | DKPHDVEI    | 62.7500 | 0.000062 | ⋯ | INK/IX | - | 0 | 8:8   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7395 | HLA-C03:03 | DKPHDVEI    | 44.1667 | 0.000062 | 45618.5 | 74.7301 | 0.008476 | DKPHDVEI    | 44.1667 | 0.000062 | ⋯ | INK/IX | - | 0 | 8:8   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7396 | HLA-A02:01 | DKPHDVEI    | 61.7857 | 0.000045 | 44319.1 | 88.4493 | 0.011147 | DKPHDVEI    | 61.7857 | 0.000045 | ⋯ | INK/IX | - | 0 | 8:8   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7397 | HLA-C08:01 | DKPHDVEI    | 38.6000 | 0.000107 | 45951.5 | 73.6098 | 0.007804 | DKPHDVEI    | 38.6000 | 0.000107 | ⋯ | INK/IX | - | 0 | 8:8   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7398 | HLA-C08:01 | NDKPHDVEI   | 16.5369 | 0.001357 | 39801.3 | 39.7903 | 0.021084 | NDKPHDVEI   | 16.5369 | 0.001357 | ⋯ | INK/IX | - | 0 | 9:9   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7399 | HLA-A02:01 | NDKPHDVEI   | 34.5385 | 0.000296 | 39576.3 | 69.3201 | 0.021608 | NDKPHDVEI   | 34.5385 | 0.000296 | ⋯ | INK/IX | - | 0 | 9:9   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7400 | HLA-C03:03 | NDKPHDVEI   | 18.7625 | 0.001063 | 39753.9 | 46.4034 | 0.021194 | NDKPHDVEI   | 18.7625 | 0.001063 | ⋯ | INK/IX | - | 0 | 9:9   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7401 | HLA-B15:01 | NDKPHDVEI   | 19.9500 | 0.001588 | 34641.0 | 64.4962 | 0.033918 | NDKPHDVEI   | 19.9500 | 0.001588 | ⋯ | INK/IX | - | 0 | 9:9   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7402 | HLA-A11:01 | NDKPHDVEI   | 37.4545 | 0.000149 | 40984.7 | 74.1964 | 0.018376 | NDKPHDVEI   | 37.4545 | 0.000149 | ⋯ | INK/IX | - | 0 | 9:9   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7403 | HLA-C08:01 | PPTRGPPP    | 95.0000 | 0.000001 | 48617.8 | 95.8047 | 0.002591 | PPTRGPPP    | 95.0000 | 0.000001 | ⋯ | P/PX   | - | 0 | 8:8   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7404 | HLA-A02:01 | PPTRGPPP    | 98.3333 | 0.000001 | 47580.6 | 96.9123 | 0.004584 | PPTRGPPP    | 98.3333 | 0.000001 | ⋯ | P/PX   | - | 0 | 8:8   | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7405 | HLA-B15:02 | DKPHDVEI    | 59.7059 | 0.000042 | 44008.5 | 91.0435 | 0.011797 | DKPHDVEI    | 59.7059 | 0.000042 | ⋯ | INK/IX | - | 0 | 8:8   | 20 | 58440921-58440925   | 137     | F | VAPB | No |\n",
       "| 7406 | HLA-A11:01 | RSAPPTRGPPP | 44.6000 | 0.000094 | 34852.7 | 47.0194 | 0.033355 | RSAPPTRGPPP | 44.6000 | 0.000094 | ⋯ | P/PX   | - | 0 | 11:11 | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7407 | HLA-B15:02 | RSAPPTRGPPP | 67.7778 | 0.000025 | 40459.1 | 76.3904 | 0.019569 | RSAPPTRGPPP | 67.7778 | 0.000025 | ⋯ | P/PX   | - | 0 | 11:11 | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7408 | HLA-C08:01 | RSAPPTRGPPP | 57.2222 | 0.000023 | 41704.9 | 47.1520 | 0.016766 | RSAPPTRGPPP | 57.2222 | 0.000023 | ⋯ | P/PX   | - | 0 | 11:11 | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7409 | HLA-A02:01 | RSAPPTRGPPP | 67.0000 | 0.000032 | 38570.6 | 65.5582 | 0.023987 | RSAPPTRGPPP | 67.0000 | 0.000032 | ⋯ | P/PX   | - | 0 | 11:11 | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| 7410 | HLA-C03:03 | RSAPPTRGPPP | 55.0000 | 0.000024 | 40282.2 | 48.2192 | 0.019974 | RSAPPTRGPPP | 55.0000 | 0.000024 | ⋯ | P/PX   | - | 0 | 11:11 | X  | 136874412-136874413 | 302,174 | F | RBMX | No |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 8730 | HLA-A03:01 | ALPEEYRD    |  81.6667 | 0.000018 | 45655.6 | 95.9159 | 0.008401 | ALPEEYRD    |  81.6667 | 0.000018 | ⋯ | D/X | - | 0 | 8:8   | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8731 | HLA-C17:01 | ALPEEYRD    |  83.8889 | 0.000013 | 47918.0 | 91.7732 | 0.003931 | ALPEEYRD    |  83.8889 | 0.000013 | ⋯ | D/X | - | 0 | 8:8   | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8737 | HLA-B41:01 | ALPEEYRD    |  80.7143 | 0.000019 | 45060.3 | 95.8047 | 0.009614 | ALPEEYRD    |  80.7143 | 0.000019 | ⋯ | D/X | - | 0 | 8:8   | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8738 | HLA-C17:01 | MALPEEYRD   |  57.0000 | 0.000159 | 44248.1 | 65.1053 | 0.011295 | MALPEEYRD   |  57.0000 | 0.000159 | ⋯ | D/X | - | 0 | 9:9   | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8739 | HLA-A02:05 | FMALPEEYRD  |  33.3578 | 0.001381 | 10283.4 | 21.7312 | 0.146167 | FMALPEEYRD  |  33.3578 | 0.001381 | ⋯ | D/X | - | 0 | 10:10 | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8740 | HLA-A03:01 | MALPEEYRD   |  60.7143 | 0.000058 | 42602.0 | 86.7146 | 0.014799 | MALPEEYRD   |  60.7143 | 0.000058 | ⋯ | D/X | - | 0 | 9:9   | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8741 | HLA-C16:02 | FMALPEEYRD  |  59.5763 | 0.000131 | 35916.5 | 52.6243 | 0.030576 | FMALPEEYRD  |  59.5763 | 0.000131 | ⋯ | D/X | - | 0 | 10:10 | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 8742 | HLA-B41:01 | MALPEEYRD   |  67.3333 | 0.000048 | 40328.4 | 78.7388 | 0.019868 | MALPEEYRD   |  67.3333 | 0.000048 | ⋯ | D/X | - | 0 | 9:9   | 8  | 28113105            | 183,91,169,169,111,64                                            | F | ELP3  | No  |\n",
       "| 38921 | HLA-A02:01 | AATTAIGG    |  85.0000 | 0.000008 | 43745.5 | 86.1357 | 0.012351 | AATTAIGG    |  85.0000 | 0.000008 | ⋯ | G/X | - | 0 | 8:8   | 20 | 32434639            | 638,643,643,643,582                                              | F | ASXL1 | Yes |\n",
       "| 38922 | HLA-C07:01 | AATTAIGG    |  85.0000 | 0.000012 | 46677.0 | 93.0558 | 0.006356 | AATTAIGG    |  85.0000 | 0.000012 | ⋯ | G/X | - | 0 | 8:8   | 20 | 32434639            | 638,643,643,643,582                                              | F | ASXL1 | Yes |\n",
       "| 38923 | HLA-B15:09 | AATTAIGG    |  68.3333 | 0.000007 | 45417.6 | 74.8935 | 0.008884 | AATTAIGG    |  68.3333 | 0.000007 | ⋯ | G/X | - | 0 | 8:8   | 20 | 32434639            | 638,643,643,643,582                                              | F | ASXL1 | Yes |\n",
       "| 38924 | HLA-C06:02 | AATTAIGG    |  72.6316 | 0.000051 | 45921.1 | 88.8202 | 0.007865 | AATTAIGG    |  72.6316 | 0.000051 | ⋯ | G/X | - | 0 | 8:8   | 20 | 32434639            | 638,643,643,643,582                                              | F | ASXL1 | Yes |\n",
       "| 38925 | HLA-B49:01 | AATTAIGG    |  55.5263 | 0.000066 | 42744.2 | 72.9057 | 0.014491 | AATTAIGG    |  55.5263 | 0.000066 | ⋯ | G/X | - | 0 | 8:8   | 20 | 32434639            | 638,643,643,643,582                                              | F | ASXL1 | Yes |\n",
       "| 38926 | HLA-A01:01 | AATTAIGG    |  71.8571 | 0.000129 | 43823.2 | 86.5010 | 0.012187 | AATTAIGG    |  71.8571 | 0.000129 | ⋯ | G/X | - | 0 | 8:8   | 20 | 32434639            | 638,643,643,643,582                                              | F | ASXL1 | Yes |\n",
       "| 38927 | HLA-B49:01 | CVICMDTISNK |  93.3333 | 0.000003 | 42450.6 | 71.2276 | 0.015128 | CVICMDTISNK |  93.3333 | 0.000003 | ⋯ | -/X | - | 0 | 10:11 | 3  | 122569799-122569800 | 570                                                              | F | DTX3L | No  |\n",
       "| 38928 | HLA-B15:09 | CVICMDTISNK | 100.0000 | 0.000000 | 46830.8 | 88.4402 | 0.006052 | CVICMDTISNK | 100.0000 | 0.000000 | ⋯ | -/X | - | 0 | 10:11 | 3  | 122569799-122569800 | 570                                                              | F | DTX3L | No  |\n",
       "| 38932 | HLA-C07:01 | CVICMDTISNK |  62.6000 | 0.000070 | 35885.1 | 42.4715 | 0.030657 | CVICMDTISNK |  62.6000 | 0.000070 | ⋯ | -/X | - | 0 | 10:11 | 3  | 122569799-122569800 | 570                                                              | F | DTX3L | No  |\n",
       "| 38935 | HLA-A01:01 | CVICMDTISNK |  53.0978 | 0.000348 | 30357.6 | 24.5774 | 0.046117 | CVICMDTISNK |  53.0978 | 0.000348 | ⋯ | -/X | - | 0 | 10:11 | 3  | 122569799-122569800 | 570                                                              | F | DTX3L | No  |\n",
       "| 38937 | HLA-C06:02 | CVICMDTISNK |  76.5385 | 0.000038 | 39034.9 | 51.3369 | 0.022881 | CVICMDTISNK |  76.5385 | 0.000038 | ⋯ | -/X | - | 0 | 10:11 | 3  | 122569799-122569800 | 570                                                              | F | DTX3L | No  |\n",
       "| 38938 | HLA-A01:01 | NKPILLPNES  |  84.3750 | 0.000057 | 44271.6 | 88.8395 | 0.011246 | NKPILLPNES  |  84.3750 | 0.000057 | ⋯ | S/X | - | 0 | 10:10 | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38939 | HLA-C07:01 | NKPILLPNES  |  74.6154 | 0.000029 | 43911.4 | 77.1029 | 0.012001 | NKPILLPNES  |  74.6154 | 0.000029 | ⋯ | S/X | - | 0 | 10:10 | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38940 | HLA-B15:09 | NKPILLPNES  |  39.4000 | 0.000060 | 40305.3 | 39.2211 | 0.019921 | NKPILLPNES  |  39.4000 | 0.000060 | ⋯ | S/X | - | 0 | 10:10 | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38941 | HLA-C06:02 | NKPILLPNES  |  68.2692 | 0.000070 | 43931.4 | 76.2076 | 0.011959 | NKPILLPNES  |  68.2692 | 0.000070 | ⋯ | S/X | - | 0 | 10:10 | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38942 | HLA-A02:01 | NKPILLPNES  |  73.7500 | 0.000020 | 42300.7 | 80.1969 | 0.015455 | NKPILLPNES  |  73.7500 | 0.000020 | ⋯ | S/X | - | 0 | 10:10 | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38943 | HLA-A02:01 | CVICMDTISNK |  54.6296 | 0.000071 | 26168.5 | 34.0837 | 0.059841 | CVICMDTISNK |  54.6296 | 0.000071 | ⋯ | -/X | - | 0 | 10:11 | 3  | 122569799-122569800 | 570                                                              | F | DTX3L | No  |\n",
       "| 38950 | HLA-B49:01 | PILLPNES    |  88.3333 | 0.000006 | 47854.8 | 96.7679 | 0.004053 | PILLPNES    |  88.3333 | 0.000006 | ⋯ | S/X | - | 0 | 8:8   | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38951 | HLA-A01:01 | PILLPNES    |  87.3684 | 0.000045 | 45819.9 | 95.1856 | 0.008069 | PILLPNES    |  87.3684 | 0.000045 | ⋯ | S/X | - | 0 | 8:8   | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38952 | HLA-C07:01 | PILLPNES    |  93.7500 | 0.000004 | 47918.5 | 96.5023 | 0.003930 | PILLPNES    |  93.7500 | 0.000004 | ⋯ | S/X | - | 0 | 8:8   | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38953 | HLA-B15:09 | PILLPNES    |  70.0000 | 0.000006 | 46971.9 | 89.7703 | 0.005774 | PILLPNES    |  70.0000 | 0.000006 | ⋯ | S/X | - | 0 | 8:8   | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "| 38954 | HLA-C06:02 | PILLPNES    |  88.1250 | 0.000014 | 48041.5 | 96.7353 | 0.003693 | PILLPNES    |  88.1250 | 0.000014 | ⋯ | S/X | - | 0 | 8:8   | 16 | 2085231-2085232     | 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498 | F | TSC2  | Yes |\n",
