notebooks/18_define_immunegenes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Dependencies\" data-toc-modified-id=\"Dependencies-1\">Dependencies</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li><li><span><a href=\"#Determine-immune-genes\" data-toc-modified-id=\"Determine-immune-genes-5\">Determine immune genes</a></span><ul class=\"toc-item\"><li><span><a href=\"#1--Bagaev-et-al-Immune-cell-compendium\" data-toc-modified-id=\"1--Bagaev-et-al-Immune-cell-compendium-5.1\">1- Bagaev et al Immune cell compendium</a></span></li><li><span><a href=\"#2--single-cell-data-from-protein-atlas\" data-toc-modified-id=\"2--single-cell-data-from-protein-atlas-5.2\">2- single-cell data from protein atlas</a></span></li><li><span><a href=\"#3--protein-atlas-tissue-specificity-data\" data-toc-modified-id=\"3--protein-atlas-tissue-specificity-data-5.3\">3- protein atlas tissue specificity data</a></span></li><li><span><a href=\"#4--ImmuneScore-genes-from-ESTIMATE\" data-toc-modified-id=\"4--ImmuneScore-genes-from-ESTIMATE-5.4\">4- ImmuneScore genes from ESTIMATE</a></span></li><li><span><a href=\"#Compile-all-immune-genes\" data-toc-modified-id=\"Compile-all-immune-genes-5.5\">Compile all immune genes</a></span></li></ul></li><li><span><a href=\"#Filter-out-genes-expressed-in-pediatric-tumour/non-immune-cells\" data-toc-modified-id=\"Filter-out-genes-expressed-in-pediatric-tumour/non-immune-cells-6\">Filter out genes expressed in pediatric tumour/non-immune cells</a></span><ul class=\"toc-item\"><li><span><a href=\"#1--PDX\" data-toc-modified-id=\"1--PDX-6.1\">1- PDX</a></span></li><li><span><a href=\"#2--scRNA\" data-toc-modified-id=\"2--scRNA-6.2\">2- scRNA</a></span><ul class=\"toc-item\"><li><span><a href=\"#1--Frank's-NBL\" data-toc-modified-id=\"1--Frank's-NBL-6.2.1\">1- Frank's NBL</a></span></li><li><span><a href=\"#2--Gojo's-EPN\" data-toc-modified-id=\"2--Gojo's-EPN-6.2.2\">2- Gojo's EPN</a></span></li><li><span><a href=\"#3--Gillen's-EPN\" data-toc-modified-id=\"3--Gillen's-EPN-6.2.3\">3- Gillen's EPN</a></span></li></ul></li><li><span><a href=\"#3--Cell-lines-from-CBTN\" data-toc-modified-id=\"3--Cell-lines-from-CBTN-6.3\">3- Cell lines from CBTN</a></span></li><li><span><a href=\"#Bind-all\" data-toc-modified-id=\"Bind-all-6.4\">Bind all</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: BiocGenerics\n",
      "\n",
      "Loading required package: parallel\n",
      "\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
      "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
      "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
      "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
      "    union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(Biobase)\n",
    "library(tidyr)\n",
    "library(biomaRt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "longtowidefx <- function(long_exp_matrix){\n",
    "    wide_exp_matrix <- tidyr::spread(long_exp_matrix, \n",
    "                              key = \"sample\", \n",
    "                              value = \"tpm\")\n",
    "    return(wide_exp_matrix)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcentr <- function(x) {\n",
    "    meds <- median(x, na.rm=T)\n",
    "    mads <- mad(x, na.rm=T)\n",
    "    if (mads == 0) {\n",
    "        rep(0, length(x))} else {\n",
    "        (x - meds) / mads}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "manifestpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Manifests/\"\n",
    "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(paste0(datapath, \"exp_mat/IPD_ExprSet_log2_combat.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vars <- pData(IPD_Set_log2_combat)\n",
    "tpms <- exprs(IPD_Set_log2_combat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  1   2   3   4   5   6 \n",
       " 64  26 148 554 110   6 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(vars$CRI_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename TCGA clusters\n",
    "vars$CRI_cluster[which(vars$CRI_cluster == 1)] <- \"Wound Healing\"\n",
    "vars$CRI_cluster[which(vars$CRI_cluster == 2)] <- \"IFN-gamma dominant\"\n",
    "vars$CRI_cluster[which(vars$CRI_cluster == 3)] <- \"Inflammatory\"\n",
    "vars$CRI_cluster[which(vars$CRI_cluster == 4)] <- \"Lymphocyte depleted\"\n",
    "vars$CRI_cluster[which(vars$CRI_cluster == 5)] <- \"Immunologically quiet\"\n",
    "vars$CRI_cluster[which(vars$CRI_cluster == 6)] <- \"TGFbeta dominant\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determine immune genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Bagaev et al Immune cell compendium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TableS2 from Bagaev et al\n",
    "bagaev_median <- read.csv(file = paste0(datapath, \"TME_clusters/immunecells_compendium_Median.csv\"),\n",
    "                          header = T, stringsAsFactors = F, row.names = 1, check.names = F, skip = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 18</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>T_helpers</th><th scope=col>CD4_T_cells</th><th scope=col>Tregs</th><th scope=col>CD8_T_cells</th><th scope=col>NK_cells</th><th scope=col>B_cells</th><th scope=col>Plasmacytoid_dendritic_cells</th><th scope=col>Dendritic_cells</th><th scope=col>Myeloid_cells</th><th scope=col>Monocytes</th><th scope=col>Macrophages</th><th scope=col>Macrophages_infected</th><th scope=col>Neutrophils</th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "\t<tr><th></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;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;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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A1BG</th><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.0000000</td><td>0.00000000</td><td>0.0000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.000000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>A1CF</th><td>0.08406795</td><td>0.06069306</td><td>0.07641884</td><td>0.1037934</td><td>0.04505663</td><td>0.1237914</td><td>0.07698022</td><td>0.06204041</td><td>0.06235984</td><td>0.08430018</td><td>0.02002451</td><td>0.02304037</td><td>0.052535261</td><td>0.01838697</td><td>0.01734924</td><td>0.03327247</td><td>0.01692311</td><td>0.01555221</td></tr>\n",
       "\t<tr><th scope=row>A2M</th><td>0.81805629</td><td>0.12240906</td><td>0.29794129</td><td>2.7971061</td><td>1.21782913</td><td>0.0716964</td><td>2.04524029</td><td>1.95675809</td><td>3.92244810</td><td>0.43032473</td><td>7.94848369</td><td>8.44845815</td><td>0.009030409</td><td>1.67354797</td><td>3.17858054</td><td>4.74040310</td><td>8.17271950</td><td>8.64940635</td></tr>\n",
       "\t<tr><th scope=row>A2ML1</th><td>0.31759809</td><td>0.16653402</td><td>0.43406171</td><td>0.4959649</td><td>0.36339632</td><td>0.8024651</td><td>0.20140618</td><td>0.25896520</td><td>0.56097638</td><td>0.43838215</td><td>0.11274752</td><td>0.07202006</td><td>0.408781112</td><td>0.07351740</td><td>0.08057154</td><td>0.12526228</td><td>0.74466755</td><td>0.20275951</td></tr>\n",
       "\t<tr><th scope=row>A3GALT2</th><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.0000000</td><td>0.00000000</td><td>0.0000000</td><td>0.00000000</td><td>0.00000000</td><td>0.15485671</td><td>0.33359313</td><td>0.00000000</td><td>0.00000000</td><td>0.833714164</td><td>0.00000000</td><td>0.00000000</td><td>0.08447119</td><td>0.05212233</td><td>0.04590313</td></tr>\n",
       "\t<tr><th scope=row>A4GALT</th><td>0.00000000</td><td>0.00000000</td><td>0.00000000</td><td>0.0000000</td><td>0.02202133</td><td>0.1131194</td><td>1.75907437</td><td>0.11421613</td><td>2.28751220</td><td>0.00000000</td><td>1.76635257</td><td>1.69764588</td><td>0.000000000</td><td>4.36188996</td><td>4.01562797</td><td>0.54605123</td><td>2.19565010</td><td>2.14657694</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 18\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & T\\_helpers & CD4\\_T\\_cells & Tregs & CD8\\_T\\_cells & NK\\_cells & B\\_cells & Plasmacytoid\\_dendritic\\_cells & Dendritic\\_cells & Myeloid\\_cells & Monocytes & Macrophages & Macrophages\\_infected & Neutrophils & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tA1BG & 0.00000000 & 0.00000000 & 0.00000000 & 0.0000000 & 0.00000000 & 0.0000000 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000 & 0.000000000 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000 & 0.00000000\\\\\n",
       "\tA1CF & 0.08406795 & 0.06069306 & 0.07641884 & 0.1037934 & 0.04505663 & 0.1237914 & 0.07698022 & 0.06204041 & 0.06235984 & 0.08430018 & 0.02002451 & 0.02304037 & 0.052535261 & 0.01838697 & 0.01734924 & 0.03327247 & 0.01692311 & 0.01555221\\\\\n",
       "\tA2M & 0.81805629 & 0.12240906 & 0.29794129 & 2.7971061 & 1.21782913 & 0.0716964 & 2.04524029 & 1.95675809 & 3.92244810 & 0.43032473 & 7.94848369 & 8.44845815 & 0.009030409 & 1.67354797 & 3.17858054 & 4.74040310 & 8.17271950 & 8.64940635\\\\\n",
       "\tA2ML1 & 0.31759809 & 0.16653402 & 0.43406171 & 0.4959649 & 0.36339632 & 0.8024651 & 0.20140618 & 0.25896520 & 0.56097638 & 0.43838215 & 0.11274752 & 0.07202006 & 0.408781112 & 0.07351740 & 0.08057154 & 0.12526228 & 0.74466755 & 0.20275951\\\\\n",
       "\tA3GALT2 & 0.00000000 & 0.00000000 & 0.00000000 & 0.0000000 & 0.00000000 & 0.0000000 & 0.00000000 & 0.00000000 & 0.15485671 & 0.33359313 & 0.00000000 & 0.00000000 & 0.833714164 & 0.00000000 & 0.00000000 & 0.08447119 & 0.05212233 & 0.04590313\\\\\n",
       "\tA4GALT & 0.00000000 & 0.00000000 & 0.00000000 & 0.0000000 & 0.02202133 & 0.1131194 & 1.75907437 & 0.11421613 & 2.28751220 & 0.00000000 & 1.76635257 & 1.69764588 & 0.000000000 & 4.36188996 & 4.01562797 & 0.54605123 & 2.19565010 & 2.14657694\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 18\n",
       "\n",
       "| <!--/--> | T_helpers &lt;dbl&gt; | CD4_T_cells &lt;dbl&gt; | Tregs &lt;dbl&gt; | CD8_T_cells &lt;dbl&gt; | NK_cells &lt;dbl&gt; | B_cells &lt;dbl&gt; | Plasmacytoid_dendritic_cells &lt;dbl&gt; | Dendritic_cells &lt;dbl&gt; | Myeloid_cells &lt;dbl&gt; | Monocytes &lt;dbl&gt; | Macrophages &lt;dbl&gt; | Macrophages_infected &lt;dbl&gt; | Neutrophils &lt;dbl&gt; | Fibroblasts &lt;dbl&gt; | Endothelium &lt;dbl&gt; | Melanoma &lt;dbl&gt; | Melanoma Primary &lt;dbl&gt; | Melanoma Metastasis &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| A1BG | 0.00000000 | 0.00000000 | 0.00000000 | 0.0000000 | 0.00000000 | 0.0000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.000000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 | 0.00000000 |\n",
       "| A1CF | 0.08406795 | 0.06069306 | 0.07641884 | 0.1037934 | 0.04505663 | 0.1237914 | 0.07698022 | 0.06204041 | 0.06235984 | 0.08430018 | 0.02002451 | 0.02304037 | 0.052535261 | 0.01838697 | 0.01734924 | 0.03327247 | 0.01692311 | 0.01555221 |\n",
       "| A2M | 0.81805629 | 0.12240906 | 0.29794129 | 2.7971061 | 1.21782913 | 0.0716964 | 2.04524029 | 1.95675809 | 3.92244810 | 0.43032473 | 7.94848369 | 8.44845815 | 0.009030409 | 1.67354797 | 3.17858054 | 4.74040310 | 8.17271950 | 8.64940635 |\n",
       "| A2ML1 | 0.31759809 | 0.16653402 | 0.43406171 | 0.4959649 | 0.36339632 | 0.8024651 | 0.20140618 | 0.25896520 | 0.56097638 | 0.43838215 | 0.11274752 | 0.07202006 | 0.408781112 | 0.07351740 | 0.08057154 | 0.12526228 | 0.74466755 | 0.20275951 |\n",
       "| A3GALT2 | 0.00000000 | 0.00000000 | 0.00000000 | 0.0000000 | 0.00000000 | 0.0000000 | 0.00000000 | 0.00000000 | 0.15485671 | 0.33359313 | 0.00000000 | 0.00000000 | 0.833714164 | 0.00000000 | 0.00000000 | 0.08447119 | 0.05212233 | 0.04590313 |\n",
       "| A4GALT | 0.00000000 | 0.00000000 | 0.00000000 | 0.0000000 | 0.02202133 | 0.1131194 | 1.75907437 | 0.11421613 | 2.28751220 | 0.00000000 | 1.76635257 | 1.69764588 | 0.000000000 | 4.36188996 | 4.01562797 | 0.54605123 | 2.19565010 | 2.14657694 |\n",
       "\n"
      ],
      "text/plain": [
       "        T_helpers  CD4_T_cells Tregs      CD8_T_cells NK_cells   B_cells  \n",
       "A1BG    0.00000000 0.00000000  0.00000000 0.0000000   0.00000000 0.0000000\n",
       "A1CF    0.08406795 0.06069306  0.07641884 0.1037934   0.04505663 0.1237914\n",
       "A2M     0.81805629 0.12240906  0.29794129 2.7971061   1.21782913 0.0716964\n",
       "A2ML1   0.31759809 0.16653402  0.43406171 0.4959649   0.36339632 0.8024651\n",
       "A3GALT2 0.00000000 0.00000000  0.00000000 0.0000000   0.00000000 0.0000000\n",
       "A4GALT  0.00000000 0.00000000  0.00000000 0.0000000   0.02202133 0.1131194\n",
       "        Plasmacytoid_dendritic_cells Dendritic_cells Myeloid_cells Monocytes \n",
       "A1BG    0.00000000                   0.00000000      0.00000000    0.00000000\n",
       "A1CF    0.07698022                   0.06204041      0.06235984    0.08430018\n",
       "A2M     2.04524029                   1.95675809      3.92244810    0.43032473\n",
       "A2ML1   0.20140618                   0.25896520      0.56097638    0.43838215\n",
       "A3GALT2 0.00000000                   0.00000000      0.15485671    0.33359313\n",
       "A4GALT  1.75907437                   0.11421613      2.28751220    0.00000000\n",
       "        Macrophages Macrophages_infected Neutrophils Fibroblasts Endothelium\n",
       "A1BG    0.00000000  0.00000000           0.000000000 0.00000000  0.00000000 \n",
       "A1CF    0.02002451  0.02304037           0.052535261 0.01838697  0.01734924 \n",
       "A2M     7.94848369  8.44845815           0.009030409 1.67354797  3.17858054 \n",
       "A2ML1   0.11274752  0.07202006           0.408781112 0.07351740  0.08057154 \n",
       "A3GALT2 0.00000000  0.00000000           0.833714164 0.00000000  0.00000000 \n",