       "\n"
      ],
      "text/plain": [
       "      HLA_allele Norm_peptide Norm_MHCrank_EL Norm_MHCscore_EL Norm_MHCaffinity\n",
       "7381  HLA-B15:02 APPTRGPPP    32.4167         0.000276         37281.8         \n",
       "7382  HLA-B15:01 APPTRGPPP    67.0000         0.000047         41264.6         \n",
       "7383  HLA-A11:01 APPTRGPPP    57.0833         0.000046         42153.6         \n",
       "7384  HLA-C03:03 APPTRGPPP    34.2667         0.000160         37735.9         \n",
       "7385  HLA-A02:01 APPTRGPPP    65.0000         0.000036         42610.8         \n",
       "7386  HLA-C08:01 SAPPTRGPPP   53.4615         0.000031         41049.5         \n",
       "7387  HLA-A11:01 AENDKPHDVEI  44.2000         0.000096         41386.6         \n",
       "7388  HLA-C08:01 APPTRGPPP    44.1667         0.000065         41920.2         \n",
       "7389  HLA-B15:02 PPTRGPPP     92.5000         0.000003         46658.9         \n",
       "7390  HLA-A11:01 PPTRGPPP     98.3333         0.000001         47700.7         \n",
       "7391  HLA-B15:01 PPTRGPPP     96.6667         0.000002         47344.4         \n",
       "7392  HLA-C03:03 PPTRGPPP     90.0000         0.000001         48403.6         \n",
       "7393  HLA-B15:02 NDKPHDVEI     9.3652         0.004685         32303.8         \n",
       "7394  HLA-B15:01 DKPHDVEI     62.7500         0.000062         43607.5         \n",
       "7395  HLA-C03:03 DKPHDVEI     44.1667         0.000062         45618.5         \n",
       "7396  HLA-A02:01 DKPHDVEI     61.7857         0.000045         44319.1         \n",
       "7397  HLA-C08:01 DKPHDVEI     38.6000         0.000107         45951.5         \n",
       "7398  HLA-C08:01 NDKPHDVEI    16.5369         0.001357         39801.3         \n",
       "7399  HLA-A02:01 NDKPHDVEI    34.5385         0.000296         39576.3         \n",
       "7400  HLA-C03:03 NDKPHDVEI    18.7625         0.001063         39753.9         \n",
       "7401  HLA-B15:01 NDKPHDVEI    19.9500         0.001588         34641.0         \n",
       "7402  HLA-A11:01 NDKPHDVEI    37.4545         0.000149         40984.7         \n",
       "7403  HLA-C08:01 PPTRGPPP     95.0000         0.000001         48617.8         \n",
       "7404  HLA-A02:01 PPTRGPPP     98.3333         0.000001         47580.6         \n",
       "7405  HLA-B15:02 DKPHDVEI     59.7059         0.000042         44008.5         \n",
       "7406  HLA-A11:01 RSAPPTRGPPP  44.6000         0.000094         34852.7         \n",
       "7407  HLA-B15:02 RSAPPTRGPPP  67.7778         0.000025         40459.1         \n",
       "7408  HLA-C08:01 RSAPPTRGPPP  57.2222         0.000023         41704.9         \n",
       "7409  HLA-A02:01 RSAPPTRGPPP  67.0000         0.000032         38570.6         \n",
       "7410  HLA-C03:03 RSAPPTRGPPP  55.0000         0.000024         40282.2         \n",
       "⋮     ⋮          ⋮            ⋮               ⋮                ⋮               \n",
       "8730  HLA-A03:01 ALPEEYRD      81.6667        0.000018         45655.6         \n",
       "8731  HLA-C17:01 ALPEEYRD      83.8889        0.000013         47918.0         \n",
       "8737  HLA-B41:01 ALPEEYRD      80.7143        0.000019         45060.3         \n",
       "8738  HLA-C17:01 MALPEEYRD     57.0000        0.000159         44248.1         \n",
       "8739  HLA-A02:05 FMALPEEYRD    33.3578        0.001381         10283.4         \n",
       "8740  HLA-A03:01 MALPEEYRD     60.7143        0.000058         42602.0         \n",
       "8741  HLA-C16:02 FMALPEEYRD    59.5763        0.000131         35916.5         \n",
       "8742  HLA-B41:01 MALPEEYRD     67.3333        0.000048         40328.4         \n",
       "38921 HLA-A02:01 AATTAIGG      85.0000        0.000008         43745.5         \n",
       "38922 HLA-C07:01 AATTAIGG      85.0000        0.000012         46677.0         \n",
       "38923 HLA-B15:09 AATTAIGG      68.3333        0.000007         45417.6         \n",
       "38924 HLA-C06:02 AATTAIGG      72.6316        0.000051         45921.1         \n",
       "38925 HLA-B49:01 AATTAIGG      55.5263        0.000066         42744.2         \n",
       "38926 HLA-A01:01 AATTAIGG      71.8571        0.000129         43823.2         \n",
       "38927 HLA-B49:01 CVICMDTISNK   93.3333        0.000003         42450.6         \n",
       "38928 HLA-B15:09 CVICMDTISNK  100.0000        0.000000         46830.8         \n",
       "38932 HLA-C07:01 CVICMDTISNK   62.6000        0.000070         35885.1         \n",
       "38935 HLA-A01:01 CVICMDTISNK   53.0978        0.000348         30357.6         \n",
       "38937 HLA-C06:02 CVICMDTISNK   76.5385        0.000038         39034.9         \n",
       "38938 HLA-A01:01 NKPILLPNES    84.3750        0.000057         44271.6         \n",
       "38939 HLA-C07:01 NKPILLPNES    74.6154        0.000029         43911.4         \n",
       "38940 HLA-B15:09 NKPILLPNES    39.4000        0.000060         40305.3         \n",
       "38941 HLA-C06:02 NKPILLPNES    68.2692        0.000070         43931.4         \n",
       "38942 HLA-A02:01 NKPILLPNES    73.7500        0.000020         42300.7         \n",
       "38943 HLA-A02:01 CVICMDTISNK   54.6296        0.000071         26168.5         \n",
       "38950 HLA-B49:01 PILLPNES      88.3333        0.000006         47854.8         \n",
       "38951 HLA-A01:01 PILLPNES      87.3684        0.000045         45819.9         \n",
       "38952 HLA-C07:01 PILLPNES      93.7500        0.000004         47918.5         \n",
       "38953 HLA-B15:09 PILLPNES      70.0000        0.000006         46971.9         \n",
       "38954 HLA-C06:02 PILLPNES      88.1250        0.000014         48041.5         \n",
       "      Norm_MHCrank_BA Norm_MHCscore_BA Mut_peptide Mut_MHCrank_EL\n",
       "7381  62.7439         0.027128         APPTRGPPP   32.4167       \n",
       "7382  86.4839         0.017747         APPTRGPPP   67.0000       \n",
       "7383  80.1349         0.015777         APPTRGPPP   57.0833       \n",
       "7384  40.4554         0.026009         APPTRGPPP   34.2667       \n",
       "7385  81.4870         0.014780         APPTRGPPP   65.0000       \n",
       "7386  44.3978         0.018230         SAPPTRGPPP  53.4615       \n",
       "7387  76.2431         0.017474         AENDKPHDVEI 44.2000       \n",
       "7388  48.1296         0.016290         APPTRGPPP   44.1667       \n",
       "7389  96.5796         0.006392         PPTRGPPP    92.5000       \n",
       "7390  97.4988         0.004351         PPTRGPPP    98.3333       \n",
       "7391  97.7438         0.005044         PPTRGPPP    96.6667       \n",
       "7392  95.2994         0.002999         PPTRGPPP    90.0000       \n",
       "7393  44.7744         0.040374         NDKPHDVEI    9.3652       \n",
       "7394  93.2134         0.012643         DKPHDVEI    62.7500       \n",
       "7395  74.7301         0.008476         DKPHDVEI    44.1667       \n",
       "7396  88.4493         0.011147         DKPHDVEI    61.7857       \n",
       "7397  73.6098         0.007804         DKPHDVEI    38.6000       \n",
       "7398  39.7903         0.021084         NDKPHDVEI   16.5369       \n",
       "7399  69.3201         0.021608         NDKPHDVEI   34.5385       \n",
       "7400  46.4034         0.021194         NDKPHDVEI   18.7625       \n",
       "7401  64.4962         0.033918         NDKPHDVEI   19.9500       \n",
       "7402  74.1964         0.018376         NDKPHDVEI   37.4545       \n",
       "7403  95.8047         0.002591         PPTRGPPP    95.0000       \n",
       "7404  96.9123         0.004584         PPTRGPPP    98.3333       \n",
       "7405  91.0435         0.011797         DKPHDVEI    59.7059       \n",
       "7406  47.0194         0.033355         RSAPPTRGPPP 44.6000       \n",
       "7407  76.3904         0.019569         RSAPPTRGPPP 67.7778       \n",
       "7408  47.1520         0.016766         RSAPPTRGPPP 57.2222       \n",
       "7409  65.5582         0.023987         RSAPPTRGPPP 67.0000       \n",
       "7410  48.2192         0.019974         RSAPPTRGPPP 55.0000       \n",
       "⋮     ⋮               ⋮                ⋮           ⋮             \n",
       "8730  95.9159         0.008401         ALPEEYRD     81.6667      \n",
       "8731  91.7732         0.003931         ALPEEYRD     83.8889      \n",
       "8737  95.8047         0.009614         ALPEEYRD     80.7143      \n",
       "8738  65.1053         0.011295         MALPEEYRD    57.0000      \n",
       "8739  21.7312         0.146167         FMALPEEYRD   33.3578      \n",
       "8740  86.7146         0.014799         MALPEEYRD    60.7143      \n",
       "8741  52.6243         0.030576         FMALPEEYRD   59.5763      \n",
       "8742  78.7388         0.019868         MALPEEYRD    67.3333      \n",
       "38921 86.1357         0.012351         AATTAIGG     85.0000      \n",
       "38922 93.0558         0.006356         AATTAIGG     85.0000      \n",
       "38923 74.8935         0.008884         AATTAIGG     68.3333      \n",
       "38924 88.8202         0.007865         AATTAIGG     72.6316      \n",
       "38925 72.9057         0.014491         AATTAIGG     55.5263      \n",
       "38926 86.5010         0.012187         AATTAIGG     71.8571      \n",
       "38927 71.2276         0.015128         CVICMDTISNK  93.3333      \n",
       "38928 88.4402         0.006052         CVICMDTISNK 100.0000      \n",
       "38932 42.4715         0.030657         CVICMDTISNK  62.6000      \n",