       "A4GALT  1.76635257  1.69764588           0.000000000 4.36188996  4.01562797 \n",
       "        Melanoma   Melanoma Primary Melanoma Metastasis\n",
       "A1BG    0.00000000 0.00000000       0.00000000         \n",
       "A1CF    0.03327247 0.01692311       0.01555221         \n",
       "A2M     4.74040310 8.17271950       8.64940635         \n",
       "A2ML1   0.12526228 0.74466755       0.20275951         \n",
       "A3GALT2 0.08447119 0.05212233       0.04590313         \n",
       "A4GALT  0.54605123 2.19565010       2.14657694         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(bagaev_median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Median center MAD scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagaev_mcentr <- (bagaev_median - median(unlist(bagaev_median)))/mad(unlist(bagaev_median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.0000  0.0379  1.8536  2.4660  4.4165 16.3842 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(bagaev_median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "-0.6745 -0.6607  0.0000  0.2228  0.9326  5.2874 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(bagaev_mcentr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2/ffTAAACh/9r5id27x3LWGLT9qP/HK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vt1VhlEGTSkmQnofPDPwlAWW31m8Kp2xY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\nUBKQ2AEAAAAAKAlI7AAAAAAAlMT/AcEuEJX1NXm2AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(bagaev_mcentr)), binwidth = 0.1) + myaxis + myplot + \n",
    "scale_x_continuous(breaks = c(-1, -0.66,0,1,2,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expressed genes are expressed > 75th percentile in at least one cell type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagaev_expressed_genes <- rownames(bagaev_mcentr)[rowSums(bagaev_mcentr > quantile(unlist(bagaev_mcentr),0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "9897"
      ],
      "text/latex": [
       "9897"
      ],
      "text/markdown": [
       "9897"
      ],
      "text/plain": [
       "[1] 9897"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(bagaev_expressed_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# med center genes across all celltypes\n",
    "bagaev_median_mcentr <- t(apply(bagaev_median,1, mcentr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(bagaev_median_mcentr) <- colnames(bagaev_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 18 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>T_helpers</th><th scope=col>CD4_T_cells</th><th scope=col>Tregs</th><th scope=col>CD8_T_cells</th><th scope=col>NK_cells</th><th scope=col>B_cells</th><th scope=col>Plasmacytoid_dendritic_cells</th><th scope=col>Dendritic_cells</th><th scope=col>Myeloid_cells</th><th scope=col>Monocytes</th><th scope=col>Macrophages</th><th scope=col>Macrophages_infected</th><th scope=col>Neutrophils</th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A1BG</th><td> 0.0000000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td></tr>\n",
       "\t<tr><th scope=row>A1CF</th><td> 0.6716501</td><td> 0.09978925</td><td> 0.4845164</td><td> 1.1542292</td><td>-0.2827521</td><td> 1.6434751</td><td> 0.49825050</td><td> 0.13275183</td><td>0.1405665</td><td> 0.6773315</td><td>-0.8951566</td><td>-0.8213745</td><td>-0.09978925</td><td>-0.9352186</td><td>-0.9606063</td><td>-0.5710485</td><td>-0.9710317</td><td>-1.0045704</td></tr>\n",
       "\t<tr><th scope=row>A2M</th><td>-0.4455402</td><td>-0.70754673</td><td>-0.6414348</td><td> 0.2998434</td><td>-0.2949709</td><td>-0.7266470</td><td> 0.01666284</td><td>-0.01666284</td><td>0.7236889</td><td>-0.5915743</td><td> 2.2400433</td><td> 2.4283522</td><td>-0.75024934</td><td>-0.1233303</td><td> 0.4435208</td><td> 1.0317611</td><td> 2.3244988</td><td> 2.5040368</td></tr>\n",
       "\t<tr><th scope=row>A2ML1</th><td> 0.1263010</td><td>-0.52451253</td><td> 0.6280490</td><td> 0.8947398</td><td> 0.3236087</td><td> 2.2152028</td><td>-0.37427647</td><td>-0.12630096</td><td>1.1748222</td><td> 0.6466623</td><td>-0.7562353</td><td>-0.9316971</td><td> 0.51913522</td><td>-0.9252463</td><td>-0.8948557</td><td>-0.7023192</td><td> 1.9661996</td><td>-0.3684461</td></tr>\n",
       "\t<tr><th scope=row>A3GALT2</th><td> 0.0000000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td></tr>\n",
       "\t<tr><th scope=row>A4GALT</th><td>-0.6744908</td><td>-0.67449076</td><td>-0.6744908</td><td>-0.6744908</td><td>-0.6294993</td><td>-0.4433783</td><td> 2.91944551</td><td>-0.44113764</td><td>3.9990882</td><td>-0.6744908</td><td> 2.9343155</td><td> 2.7939420</td><td>-0.67449076</td><td> 8.2372157</td><td> 7.5297735</td><td> 0.4411376</td><td> 3.8114063</td><td> 3.7111457</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 18 of type dbl\n",
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       "  & T\\_helpers & CD4\\_T\\_cells & Tregs & CD8\\_T\\_cells & NK\\_cells & B\\_cells & Plasmacytoid\\_dendritic\\_cells & Dendritic\\_cells & Myeloid\\_cells & Monocytes & Macrophages & Macrophages\\_infected & Neutrophils & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tA1BG &  0.0000000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.00000000 &  0.00000000 & 0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000\\\\\n",
       "\tA1CF &  0.6716501 &  0.09978925 &  0.4845164 &  1.1542292 & -0.2827521 &  1.6434751 &  0.49825050 &  0.13275183 & 0.1405665 &  0.6773315 & -0.8951566 & -0.8213745 & -0.09978925 & -0.9352186 & -0.9606063 & -0.5710485 & -0.9710317 & -1.0045704\\\\\n",
       "\tA2M & -0.4455402 & -0.70754673 & -0.6414348 &  0.2998434 & -0.2949709 & -0.7266470 &  0.01666284 & -0.01666284 & 0.7236889 & -0.5915743 &  2.2400433 &  2.4283522 & -0.75024934 & -0.1233303 &  0.4435208 &  1.0317611 &  2.3244988 &  2.5040368\\\\\n",
       "\tA2ML1 &  0.1263010 & -0.52451253 &  0.6280490 &  0.8947398 &  0.3236087 &  2.2152028 & -0.37427647 & -0.12630096 & 1.1748222 &  0.6466623 & -0.7562353 & -0.9316971 &  0.51913522 & -0.9252463 & -0.8948557 & -0.7023192 &  1.9661996 & -0.3684461\\\\\n",
       "\tA3GALT2 &  0.0000000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.00000000 &  0.00000000 & 0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000\\\\\n",
       "\tA4GALT & -0.6744908 & -0.67449076 & -0.6744908 & -0.6744908 & -0.6294993 & -0.4433783 &  2.91944551 & -0.44113764 & 3.9990882 & -0.6744908 &  2.9343155 &  2.7939420 & -0.67449076 &  8.2372157 &  7.5297735 &  0.4411376 &  3.8114063 &  3.7111457\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 18 of type dbl\n",
       "\n",
       "| <!--/--> | T_helpers | CD4_T_cells | Tregs | CD8_T_cells | NK_cells | B_cells | Plasmacytoid_dendritic_cells | Dendritic_cells | Myeloid_cells | Monocytes | Macrophages | Macrophages_infected | Neutrophils | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| A1BG |  0.0000000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.00000000 |  0.00000000 | 0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |\n",
       "| A1CF |  0.6716501 |  0.09978925 |  0.4845164 |  1.1542292 | -0.2827521 |  1.6434751 |  0.49825050 |  0.13275183 | 0.1405665 |  0.6773315 | -0.8951566 | -0.8213745 | -0.09978925 | -0.9352186 | -0.9606063 | -0.5710485 | -0.9710317 | -1.0045704 |\n",
       "| A2M | -0.4455402 | -0.70754673 | -0.6414348 |  0.2998434 | -0.2949709 | -0.7266470 |  0.01666284 | -0.01666284 | 0.7236889 | -0.5915743 |  2.2400433 |  2.4283522 | -0.75024934 | -0.1233303 |  0.4435208 |  1.0317611 |  2.3244988 |  2.5040368 |\n",
       "| A2ML1 |  0.1263010 | -0.52451253 |  0.6280490 |  0.8947398 |  0.3236087 |  2.2152028 | -0.37427647 | -0.12630096 | 1.1748222 |  0.6466623 | -0.7562353 | -0.9316971 |  0.51913522 | -0.9252463 | -0.8948557 | -0.7023192 |  1.9661996 | -0.3684461 |\n",
       "| A3GALT2 |  0.0000000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.00000000 |  0.00000000 | 0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |\n",
       "| A4GALT | -0.6744908 | -0.67449076 | -0.6744908 | -0.6744908 | -0.6294993 | -0.4433783 |  2.91944551 | -0.44113764 | 3.9990882 | -0.6744908 |  2.9343155 |  2.7939420 | -0.67449076 |  8.2372157 |  7.5297735 |  0.4411376 |  3.8114063 |  3.7111457 |\n",
       "\n"
      ],
      "text/plain": [
       "        T_helpers  CD4_T_cells Tregs      CD8_T_cells NK_cells   B_cells   \n",
       "A1BG     0.0000000  0.00000000  0.0000000  0.0000000   0.0000000  0.0000000\n",
       "A1CF     0.6716501  0.09978925  0.4845164  1.1542292  -0.2827521  1.6434751\n",
       "A2M     -0.4455402 -0.70754673 -0.6414348  0.2998434  -0.2949709 -0.7266470\n",
       "A2ML1    0.1263010 -0.52451253  0.6280490  0.8947398   0.3236087  2.2152028\n",
       "A3GALT2  0.0000000  0.00000000  0.0000000  0.0000000   0.0000000  0.0000000\n",
       "A4GALT  -0.6744908 -0.67449076 -0.6744908 -0.6744908  -0.6294993 -0.4433783\n",
       "        Plasmacytoid_dendritic_cells Dendritic_cells Myeloid_cells Monocytes \n",
       "A1BG     0.00000000                   0.00000000     0.0000000      0.0000000\n",
       "A1CF     0.49825050                   0.13275183     0.1405665      0.6773315\n",
       "A2M      0.01666284                  -0.01666284     0.7236889     -0.5915743\n",
       "A2ML1   -0.37427647                  -0.12630096     1.1748222      0.6466623\n",
       "A3GALT2  0.00000000                   0.00000000     0.0000000      0.0000000\n",
       "A4GALT   2.91944551                  -0.44113764     3.9990882     -0.6744908\n",
       "        Macrophages Macrophages_infected Neutrophils Fibroblasts Endothelium\n",
       "A1BG     0.0000000   0.0000000            0.00000000  0.0000000   0.0000000 \n",
       "A1CF    -0.8951566  -0.8213745           -0.09978925 -0.9352186  -0.9606063 \n",
       "A2M      2.2400433   2.4283522           -0.75024934 -0.1233303   0.4435208 \n",
       "A2ML1   -0.7562353  -0.9316971            0.51913522 -0.9252463  -0.8948557 \n",
       "A3GALT2  0.0000000   0.0000000            0.00000000  0.0000000   0.0000000 \n",
       "A4GALT   2.9343155   2.7939420           -0.67449076  8.2372157   7.5297735 \n",
       "        Melanoma   Melanoma Primary Melanoma Metastasis\n",
       "A1BG     0.0000000  0.0000000        0.0000000         \n",
       "A1CF    -0.5710485 -0.9710317       -1.0045704         \n",
       "A2M      1.0317611  2.3244988        2.5040368         \n",
       "A2ML1   -0.7023192  1.9661996       -0.3684461         \n",
       "A3GALT2  0.0000000  0.0000000        0.0000000         \n",
       "A4GALT   0.4411376  3.8114063        3.7111457         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(bagaev_median_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a list of tables with  differences across celltype medians for each gene\n",
    "diff_gen_list <- mclapply(rownames(bagaev_median_mcentr), function(x){\n",
    "    mygen <- x\n",
    "    gen_df <- bagaev_median_mcentr[mygen,]\n",
    "    #get m-score difference across cell types\n",
    "    tab_diff <- outer(gen_df, gen_df, \"-\")\n",
    "    \n",
    "    #keep immune cells in rows and non-immune in cols   \n",
    "    return(tab_diff[1:13,14:18])    \n",
    "})\n",
    "\n",
    "names(diff_gen_list) <- rownames(bagaev_median_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 13 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>T_helpers</th><td>0.97997630</td><td>0.97997630</td><td>0.92962668</td><td>-1.2075020</td><td>-2.386453</td></tr>\n",
       "\t<tr><th scope=row>CD4_T_cells</th><td>1.20404458</td><td>1.20404458</td><td>1.15369496</td><td>-0.9834337</td><td>-2.162385</td></tr>\n",
       "\t<tr><th scope=row>Tregs</th><td>0.97705217</td><td>0.97705217</td><td>0.92670255</td><td>-1.2104261</td><td>-2.389377</td></tr>\n",
       "\t<tr><th scope=row>CD8_T_cells</th><td>9.28423066</td><td>9.28423066</td><td>9.23388103</td><td> 7.0967524</td><td> 5.917801</td></tr>\n",
       "\t<tr><th scope=row>NK_cells</th><td>5.92580988</td><td>5.92580988</td><td>5.87546026</td><td> 3.7383316</td><td> 2.559380</td></tr>\n",
       "\t<tr><th scope=row>B_cells</th><td>0.41008628</td><td>0.41008628</td><td>0.35973666</td><td>-1.7773920</td><td>-2.956343</td></tr>\n",
       "\t<tr><th scope=row>Plasmacytoid_dendritic_cells</th><td>1.83737953</td><td>1.83737953</td><td>1.78702990</td><td>-0.3500988</td><td>-1.529050</td></tr>\n",
       "\t<tr><th scope=row>Dendritic_cells</th><td>0.51221251</td><td>0.51221251</td><td>0.46186289</td><td>-1.6752658</td><td>-2.854217</td></tr>\n",
       "\t<tr><th scope=row>Myeloid_cells</th><td>0.79068485</td><td>0.79068485</td><td>0.74033522</td><td>-1.3967934</td><td>-2.575745</td></tr>\n",
       "\t<tr><th scope=row>Monocytes</th><td>0.65941584</td><td>0.65941584</td><td>0.60906622</td><td>-1.5280624</td><td>-2.707014</td></tr>\n",
       "\t<tr><th scope=row>Macrophages</th><td>0.33436192</td><td>0.33436192</td><td>0.28401229</td><td>-1.8531164</td><td>-3.032067</td></tr>\n",
       "\t<tr><th scope=row>Macrophages_infected</th><td>0.08439358</td><td>0.08439358</td><td>0.03404396</td><td>-2.1030847</td><td>-3.282036</td></tr>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>1.12396106</td><td>1.12396106</td><td>1.07361144</td><td>-1.0635172</td><td>-2.242468</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 13 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tT\\_helpers & 0.97997630 & 0.97997630 & 0.92962668 & -1.2075020 & -2.386453\\\\\n",
       "\tCD4\\_T\\_cells & 1.20404458 & 1.20404458 & 1.15369496 & -0.9834337 & -2.162385\\\\\n",
       "\tTregs & 0.97705217 & 0.97705217 & 0.92670255 & -1.2104261 & -2.389377\\\\\n",
       "\tCD8\\_T\\_cells & 9.28423066 & 9.28423066 & 9.23388103 &  7.0967524 &  5.917801\\\\\n",
       "\tNK\\_cells & 5.92580988 & 5.92580988 & 5.87546026 &  3.7383316 &  2.559380\\\\\n",
       "\tB\\_cells & 0.41008628 & 0.41008628 & 0.35973666 & -1.7773920 & -2.956343\\\\\n",
       "\tPlasmacytoid\\_dendritic\\_cells & 1.83737953 & 1.83737953 & 1.78702990 & -0.3500988 & -1.529050\\\\\n",
       "\tDendritic\\_cells & 0.51221251 & 0.51221251 & 0.46186289 & -1.6752658 & -2.854217\\\\\n",
       "\tMyeloid\\_cells & 0.79068485 & 0.79068485 & 0.74033522 & -1.3967934 & -2.575745\\\\\n",
       "\tMonocytes & 0.65941584 & 0.65941584 & 0.60906622 & -1.5280624 & -2.707014\\\\\n",
       "\tMacrophages & 0.33436192 & 0.33436192 & 0.28401229 & -1.8531164 & -3.032067\\\\\n",
       "\tMacrophages\\_infected & 0.08439358 & 0.08439358 & 0.03404396 & -2.1030847 & -3.282036\\\\\n",
       "\tNeutrophils & 1.12396106 & 1.12396106 & 1.07361144 & -1.0635172 & -2.242468\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 13 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| T_helpers | 0.97997630 | 0.97997630 | 0.92962668 | -1.2075020 | -2.386453 |\n",
       "| CD4_T_cells | 1.20404458 | 1.20404458 | 1.15369496 | -0.9834337 | -2.162385 |\n",
       "| Tregs | 0.97705217 | 0.97705217 | 0.92670255 | -1.2104261 | -2.389377 |\n",
       "| CD8_T_cells | 9.28423066 | 9.28423066 | 9.23388103 |  7.0967524 |  5.917801 |\n",
       "| NK_cells | 5.92580988 | 5.92580988 | 5.87546026 |  3.7383316 |  2.559380 |\n",
       "| B_cells | 0.41008628 | 0.41008628 | 0.35973666 | -1.7773920 | -2.956343 |\n",
       "| Plasmacytoid_dendritic_cells | 1.83737953 | 1.83737953 | 1.78702990 | -0.3500988 | -1.529050 |\n",
       "| Dendritic_cells | 0.51221251 | 0.51221251 | 0.46186289 | -1.6752658 | -2.854217 |\n",
       "| Myeloid_cells | 0.79068485 | 0.79068485 | 0.74033522 | -1.3967934 | -2.575745 |\n",
       "| Monocytes | 0.65941584 | 0.65941584 | 0.60906622 | -1.5280624 | -2.707014 |\n",
       "| Macrophages | 0.33436192 | 0.33436192 | 0.28401229 | -1.8531164 | -3.032067 |\n",
       "| Macrophages_infected | 0.08439358 | 0.08439358 | 0.03404396 | -2.1030847 | -3.282036 |\n",
       "| Neutrophils | 1.12396106 | 1.12396106 | 1.07361144 | -1.0635172 | -2.242468 |\n",
       "\n"
      ],
      "text/plain": [
       "                             Fibroblasts Endothelium Melanoma  \n",
       "T_helpers                    0.97997630  0.97997630  0.92962668\n",
       "CD4_T_cells                  1.20404458  1.20404458  1.15369496\n",
       "Tregs                        0.97705217  0.97705217  0.92670255\n",
       "CD8_T_cells                  9.28423066  9.28423066  9.23388103\n",
       "NK_cells                     5.92580988  5.92580988  5.87546026\n",
       "B_cells                      0.41008628  0.41008628  0.35973666\n",
       "Plasmacytoid_dendritic_cells 1.83737953  1.83737953  1.78702990\n",
       "Dendritic_cells              0.51221251  0.51221251  0.46186289\n",
       "Myeloid_cells                0.79068485  0.79068485  0.74033522\n",
       "Monocytes                    0.65941584  0.65941584  0.60906622\n",
       "Macrophages                  0.33436192  0.33436192  0.28401229\n",
       "Macrophages_infected         0.08439358  0.08439358  0.03404396\n",
       "Neutrophils                  1.12396106  1.12396106  1.07361144\n",
       "                             Melanoma Primary Melanoma Metastasis\n",
       "T_helpers                    -1.2075020       -2.386453          \n",
       "CD4_T_cells                  -0.9834337       -2.162385          \n",
       "Tregs                        -1.2104261       -2.389377          \n",
       "CD8_T_cells                   7.0967524        5.917801          \n",
       "NK_cells                      3.7383316        2.559380          \n",
       "B_cells                      -1.7773920       -2.956343          \n",
       "Plasmacytoid_dendritic_cells -0.3500988       -1.529050          \n",
       "Dendritic_cells              -1.6752658       -2.854217          \n",
       "Myeloid_cells                -1.3967934       -2.575745          \n",
       "Monocytes                    -1.5280624       -2.707014          \n",
       "Macrophages                  -1.8531164       -3.032067          \n",
       "Macrophages_infected         -2.1030847       -3.282036          \n",
       "Neutrophils                  -1.0635172       -2.242468          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#row value - col value\n",
    "diff_gen_list[[\"CD8A\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select genes if any immune cells express >=2 mad compared to all nonimmune cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_gen_list_immune <- lapply(diff_gen_list, function(x) x[ rowSums(x >= 2) == 5,,drop = F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rmeove empty lists\n",
    "diff_gen_list_immune <- diff_gen_list_immune[sapply(diff_gen_list_immune, function(x) length(unlist(x))) >= 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$A1CF</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>B_cells</th><td>2.578694</td><td>2.604081</td><td>2.214524</td><td>2.614507</td><td>2.648045</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$A4GNT</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Myeloid_cells</th><td>4.52825</td><td>8.814255</td><td>7.259208</td><td>7.441095</td><td>7.465274</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$AANAT</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>NK_cells</th><td>3.102306</td><td>3.132543</td><td>2.538271</td><td>2.620217</td><td>2.795156</td></tr>\n",
       "\t<tr><th scope=row>B_cells</th><td>5.223205</td><td>5.253442</td><td>4.659171</td><td>4.741116</td><td>4.916055</td></tr>\n",
       "\t<tr><th scope=row>Monocytes</th><td>3.200807</td><td>3.231044</td><td>2.636772</td><td>2.718718</td><td>2.893657</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$AATK</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>5.495904</td><td>4.814888</td><td>4.827939</td><td>3.555892</td><td>3.772472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ABAT</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>2.04903</td><td>3.688259</td><td>4.103155</td><td>3.639862</td><td>3.56384</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ABCA13</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Fibroblasts</th><th scope=col>Endothelium</th><th scope=col>Melanoma</th><th scope=col>Melanoma Primary</th><th scope=col>Melanoma Metastasis</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>4.658446</td><td>4.791459</td><td>3.000197</td><td>3.224012</td><td>3.567734</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$A1CF] A matrix: 1 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tB\\_cells & 2.578694 & 2.604081 & 2.214524 & 2.614507 & 2.648045\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$A4GNT] A matrix: 1 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tMyeloid\\_cells & 4.52825 & 8.814255 & 7.259208 & 7.441095 & 7.465274\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$AANAT] A matrix: 3 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tNK\\_cells & 3.102306 & 3.132543 & 2.538271 & 2.620217 & 2.795156\\\\\n",
       "\tB\\_cells & 5.223205 & 5.253442 & 4.659171 & 4.741116 & 4.916055\\\\\n",
       "\tMonocytes & 3.200807 & 3.231044 & 2.636772 & 2.718718 & 2.893657\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$AATK] A matrix: 1 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tNeutrophils & 5.495904 & 4.814888 & 4.827939 & 3.555892 & 3.772472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ABAT] A matrix: 1 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tNeutrophils & 2.04903 & 3.688259 & 4.103155 & 3.639862 & 3.56384\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ABCA13] A matrix: 1 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Fibroblasts & Endothelium & Melanoma & Melanoma Primary & Melanoma Metastasis\\\\\n",
       "\\hline\n",
       "\tNeutrophils & 4.658446 & 4.791459 & 3.000197 & 3.224012 & 3.567734\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$A1CF\n",
       ":   \n",
       "A matrix: 1 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| B_cells | 2.578694 | 2.604081 | 2.214524 | 2.614507 | 2.648045 |\n",
       "\n",
       "\n",
       "$A4GNT\n",
       ":   \n",
       "A matrix: 1 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| Myeloid_cells | 4.52825 | 8.814255 | 7.259208 | 7.441095 | 7.465274 |\n",
       "\n",
       "\n",
       "$AANAT\n",
       ":   \n",
       "A matrix: 3 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| NK_cells | 3.102306 | 3.132543 | 2.538271 | 2.620217 | 2.795156 |\n",
       "| B_cells | 5.223205 | 5.253442 | 4.659171 | 4.741116 | 4.916055 |\n",
       "| Monocytes | 3.200807 | 3.231044 | 2.636772 | 2.718718 | 2.893657 |\n",
       "\n",
       "\n",
       "$AATK\n",
       ":   \n",
       "A matrix: 1 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| Neutrophils | 5.495904 | 4.814888 | 4.827939 | 3.555892 | 3.772472 |\n",
       "\n",
       "\n",
       "$ABAT\n",
       ":   \n",
       "A matrix: 1 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| Neutrophils | 2.04903 | 3.688259 | 4.103155 | 3.639862 | 3.56384 |\n",
       "\n",
       "\n",
       "$ABCA13\n",
       ":   \n",
       "A matrix: 1 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | Fibroblasts | Endothelium | Melanoma | Melanoma Primary | Melanoma Metastasis |\n",
       "|---|---|---|---|---|---|\n",
       "| Neutrophils | 4.658446 | 4.791459 | 3.000197 | 3.224012 | 3.567734 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$A1CF\n",
       "        Fibroblasts Endothelium Melanoma Melanoma Primary Melanoma Metastasis\n",
       "B_cells    2.578694    2.604081 2.214524         2.614507            2.648045\n",
       "\n",
       "$A4GNT\n",
       "              Fibroblasts Endothelium Melanoma Melanoma Primary\n",
       "Myeloid_cells     4.52825    8.814255 7.259208         7.441095\n",
       "              Melanoma Metastasis\n",
       "Myeloid_cells            7.465274\n",
       "\n",
       "$AANAT\n",
       "          Fibroblasts Endothelium Melanoma Melanoma Primary Melanoma Metastasis\n",
       "NK_cells     3.102306    3.132543 2.538271         2.620217            2.795156\n",
       "B_cells      5.223205    5.253442 4.659171         4.741116            4.916055\n",
       "Monocytes    3.200807    3.231044 2.636772         2.718718            2.893657\n",
       "\n",
       "$AATK\n",
       "            Fibroblasts Endothelium Melanoma Melanoma Primary\n",
       "Neutrophils    5.495904    4.814888 4.827939         3.555892\n",
       "            Melanoma Metastasis\n",
       "Neutrophils            3.772472\n",
       "\n",
       "$ABAT\n",
       "            Fibroblasts Endothelium Melanoma Melanoma Primary\n",
       "Neutrophils     2.04903    3.688259 4.103155         3.639862\n",
       "            Melanoma Metastasis\n",
       "Neutrophils             3.56384\n",
       "\n",
       "$ABCA13\n",
       "            Fibroblasts Endothelium Melanoma Melanoma Primary\n",
       "Neutrophils    4.658446    4.791459 3.000197         3.224012\n",
       "            Melanoma Metastasis\n",
       "Neutrophils            3.567734\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(diff_gen_list_immune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3733"
      ],
      "text/latex": [
       "3733"
      ],
      "text/markdown": [
       "3733"
      ],
      "text/plain": [
       "[1] 3733"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_immune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_gen_list_immune_expressed <- diff_gen_list_immune[names(diff_gen_list_immune) %in% bagaev_expressed_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1958"
      ],
      "text/latex": [
       "1958"
      ],
      "text/markdown": [
       "1958"
      ],
      "text/plain": [
       "[1] 1958"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_immune_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "compendium_immune_genes <- names(diff_gen_list_immune_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>'AATK'</li><li>'ABCA7'</li><li>'ABCB1'</li><li>'ABCB4'</li><li>'ABHD15'</li><li>'ABHD17A'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'AATK'\n",
       "\\item 'ABCA7'\n",
       "\\item 'ABCB1'\n",
       "\\item 'ABCB4'\n",
       "\\item 'ABHD15'\n",
       "\\item 'ABHD17A'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'AATK'\n",
       "2. 'ABCA7'\n",
       "3. 'ABCB1'\n",
       "4. 'ABCB4'\n",
       "5. 'ABHD15'\n",
       "6. 'ABHD17A'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"AATK\"    \"ABCA7\"   \"ABCB1\"   \"ABCB4\"   \"ABHD15\"  \"ABHD17A\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(compendium_immune_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selected 1958 genes that are expressed more than 75th (of the entire dataset) in at least one cell type AND have >2 mad difference in expression between at least one immune celltype and all non-immune celltypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- single-cell data from protein atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteinatlas <- read.csv(paste0(datapath, \"TME_clusters/proteinatlas_v20.1.csv\"), header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>19670</li><li>292</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 19670\n",