       "38935 24.5774         0.046117         CVICMDTISNK  53.0978      \n",
       "38937 51.3369         0.022881         CVICMDTISNK  76.5385      \n",
       "38938 88.8395         0.011246         NKPILLPNES   84.3750      \n",
       "38939 77.1029         0.012001         NKPILLPNES   74.6154      \n",
       "38940 39.2211         0.019921         NKPILLPNES   39.4000      \n",
       "38941 76.2076         0.011959         NKPILLPNES   68.2692      \n",
       "38942 80.1969         0.015455         NKPILLPNES   73.7500      \n",
       "38943 34.0837         0.059841         CVICMDTISNK  54.6296      \n",
       "38950 96.7679         0.004053         PILLPNES     88.3333      \n",
       "38951 95.1856         0.008069         PILLPNES     87.3684      \n",
       "38952 96.5023         0.003930         PILLPNES     93.7500      \n",
       "38953 89.7703         0.005774         PILLPNES     70.0000      \n",
       "38954 96.7353         0.003693         PILLPNES     88.1250      \n",
       "      Mut_MHCscore_EL ⋯ Amino_Acid_Change Allele_Frequency Mismatches\n",
       "7381  0.000276        ⋯ P/PX              -                0         \n",
       "7382  0.000047        ⋯ P/PX              -                0         \n",
       "7383  0.000046        ⋯ P/PX              -                0         \n",
       "7384  0.000160        ⋯ P/PX              -                0         \n",
       "7385  0.000036        ⋯ P/PX              -                0         \n",
       "7386  0.000031        ⋯ P/PX              -                0         \n",
       "7387  0.000096        ⋯ INK/IX            -                0         \n",
       "7388  0.000065        ⋯ P/PX              -                0         \n",
       "7389  0.000003        ⋯ P/PX              -                0         \n",
       "7390  0.000001        ⋯ P/PX              -                0         \n",
       "7391  0.000002        ⋯ P/PX              -                0         \n",
       "7392  0.000001        ⋯ P/PX              -                0         \n",
       "7393  0.004685        ⋯ INK/IX            -                0         \n",
       "7394  0.000062        ⋯ INK/IX            -                0         \n",
       "7395  0.000062        ⋯ INK/IX            -                0         \n",
       "7396  0.000045        ⋯ INK/IX            -                0         \n",
       "7397  0.000107        ⋯ INK/IX            -                0         \n",
       "7398  0.001357        ⋯ INK/IX            -                0         \n",
       "7399  0.000296        ⋯ INK/IX            -                0         \n",
       "7400  0.001063        ⋯ INK/IX            -                0         \n",
       "7401  0.001588        ⋯ INK/IX            -                0         \n",
       "7402  0.000149        ⋯ INK/IX            -                0         \n",
       "7403  0.000001        ⋯ P/PX              -                0         \n",
       "7404  0.000001        ⋯ P/PX              -                0         \n",
       "7405  0.000042        ⋯ INK/IX            -                0         \n",
       "7406  0.000094        ⋯ P/PX              -                0         \n",
       "7407  0.000025        ⋯ P/PX              -                0         \n",
       "7408  0.000023        ⋯ P/PX              -                0         \n",
       "7409  0.000032        ⋯ P/PX              -                0         \n",
       "7410  0.000024        ⋯ P/PX              -                0         \n",
       "⋮     ⋮               ⋱ ⋮                 ⋮                ⋮         \n",
       "8730  0.000018        ⋯ D/X               -                0         \n",
       "8731  0.000013        ⋯ D/X               -                0         \n",
       "8737  0.000019        ⋯ D/X               -                0         \n",
       "8738  0.000159        ⋯ D/X               -                0         \n",
       "8739  0.001381        ⋯ D/X               -                0         \n",
       "8740  0.000058        ⋯ D/X               -                0         \n",
       "8741  0.000131        ⋯ D/X               -                0         \n",
       "8742  0.000048        ⋯ D/X               -                0         \n",
       "38921 0.000008        ⋯ G/X               -                0         \n",
       "38922 0.000012        ⋯ G/X               -                0         \n",
       "38923 0.000007        ⋯ G/X               -                0         \n",
       "38924 0.000051        ⋯ G/X               -                0         \n",
       "38925 0.000066        ⋯ G/X               -                0         \n",
       "38926 0.000129        ⋯ G/X               -                0         \n",
       "38927 0.000003        ⋯ -/X               -                0         \n",
       "38928 0.000000        ⋯ -/X               -                0         \n",
       "38932 0.000070        ⋯ -/X               -                0         \n",
       "38935 0.000348        ⋯ -/X               -                0         \n",
       "38937 0.000038        ⋯ -/X               -                0         \n",
       "38938 0.000057        ⋯ S/X               -                0         \n",
       "38939 0.000029        ⋯ S/X               -                0         \n",
       "38940 0.000060        ⋯ S/X               -                0         \n",
       "38941 0.000070        ⋯ S/X               -                0         \n",
       "38942 0.000020        ⋯ S/X               -                0         \n",
       "38943 0.000071        ⋯ -/X               -                0         \n",
       "38950 0.000006        ⋯ S/X               -                0         \n",
       "38951 0.000045        ⋯ S/X               -                0         \n",
       "38952 0.000004        ⋯ S/X               -                0         \n",
       "38953 0.000006        ⋯ S/X               -                0         \n",
       "38954 0.000014        ⋯ S/X               -                0         \n",
       "      peptide_position Chr Genomic_Position   \n",
       "7381  9:9              X   136874412-136874413\n",
       "7382  9:9              X   136874412-136874413\n",
       "7383  9:9              X   136874412-136874413\n",
       "7384  9:9              X   136874412-136874413\n",
       "7385  9:9              X   136874412-136874413\n",
       "7386  10:10            X   136874412-136874413\n",
       "7387  11:11            20  58440921-58440925  \n",
       "7388  9:9              X   136874412-136874413\n",
       "7389  8:8              X   136874412-136874413\n",
       "7390  8:8              X   136874412-136874413\n",
       "7391  8:8              X   136874412-136874413\n",
       "7392  8:8              X   136874412-136874413\n",
       "7393  9:9              20  58440921-58440925  \n",
       "7394  8:8              20  58440921-58440925  \n",
       "7395  8:8              20  58440921-58440925  \n",
       "7396  8:8              20  58440921-58440925  \n",
       "7397  8:8              20  58440921-58440925  \n",
       "7398  9:9              20  58440921-58440925  \n",
       "7399  9:9              20  58440921-58440925  \n",
       "7400  9:9              20  58440921-58440925  \n",
       "7401  9:9              20  58440921-58440925  \n",
       "7402  9:9              20  58440921-58440925  \n",
       "7403  8:8              X   136874412-136874413\n",
       "7404  8:8              X   136874412-136874413\n",
       "7405  8:8              20  58440921-58440925  \n",
       "7406  11:11            X   136874412-136874413\n",
       "7407  11:11            X   136874412-136874413\n",
       "7408  11:11            X   136874412-136874413\n",
       "7409  11:11            X   136874412-136874413\n",
       "7410  11:11            X   136874412-136874413\n",
       "⋮     ⋮                ⋮   ⋮                  \n",
       "8730  8:8              8   28113105           \n",
       "8731  8:8              8   28113105           \n",
       "8737  8:8              8   28113105           \n",
       "8738  9:9              8   28113105           \n",
       "8739  10:10            8   28113105           \n",
       "8740  9:9              8   28113105           \n",
       "8741  10:10            8   28113105           \n",
       "8742  9:9              8   28113105           \n",
       "38921 8:8              20  32434639           \n",
       "38922 8:8              20  32434639           \n",
       "38923 8:8              20  32434639           \n",
       "38924 8:8              20  32434639           \n",
       "38925 8:8              20  32434639           \n",
       "38926 8:8              20  32434639           \n",
       "38927 10:11            3   122569799-122569800\n",
       "38928 10:11            3   122569799-122569800\n",
       "38932 10:11            3   122569799-122569800\n",
       "38935 10:11            3   122569799-122569800\n",
       "38937 10:11            3   122569799-122569800\n",
       "38938 10:10            16  2085231-2085232    \n",
       "38939 10:10            16  2085231-2085232    \n",
       "38940 10:10            16  2085231-2085232    \n",
       "38941 10:10            16  2085231-2085232    \n",
       "38942 10:10            16  2085231-2085232    \n",
       "38943 10:11            3   122569799-122569800\n",
       "38950 8:8              16  2085231-2085232    \n",
       "38951 8:8              16  2085231-2085232    \n",
       "38952 8:8              16  2085231-2085232    \n",
       "38953 8:8              16  2085231-2085232    \n",
       "38954 8:8              16  2085231-2085232    \n",
       "      Protein_position                                                \n",
       "7381  302,174                                                         \n",
       "7382  302,174                                                         \n",