       "\\item 292\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 19670\n",
       "2. 292\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 19670   292"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(proteinatlas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove irrelevant columns\n",
    "rmcols <- colnames(proteinatlas)[ grepl(\"Cell.RNA...\", colnames(proteinatlas))]\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"mouse\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"pig\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"Pathology\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"score\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"distribution\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"Blood.concentration\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"Secretome\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"Subcellular\", colnames(proteinatlas))])\n",
    "rmcols <- c(rmcols, colnames(proteinatlas)[ grepl(\"Brain.RNA\", colnames(proteinatlas))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteinatlas <- proteinatlas[,! colnames(proteinatlas) %in% rmcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get single-cell columns and dedup genes\n",
    "sc_rna <- proteinatlas[!duplicated(proteinatlas$Gene),grepl(\"Single.Cell.Type\", colnames(proteinatlas))]\n",
    "rownames(sc_rna) <- proteinatlas$Gene[!duplicated(proteinatlas$Gene)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>'Single.Cell.Type.RNA...Alveolar.cells.type.1..NX.'</li><li>'Single.Cell.Type.RNA...Alveolar.cells.type.2..NX.'</li><li>'Single.Cell.Type.RNA...B.cells..NX.'</li><li>'Single.Cell.Type.RNA...Basal.glandular.cells..NX.'</li><li>'Single.Cell.Type.RNA...Basal.keratinocytes..NX.'</li><li>'Single.Cell.Type.RNA...Bipolar.cells..NX.'</li><li>'Single.Cell.Type.RNA...Cardiomyocytes..NX.'</li><li>'Single.Cell.Type.RNA...Cholangiocytes..NX.'</li><li>'Single.Cell.Type.RNA...Ciliated.cells..NX.'</li><li>'Single.Cell.Type.RNA...Club.cells..NX.'</li><li>'Single.Cell.Type.RNA...Collecting.duct.cells..NX.'</li><li>'Single.Cell.Type.RNA...Cone.photoreceptor.cells..NX.'</li><li>'Single.Cell.Type.RNA...Cytotrophoblasts..NX.'</li><li>'Single.Cell.Type.RNA...Distal.tubular.cells..NX.'</li><li>'Single.Cell.Type.RNA...Ductal.cells..NX.'</li><li>'Single.Cell.Type.RNA...Early.spermatids..NX.'</li><li>'Single.Cell.Type.RNA...Endothelial.cells..NX.'</li><li>'Single.Cell.Type.RNA...Enterocytes..NX.'</li><li>'Single.Cell.Type.RNA...Erythroid.cells..NX.'</li><li>'Single.Cell.Type.RNA...Exocrine.glandular.cells..NX.'</li><li>'Single.Cell.Type.RNA...Extravillous.trophoblasts..NX.'</li><li>'Single.Cell.Type.RNA...Fibroblasts..NX.'</li><li>'Single.Cell.Type.RNA...Glandular.cells..NX.'</li><li>'Single.Cell.Type.RNA...granulocytes..NX.'</li><li>'Single.Cell.Type.RNA...Hepatocytes..NX.'</li><li>'Single.Cell.Type.RNA...Hofbauer.cells..NX.'</li><li>'Single.Cell.Type.RNA...Horizontal.cells..NX.'</li><li>'Single.Cell.Type.RNA...Intestinal.endocrine.cells..NX.'</li><li>'Single.Cell.Type.RNA...Ito.cells..NX.'</li><li>'Single.Cell.Type.RNA...Kupffer.cells..NX.'</li><li>'Single.Cell.Type.RNA...Late.spermatids..NX.'</li><li>'Single.Cell.Type.RNA...Leydig.cells..NX.'</li><li>'Single.Cell.Type.RNA...Macrophages..NX.'</li><li>'Single.Cell.Type.RNA...Melanocytes..NX.'</li><li>'Single.Cell.Type.RNA...monocytes..NX.'</li><li>'Single.Cell.Type.RNA...Mucus.secreting.cells..NX.'</li><li>'Single.Cell.Type.RNA...Muller.glia.cells..NX.'</li><li>'Single.Cell.Type.RNA...Pancreatic.endocrine.cells..NX.'</li><li>'Single.Cell.Type.RNA...Paneth.cells..NX.'</li><li>'Single.Cell.Type.RNA...Peritubular.cells..NX.'</li><li>'Single.Cell.Type.RNA...Proximal.tubular.cells..NX.'</li><li>'Single.Cell.Type.RNA...Rod.photoreceptor.cells..NX.'</li><li>'Single.Cell.Type.RNA...Sertoli.cells..NX.'</li><li>'Single.Cell.Type.RNA...Smooth.muscle.cells..NX.'</li><li>'Single.Cell.Type.RNA...Spermatocytes..NX.'</li><li>'Single.Cell.Type.RNA...Spermatogonia..NX.'</li><li>'Single.Cell.Type.RNA...Suprabasal.keratinocytes..NX.'</li><li>'Single.Cell.Type.RNA...Syncytiotrophoblasts..NX.'</li><li>'Single.Cell.Type.RNA...T.cells..NX.'</li><li>'Single.Cell.Type.RNA...Undifferentiated.cells..NX.'</li><li>'Single.Cell.Type.RNA...Urothelial.cells..NX.'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Single.Cell.Type.RNA...Alveolar.cells.type.1..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Alveolar.cells.type.2..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...B.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Basal.glandular.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Basal.keratinocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Bipolar.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Cardiomyocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Cholangiocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Ciliated.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Club.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Collecting.duct.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Cone.photoreceptor.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Cytotrophoblasts..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Distal.tubular.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Ductal.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Early.spermatids..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Endothelial.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Enterocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Erythroid.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Exocrine.glandular.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Extravillous.trophoblasts..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Fibroblasts..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Glandular.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...granulocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Hepatocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Hofbauer.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Horizontal.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Intestinal.endocrine.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Ito.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Kupffer.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Late.spermatids..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Leydig.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Macrophages..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Melanocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...monocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Mucus.secreting.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Muller.glia.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Pancreatic.endocrine.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Paneth.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Peritubular.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Proximal.tubular.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Rod.photoreceptor.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Sertoli.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Smooth.muscle.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Spermatocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Spermatogonia..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Suprabasal.keratinocytes..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Syncytiotrophoblasts..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...T.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Undifferentiated.cells..NX.'\n",
       "\\item 'Single.Cell.Type.RNA...Urothelial.cells..NX.'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Single.Cell.Type.RNA...Alveolar.cells.type.1..NX.'\n",
       "2. 'Single.Cell.Type.RNA...Alveolar.cells.type.2..NX.'\n",
       "3. 'Single.Cell.Type.RNA...B.cells..NX.'\n",
       "4. 'Single.Cell.Type.RNA...Basal.glandular.cells..NX.'\n",
       "5. 'Single.Cell.Type.RNA...Basal.keratinocytes..NX.'\n",
       "6. 'Single.Cell.Type.RNA...Bipolar.cells..NX.'\n",
       "7. 'Single.Cell.Type.RNA...Cardiomyocytes..NX.'\n",
       "8. 'Single.Cell.Type.RNA...Cholangiocytes..NX.'\n",
       "9. 'Single.Cell.Type.RNA...Ciliated.cells..NX.'\n",
       "10. 'Single.Cell.Type.RNA...Club.cells..NX.'\n",
       "11. 'Single.Cell.Type.RNA...Collecting.duct.cells..NX.'\n",
       "12. 'Single.Cell.Type.RNA...Cone.photoreceptor.cells..NX.'\n",
       "13. 'Single.Cell.Type.RNA...Cytotrophoblasts..NX.'\n",
       "14. 'Single.Cell.Type.RNA...Distal.tubular.cells..NX.'\n",
       "15. 'Single.Cell.Type.RNA...Ductal.cells..NX.'\n",
       "16. 'Single.Cell.Type.RNA...Early.spermatids..NX.'\n",
       "17. 'Single.Cell.Type.RNA...Endothelial.cells..NX.'\n",
       "18. 'Single.Cell.Type.RNA...Enterocytes..NX.'\n",
       "19. 'Single.Cell.Type.RNA...Erythroid.cells..NX.'\n",
       "20. 'Single.Cell.Type.RNA...Exocrine.glandular.cells..NX.'\n",
       "21. 'Single.Cell.Type.RNA...Extravillous.trophoblasts..NX.'\n",
       "22. 'Single.Cell.Type.RNA...Fibroblasts..NX.'\n",
       "23. 'Single.Cell.Type.RNA...Glandular.cells..NX.'\n",
       "24. 'Single.Cell.Type.RNA...granulocytes..NX.'\n",
       "25. 'Single.Cell.Type.RNA...Hepatocytes..NX.'\n",
       "26. 'Single.Cell.Type.RNA...Hofbauer.cells..NX.'\n",
       "27. 'Single.Cell.Type.RNA...Horizontal.cells..NX.'\n",
       "28. 'Single.Cell.Type.RNA...Intestinal.endocrine.cells..NX.'\n",
       "29. 'Single.Cell.Type.RNA...Ito.cells..NX.'\n",
       "30. 'Single.Cell.Type.RNA...Kupffer.cells..NX.'\n",
       "31. 'Single.Cell.Type.RNA...Late.spermatids..NX.'\n",
       "32. 'Single.Cell.Type.RNA...Leydig.cells..NX.'\n",
       "33. 'Single.Cell.Type.RNA...Macrophages..NX.'\n",
       "34. 'Single.Cell.Type.RNA...Melanocytes..NX.'\n",
       "35. 'Single.Cell.Type.RNA...monocytes..NX.'\n",
       "36. 'Single.Cell.Type.RNA...Mucus.secreting.cells..NX.'\n",
       "37. 'Single.Cell.Type.RNA...Muller.glia.cells..NX.'\n",
       "38. 'Single.Cell.Type.RNA...Pancreatic.endocrine.cells..NX.'\n",
       "39. 'Single.Cell.Type.RNA...Paneth.cells..NX.'\n",
       "40. 'Single.Cell.Type.RNA...Peritubular.cells..NX.'\n",
       "41. 'Single.Cell.Type.RNA...Proximal.tubular.cells..NX.'\n",
       "42. 'Single.Cell.Type.RNA...Rod.photoreceptor.cells..NX.'\n",
       "43. 'Single.Cell.Type.RNA...Sertoli.cells..NX.'\n",
       "44. 'Single.Cell.Type.RNA...Smooth.muscle.cells..NX.'\n",
       "45. 'Single.Cell.Type.RNA...Spermatocytes..NX.'\n",
       "46. 'Single.Cell.Type.RNA...Spermatogonia..NX.'\n",
       "47. 'Single.Cell.Type.RNA...Suprabasal.keratinocytes..NX.'\n",
       "48. 'Single.Cell.Type.RNA...Syncytiotrophoblasts..NX.'\n",
       "49. 'Single.Cell.Type.RNA...T.cells..NX.'\n",
       "50. 'Single.Cell.Type.RNA...Undifferentiated.cells..NX.'\n",
       "51. 'Single.Cell.Type.RNA...Urothelial.cells..NX.'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Single.Cell.Type.RNA...Alveolar.cells.type.1..NX.\"     \n",
       " [2] \"Single.Cell.Type.RNA...Alveolar.cells.type.2..NX.\"     \n",
       " [3] \"Single.Cell.Type.RNA...B.cells..NX.\"                   \n",
       " [4] \"Single.Cell.Type.RNA...Basal.glandular.cells..NX.\"     \n",
       " [5] \"Single.Cell.Type.RNA...Basal.keratinocytes..NX.\"       \n",
       " [6] \"Single.Cell.Type.RNA...Bipolar.cells..NX.\"             \n",
       " [7] \"Single.Cell.Type.RNA...Cardiomyocytes..NX.\"            \n",
       " [8] \"Single.Cell.Type.RNA...Cholangiocytes..NX.\"            \n",
       " [9] \"Single.Cell.Type.RNA...Ciliated.cells..NX.\"            \n",
       "[10] \"Single.Cell.Type.RNA...Club.cells..NX.\"                \n",
       "[11] \"Single.Cell.Type.RNA...Collecting.duct.cells..NX.\"     \n",
       "[12] \"Single.Cell.Type.RNA...Cone.photoreceptor.cells..NX.\"  \n",
       "[13] \"Single.Cell.Type.RNA...Cytotrophoblasts..NX.\"          \n",
       "[14] \"Single.Cell.Type.RNA...Distal.tubular.cells..NX.\"      \n",
       "[15] \"Single.Cell.Type.RNA...Ductal.cells..NX.\"              \n",
       "[16] \"Single.Cell.Type.RNA...Early.spermatids..NX.\"          \n",
       "[17] \"Single.Cell.Type.RNA...Endothelial.cells..NX.\"         \n",
       "[18] \"Single.Cell.Type.RNA...Enterocytes..NX.\"               \n",
       "[19] \"Single.Cell.Type.RNA...Erythroid.cells..NX.\"           \n",
       "[20] \"Single.Cell.Type.RNA...Exocrine.glandular.cells..NX.\"  \n",
       "[21] \"Single.Cell.Type.RNA...Extravillous.trophoblasts..NX.\" \n",
       "[22] \"Single.Cell.Type.RNA...Fibroblasts..NX.\"               \n",
       "[23] \"Single.Cell.Type.RNA...Glandular.cells..NX.\"           \n",
       "[24] \"Single.Cell.Type.RNA...granulocytes..NX.\"              \n",
       "[25] \"Single.Cell.Type.RNA...Hepatocytes..NX.\"               \n",
       "[26] \"Single.Cell.Type.RNA...Hofbauer.cells..NX.\"            \n",
       "[27] \"Single.Cell.Type.RNA...Horizontal.cells..NX.\"          \n",
       "[28] \"Single.Cell.Type.RNA...Intestinal.endocrine.cells..NX.\"\n",
       "[29] \"Single.Cell.Type.RNA...Ito.cells..NX.\"                 \n",
       "[30] \"Single.Cell.Type.RNA...Kupffer.cells..NX.\"             \n",
       "[31] \"Single.Cell.Type.RNA...Late.spermatids..NX.\"           \n",
       "[32] \"Single.Cell.Type.RNA...Leydig.cells..NX.\"              \n",
       "[33] \"Single.Cell.Type.RNA...Macrophages..NX.\"               \n",
       "[34] \"Single.Cell.Type.RNA...Melanocytes..NX.\"               \n",
       "[35] \"Single.Cell.Type.RNA...monocytes..NX.\"                 \n",
       "[36] \"Single.Cell.Type.RNA...Mucus.secreting.cells..NX.\"     \n",
       "[37] \"Single.Cell.Type.RNA...Muller.glia.cells..NX.\"         \n",
       "[38] \"Single.Cell.Type.RNA...Pancreatic.endocrine.cells..NX.\"\n",
       "[39] \"Single.Cell.Type.RNA...Paneth.cells..NX.\"              \n",
       "[40] \"Single.Cell.Type.RNA...Peritubular.cells..NX.\"         \n",
       "[41] \"Single.Cell.Type.RNA...Proximal.tubular.cells..NX.\"    \n",
       "[42] \"Single.Cell.Type.RNA...Rod.photoreceptor.cells..NX.\"   \n",
       "[43] \"Single.Cell.Type.RNA...Sertoli.cells..NX.\"             \n",
       "[44] \"Single.Cell.Type.RNA...Smooth.muscle.cells..NX.\"       \n",
       "[45] \"Single.Cell.Type.RNA...Spermatocytes..NX.\"             \n",
       "[46] \"Single.Cell.Type.RNA...Spermatogonia..NX.\"             \n",
       "[47] \"Single.Cell.Type.RNA...Suprabasal.keratinocytes..NX.\"  \n",
       "[48] \"Single.Cell.Type.RNA...Syncytiotrophoblasts..NX.\"      \n",
       "[49] \"Single.Cell.Type.RNA...T.cells..NX.\"                   \n",
       "[50] \"Single.Cell.Type.RNA...Undifferentiated.cells..NX.\"    \n",
       "[51] \"Single.Cell.Type.RNA...Urothelial.cells..NX.\"          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(sc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleanup\n",
    "colnames(sc_rna) <- gsub(\"Single.Cell.Type.RNA...\", \"\", colnames(sc_rna))\n",
    "colnames(sc_rna) <- gsub(\"..NX.\", \"\", colnames(sc_rna))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 51</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Alveolar.cells.type.1</th><th scope=col>Alveolar.cells.type.2</th><th scope=col>B.cells</th><th scope=col>Basal.glandular.cells</th><th scope=col>Basal.keratinocytes</th><th scope=col>Bipolar.cells</th><th scope=col>Cardiomyocytes</th><th scope=col>Cholangiocytes</th><th scope=col>Ciliated.cells</th><th scope=col>Club.cells</th><th scope=col>⋯</th><th scope=col>Rod.photoreceptor.cells</th><th scope=col>Sertoli.cells</th><th scope=col>Smooth.muscle.cells</th><th scope=col>Spermatocytes</th><th scope=col>Spermatogonia</th><th scope=col>Suprabasal.keratinocytes</th><th scope=col>Syncytiotrophoblasts</th><th scope=col>T.cells</th><th scope=col>Undifferentiated.cells</th><th scope=col>Urothelial.cells</th></tr>\n",
       "\t<tr><th></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;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>⋯</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;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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TSPAN6</th><td> 3.0</td><td> 8.5</td><td> 7.8</td><td> 36.0</td><td>  6.4</td><td> 0.7</td><td> 7.3</td><td>61.1</td><td>115.1</td><td>21.6</td><td>⋯</td><td> 1.5</td><td>19.4</td><td> 7.5</td><td> 19.0</td><td> 32.2</td><td> 4.6</td><td> 11.3</td><td> 3.5</td><td>78.3</td><td> 78.2</td></tr>\n",
       "\t<tr><th scope=row>TNMD</th><td> 0.0</td><td> 0.0</td><td> 0.0</td><td>  0.0</td><td>  1.8</td><td> 0.0</td><td> 0.0</td><td> 0.0</td><td>  0.0</td><td> 0.0</td><td>⋯</td><td> 0.0</td><td> 0.0</td><td> 0.0</td><td>  0.0</td><td>  0.0</td><td> 0.9</td><td>  0.0</td><td> 0.0</td><td> 0.5</td><td>  0.0</td></tr>\n",
       "\t<tr><th scope=row>DPM1</th><td>17.9</td><td>40.2</td><td>52.1</td><td>126.5</td><td>103.5</td><td>11.0</td><td>36.2</td><td>20.4</td><td> 77.6</td><td>43.3</td><td>⋯</td><td>10.2</td><td>71.1</td><td>61.3</td><td>110.0</td><td>113.4</td><td>92.2</td><td>205.3</td><td>55.3</td><td>48.2</td><td>127.0</td></tr>\n",
       "\t<tr><th scope=row>SCYL3</th><td> 6.0</td><td>11.8</td><td> 5.2</td><td>  1.7</td><td>  2.2</td><td> 9.6</td><td> 3.4</td><td>22.9</td><td>  2.7</td><td> 9.1</td><td>⋯</td><td>17.6</td><td> 6.5</td><td> 4.1</td><td>  7.2</td><td>  6.4</td><td> 1.6</td><td>  9.7</td><td>10.2</td><td> 9.9</td><td>  2.3</td></tr>\n",
       "\t<tr><th scope=row>C1orf112</th><td> 0.0</td><td> 3.5</td><td> 2.2</td><td>  2.6</td><td>  1.5</td><td> 3.8</td><td> 0.4</td><td> 0.0</td><td>  2.7</td><td> 1.2</td><td>⋯</td><td> 6.5</td><td> 6.5</td><td> 1.2</td><td> 27.7</td><td> 17.7</td><td> 0.9</td><td>  3.2</td><td> 1.9</td><td> 1.6</td><td>  1.4</td></tr>\n",
       "\t<tr><th scope=row>FGR</th><td> 6.0</td><td>30.7</td><td>12.5</td><td>  0.5</td><td>  2.5</td><td> 0.0</td><td> 1.6</td><td> 2.5</td><td>  2.7</td><td>10.3</td><td>⋯</td><td> 0.0</td><td> 0.0</td><td> 1.0</td><td>  0.2</td><td>  0.5</td><td> 1.0</td><td>  0.8</td><td>45.8</td><td> 0.0</td><td>  0.9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 51\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Alveolar.cells.type.1 & Alveolar.cells.type.2 & B.cells & Basal.glandular.cells & Basal.keratinocytes & Bipolar.cells & Cardiomyocytes & Cholangiocytes & Ciliated.cells & Club.cells & ⋯ & Rod.photoreceptor.cells & Sertoli.cells & Smooth.muscle.cells & Spermatocytes & Spermatogonia & Suprabasal.keratinocytes & Syncytiotrophoblasts & T.cells & Undifferentiated.cells & Urothelial.cells\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTSPAN6 &  3.0 &  8.5 &  7.8 &  36.0 &   6.4 &  0.7 &  7.3 & 61.1 & 115.1 & 21.6 & ⋯ &  1.5 & 19.4 &  7.5 &  19.0 &  32.2 &  4.6 &  11.3 &  3.5 & 78.3 &  78.2\\\\\n",
       "\tTNMD &  0.0 &  0.0 &  0.0 &   0.0 &   1.8 &  0.0 &  0.0 &  0.0 &   0.0 &  0.0 & ⋯ &  0.0 &  0.0 &  0.0 &   0.0 &   0.0 &  0.9 &   0.0 &  0.0 &  0.5 &   0.0\\\\\n",
       "\tDPM1 & 17.9 & 40.2 & 52.1 & 126.5 & 103.5 & 11.0 & 36.2 & 20.4 &  77.6 & 43.3 & ⋯ & 10.2 & 71.1 & 61.3 & 110.0 & 113.4 & 92.2 & 205.3 & 55.3 & 48.2 & 127.0\\\\\n",
       "\tSCYL3 &  6.0 & 11.8 &  5.2 &   1.7 &   2.2 &  9.6 &  3.4 & 22.9 &   2.7 &  9.1 & ⋯ & 17.6 &  6.5 &  4.1 &   7.2 &   6.4 &  1.6 &   9.7 & 10.2 &  9.9 &   2.3\\\\\n",
       "\tC1orf112 &  0.0 &  3.5 &  2.2 &   2.6 &   1.5 &  3.8 &  0.4 &  0.0 &   2.7 &  1.2 & ⋯ &  6.5 &  6.5 &  1.2 &  27.7 &  17.7 &  0.9 &   3.2 &  1.9 &  1.6 &   1.4\\\\\n",
       "\tFGR &  6.0 & 30.7 & 12.5 &   0.5 &   2.5 &  0.0 &  1.6 &  2.5 &   2.7 & 10.3 & ⋯ &  0.0 &  0.0 &  1.0 &   0.2 &   0.5 &  1.0 &   0.8 & 45.8 &  0.0 &   0.9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 51\n",
       "\n",
       "| <!--/--> | Alveolar.cells.type.1 &lt;dbl&gt; | Alveolar.cells.type.2 &lt;dbl&gt; | B.cells &lt;dbl&gt; | Basal.glandular.cells &lt;dbl&gt; | Basal.keratinocytes &lt;dbl&gt; | Bipolar.cells &lt;dbl&gt; | Cardiomyocytes &lt;dbl&gt; | Cholangiocytes &lt;dbl&gt; | Ciliated.cells &lt;dbl&gt; | Club.cells &lt;dbl&gt; | ⋯ ⋯ | Rod.photoreceptor.cells &lt;dbl&gt; | Sertoli.cells &lt;dbl&gt; | Smooth.muscle.cells &lt;dbl&gt; | Spermatocytes &lt;dbl&gt; | Spermatogonia &lt;dbl&gt; | Suprabasal.keratinocytes &lt;dbl&gt; | Syncytiotrophoblasts &lt;dbl&gt; | T.cells &lt;dbl&gt; | Undifferentiated.cells &lt;dbl&gt; | Urothelial.cells &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TSPAN6 |  3.0 |  8.5 |  7.8 |  36.0 |   6.4 |  0.7 |  7.3 | 61.1 | 115.1 | 21.6 | ⋯ |  1.5 | 19.4 |  7.5 |  19.0 |  32.2 |  4.6 |  11.3 |  3.5 | 78.3 |  78.2 |\n",
       "| TNMD |  0.0 |  0.0 |  0.0 |   0.0 |   1.8 |  0.0 |  0.0 |  0.0 |   0.0 |  0.0 | ⋯ |  0.0 |  0.0 |  0.0 |   0.0 |   0.0 |  0.9 |   0.0 |  0.0 |  0.5 |   0.0 |\n",
       "| DPM1 | 17.9 | 40.2 | 52.1 | 126.5 | 103.5 | 11.0 | 36.2 | 20.4 |  77.6 | 43.3 | ⋯ | 10.2 | 71.1 | 61.3 | 110.0 | 113.4 | 92.2 | 205.3 | 55.3 | 48.2 | 127.0 |\n",
       "| SCYL3 |  6.0 | 11.8 |  5.2 |   1.7 |   2.2 |  9.6 |  3.4 | 22.9 |   2.7 |  9.1 | ⋯ | 17.6 |  6.5 |  4.1 |   7.2 |   6.4 |  1.6 |   9.7 | 10.2 |  9.9 |   2.3 |\n",
       "| C1orf112 |  0.0 |  3.5 |  2.2 |   2.6 |   1.5 |  3.8 |  0.4 |  0.0 |   2.7 |  1.2 | ⋯ |  6.5 |  6.5 |  1.2 |  27.7 |  17.7 |  0.9 |   3.2 |  1.9 |  1.6 |   1.4 |\n",
       "| FGR |  6.0 | 30.7 | 12.5 |   0.5 |   2.5 |  0.0 |  1.6 |  2.5 |   2.7 | 10.3 | ⋯ |  0.0 |  0.0 |  1.0 |   0.2 |   0.5 |  1.0 |   0.8 | 45.8 |  0.0 |   0.9 |\n",
       "\n"
      ],
      "text/plain": [
       "         Alveolar.cells.type.1 Alveolar.cells.type.2 B.cells\n",
       "TSPAN6    3.0                   8.5                   7.8   \n",
       "TNMD      0.0                   0.0                   0.0   \n",
       "DPM1     17.9                  40.2                  52.1   \n",
       "SCYL3     6.0                  11.8                   5.2   \n",
       "C1orf112  0.0                   3.5                   2.2   \n",
       "FGR       6.0                  30.7                  12.5   \n",
       "         Basal.glandular.cells Basal.keratinocytes Bipolar.cells Cardiomyocytes\n",
       "TSPAN6    36.0                   6.4                0.7           7.3          \n",
       "TNMD       0.0                   1.8                0.0           0.0          \n",
       "DPM1     126.5                 103.5               11.0          36.2          \n",
       "SCYL3      1.7                   2.2                9.6           3.4          \n",
       "C1orf112   2.6                   1.5                3.8           0.4          \n",
       "FGR        0.5                   2.5                0.0           1.6          \n",
       "         Cholangiocytes Ciliated.cells Club.cells ⋯ Rod.photoreceptor.cells\n",
       "TSPAN6   61.1           115.1          21.6       ⋯  1.5                   \n",
       "TNMD      0.0             0.0           0.0       ⋯  0.0                   \n",
       "DPM1     20.4            77.6          43.3       ⋯ 10.2                   \n",
       "SCYL3    22.9             2.7           9.1       ⋯ 17.6                   \n",
       "C1orf112  0.0             2.7           1.2       ⋯  6.5                   \n",
       "FGR       2.5             2.7          10.3       ⋯  0.0                   \n",
       "         Sertoli.cells Smooth.muscle.cells Spermatocytes Spermatogonia\n",
       "TSPAN6   19.4           7.5                 19.0          32.2        \n",
       "TNMD      0.0           0.0                  0.0           0.0        \n",
       "DPM1     71.1          61.3                110.0         113.4        \n",
       "SCYL3     6.5           4.1                  7.2           6.4        \n",
       "C1orf112  6.5           1.2                 27.7          17.7        \n",
       "FGR       0.0           1.0                  0.2           0.5        \n",
       "         Suprabasal.keratinocytes Syncytiotrophoblasts T.cells\n",
       "TSPAN6    4.6                      11.3                 3.5   \n",
       "TNMD      0.9                       0.0                 0.0   \n",
       "DPM1     92.2                     205.3                55.3   \n",
       "SCYL3     1.6                       9.7                10.2   \n",
       "C1orf112  0.9                       3.2                 1.9   \n",
       "FGR       1.0                       0.8                45.8   \n",
       "         Undifferentiated.cells Urothelial.cells\n",
       "TSPAN6   78.3                    78.2           \n",
       "TNMD      0.5                     0.0           \n",
       "DPM1     48.2                   127.0           \n",
       "SCYL3     9.9                     2.3           \n",
       "C1orf112  1.6                     1.4           \n",
       "FGR       0.0                     0.9           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(sc_rna)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "median center MAD scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_rna_m <- (sc_rna - median(unlist(sc_rna)))/mad(unlist(sc_rna))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "     0.0      0.0      3.0     48.6     18.4 465852.4 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(sc_rna))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "    -0.67     -0.67      0.00     10.26      3.46 104737.04 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(sc_rna_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ah2AACREOwAACIh2AEAREKwAwCIhGAH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\nZtxvweNKAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(sc_rna_m)), binwidth = 2) + myaxis + myplot +\n",
    "coord_cartesian(xlim = c(-1,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose genes expressed > 75th percentile in at least one dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_rna_genes <- rownames(sc_rna_m)[rowSums(sc_rna_m > quantile(unlist(sc_rna_m), 0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "15302"
      ],
      "text/latex": [
       "15302"
      ],
      "text/markdown": [
       "15302"
      ],
      "text/plain": [
       "[1] 15302"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(sc_rna_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 51</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Alveolar.cells.type.1</th><th scope=col>Alveolar.cells.type.2</th><th scope=col>B.cells</th><th scope=col>Basal.glandular.cells</th><th scope=col>Basal.keratinocytes</th><th scope=col>Bipolar.cells</th><th scope=col>Cardiomyocytes</th><th scope=col>Cholangiocytes</th><th scope=col>Ciliated.cells</th><th scope=col>Club.cells</th><th scope=col>⋯</th><th scope=col>Rod.photoreceptor.cells</th><th scope=col>Sertoli.cells</th><th scope=col>Smooth.muscle.cells</th><th scope=col>Spermatocytes</th><th scope=col>Spermatogonia</th><th scope=col>Suprabasal.keratinocytes</th><th scope=col>Syncytiotrophoblasts</th><th scope=col>T.cells</th><th scope=col>Undifferentiated.cells</th><th scope=col>Urothelial.cells</th></tr>\n",