       "7383  302,174                                                         \n",
       "7384  302,174                                                         \n",
       "7385  302,174                                                         \n",
       "7386  302,174                                                         \n",
       "7387  137                                                             \n",
       "7388  302,174                                                         \n",
       "7389  302,174                                                         \n",
       "7390  302,174                                                         \n",
       "7391  302,174                                                         \n",
       "7392  302,174                                                         \n",
       "7393  137                                                             \n",
       "7394  137                                                             \n",
       "7395  137                                                             \n",
       "7396  137                                                             \n",
       "7397  137                                                             \n",
       "7398  137                                                             \n",
       "7399  137                                                             \n",
       "7400  137                                                             \n",
       "7401  137                                                             \n",
       "7402  137                                                             \n",
       "7403  302,174                                                         \n",
       "7404  302,174                                                         \n",
       "7405  137                                                             \n",
       "7406  302,174                                                         \n",
       "7407  302,174                                                         \n",
       "7408  302,174                                                         \n",
       "7409  302,174                                                         \n",
       "7410  302,174                                                         \n",
       "⋮     ⋮                                                               \n",
       "8730  183,91,169,169,111,64                                           \n",
       "8731  183,91,169,169,111,64                                           \n",
       "8737  183,91,169,169,111,64                                           \n",
       "8738  183,91,169,169,111,64                                           \n",
       "8739  183,91,169,169,111,64                                           \n",
       "8740  183,91,169,169,111,64                                           \n",
       "8741  183,91,169,169,111,64                                           \n",
       "8742  183,91,169,169,111,64                                           \n",
       "38921 638,643,643,643,582                                             \n",
       "38922 638,643,643,643,582                                             \n",
       "38923 638,643,643,643,582                                             \n",
       "38924 638,643,643,643,582                                             \n",
       "38925 638,643,643,643,582                                             \n",
       "38926 638,643,643,643,582                                             \n",
       "38927 570                                                             \n",
       "38928 570                                                             \n",
       "38932 570                                                             \n",
       "38935 570                                                             \n",
       "38937 570                                                             \n",
       "38938 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38939 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38940 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38941 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38942 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38943 570                                                             \n",
       "38950 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38951 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38952 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38953 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "38954 1524,1501,1409,1457,1421,1468,1076,1481,1458,1480,1481,1457,1498\n",
       "      Mutation_Consequence Gene_Symbol Cancer_Driver_Gene\n",
       "7381  F                    RBMX        No                \n",
       "7382  F                    RBMX        No                \n",
       "7383  F                    RBMX        No                \n",
       "7384  F                    RBMX        No                \n",
       "7385  F                    RBMX        No                \n",
       "7386  F                    RBMX        No                \n",
       "7387  F                    VAPB        No                \n",
       "7388  F                    RBMX        No                \n",
       "7389  F                    RBMX        No                \n",
       "7390  F                    RBMX        No                \n",
       "7391  F                    RBMX        No                \n",
       "7392  F                    RBMX        No                \n",
       "7393  F                    VAPB        No                \n",
       "7394  F                    VAPB        No                \n",
       "7395  F                    VAPB        No                \n",
       "7396  F                    VAPB        No                \n",
       "7397  F                    VAPB        No                \n",
       "7398  F                    VAPB        No                \n",
       "7399  F                    VAPB        No                \n",
       "7400  F                    VAPB        No                \n",
       "7401  F                    VAPB        No                \n",
       "7402  F                    VAPB        No                \n",
       "7403  F                    RBMX        No                \n",
       "7404  F                    RBMX        No                \n",
       "7405  F                    VAPB        No                \n",
       "7406  F                    RBMX        No                \n",
       "7407  F                    RBMX        No                \n",
       "7408  F                    RBMX        No                \n",
       "7409  F                    RBMX        No                \n",
       "7410  F                    RBMX        No                \n",
       "⋮     ⋮                    ⋮           ⋮                 \n",
       "8730  F                    ELP3        No                \n",
       "8731  F                    ELP3        No                \n",
       "8737  F                    ELP3        No                \n",
       "8738  F                    ELP3        No                \n",
       "8739  F                    ELP3        No                \n",
       "8740  F                    ELP3        No                \n",
       "8741  F                    ELP3        No                \n",
       "8742  F                    ELP3        No                \n",
       "38921 F                    ASXL1       Yes               \n",
       "38922 F                    ASXL1       Yes               \n",
       "38923 F                    ASXL1       Yes               \n",
       "38924 F                    ASXL1       Yes               \n",
       "38925 F                    ASXL1       Yes               \n",
       "38926 F                    ASXL1       Yes               \n",
       "38927 F                    DTX3L       No                \n",
       "38928 F                    DTX3L       No                \n",
       "38932 F                    DTX3L       No                \n",
       "38935 F                    DTX3L       No                \n",
       "38937 F                    DTX3L       No                \n",
       "38938 F                    TSC2        Yes               \n",
       "38939 F                    TSC2        Yes               \n",
       "38940 F                    TSC2        Yes               \n",
       "38941 F                    TSC2        Yes               \n",
       "38942 F                    TSC2        Yes               \n",
       "38943 F                    DTX3L       No                \n",
       "38950 F                    TSC2        Yes               \n",
       "38951 F                    TSC2        Yes               \n",
       "38952 F                    TSC2        Yes               \n",
       "38953 F                    TSC2        Yes               \n",
       "38954 F                    TSC2        Yes               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(all.mupexi[ all.mupexi$Mismatches == 0,1:25],100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "Listofpatients_numberof2 <- list()\n",
    "\n",
    "for(p in unique(onemis_mupexi$sample_id)){\n",
    "\n",
    "    mysample <- onemis_mupexi[ onemis_mupexi$sample_id == p,]\n",
    "    \n",
    "    mysample$HLA_residue <- paste(mysample$HLA_allele, mysample$Amino_Acid_Change, sep = \"-\")    \n",
    "    \n",
    "    mygenes <- unique(mysample$Gene_Symbol)\n",
    "    myres <- unique(mysample$Amino_Acid_Change)\n",
    "    myhla <- unique(mysample$HLA_allele)\n",
    "    \n",
    "    hla_res <- as.vector(sapply(unique(mysample$HLA_allele), function(x) paste(x,unique(mysample$Amino_Acid_Change), sep = \"-\" )))\n",
    "                                \n",
    "    gene_reshlamat <- matrix(nrow = length(mygenes), ncol = length(hla_res),\n",
    "                             dimnames = list(mygenes, hla_res))\n",