       "\t<tr><th></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;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>⋯</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;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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TSPAN6</th><td> 3.0</td><td> 8.5</td><td> 7.8</td><td> 36.0</td><td>  6.4</td><td> 0.7</td><td> 7.3</td><td>61.1</td><td>115.1</td><td>21.6</td><td>⋯</td><td> 1.5</td><td>19.4</td><td> 7.5</td><td> 19.0</td><td> 32.2</td><td> 4.6</td><td> 11.3</td><td> 3.5</td><td>78.3</td><td> 78.2</td></tr>\n",
       "\t<tr><th scope=row>TNMD</th><td> 0.0</td><td> 0.0</td><td> 0.0</td><td>  0.0</td><td>  1.8</td><td> 0.0</td><td> 0.0</td><td> 0.0</td><td>  0.0</td><td> 0.0</td><td>⋯</td><td> 0.0</td><td> 0.0</td><td> 0.0</td><td>  0.0</td><td>  0.0</td><td> 0.9</td><td>  0.0</td><td> 0.0</td><td> 0.5</td><td>  0.0</td></tr>\n",
       "\t<tr><th scope=row>DPM1</th><td>17.9</td><td>40.2</td><td>52.1</td><td>126.5</td><td>103.5</td><td>11.0</td><td>36.2</td><td>20.4</td><td> 77.6</td><td>43.3</td><td>⋯</td><td>10.2</td><td>71.1</td><td>61.3</td><td>110.0</td><td>113.4</td><td>92.2</td><td>205.3</td><td>55.3</td><td>48.2</td><td>127.0</td></tr>\n",
       "\t<tr><th scope=row>SCYL3</th><td> 6.0</td><td>11.8</td><td> 5.2</td><td>  1.7</td><td>  2.2</td><td> 9.6</td><td> 3.4</td><td>22.9</td><td>  2.7</td><td> 9.1</td><td>⋯</td><td>17.6</td><td> 6.5</td><td> 4.1</td><td>  7.2</td><td>  6.4</td><td> 1.6</td><td>  9.7</td><td>10.2</td><td> 9.9</td><td>  2.3</td></tr>\n",
       "\t<tr><th scope=row>C1orf112</th><td> 0.0</td><td> 3.5</td><td> 2.2</td><td>  2.6</td><td>  1.5</td><td> 3.8</td><td> 0.4</td><td> 0.0</td><td>  2.7</td><td> 1.2</td><td>⋯</td><td> 6.5</td><td> 6.5</td><td> 1.2</td><td> 27.7</td><td> 17.7</td><td> 0.9</td><td>  3.2</td><td> 1.9</td><td> 1.6</td><td>  1.4</td></tr>\n",
       "\t<tr><th scope=row>FGR</th><td> 6.0</td><td>30.7</td><td>12.5</td><td>  0.5</td><td>  2.5</td><td> 0.0</td><td> 1.6</td><td> 2.5</td><td>  2.7</td><td>10.3</td><td>⋯</td><td> 0.0</td><td> 0.0</td><td> 1.0</td><td>  0.2</td><td>  0.5</td><td> 1.0</td><td>  0.8</td><td>45.8</td><td> 0.0</td><td>  0.9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 51\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Alveolar.cells.type.1 & Alveolar.cells.type.2 & B.cells & Basal.glandular.cells & Basal.keratinocytes & Bipolar.cells & Cardiomyocytes & Cholangiocytes & Ciliated.cells & Club.cells & ⋯ & Rod.photoreceptor.cells & Sertoli.cells & Smooth.muscle.cells & Spermatocytes & Spermatogonia & Suprabasal.keratinocytes & Syncytiotrophoblasts & T.cells & Undifferentiated.cells & Urothelial.cells\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTSPAN6 &  3.0 &  8.5 &  7.8 &  36.0 &   6.4 &  0.7 &  7.3 & 61.1 & 115.1 & 21.6 & ⋯ &  1.5 & 19.4 &  7.5 &  19.0 &  32.2 &  4.6 &  11.3 &  3.5 & 78.3 &  78.2\\\\\n",
       "\tTNMD &  0.0 &  0.0 &  0.0 &   0.0 &   1.8 &  0.0 &  0.0 &  0.0 &   0.0 &  0.0 & ⋯ &  0.0 &  0.0 &  0.0 &   0.0 &   0.0 &  0.9 &   0.0 &  0.0 &  0.5 &   0.0\\\\\n",
       "\tDPM1 & 17.9 & 40.2 & 52.1 & 126.5 & 103.5 & 11.0 & 36.2 & 20.4 &  77.6 & 43.3 & ⋯ & 10.2 & 71.1 & 61.3 & 110.0 & 113.4 & 92.2 & 205.3 & 55.3 & 48.2 & 127.0\\\\\n",
       "\tSCYL3 &  6.0 & 11.8 &  5.2 &   1.7 &   2.2 &  9.6 &  3.4 & 22.9 &   2.7 &  9.1 & ⋯ & 17.6 &  6.5 &  4.1 &   7.2 &   6.4 &  1.6 &   9.7 & 10.2 &  9.9 &   2.3\\\\\n",
       "\tC1orf112 &  0.0 &  3.5 &  2.2 &   2.6 &   1.5 &  3.8 &  0.4 &  0.0 &   2.7 &  1.2 & ⋯ &  6.5 &  6.5 &  1.2 &  27.7 &  17.7 &  0.9 &   3.2 &  1.9 &  1.6 &   1.4\\\\\n",
       "\tFGR &  6.0 & 30.7 & 12.5 &   0.5 &   2.5 &  0.0 &  1.6 &  2.5 &   2.7 & 10.3 & ⋯ &  0.0 &  0.0 &  1.0 &   0.2 &   0.5 &  1.0 &   0.8 & 45.8 &  0.0 &   0.9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 51\n",
       "\n",
       "| <!--/--> | Alveolar.cells.type.1 &lt;dbl&gt; | Alveolar.cells.type.2 &lt;dbl&gt; | B.cells &lt;dbl&gt; | Basal.glandular.cells &lt;dbl&gt; | Basal.keratinocytes &lt;dbl&gt; | Bipolar.cells &lt;dbl&gt; | Cardiomyocytes &lt;dbl&gt; | Cholangiocytes &lt;dbl&gt; | Ciliated.cells &lt;dbl&gt; | Club.cells &lt;dbl&gt; | ⋯ ⋯ | Rod.photoreceptor.cells &lt;dbl&gt; | Sertoli.cells &lt;dbl&gt; | Smooth.muscle.cells &lt;dbl&gt; | Spermatocytes &lt;dbl&gt; | Spermatogonia &lt;dbl&gt; | Suprabasal.keratinocytes &lt;dbl&gt; | Syncytiotrophoblasts &lt;dbl&gt; | T.cells &lt;dbl&gt; | Undifferentiated.cells &lt;dbl&gt; | Urothelial.cells &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TSPAN6 |  3.0 |  8.5 |  7.8 |  36.0 |   6.4 |  0.7 |  7.3 | 61.1 | 115.1 | 21.6 | ⋯ |  1.5 | 19.4 |  7.5 |  19.0 |  32.2 |  4.6 |  11.3 |  3.5 | 78.3 |  78.2 |\n",
       "| TNMD |  0.0 |  0.0 |  0.0 |   0.0 |   1.8 |  0.0 |  0.0 |  0.0 |   0.0 |  0.0 | ⋯ |  0.0 |  0.0 |  0.0 |   0.0 |   0.0 |  0.9 |   0.0 |  0.0 |  0.5 |   0.0 |\n",
       "| DPM1 | 17.9 | 40.2 | 52.1 | 126.5 | 103.5 | 11.0 | 36.2 | 20.4 |  77.6 | 43.3 | ⋯ | 10.2 | 71.1 | 61.3 | 110.0 | 113.4 | 92.2 | 205.3 | 55.3 | 48.2 | 127.0 |\n",
       "| SCYL3 |  6.0 | 11.8 |  5.2 |   1.7 |   2.2 |  9.6 |  3.4 | 22.9 |   2.7 |  9.1 | ⋯ | 17.6 |  6.5 |  4.1 |   7.2 |   6.4 |  1.6 |   9.7 | 10.2 |  9.9 |   2.3 |\n",
       "| C1orf112 |  0.0 |  3.5 |  2.2 |   2.6 |   1.5 |  3.8 |  0.4 |  0.0 |   2.7 |  1.2 | ⋯ |  6.5 |  6.5 |  1.2 |  27.7 |  17.7 |  0.9 |   3.2 |  1.9 |  1.6 |   1.4 |\n",
       "| FGR |  6.0 | 30.7 | 12.5 |   0.5 |   2.5 |  0.0 |  1.6 |  2.5 |   2.7 | 10.3 | ⋯ |  0.0 |  0.0 |  1.0 |   0.2 |   0.5 |  1.0 |   0.8 | 45.8 |  0.0 |   0.9 |\n",
       "\n"
      ],
      "text/plain": [
       "         Alveolar.cells.type.1 Alveolar.cells.type.2 B.cells\n",
       "TSPAN6    3.0                   8.5                   7.8   \n",
       "TNMD      0.0                   0.0                   0.0   \n",
       "DPM1     17.9                  40.2                  52.1   \n",
       "SCYL3     6.0                  11.8                   5.2   \n",
       "C1orf112  0.0                   3.5                   2.2   \n",
       "FGR       6.0                  30.7                  12.5   \n",
       "         Basal.glandular.cells Basal.keratinocytes Bipolar.cells Cardiomyocytes\n",
       "TSPAN6    36.0                   6.4                0.7           7.3          \n",
       "TNMD       0.0                   1.8                0.0           0.0          \n",
       "DPM1     126.5                 103.5               11.0          36.2          \n",
       "SCYL3      1.7                   2.2                9.6           3.4          \n",
       "C1orf112   2.6                   1.5                3.8           0.4          \n",
       "FGR        0.5                   2.5                0.0           1.6          \n",
       "         Cholangiocytes Ciliated.cells Club.cells ⋯ Rod.photoreceptor.cells\n",
       "TSPAN6   61.1           115.1          21.6       ⋯  1.5                   \n",
       "TNMD      0.0             0.0           0.0       ⋯  0.0                   \n",
       "DPM1     20.4            77.6          43.3       ⋯ 10.2                   \n",
       "SCYL3    22.9             2.7           9.1       ⋯ 17.6                   \n",
       "C1orf112  0.0             2.7           1.2       ⋯  6.5                   \n",
       "FGR       2.5             2.7          10.3       ⋯  0.0                   \n",
       "         Sertoli.cells Smooth.muscle.cells Spermatocytes Spermatogonia\n",
       "TSPAN6   19.4           7.5                 19.0          32.2        \n",
       "TNMD      0.0           0.0                  0.0           0.0        \n",
       "DPM1     71.1          61.3                110.0         113.4        \n",
       "SCYL3     6.5           4.1                  7.2           6.4        \n",
       "C1orf112  6.5           1.2                 27.7          17.7        \n",
       "FGR       0.0           1.0                  0.2           0.5        \n",
       "         Suprabasal.keratinocytes Syncytiotrophoblasts T.cells\n",
       "TSPAN6    4.6                      11.3                 3.5   \n",
       "TNMD      0.9                       0.0                 0.0   \n",
       "DPM1     92.2                     205.3                55.3   \n",
       "SCYL3     1.6                       9.7                10.2   \n",
       "C1orf112  0.9                       3.2                 1.9   \n",
       "FGR       1.0                       0.8                45.8   \n",
       "         Undifferentiated.cells Urothelial.cells\n",
       "TSPAN6   78.3                    78.2           \n",
       "TNMD      0.5                     0.0           \n",
       "DPM1     48.2                   127.0           \n",
       "SCYL3     9.9                     2.3           \n",
       "C1orf112  1.6                     1.4           \n",
       "FGR       0.0                     0.9           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(sc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get mad/med scores for all genes across all celltypes\n",
    "sc_rna_mcentr <- t(apply(sc_rna,1, mcentr))\n",
    "\n",
    "colnames(sc_rna_mcentr) <- colnames(sc_rna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 51 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Alveolar.cells.type.1</th><th scope=col>Alveolar.cells.type.2</th><th scope=col>B.cells</th><th scope=col>Basal.glandular.cells</th><th scope=col>Basal.keratinocytes</th><th scope=col>Bipolar.cells</th><th scope=col>Cardiomyocytes</th><th scope=col>Cholangiocytes</th><th scope=col>Ciliated.cells</th><th scope=col>Club.cells</th><th scope=col>⋯</th><th scope=col>Rod.photoreceptor.cells</th><th scope=col>Sertoli.cells</th><th scope=col>Smooth.muscle.cells</th><th scope=col>Spermatocytes</th><th scope=col>Spermatogonia</th><th scope=col>Suprabasal.keratinocytes</th><th scope=col>Syncytiotrophoblasts</th><th scope=col>T.cells</th><th scope=col>Undifferentiated.cells</th><th scope=col>Urothelial.cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TSPAN6</th><td>-0.7060459</td><td>-0.4891044</td><td>-0.5167151</td><td> 0.5956030</td><td>-0.5719366</td><td>-0.7967669</td><td>-0.5364371</td><td> 1.585645</td><td> 3.7156158</td><td> 0.02761073</td><td>⋯</td><td>-0.7652117</td><td>-0.05916586</td><td>-0.5285483</td><td>-0.07494342</td><td>0.44571612</td><td>-0.6429356</td><td>-0.3786615</td><td>-0.6863239</td><td> 2.2640801</td><td> 2.2601357</td></tr>\n",
       "\t<tr><th scope=row>TNMD</th><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.000000</td><td> 0.0000000</td><td> 0.00000000</td><td>⋯</td><td> 0.0000000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.00000000</td><td>0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.0000000</td></tr>\n",
       "\t<tr><th scope=row>DPM1</th><td>-1.3207306</td><td>-0.5332362</td><td>-0.1130037</td><td> 2.5143320</td><td> 1.7021180</td><td>-1.5643948</td><td>-0.6744908</td><td>-1.232446</td><td> 0.7874944</td><td>-0.42376383</td><td>⋯</td><td>-1.5926457</td><td> 0.55795571</td><td> 0.2118819</td><td> 1.93165678</td><td>2.05172320</td><td> 1.3030738</td><td> 5.2970478</td><td> 0.0000000</td><td>-0.2507269</td><td> 2.5319889</td></tr>\n",
       "\t<tr><th scope=row>SCYL3</th><td> 0.0000000</td><td> 1.3489815</td><td>-0.1860664</td><td>-1.0001070</td><td>-0.8838155</td><td> 0.8372989</td><td>-0.6047159</td><td> 3.930653</td><td>-0.7675240</td><td> 0.72100736</td><td>⋯</td><td> 2.6979630</td><td> 0.11629151</td><td>-0.4419077</td><td> 0.27909962</td><td>0.09303321</td><td>-1.0233653</td><td> 0.8605572</td><td> 0.9768487</td><td> 0.9070738</td><td>-0.8605572</td></tr>\n",
       "\t<tr><th scope=row>C1orf112</th><td>-1.1933298</td><td> 0.6226069</td><td>-0.0518839</td><td> 0.1556517</td><td>-0.4150712</td><td> 0.7782586</td><td>-0.9857942</td><td>-1.193330</td><td> 0.2075356</td><td>-0.57072295</td><td>⋯</td><td> 2.1791240</td><td> 2.17912399</td><td>-0.5707230</td><td>13.17851176</td><td>7.99012130</td><td>-0.7263747</td><td> 0.4669551</td><td>-0.2075356</td><td>-0.3631873</td><td>-0.4669551</td></tr>\n",
       "\t<tr><th scope=row>FGR</th><td> 7.4193984</td><td>40.7392419</td><td>16.1877782</td><td> 0.0000000</td><td> 2.6979630</td><td>-0.6744908</td><td> 1.4838797</td><td> 2.697963</td><td> 2.9677593</td><td>13.22001889</td><td>⋯</td><td>-0.6744908</td><td>-0.67449076</td><td> 0.6744908</td><td>-0.40469446</td><td>0.00000000</td><td> 0.6744908</td><td> 0.4046945</td><td>61.1088628</td><td>-0.6744908</td><td> 0.5395926</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 51 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Alveolar.cells.type.1 & Alveolar.cells.type.2 & B.cells & Basal.glandular.cells & Basal.keratinocytes & Bipolar.cells & Cardiomyocytes & Cholangiocytes & Ciliated.cells & Club.cells & ⋯ & Rod.photoreceptor.cells & Sertoli.cells & Smooth.muscle.cells & Spermatocytes & Spermatogonia & Suprabasal.keratinocytes & Syncytiotrophoblasts & T.cells & Undifferentiated.cells & Urothelial.cells\\\\\n",
       "\\hline\n",
       "\tTSPAN6 & -0.7060459 & -0.4891044 & -0.5167151 &  0.5956030 & -0.5719366 & -0.7967669 & -0.5364371 &  1.585645 &  3.7156158 &  0.02761073 & ⋯ & -0.7652117 & -0.05916586 & -0.5285483 & -0.07494342 & 0.44571612 & -0.6429356 & -0.3786615 & -0.6863239 &  2.2640801 &  2.2601357\\\\\n",
       "\tTNMD &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.000000 &  0.0000000 &  0.00000000 & ⋯ &  0.0000000 &  0.00000000 &  0.0000000 &  0.00000000 & 0.00000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000 &  0.0000000\\\\\n",
       "\tDPM1 & -1.3207306 & -0.5332362 & -0.1130037 &  2.5143320 &  1.7021180 & -1.5643948 & -0.6744908 & -1.232446 &  0.7874944 & -0.42376383 & ⋯ & -1.5926457 &  0.55795571 &  0.2118819 &  1.93165678 & 2.05172320 &  1.3030738 &  5.2970478 &  0.0000000 & -0.2507269 &  2.5319889\\\\\n",
       "\tSCYL3 &  0.0000000 &  1.3489815 & -0.1860664 & -1.0001070 & -0.8838155 &  0.8372989 & -0.6047159 &  3.930653 & -0.7675240 &  0.72100736 & ⋯ &  2.6979630 &  0.11629151 & -0.4419077 &  0.27909962 & 0.09303321 & -1.0233653 &  0.8605572 &  0.9768487 &  0.9070738 & -0.8605572\\\\\n",
       "\tC1orf112 & -1.1933298 &  0.6226069 & -0.0518839 &  0.1556517 & -0.4150712 &  0.7782586 & -0.9857942 & -1.193330 &  0.2075356 & -0.57072295 & ⋯ &  2.1791240 &  2.17912399 & -0.5707230 & 13.17851176 & 7.99012130 & -0.7263747 &  0.4669551 & -0.2075356 & -0.3631873 & -0.4669551\\\\\n",
       "\tFGR &  7.4193984 & 40.7392419 & 16.1877782 &  0.0000000 &  2.6979630 & -0.6744908 &  1.4838797 &  2.697963 &  2.9677593 & 13.22001889 & ⋯ & -0.6744908 & -0.67449076 &  0.6744908 & -0.40469446 & 0.00000000 &  0.6744908 &  0.4046945 & 61.1088628 & -0.6744908 &  0.5395926\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 51 of type dbl\n",
       "\n",
       "| <!--/--> | Alveolar.cells.type.1 | Alveolar.cells.type.2 | B.cells | Basal.glandular.cells | Basal.keratinocytes | Bipolar.cells | Cardiomyocytes | Cholangiocytes | Ciliated.cells | Club.cells | ⋯ | Rod.photoreceptor.cells | Sertoli.cells | Smooth.muscle.cells | Spermatocytes | Spermatogonia | Suprabasal.keratinocytes | Syncytiotrophoblasts | T.cells | Undifferentiated.cells | Urothelial.cells |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TSPAN6 | -0.7060459 | -0.4891044 | -0.5167151 |  0.5956030 | -0.5719366 | -0.7967669 | -0.5364371 |  1.585645 |  3.7156158 |  0.02761073 | ⋯ | -0.7652117 | -0.05916586 | -0.5285483 | -0.07494342 | 0.44571612 | -0.6429356 | -0.3786615 | -0.6863239 |  2.2640801 |  2.2601357 |\n",
       "| TNMD |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.000000 |  0.0000000 |  0.00000000 | ⋯ |  0.0000000 |  0.00000000 |  0.0000000 |  0.00000000 | 0.00000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |  0.0000000 |\n",
       "| DPM1 | -1.3207306 | -0.5332362 | -0.1130037 |  2.5143320 |  1.7021180 | -1.5643948 | -0.6744908 | -1.232446 |  0.7874944 | -0.42376383 | ⋯ | -1.5926457 |  0.55795571 |  0.2118819 |  1.93165678 | 2.05172320 |  1.3030738 |  5.2970478 |  0.0000000 | -0.2507269 |  2.5319889 |\n",
       "| SCYL3 |  0.0000000 |  1.3489815 | -0.1860664 | -1.0001070 | -0.8838155 |  0.8372989 | -0.6047159 |  3.930653 | -0.7675240 |  0.72100736 | ⋯ |  2.6979630 |  0.11629151 | -0.4419077 |  0.27909962 | 0.09303321 | -1.0233653 |  0.8605572 |  0.9768487 |  0.9070738 | -0.8605572 |\n",
       "| C1orf112 | -1.1933298 |  0.6226069 | -0.0518839 |  0.1556517 | -0.4150712 |  0.7782586 | -0.9857942 | -1.193330 |  0.2075356 | -0.57072295 | ⋯ |  2.1791240 |  2.17912399 | -0.5707230 | 13.17851176 | 7.99012130 | -0.7263747 |  0.4669551 | -0.2075356 | -0.3631873 | -0.4669551 |\n",
       "| FGR |  7.4193984 | 40.7392419 | 16.1877782 |  0.0000000 |  2.6979630 | -0.6744908 |  1.4838797 |  2.697963 |  2.9677593 | 13.22001889 | ⋯ | -0.6744908 | -0.67449076 |  0.6744908 | -0.40469446 | 0.00000000 |  0.6744908 |  0.4046945 | 61.1088628 | -0.6744908 |  0.5395926 |\n",
       "\n"
      ],
      "text/plain": [
       "         Alveolar.cells.type.1 Alveolar.cells.type.2 B.cells   \n",
       "TSPAN6   -0.7060459            -0.4891044            -0.5167151\n",
       "TNMD      0.0000000             0.0000000             0.0000000\n",
       "DPM1     -1.3207306            -0.5332362            -0.1130037\n",
       "SCYL3     0.0000000             1.3489815            -0.1860664\n",
       "C1orf112 -1.1933298             0.6226069            -0.0518839\n",
       "FGR       7.4193984            40.7392419            16.1877782\n",
       "         Basal.glandular.cells Basal.keratinocytes Bipolar.cells Cardiomyocytes\n",
       "TSPAN6    0.5956030            -0.5719366          -0.7967669    -0.5364371    \n",
       "TNMD      0.0000000             0.0000000           0.0000000     0.0000000    \n",
       "DPM1      2.5143320             1.7021180          -1.5643948    -0.6744908    \n",
       "SCYL3    -1.0001070            -0.8838155           0.8372989    -0.6047159    \n",
       "C1orf112  0.1556517            -0.4150712           0.7782586    -0.9857942    \n",
       "FGR       0.0000000             2.6979630          -0.6744908     1.4838797    \n",
       "         Cholangiocytes Ciliated.cells Club.cells  ⋯ Rod.photoreceptor.cells\n",
       "TSPAN6    1.585645       3.7156158      0.02761073 ⋯ -0.7652117             \n",
       "TNMD      0.000000       0.0000000      0.00000000 ⋯  0.0000000             \n",
       "DPM1     -1.232446       0.7874944     -0.42376383 ⋯ -1.5926457             \n",
       "SCYL3     3.930653      -0.7675240      0.72100736 ⋯  2.6979630             \n",
       "C1orf112 -1.193330       0.2075356     -0.57072295 ⋯  2.1791240             \n",
       "FGR       2.697963       2.9677593     13.22001889 ⋯ -0.6744908             \n",
       "         Sertoli.cells Smooth.muscle.cells Spermatocytes Spermatogonia\n",
       "TSPAN6   -0.05916586   -0.5285483          -0.07494342   0.44571612   \n",
       "TNMD      0.00000000    0.0000000           0.00000000   0.00000000   \n",
       "DPM1      0.55795571    0.2118819           1.93165678   2.05172320   \n",
       "SCYL3     0.11629151   -0.4419077           0.27909962   0.09303321   \n",
       "C1orf112  2.17912399   -0.5707230          13.17851176   7.99012130   \n",
       "FGR      -0.67449076    0.6744908          -0.40469446   0.00000000   \n",
       "         Suprabasal.keratinocytes Syncytiotrophoblasts T.cells   \n",
       "TSPAN6   -0.6429356               -0.3786615           -0.6863239\n",
       "TNMD      0.0000000                0.0000000            0.0000000\n",
       "DPM1      1.3030738                5.2970478            0.0000000\n",
       "SCYL3    -1.0233653                0.8605572            0.9768487\n",
       "C1orf112 -0.7263747                0.4669551           -0.2075356\n",
       "FGR       0.6744908                0.4046945           61.1088628\n",
       "         Undifferentiated.cells Urothelial.cells\n",
       "TSPAN6    2.2640801              2.2601357      \n",
       "TNMD      0.0000000              0.0000000      \n",
       "DPM1     -0.2507269              2.5319889      \n",
       "SCYL3     0.9070738             -0.8605572      \n",
       "C1orf112 -0.3631873             -0.4669551      \n",
       "FGR      -0.6744908              0.5395926      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(sc_rna_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a list of tables with differences in med/mad scores among cell types\n",
    "diff_gen_list_sc <- mclapply(rownames(sc_rna_mcentr), function(x){\n",
    "    mygen <- x\n",
    "    gen_df <- sc_rna_mcentr[mygen,]\n",
    "    #get m-score difference across cell types\n",
    "    tab_diff <- outer(gen_df, gen_df, \"-\")\n",
    "    #keep immune cells in rows and non-immune in cols    \n",
    "    immunecells <- c(\"B.cells\", \"T.cells\", \"granulocytes\",\"Macrophages\", \"monocytes\")\n",
    "    \n",
    "    return(tab_diff[immunecells, !colnames(tab_diff) %in% immunecells])\n",
    "})\n",
    "\n",
    "names(diff_gen_list_sc) <- rownames(sc_rna_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$NUB1</strong> = <table class=\"dataframe\">\n",
       "<caption>A matrix: 5 × 46 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Alveolar.cells.type.1</th><th scope=col>Alveolar.cells.type.2</th><th scope=col>Basal.glandular.cells</th><th scope=col>Basal.keratinocytes</th><th scope=col>Bipolar.cells</th><th scope=col>Cardiomyocytes</th><th scope=col>Cholangiocytes</th><th scope=col>Ciliated.cells</th><th scope=col>Club.cells</th><th scope=col>Collecting.duct.cells</th><th scope=col>⋯</th><th scope=col>Proximal.tubular.cells</th><th scope=col>Rod.photoreceptor.cells</th><th scope=col>Sertoli.cells</th><th scope=col>Smooth.muscle.cells</th><th scope=col>Spermatocytes</th><th scope=col>Spermatogonia</th><th scope=col>Suprabasal.keratinocytes</th><th scope=col>Syncytiotrophoblasts</th><th scope=col>Undifferentiated.cells</th><th scope=col>Urothelial.cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>B.cells</th><td>-1.56096433</td><td>-0.8415075</td><td> 1.0085243</td><td> 1.426066</td><td> 0.5652875</td><td>-0.4817791</td><td> 0.8736261</td><td>-1.8050657</td><td>-1.7665234</td><td> 1.271897</td><td>⋯</td><td> 0.8800498</td><td> 0.2633726</td><td> 1.181965</td><td> 0.3533047</td><td>-3.327488</td><td> 2.023472</td><td> 1.214083</td><td> 0.14774559</td><td> 1.432490</td><td> 0.8736261</td></tr>\n",
       "\t<tr><th scope=row>T.cells</th><td>-1.31686291</td><td>-0.5974061</td><td> 1.2526257</td><td> 1.670168</td><td> 0.8093889</td><td>-0.2376777</td><td> 1.1177275</td><td>-1.5609643</td><td>-1.5224220</td><td> 1.515998</td><td>⋯</td><td> 1.1241513</td><td> 0.5074740</td><td> 1.426066</td><td> 0.5974061</td><td>-3.083386</td><td> 2.267574</td><td> 1.458185</td><td> 0.39184701</td><td> 1.676591</td><td> 1.1177275</td></tr>\n",
       "\t<tr><th scope=row>granulocytes</th><td> 0.05781349</td><td> 0.7772703</td><td> 2.6273021</td><td> 3.044844</td><td> 2.1840653</td><td> 1.1369987</td><td> 2.4924039</td><td>-0.1862879</td><td>-0.1477456</td><td> 2.890675</td><td>⋯</td><td> 2.4988277</td><td> 1.8821504</td><td> 2.800743</td><td> 1.9720825</td><td>-1.708710</td><td> 3.642250</td><td> 2.832861</td><td> 1.76652342</td><td> 3.051268</td><td> 2.4924039</td></tr>\n",
       "\t<tr><th scope=row>Macrophages</th><td>-1.66374387</td><td>-0.9442871</td><td> 0.9057447</td><td> 1.323287</td><td> 0.4625079</td><td>-0.5845587</td><td> 0.7708466</td><td>-1.9078453</td><td>-1.8693030</td><td> 1.169117</td><td>⋯</td><td> 0.7772703</td><td> 0.1605930</td><td> 1.079185</td><td> 0.2505251</td><td>-3.430267</td><td> 1.920693</td><td> 1.111304</td><td> 0.04496605</td><td> 1.329710</td><td> 0.7708466</td></tr>\n",
       "\t<tr><th scope=row>monocytes</th><td>55.37890321</td><td>56.0983600</td><td>57.9483918</td><td>58.365934</td><td>57.5051550</td><td>56.4580884</td><td>57.8134937</td><td>55.1348018</td><td>55.1733441</td><td>58.211764</td><td>⋯</td><td>57.8199174</td><td>57.2032401</td><td>58.121832</td><td>57.2931722</td><td>53.612380</td><td>58.963340</td><td>58.153951</td><td>57.08761314</td><td>58.372357</td><td>57.8134937</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\textbf{\\$NUB1} = A matrix: 5 × 46 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Alveolar.cells.type.1 & Alveolar.cells.type.2 & Basal.glandular.cells & Basal.keratinocytes & Bipolar.cells & Cardiomyocytes & Cholangiocytes & Ciliated.cells & Club.cells & Collecting.duct.cells & ⋯ & Proximal.tubular.cells & Rod.photoreceptor.cells & Sertoli.cells & Smooth.muscle.cells & Spermatocytes & Spermatogonia & Suprabasal.keratinocytes & Syncytiotrophoblasts & Undifferentiated.cells & Urothelial.cells\\\\\n",
       "\\hline\n",
       "\tB.cells & -1.56096433 & -0.8415075 &  1.0085243 &  1.426066 &  0.5652875 & -0.4817791 &  0.8736261 & -1.8050657 & -1.7665234 &  1.271897 & ⋯ &  0.8800498 &  0.2633726 &  1.181965 &  0.3533047 & -3.327488 &  2.023472 &  1.214083 &  0.14774559 &  1.432490 &  0.8736261\\\\\n",
       "\tT.cells & -1.31686291 & -0.5974061 &  1.2526257 &  1.670168 &  0.8093889 & -0.2376777 &  1.1177275 & -1.5609643 & -1.5224220 &  1.515998 & ⋯ &  1.1241513 &  0.5074740 &  1.426066 &  0.5974061 & -3.083386 &  2.267574 &  1.458185 &  0.39184701 &  1.676591 &  1.1177275\\\\\n",
       "\tgranulocytes &  0.05781349 &  0.7772703 &  2.6273021 &  3.044844 &  2.1840653 &  1.1369987 &  2.4924039 & -0.1862879 & -0.1477456 &  2.890675 & ⋯ &  2.4988277 &  1.8821504 &  2.800743 &  1.9720825 & -1.708710 &  3.642250 &  2.832861 &  1.76652342 &  3.051268 &  2.4924039\\\\\n",
       "\tMacrophages & -1.66374387 & -0.9442871 &  0.9057447 &  1.323287 &  0.4625079 & -0.5845587 &  0.7708466 & -1.9078453 & -1.8693030 &  1.169117 & ⋯ &  0.7772703 &  0.1605930 &  1.079185 &  0.2505251 & -3.430267 &  1.920693 &  1.111304 &  0.04496605 &  1.329710 &  0.7708466\\\\\n",
       "\tmonocytes & 55.37890321 & 56.0983600 & 57.9483918 & 58.365934 & 57.5051550 & 56.4580884 & 57.8134937 & 55.1348018 & 55.1733441 & 58.211764 & ⋯ & 57.8199174 & 57.2032401 & 58.121832 & 57.2931722 & 53.612380 & 58.963340 & 58.153951 & 57.08761314 & 58.372357 & 57.8134937\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "**$NUB1** = \n",
       "A matrix: 5 × 46 of type dbl\n",
       "\n",
       "| <!--/--> | Alveolar.cells.type.1 | Alveolar.cells.type.2 | Basal.glandular.cells | Basal.keratinocytes | Bipolar.cells | Cardiomyocytes | Cholangiocytes | Ciliated.cells | Club.cells | Collecting.duct.cells | ⋯ | Proximal.tubular.cells | Rod.photoreceptor.cells | Sertoli.cells | Smooth.muscle.cells | Spermatocytes | Spermatogonia | Suprabasal.keratinocytes | Syncytiotrophoblasts | Undifferentiated.cells | Urothelial.cells |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| B.cells | -1.56096433 | -0.8415075 |  1.0085243 |  1.426066 |  0.5652875 | -0.4817791 |  0.8736261 | -1.8050657 | -1.7665234 |  1.271897 | ⋯ |  0.8800498 |  0.2633726 |  1.181965 |  0.3533047 | -3.327488 |  2.023472 |  1.214083 |  0.14774559 |  1.432490 |  0.8736261 |\n",
       "| T.cells | -1.31686291 | -0.5974061 |  1.2526257 |  1.670168 |  0.8093889 | -0.2376777 |  1.1177275 | -1.5609643 | -1.5224220 |  1.515998 | ⋯ |  1.1241513 |  0.5074740 |  1.426066 |  0.5974061 | -3.083386 |  2.267574 |  1.458185 |  0.39184701 |  1.676591 |  1.1177275 |\n",