    "                                \n",
    "    for(r in colnames(gene_reshlamat)){\n",
    "        \n",
    "    hlares <- mysample[ mysample$HLA_residue == r,]\n",
    "    for(g in unique(hlares$Gene_Symbol)){\n",
    "        myresmygen <- hlares[ hlares$Gene_Symbol == g,]\n",
    "        lessthan2 <- sum(myresmygen$Mut_MHCrank_BA < 2)\n",
    "        BestRank <- lessthan2\n",
    "        gene_reshlamat[g,r] <- BestRank\n",
    "    } \n",
    "                \n",
    "    }\n",
    "                                \n",
    "    Listofpatients_numberof2[length(Listofpatients_numberof2) + 1] <- list(gene_reshlamat)\n",
    "                            \n",
    "                                }\n",
    "    \n",
    "names(Listofpatients_numberof2) <- unique(onemis_mupexi$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(Listofpatients_numberof2, file = paste0(datapath, \"Mupexi/Listofpatients_count2.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$`7316-1075`</strong> = <table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 12 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>HLA-C04:01-G/R</th><th scope=col>HLA-C04:01-V/I</th><th scope=col>HLA-A68:01-G/R</th><th scope=col>HLA-A68:01-V/I</th><th scope=col>HLA-B55:01-G/R</th><th scope=col>HLA-B55:01-V/I</th><th scope=col>HLA-C03:03-G/R</th><th scope=col>HLA-C03:03-V/I</th><th scope=col>HLA-B35:02-G/R</th><th scope=col>HLA-B35:02-V/I</th><th scope=col>HLA-A26:01-G/R</th><th scope=col>HLA-A26:01-V/I</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CTNNB1</th><td> 0</td><td>NA</td><td> 0</td><td>NA</td><td> 0</td><td>NA</td><td> 0</td><td>NA</td><td> 0</td><td>NA</td><td> 0</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>AHNAK2</th><td>NA</td><td> 0</td><td>NA</td><td> 0</td><td>NA</td><td> 0</td><td>NA</td><td> 2</td><td>NA</td><td> 0</td><td>NA</td><td> 0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\textbf{\\$`7316-1075`} = A matrix: 2 × 12 of type int\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & HLA-C04:01-G/R & HLA-C04:01-V/I & HLA-A68:01-G/R & HLA-A68:01-V/I & HLA-B55:01-G/R & HLA-B55:01-V/I & HLA-C03:03-G/R & HLA-C03:03-V/I & HLA-B35:02-G/R & HLA-B35:02-V/I & HLA-A26:01-G/R & HLA-A26:01-V/I\\\\\n",
       "\\hline\n",
       "\tCTNNB1 &  0 & NA &  0 & NA &  0 & NA &  0 & NA &  0 & NA &  0 & NA\\\\\n",
       "\tAHNAK2 & NA &  0 & NA &  0 & NA &  0 & NA &  2 & NA &  0 & NA &  0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "**$`7316-1075`** = \n",
       "A matrix: 2 × 12 of type int\n",
       "\n",
       "| <!--/--> | HLA-C04:01-G/R | HLA-C04:01-V/I | HLA-A68:01-G/R | HLA-A68:01-V/I | HLA-B55:01-G/R | HLA-B55:01-V/I | HLA-C03:03-G/R | HLA-C03:03-V/I | HLA-B35:02-G/R | HLA-B35:02-V/I | HLA-A26:01-G/R | HLA-A26:01-V/I |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CTNNB1 |  0 | NA |  0 | NA |  0 | NA |  0 | NA |  0 | NA |  0 | NA |\n",
       "| AHNAK2 | NA |  0 | NA |  0 | NA |  0 | NA |  2 | NA |  0 | NA |  0 |\n",
       "\n"
      ],
      "text/plain": [
       "$`7316-1075`\n",
       "       HLA-C04:01-G/R HLA-C04:01-V/I HLA-A68:01-G/R HLA-A68:01-V/I\n",
       "CTNNB1              0             NA              0             NA\n",
       "AHNAK2             NA              0             NA              0\n",
       "       HLA-B55:01-G/R HLA-B55:01-V/I HLA-C03:03-G/R HLA-C03:03-V/I\n",
       "CTNNB1              0             NA              0             NA\n",
       "AHNAK2             NA              0             NA              2\n",
       "       HLA-B35:02-G/R HLA-B35:02-V/I HLA-A26:01-G/R HLA-A26:01-V/I\n",
       "CTNNB1              0             NA              0             NA\n",
       "AHNAK2             NA              0             NA              0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Listofpatients_numberof2[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "Listofpatients_minRank <- list()\n",
    "\n",
    "for(p in unique(onemis_mupexi$sample_id)){\n",
    "\n",
    "    mysample <- onemis_mupexi[ onemis_mupexi$sample_id == p,]\n",
    "    \n",
    "    mysample$HLA_residue <- paste(mysample$HLA_allele, mysample$Amino_Acid_Change, sep = \"-\")    \n",
    "    \n",
    "    mygenes <- unique(mysample$Gene_Symbol)\n",
    "    myres <- unique(mysample$Amino_Acid_Change)\n",
    "    myhla <- unique(mysample$HLA_allele)\n",
    "    \n",
    "    hla_res <- as.vector(sapply(unique(mysample$HLA_allele), function(x) paste(x,unique(mysample$Amino_Acid_Change), sep = \"-\" )))\n",
    "                                \n",
    "    gene_reshlamat <- matrix(nrow = length(mygenes), ncol = length(hla_res),\n",
    "                             dimnames = list(mygenes, hla_res))\n",
    "                                \n",
    "    for(r in colnames(gene_reshlamat)){\n",
    "        \n",
    "    hlares <- mysample[ mysample$HLA_residue == r,]\n",
    "    for(g in unique(hlares$Gene_Symbol)){\n",
    "        myresmygen <- hlares[ hlares$Gene_Symbol == g,]\n",
    "        summarytab <- summary(myresmygen$Mut_MHCrank_BA)\n",
    "        BestRank <- min(summarytab)\n",
    "        gene_reshlamat[g,r] <- BestRank\n",
    "    } \n",
    "                \n",
    "    }\n",
    "                                \n",
    "    Listofpatients_minRank[length(Listofpatients_minRank) + 1] <- list(gene_reshlamat)\n",
    "                                }\n",
    "    \n",
    "names(Listofpatients_minRank) <- unique(onemis_mupexi$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(Listofpatients_minRank, file = paste0(datapath, \"Mupexi/Listofpatients_minRank.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$`7316-1075`</strong> = <table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 12 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>HLA-C04:01-G/R</th><th scope=col>HLA-C04:01-V/I</th><th scope=col>HLA-A68:01-G/R</th><th scope=col>HLA-A68:01-V/I</th><th scope=col>HLA-B55:01-G/R</th><th scope=col>HLA-B55:01-V/I</th><th scope=col>HLA-C03:03-G/R</th><th scope=col>HLA-C03:03-V/I</th><th scope=col>HLA-B35:02-G/R</th><th scope=col>HLA-B35:02-V/I</th><th scope=col>HLA-A26:01-G/R</th><th scope=col>HLA-A26:01-V/I</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CTNNB1</th><td>2.0276</td><td>    NA</td><td>6.2125</td><td>     NA</td><td>3.8436</td><td>    NA</td><td>9.2932</td><td>    NA</td><td>11.1415</td><td>   NA</td><td>9.7442</td><td>    NA</td></tr>\n",
       "\t<tr><th scope=row>AHNAK2</th><td>    NA</td><td>3.1029</td><td>    NA</td><td>14.0262</td><td>    NA</td><td>9.3076</td><td>    NA</td><td>0.7069</td><td>     NA</td><td>3.412</td><td>    NA</td><td>7.8231</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\textbf{\\$`7316-1075`} = A matrix: 2 × 12 of type dbl\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & HLA-C04:01-G/R & HLA-C04:01-V/I & HLA-A68:01-G/R & HLA-A68:01-V/I & HLA-B55:01-G/R & HLA-B55:01-V/I & HLA-C03:03-G/R & HLA-C03:03-V/I & HLA-B35:02-G/R & HLA-B35:02-V/I & HLA-A26:01-G/R & HLA-A26:01-V/I\\\\\n",
       "\\hline\n",
       "\tCTNNB1 & 2.0276 &     NA & 6.2125 &      NA & 3.8436 &     NA & 9.2932 &     NA & 11.1415 &    NA & 9.7442 &     NA\\\\\n",
       "\tAHNAK2 &     NA & 3.1029 &     NA & 14.0262 &     NA & 9.3076 &     NA & 0.7069 &      NA & 3.412 &     NA & 7.8231\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "**$`7316-1075`** = \n",
       "A matrix: 2 × 12 of type dbl\n",
       "\n",
       "| <!--/--> | HLA-C04:01-G/R | HLA-C04:01-V/I | HLA-A68:01-G/R | HLA-A68:01-V/I | HLA-B55:01-G/R | HLA-B55:01-V/I | HLA-C03:03-G/R | HLA-C03:03-V/I | HLA-B35:02-G/R | HLA-B35:02-V/I | HLA-A26:01-G/R | HLA-A26:01-V/I |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CTNNB1 | 2.0276 |     NA | 6.2125 |      NA | 3.8436 |     NA | 9.2932 |     NA | 11.1415 |    NA | 9.7442 |     NA |\n",
       "| AHNAK2 |     NA | 3.1029 |     NA | 14.0262 |     NA | 9.3076 |     NA | 0.7069 |      NA | 3.412 |     NA | 7.8231 |\n",
       "\n"
      ],
      "text/plain": [
       "$`7316-1075`\n",
       "       HLA-C04:01-G/R HLA-C04:01-V/I HLA-A68:01-G/R HLA-A68:01-V/I\n",
       "CTNNB1         2.0276             NA         6.2125             NA\n",
       "AHNAK2             NA         3.1029             NA        14.0262\n",
       "       HLA-B55:01-G/R HLA-B55:01-V/I HLA-C03:03-G/R HLA-C03:03-V/I\n",
       "CTNNB1         3.8436             NA         9.2932             NA\n",
       "AHNAK2             NA         9.3076             NA         0.7069\n",
       "       HLA-B35:02-G/R HLA-B35:02-V/I HLA-A26:01-G/R HLA-A26:01-V/I\n",
       "CTNNB1        11.1415             NA         9.7442             NA\n",
       "AHNAK2             NA          3.412             NA         7.8231\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Listofpatients_minRank[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PBR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minimum rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"Mupexi/Listofpatients_minRank.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a sample x Gene matrix to hold PBR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbrmat <- matrix(nrow = length(unique(all.mupexi$sample_id)), \n",
    "                 ncol = length(unique(all.mupexi$Gene_Symbol)),\n",
    "                 dimnames = list(\n",
    "                 unique(all.mupexi$sample_id),\n",
    "                 unique(all.mupexi$Gene_Symbol)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(s in rownames(pbrmat)){\n",
    "    mypt <- Listofpatients_minRank[[s]]\n",
    "    bestGene <- rownames(which(mypt == min(mypt, na.rm = T), arr.ind = TRUE))\n",
    "    bestRank <- min(mypt, na.rm = T)\n",
    "    pbrmat[s,bestGene] <- bestRank\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'AHNAK2'"
      ],
      "text/latex": [
       "'AHNAK2'"
      ],
      "text/markdown": [
       "'AHNAK2'"
      ],
      "text/plain": [
       "[1] \"AHNAK2\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rownames(which(Listofpatients_minRank[[3]] == min(Listofpatients_minRank[[3]], na.rm = T), arr.ind = TRUE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$`7316-1075`</strong> = <table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 12 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>HLA-C04:01-G/R</th><th scope=col>HLA-C04:01-V/I</th><th scope=col>HLA-A68:01-G/R</th><th scope=col>HLA-A68:01-V/I</th><th scope=col>HLA-B55:01-G/R</th><th scope=col>HLA-B55:01-V/I</th><th scope=col>HLA-C03:03-G/R</th><th scope=col>HLA-C03:03-V/I</th><th scope=col>HLA-B35:02-G/R</th><th scope=col>HLA-B35:02-V/I</th><th scope=col>HLA-A26:01-G/R</th><th scope=col>HLA-A26:01-V/I</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CTNNB1</th><td>2.0276</td><td>    NA</td><td>6.2125</td><td>     NA</td><td>3.8436</td><td>    NA</td><td>9.2932</td><td>    NA</td><td>11.1415</td><td>   NA</td><td>9.7442</td><td>    NA</td></tr>\n",
       "\t<tr><th scope=row>AHNAK2</th><td>    NA</td><td>3.1029</td><td>    NA</td><td>14.0262</td><td>    NA</td><td>9.3076</td><td>    NA</td><td>0.7069</td><td>     NA</td><td>3.412</td><td>    NA</td><td>7.8231</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\textbf{\\$`7316-1075`} = A matrix: 2 × 12 of type dbl\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & HLA-C04:01-G/R & HLA-C04:01-V/I & HLA-A68:01-G/R & HLA-A68:01-V/I & HLA-B55:01-G/R & HLA-B55:01-V/I & HLA-C03:03-G/R & HLA-C03:03-V/I & HLA-B35:02-G/R & HLA-B35:02-V/I & HLA-A26:01-G/R & HLA-A26:01-V/I\\\\\n",
       "\\hline\n",
       "\tCTNNB1 & 2.0276 &     NA & 6.2125 &      NA & 3.8436 &     NA & 9.2932 &     NA & 11.1415 &    NA & 9.7442 &     NA\\\\\n",
       "\tAHNAK2 &     NA & 3.1029 &     NA & 14.0262 &     NA & 9.3076 &     NA & 0.7069 &      NA & 3.412 &     NA & 7.8231\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "**$`7316-1075`** = \n",
       "A matrix: 2 × 12 of type dbl\n",
       "\n",
       "| <!--/--> | HLA-C04:01-G/R | HLA-C04:01-V/I | HLA-A68:01-G/R | HLA-A68:01-V/I | HLA-B55:01-G/R | HLA-B55:01-V/I | HLA-C03:03-G/R | HLA-C03:03-V/I | HLA-B35:02-G/R | HLA-B35:02-V/I | HLA-A26:01-G/R | HLA-A26:01-V/I |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CTNNB1 | 2.0276 |     NA | 6.2125 |      NA | 3.8436 |     NA | 9.2932 |     NA | 11.1415 |    NA | 9.7442 |     NA |\n",
       "| AHNAK2 |     NA | 3.1029 |     NA | 14.0262 |     NA | 9.3076 |     NA | 0.7069 |      NA | 3.412 |     NA | 7.8231 |\n",
       "\n"
      ],
      "text/plain": [
       "$`7316-1075`\n",
       "       HLA-C04:01-G/R HLA-C04:01-V/I HLA-A68:01-G/R HLA-A68:01-V/I\n",
       "CTNNB1         2.0276             NA         6.2125             NA\n",
       "AHNAK2             NA         3.1029             NA        14.0262\n",
       "       HLA-B55:01-G/R HLA-B55:01-V/I HLA-C03:03-G/R HLA-C03:03-V/I\n",
       "CTNNB1         3.8436             NA         9.2932             NA\n",
       "AHNAK2             NA         9.3076             NA         0.7069\n",
       "       HLA-B35:02-G/R HLA-B35:02-V/I HLA-A26:01-G/R HLA-A26:01-V/I\n",
       "CTNNB1        11.1415             NA         9.7442             NA\n",
       "AHNAK2             NA          3.412             NA         7.8231\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Listofpatients_minRank[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 10 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>PTGER4</th><th scope=col>COQ10A</th><th scope=col>ENTPD2</th><th scope=col>UTP23</th><th scope=col>SLC2A13</th><th scope=col>ATP8B2</th><th scope=col>SLC44A5</th><th scope=col>IGDCC3</th><th scope=col>IRF2BPL</th><th scope=col>C12orf45</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7316-10</th><td>NA</td><td>0.0291</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>7316-101</th><td>NA</td><td>    NA</td><td>NA</td><td>NA</td><td>0.082</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>7316-1075</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></tr>\n",
       "\t<tr><th scope=row>7316-1077</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></tr>\n",
       "\t<tr><th scope=row>7316-1078</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></tr>\n",
       "\t<tr><th scope=row>7316-1081</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></tr>\n",
       "\t<tr><th scope=row>7316-1082</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></tr>\n",
       "\t<tr><th scope=row>7316-1083</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></tr>\n",
       "\t<tr><th scope=row>7316-1084</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></tr>\n",
       "\t<tr><th scope=row>7316-1085</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></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 10 of type dbl\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & PTGER4 & COQ10A & ENTPD2 & UTP23 & SLC2A13 & ATP8B2 & SLC44A5 & IGDCC3 & IRF2BPL & C12orf45\\\\\n",
       "\\hline\n",
       "\t7316-10 & NA & 0.0291 & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-101 & NA &     NA & NA & NA & 0.082 & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1075 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1077 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1078 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1081 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1082 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1083 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1084 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\t7316-1085 & NA &     NA & NA & NA &    NA & NA & NA & NA & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 10 of type dbl\n",
       "\n",
       "| <!--/--> | PTGER4 | COQ10A | ENTPD2 | UTP23 | SLC2A13 | ATP8B2 | SLC44A5 | IGDCC3 | IRF2BPL | C12orf45 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 7316-10 | NA | 0.0291 | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-101 | NA |     NA | NA | NA | 0.082 | NA | NA | NA | NA | NA |\n",
       "| 7316-1075 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1077 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1078 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1081 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1082 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1083 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1084 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "| 7316-1085 | NA |     NA | NA | NA |    NA | NA | NA | NA | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "          PTGER4 COQ10A ENTPD2 UTP23 SLC2A13 ATP8B2 SLC44A5 IGDCC3 IRF2BPL\n",
       "7316-10   NA     0.0291 NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-101  NA         NA NA     NA    0.082   NA     NA      NA     NA     \n",
       "7316-1075 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1077 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1078 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1081 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1082 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1083 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1084 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "7316-1085 NA         NA NA     NA       NA   NA     NA      NA     NA     \n",
       "          C12orf45\n",
       "7316-10   NA      \n",
       "7316-101  NA      \n",
       "7316-1075 NA      \n",
       "7316-1077 NA      \n",
       "7316-1078 NA      \n",
       "7316-1081 NA      \n",
       "7316-1082 NA      \n",
       "7316-1083 NA      \n",
       "7316-1084 NA      \n",
       "7316-1085 NA      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbrmat[1:10, 1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>549</li><li>4038</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 549\n",
       "\\item 4038\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 549\n",
       "2. 4038\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  549 4038"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pbrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbrmat <- pbrmat[, colnames(pbrmat) %in% allmutated_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>549</li><li>4000</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 549\n",
       "\\item 4000\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 549\n",
       "2. 4000\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  549 4000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pbrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset <- metadata_IC[metadata_IC$sample_id %in% rownames(pbrmat),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>543</li><li>24</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 543\n",
       "\\item 24\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 543\n",
       "2. 24\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 543  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset <- metadata_IC_subset[order(metadata_IC_subset$immune_cluster),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbrmat <- pbrmat[metadata_IC_subset$sample_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 10 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>PTGER4</th><th scope=col>COQ10A</th><th scope=col>ENTPD2</th><th scope=col>UTP23</th><th scope=col>SLC2A13</th><th scope=col>ATP8B2</th><th scope=col>SLC44A5</th><th scope=col>IGDCC3</th><th scope=col>IRF2BPL</th><th scope=col>C12orf45</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TARGET-30-PAIPGU-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PALETP-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PALTEG-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PANKFE-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PANXJL-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PAPEFE-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PAPUWY-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PAPVXS-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PARDIW-01A</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></tr>\n",
       "\t<tr><th scope=row>TARGET-30-PARSBI-01A</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></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 10 of type dbl\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & PTGER4 & COQ10A & ENTPD2 & UTP23 & SLC2A13 & ATP8B2 & SLC44A5 & IGDCC3 & IRF2BPL & C12orf45\\\\\n",
       "\\hline\n",
       "\tTARGET-30-PAIPGU-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PALETP-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PALTEG-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PANKFE-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PANXJL-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PAPEFE-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PAPUWY-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PAPVXS-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PARDIW-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\tTARGET-30-PARSBI-01A & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 10 of type dbl\n",
       "\n",
       "| <!--/--> | PTGER4 | COQ10A | ENTPD2 | UTP23 | SLC2A13 | ATP8B2 | SLC44A5 | IGDCC3 | IRF2BPL | C12orf45 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TARGET-30-PAIPGU-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PALETP-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PALTEG-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PANKFE-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PANXJL-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PAPEFE-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PAPUWY-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PAPVXS-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PARDIW-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| TARGET-30-PARSBI-01A | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "                     PTGER4 COQ10A ENTPD2 UTP23 SLC2A13 ATP8B2 SLC44A5 IGDCC3\n",
       "TARGET-30-PAIPGU-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PALETP-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PALTEG-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PANKFE-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PANXJL-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PAPEFE-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PAPUWY-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PAPVXS-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PARDIW-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "TARGET-30-PARSBI-01A NA     NA     NA     NA    NA      NA     NA      NA    \n",
       "                     IRF2BPL C12orf45\n",
       "TARGET-30-PAIPGU-01A NA      NA      \n",
       "TARGET-30-PALETP-01A NA      NA      \n",
       "TARGET-30-PALTEG-01A NA      NA      \n",
       "TARGET-30-PANKFE-01A NA      NA      \n",
       "TARGET-30-PANXJL-01A NA      NA      \n",
       "TARGET-30-PAPEFE-01A NA      NA      \n",
       "TARGET-30-PAPUWY-01A NA      NA      \n",
       "TARGET-30-PAPVXS-01A NA      NA      \n",
       "TARGET-30-PARDIW-01A NA      NA      \n",
       "TARGET-30-PARSBI-01A NA      NA      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbrmat[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in min(x, na.rm = T):\n",
      "“no non-missing arguments to min; returning Inf”\n",
      "Warning message in min(x, na.rm = T):\n",
      "“no non-missing arguments to min; returning Inf”\n",
      "Warning message in min(x, na.rm = T):\n",
      "“no non-missing arguments to min; returning Inf”\n",
      "Warning message in min(x, na.rm = T):\n",
      "“no non-missing arguments to min; returning Inf”\n"
     ]
    }
   ],
   "source": [
    "pbrs <- apply(pbrmat, 1, function(x) min(x, na.rm = T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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>TARGET-30-PAIPGU-01A</dt><dd>0.0145</dd><dt>TARGET-30-PALETP-01A</dt><dd>0.0559</dd><dt>TARGET-30-PALTEG-01A</dt><dd>0.029</dd><dt>TARGET-30-PANKFE-01A</dt><dd>0.3373</dd><dt>TARGET-30-PANXJL-01A</dt><dd>0.0257</dd><dt>TARGET-30-PAPEFE-01A</dt><dd>0.0076</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[TARGET-30-PAIPGU-01A] 0.0145\n",
       "\\item[TARGET-30-PALETP-01A] 0.0559\n",
       "\\item[TARGET-30-PALTEG-01A] 0.029\n",
       "\\item[TARGET-30-PANKFE-01A] 0.3373\n",
       "\\item[TARGET-30-PANXJL-01A] 0.0257\n",
       "\\item[TARGET-30-PAPEFE-01A] 0.0076\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "TARGET-30-PAIPGU-01A\n",
       ":   0.0145TARGET-30-PALETP-01A\n",
       ":   0.0559TARGET-30-PALTEG-01A\n",
       ":   0.029TARGET-30-PANKFE-01A\n",
       ":   0.3373TARGET-30-PANXJL-01A\n",
       ":   0.0257TARGET-30-PAPEFE-01A\n",
       ":   0.0076\n",
       "\n"
      ],
      "text/plain": [
       "TARGET-30-PAIPGU-01A TARGET-30-PALETP-01A TARGET-30-PALTEG-01A \n",
       "              0.0145               0.0559               0.0290 \n",
       "TARGET-30-PANKFE-01A TARGET-30-PANXJL-01A TARGET-30-PAPEFE-01A \n",
       "              0.3373               0.0257               0.0076 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset_pbr <- cbind(metadata_IC_subset, pbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset_pbr <- metadata_IC_subset_pbr[ metadata_IC_subset_pbr$pbrs != Inf,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "aov(formula = pbrs ~ immune_cluster + cohort, data = metadata_IC_subset_pbr)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-1.7374 -0.5691 -0.1526  0.0279 12.2519 \n",
       "\n",
       "Coefficients:\n",
       "                              Estimate Std. Error t value Pr(>|t|)  \n",
       "(Intercept)                    0.74562    0.34346   2.171   0.0304 *\n",
       "immune_clusterMyeloid-driven  -0.21594    0.20441  -1.056   0.2913  \n",
       "immune_clusterPediatric cold  -0.18633    0.20842  -0.894   0.3717  \n",
       "immune_clusterImmune excluded -0.02284    0.23064  -0.099   0.9211  \n",
       "cohortCP                      -0.43374    0.53224  -0.815   0.4155  \n",
       "cohortCPH                      0.37866    0.45850   0.826   0.4093  \n",
       "cohortEPN                      0.17471    0.35404   0.493   0.6219  \n",
       "cohortETMR                    -0.58209    0.98291  -0.592   0.5540  \n",
       "cohortMB                      -0.54354    0.33574  -1.619   0.1061  \n",
       "cohortMNG                     -0.29436    0.51730  -0.569   0.5696  \n",
       "cohortNBL                     -0.56171    0.32668  -1.719   0.0861 .\n",
       "cohortNFB                     -0.47000    0.58934  -0.798   0.4255  \n",
       "cohortpedHGG                  -0.47291    0.36961  -1.279   0.2013  \n",
       "cohortpedLGG                   0.13162    0.31217   0.422   0.6735  \n",
       "cohortSCHW                     1.00154    0.51367   1.950   0.0517 .\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 1.324 on 524 degrees of freedom\n",
       "Multiple R-squared:  0.07323,\tAdjusted R-squared:  0.04847 \n",
       "F-statistic: 2.958 on 14 and 524 DF,  p-value: 0.0002286\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_pbrs <- aov(pbrs ~ immune_cluster + cohort, metadata_IC_subset_pbr)\n",
    "summary.lm(fit_pbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$`Pediatric inflamed`\n",
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       " 0.00750  0.03207  0.18225  0.74528  0.48883 11.80910 \n",
       "\n",
       "$`Myeloid-driven`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "0.00440 0.02525 0.10480 0.49581 0.35750 7.04310 \n",
       "\n",
       "$`Pediatric cold`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.0056  0.0185  0.0739  0.4315  0.2891 12.9428 \n",
       "\n",
       "$`Immune excluded`\n",
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       " 0.00590  0.01915  0.04560  0.35868  0.16755 11.64050 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(metadata_IC_subset_pbr$pbrs,\n",
    "       metadata_IC_subset_pbr$immune_cluster, \n",
    "       summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count number of rank 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"Mupexi/Listofpatients_count2.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a sample x Gene matrix to hold PBR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbrmat <- matrix(nrow = length(unique(all.mupexi$sample_id)), \n",
    "                 ncol = length(unique(all.mupexi$Gene_Symbol)),\n",
    "                 dimnames = list(\n",
    "                 unique(all.mupexi$sample_id),\n",
    "                 unique(all.mupexi$Gene_Symbol)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(s in rownames(pbrmat)){\n",
    "    mypt <- Listofpatients_numberof2[[s]]\n",
    "    bestGene <- rownames(which(mypt == max(mypt, na.rm = T), arr.ind = TRUE))\n",
    "    bestRank <- max(mypt, na.rm = T)\n",
    "    pbrmat[s,bestGene] <- bestRank\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbrmat <- pbrmat[, colnames(pbrmat) %in% allmutated_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>549</li><li>4000</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 549\n",
       "\\item 4000\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 549\n",
       "2. 4000\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  549 4000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pbrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset <- metadata_IC[metadata_IC$sample_id %in% rownames(pbrmat),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>543</li><li>24</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 543\n",
       "\\item 24\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 543\n",
       "2. 24\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 543  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset <- metadata_IC_subset[order(metadata_IC_subset$immune_cluster),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbrmat <- pbrmat[metadata_IC_subset$sample_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in max(x, na.rm = T):\n",
      "“no non-missing arguments to max; returning -Inf”\n",
      "Warning message in max(x, na.rm = T):\n",
      "“no non-missing arguments to max; returning -Inf”\n",
      "Warning message in max(x, na.rm = T):\n",
      "“no non-missing arguments to max; returning -Inf”\n",
      "Warning message in max(x, na.rm = T):\n",
      "“no non-missing arguments to max; returning -Inf”\n",
      "Warning message in max(x, na.rm = T):\n",
      "“no non-missing arguments to max; returning -Inf”\n",
      "Warning message in max(x, na.rm = T):\n",
      "“no non-missing arguments to max; returning -Inf”\n"
     ]
    }
   ],
   "source": [
    "pbrs <- apply(pbrmat, 1, function(x) max(x, na.rm = T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>TARGET-30-PAIPGU-01A</dt><dd>8</dd><dt>TARGET-30-PALETP-01A</dt><dd>6</dd><dt>TARGET-30-PALTEG-01A</dt><dd>5</dd><dt>TARGET-30-PANKFE-01A</dt><dd>2</dd><dt>TARGET-30-PANXJL-01A</dt><dd>7</dd><dt>TARGET-30-PAPEFE-01A</dt><dd>5</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[TARGET-30-PAIPGU-01A] 8\n",
       "\\item[TARGET-30-PALETP-01A] 6\n",
       "\\item[TARGET-30-PALTEG-01A] 5\n",
       "\\item[TARGET-30-PANKFE-01A] 2\n",
       "\\item[TARGET-30-PANXJL-01A] 7\n",
       "\\item[TARGET-30-PAPEFE-01A] 5\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "TARGET-30-PAIPGU-01A\n",
       ":   8TARGET-30-PALETP-01A\n",
       ":   6TARGET-30-PALTEG-01A\n",
       ":   5TARGET-30-PANKFE-01A\n",
       ":   2TARGET-30-PANXJL-01A\n",
       ":   7TARGET-30-PAPEFE-01A\n",
       ":   5\n",
       "\n"
      ],
      "text/plain": [
       "TARGET-30-PAIPGU-01A TARGET-30-PALETP-01A TARGET-30-PALTEG-01A \n",
       "                   8                    6                    5 \n",
       "TARGET-30-PANKFE-01A TARGET-30-PANXJL-01A TARGET-30-PAPEFE-01A \n",
       "                   2                    7                    5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset_pbr <- cbind(metadata_IC_subset, pbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_subset_pbr <- metadata_IC_subset_pbr[ metadata_IC_subset_pbr$pbrs != -Inf,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$`Pediatric inflamed`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   2.000   4.000   3.922   6.000  10.000 \n",
       "\n",
       "$`Myeloid-driven`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   2.000   4.000   4.346   6.000  12.000 \n",
       "\n",
       "$`Pediatric cold`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   3.000   4.000   4.792   7.000  17.000 \n",
       "\n",
       "$`Immune excluded`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   3.000   5.000   5.284   7.000  19.000 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(metadata_IC_subset_pbr$pbrs,\n",
    "       metadata_IC_subset_pbr$immune_cluster, \n",
    "       summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_subset_pbr$pbrs[metadata_IC_subset_pbr$cohort != \"NBL\"] and metadata_IC_subset_pbr$immune_cluster[metadata_IC_subset_pbr$cohort != \"NBL\"] \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  0.2179             -              -             \n",
       "Pediatric cold  0.0038             0.0193         -             \n",
       "Immune excluded 0.0011             0.0055         0.3515        \n",
       "\n",
       "P value adjustment method: none "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_subset_pbr$pbrs[ metadata_IC_subset_pbr$cohort != \"NBL\"],\n",
    "                metadata_IC_subset_pbr$immune_cluster[ metadata_IC_subset_pbr$cohort != \"NBL\"],\n",
    "                p.adjust = \"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "aov(formula = pbrs ~ immune_cluster + cohort, data = metadata_IC_subset_pbr)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-5.6720 -1.7447 -0.4384  1.4443 13.1331 \n",
       "\n",
       "Coefficients:\n",
       "                              Estimate Std. Error t value    Pr(>|t|)    \n",
       "(Intercept)                     3.4638     0.6663   5.199 0.000000289 ***\n",
       "immune_clusterMyeloid-driven    0.7328     0.3967   1.847    0.065283 .  \n",
       "immune_clusterPediatric cold    0.9218     0.4043   2.280    0.023020 *  \n",
       "immune_clusterImmune excluded   0.4992     0.4485   1.113    0.266283    \n",
       "cohortCP                        0.6178     0.9967   0.620    0.535623    \n",
       "cohortCPH                      -1.8746     0.8894  -2.108    0.035528 *  \n",
       "cohortEPN                      -0.5986     0.6867  -0.872    0.383805    \n",
       "cohortETMR                     -1.1743     1.9066  -0.616    0.538202    \n",
       "cohortMB                        1.9039     0.6519   2.920    0.003647 ** \n",
       "cohortMNG                       1.3854     1.0034   1.381    0.167978    \n",
       "cohortNBL                       2.4754     0.6342   3.903    0.000107 ***\n",
       "cohortNFB                       1.2603     1.1432   1.102    0.270780    \n",
       "cohortpedHGG                    1.7792     0.7169   2.482    0.013387 *  \n",
       "cohortpedLGG                   -0.6409     0.6056  -1.058    0.290397    \n",
       "cohortSCHW                     -0.1182     0.9964  -0.119    0.905590    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 2.568 on 522 degrees of freedom\n",
       "Multiple R-squared:  0.2286,\tAdjusted R-squared:  0.2079 \n",
       "F-statistic: 11.05 on 14 and 522 DF,  p-value: < 0.00000000000000022\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_pbrs <- aov(pbrs ~ immune_cluster + cohort, metadata_IC_subset_pbr)\n",
    "summary.lm(fit_pbrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deprecated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "myclass <- as.character(metadata_IC_subset$immune_cluster)\n",
    "names(myclass) <- metadata_IC_subset$sample_id\n",
    "class_hm <- class_hm.fx(myclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbr_hm.fx <- function(pbr_mat){\n",
    "\n",
    "    col_fun= colorRamp2(c(0, 2), c(\"red\", \"white\"))\n",
    "    \n",
    "    pbr_hm = Heatmap(pbr_mat,\n",
    "                       #titles and names   \n",
    "                       name = \"PBR score\",   \n",
    "                       show_row_names = TRUE,\n",
    "                       show_column_names = FALSE,     \n",
    "                       #clusters and orders  \n",
    "                       cluster_columns = FALSE,\n",
    "                       cluster_rows = FALSE,\n",
    "                       show_column_dend = TRUE,\n",
    "                       #row_dend_width = unit(8, \"cm\"),\n",
    "                       #aesthestics\n",
    "                       col = col_fun,\n",
    "                       column_names_gp = gpar(fontsize = 20),\n",
    "                       row_names_gp = gpar(fontsize = 20),\n",
    "                       height = unit(nrow(pbr_mat), \"cm\"),\n",
    "                       column_title_gp = gpar(fontsize = 20),\n",
    "                       column_title = NULL,\n",
    "                       row_title = NULL)\n",
    "    return(pbr_hm)   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clustercolors = structure(c(\"#d73027\", \"#fc8d59\", \"#fee090\", \"#e0f3f8\", \"#91bfdb\", \"#4575b4\"),\n",
    "                          names = c(\"C1\", \"C2\", \"C3\", \"C4\", \"C5\", \"C6\"))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 4 rows containing non-finite values (stat_ydensity).”\n",
      "Warning message:\n",
      "“Removed 4 rows containing non-finite values (stat_boxplot).”\n"
     ]
    },
    {
     "data": {
      "image/png": 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0QrIiqVqvprezZvrkh9EnLbbe1NHJ2dnW1o1KtXz05VAgAAp9I+zHfHufPRoT4j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RmwsTGRk5aNCgXbt2LeMMm7R6TqpProLT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\n0vdu+vXHNUfK2MqjwD4PTZvUU5kLSkB9EOzaljv95x8Oscm7Q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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbrplot <- ggplot(data = metadata_IC_subset_pbr, aes(x = immune_cluster, y = pbrs, fill = immune_cluster)) + \n",
    "geom_violin() + geom_boxplot(width = 0.2, outlier.colour = NA) + myaxis + myplot +\n",
    "scale_fill_manual(values = Clustercolors) \n",
    "pbrplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Listofpatients <- list()\n",
    "\n",
    "for(p in unique(all.mupexi$sample_id)){\n",
    "    mysample <- all.mupexi[ all.mupexi$sample_id == p,]\n",
    "    \n",
    "    genemat <- matrix(nrow = length(unique(mysample$Gene_Symbol)), \n",
    "                  ncol = length(unique(mysample$Amino_Acid_Change)),\n",
    "                  dimnames = list(\n",
    "                  unique(mysample$Gene_Symbol),\n",
    "                  unique(mysample$Amino_Acid_Change)))\n",
    "    \n",
    "    for(r in colnames(genemat)){\n",
    "        \n",
    "    myres <- mysample[ mysample$Amino_Acid_Change == r,]\n",
    "    for(g in unique(myres$Gene_Symbol)){\n",
    "        myresmygen <- myres[ myres$Gene_Symbol == g,]\n",
    "        summarytab <- tapply(myresmygen$Mut_MHCrank_BA, myresmygen$HLA_allele,summary)\n",
    "        BestRank <- min(unlist(summarytab))\n",
    "        genemat[g,r] <- BestRank\n",
    "    }  \n",
    "}\n",
    "    Listofpatients[length(Listofpatients) + 1] <- list(genemat)\n",
    "\n",
    "}\n",
    "\n",
    "names(Listofpatients) <- unique(all.mupexi$sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(Listofpatients, file = paste0(datapath, \"Mupexi/Listofpatients.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "R",
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   "base_numbering": 1,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "215.739px"
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