       "| granulocytes |  0.05781349 |  0.7772703 |  2.6273021 |  3.044844 |  2.1840653 |  1.1369987 |  2.4924039 | -0.1862879 | -0.1477456 |  2.890675 | ⋯ |  2.4988277 |  1.8821504 |  2.800743 |  1.9720825 | -1.708710 |  3.642250 |  2.832861 |  1.76652342 |  3.051268 |  2.4924039 |\n",
       "| Macrophages | -1.66374387 | -0.9442871 |  0.9057447 |  1.323287 |  0.4625079 | -0.5845587 |  0.7708466 | -1.9078453 | -1.8693030 |  1.169117 | ⋯ |  0.7772703 |  0.1605930 |  1.079185 |  0.2505251 | -3.430267 |  1.920693 |  1.111304 |  0.04496605 |  1.329710 |  0.7708466 |\n",
       "| monocytes | 55.37890321 | 56.0983600 | 57.9483918 | 58.365934 | 57.5051550 | 56.4580884 | 57.8134937 | 55.1348018 | 55.1733441 | 58.211764 | ⋯ | 57.8199174 | 57.2032401 | 58.121832 | 57.2931722 | 53.612380 | 58.963340 | 58.153951 | 57.08761314 | 58.372357 | 57.8134937 |\n",
       "\n"
      ],
      "text/plain": [
       "$NUB1\n",
       "             Alveolar.cells.type.1 Alveolar.cells.type.2 Basal.glandular.cells\n",
       "B.cells                -1.56096433            -0.8415075             1.0085243\n",
       "T.cells                -1.31686291            -0.5974061             1.2526257\n",
       "granulocytes            0.05781349             0.7772703             2.6273021\n",
       "Macrophages            -1.66374387            -0.9442871             0.9057447\n",
       "monocytes              55.37890321            56.0983600            57.9483918\n",
       "             Basal.keratinocytes Bipolar.cells Cardiomyocytes Cholangiocytes\n",
       "B.cells                 1.426066     0.5652875     -0.4817791      0.8736261\n",
       "T.cells                 1.670168     0.8093889     -0.2376777      1.1177275\n",
       "granulocytes            3.044844     2.1840653      1.1369987      2.4924039\n",
       "Macrophages             1.323287     0.4625079     -0.5845587      0.7708466\n",
       "monocytes              58.365934    57.5051550     56.4580884     57.8134937\n",
       "             Ciliated.cells Club.cells Collecting.duct.cells\n",
       "B.cells          -1.8050657 -1.7665234              1.271897\n",
       "T.cells          -1.5609643 -1.5224220              1.515998\n",
       "granulocytes     -0.1862879 -0.1477456              2.890675\n",
       "Macrophages      -1.9078453 -1.8693030              1.169117\n",
       "monocytes        55.1348018 55.1733441             58.211764\n",
       "             Cone.photoreceptor.cells Cytotrophoblasts Distal.tubular.cells\n",
       "B.cells                      2.081286        0.3276098           -0.4625079\n",
       "T.cells                      2.325387        0.5717112           -0.2184065\n",
       "granulocytes                 3.700064        1.9463876            1.1562699\n",
       "Macrophages                  1.978506        0.2248303           -0.5652875\n",
       "monocytes                   59.021153       57.2674773           56.4773596\n",
       "             Ductal.cells Early.spermatids Endothelial.cells Enterocytes\n",
       "B.cells        0.03854233        -8.813346         0.5267452  0.13489815\n",
       "T.cells        0.28264375        -8.569245         0.7708466  0.37899957\n",
       "granulocytes   1.65732015        -7.194568         2.1455230  1.75367597\n",
       "Macrophages   -0.06423722        -8.916125         0.4239656  0.03211861\n",
       "monocytes     56.97840987        48.126522        57.4666127 57.07476569\n",
       "             Erythroid.cells Exocrine.glandular.cells Extravillous.trophoblasts\n",
       "B.cells             2.839285               -1.0534903                 0.4175419\n",
       "T.cells             3.083386               -0.8093889                 0.6616433\n",
       "granulocytes        4.458063                0.5652875                 2.0363197\n",
       "Macrophages         2.736505               -1.1562699                 0.3147624\n",
       "monocytes          59.779152               55.8863772                57.3574094\n",
       "             Fibroblasts Glandular.cells Hepatocytes Hofbauer.cells\n",
       "B.cells        1.0149480      0.17986420   0.6937619      1.0856089\n",
       "T.cells        1.2590494      0.42396562   0.9378633      1.3297104\n",
       "granulocytes   2.6337258      1.79864203   2.3125397      2.7043868\n",
       "Macrophages    0.9121685      0.07708466   0.5909824      0.9828294\n",
       "monocytes     57.9548155     57.11973175  57.6336295     58.0254765\n",
       "             Horizontal.cells Intestinal.endocrine.cells  Ito.cells\n",
       "B.cells            -0.1220507                  1.0277954  0.8672024\n",
       "T.cells             0.1220507                  1.2718969  1.1113038\n",
       "granulocytes        1.4967271                  2.6465733  2.4859802\n",
       "Macrophages        -0.2248303                  0.9250159  0.7644229\n",
       "monocytes          56.8178168                 57.9676630 57.8070699\n",
       "             Kupffer.cells Late.spermatids Leydig.cells Melanocytes\n",
       "B.cells          0.6038298        1.850032    0.2119828    1.869303\n",
       "T.cells          0.8479312        2.094133    0.4560842    2.113404\n",
       "granulocytes     2.2226076        3.468810    1.8307606    3.488081\n",
       "Macrophages      0.5010503        1.747252    0.1092033    1.766523\n",
       "monocytes       57.5436974       58.789899   57.1518504   58.809171\n",
       "             Mucus.secreting.cells Muller.glia.cells Pancreatic.endocrine.cells\n",
       "B.cells                 0.17986420        -0.5460163                 -0.4689317\n",
       "T.cells                 0.42396562        -0.3019149                 -0.2248303\n",
       "granulocytes            1.79864203         1.0727615                  1.1498462\n",
       "Macrophages             0.07708466        -0.6487959                 -0.5717112\n",
       "monocytes              57.11973175        56.3938512                 56.4709359\n",
       "             Paneth.cells Peritubular.cells Proximal.tubular.cells\n",
       "B.cells          1.715134         0.3661521              0.8800498\n",
       "T.cells          1.959235         0.6102535              1.1241513\n",
       "granulocytes     3.333911         1.9849299              2.4988277\n",
       "Macrophages      1.612354         0.2633726              0.7772703\n",
       "monocytes       58.655001        57.3060197             57.8199174\n",
       "             Rod.photoreceptor.cells Sertoli.cells Smooth.muscle.cells\n",
       "B.cells                    0.2633726      1.181965           0.3533047\n",
       "T.cells                    0.5074740      1.426066           0.5974061\n",
       "granulocytes               1.8821504      2.800743           1.9720825\n",
       "Macrophages                0.1605930      1.079185           0.2505251\n",
       "monocytes                 57.2032401     58.121832          57.2931722\n",
       "             Spermatocytes Spermatogonia Suprabasal.keratinocytes\n",
       "B.cells          -3.327488      2.023472                 1.214083\n",
       "T.cells          -3.083386      2.267574                 1.458185\n",
       "granulocytes     -1.708710      3.642250                 2.832861\n",
       "Macrophages      -3.430267      1.920693                 1.111304\n",
       "monocytes        53.612380     58.963340                58.153951\n",
       "             Syncytiotrophoblasts Undifferentiated.cells Urothelial.cells\n",
       "B.cells                0.14774559               1.432490        0.8736261\n",
       "T.cells                0.39184701               1.676591        1.1177275\n",
       "granulocytes           1.76652342               3.051268        2.4924039\n",
       "Macrophages            0.04496605               1.329710        0.7708466\n",
       "monocytes             57.08761314              58.372357       57.8134937\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_gen_list_sc[\"NUB1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select genes if any immune cells express >=2 m compared to all nonimmune cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_gen_list_sc_immune <- lapply(diff_gen_list_sc, function(x) x[ rowSums(x >= 2) == 46,,drop = F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove empty lists\n",
    "diff_gen_list_sc_immune <- diff_gen_list_sc_immune[sapply(diff_gen_list_sc_immune, function(x) length(unlist(x))) >= 46]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "554"
      ],
      "text/latex": [
       "554"
      ],
      "text/markdown": [
       "554"
      ],
      "text/plain": [
       "[1] 554"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_sc_immune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_gen_list_sc_immune_expressed <- diff_gen_list_sc_immune[names(diff_gen_list_sc_immune) %in% sc_rna_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "535"
      ],
      "text/latex": [
       "535"
      ],
      "text/markdown": [
       "535"
      ],
      "text/plain": [
       "[1] 535"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_sc_immune_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_sc_genes <- names(diff_gen_list_sc_immune_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>'SNX11'</li><li>'ALS2'</li><li>'ITGAL'</li><li>'MAP3K14'</li><li>'MATK'</li><li>'CD79B'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'SNX11'\n",
       "\\item 'ALS2'\n",
       "\\item 'ITGAL'\n",
       "\\item 'MAP3K14'\n",
       "\\item 'MATK'\n",
       "\\item 'CD79B'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'SNX11'\n",
       "2. 'ALS2'\n",
       "3. 'ITGAL'\n",
       "4. 'MAP3K14'\n",
       "5. 'MATK'\n",
       "6. 'CD79B'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"SNX11\"   \"ALS2\"    \"ITGAL\"   \"MAP3K14\" \"MATK\"    \"CD79B\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pa_sc_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selected 535 genes that are expressed more than 75th (of the entire dataset) in at least one cell type AND have >2 mad difference in expression between at least one immune celltype and all non-immune celltypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- protein atlas tissue specificity data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteinatlas <- read.csv(paste0(datapath, \"TME_clusters/proteinatlas_v20.1.csv\"), header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#match genes from the immune cell compendium with the proteinatlas\n",
    "gene_cells_tissuespecificity <- as.data.frame(proteinatlas$Gene)\n",
    "gene_cells_tissuespecificity$tissue <- proteinatlas$RNA.tissue.specific.NX\n",
    "colnames(gene_cells_tissuespecificity) <- c(\"Gene\", \"Tissue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_cells_tissuespecificity <- gene_cells_tissuespecificity[gene_cells_tissuespecificity$Tissue != \"\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Gene</th><th scope=col>Tissue</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>TSPAN6  </td><td>salivary gland: 88.4                                                               </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>TNMD    </td><td>adipose tissue: 18.4;seminal vesicle: 25.0                                         </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>C1orf112</td><td>lymphoid tissue: 24.6                                                              </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>FGR     </td><td>adipose tissue: 20.8;blood: 68.0;bone marrow: 73.6;lung: 30.9;lymphoid tissue: 54.3</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>CFH     </td><td>liver: 295.2                                                                       </td></tr>\n",
       "\t<tr><th scope=row>9</th><td>GCLC    </td><td>liver: 69.2                                                                        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Gene & Tissue\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & TSPAN6   & salivary gland: 88.4                                                               \\\\\n",
       "\t2 & TNMD     & adipose tissue: 18.4;seminal vesicle: 25.0                                         \\\\\n",
       "\t5 & C1orf112 & lymphoid tissue: 24.6                                                              \\\\\n",
       "\t6 & FGR      & adipose tissue: 20.8;blood: 68.0;bone marrow: 73.6;lung: 30.9;lymphoid tissue: 54.3\\\\\n",
       "\t7 & CFH      & liver: 295.2                                                                       \\\\\n",
       "\t9 & GCLC     & liver: 69.2                                                                        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Tissue &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | TSPAN6   | salivary gland: 88.4                                                                |\n",
       "| 2 | TNMD     | adipose tissue: 18.4;seminal vesicle: 25.0                                          |\n",
       "| 5 | C1orf112 | lymphoid tissue: 24.6                                                               |\n",
       "| 6 | FGR      | adipose tissue: 20.8;blood: 68.0;bone marrow: 73.6;lung: 30.9;lymphoid tissue: 54.3 |\n",
       "| 7 | CFH      | liver: 295.2                                                                        |\n",
       "| 9 | GCLC     | liver: 69.2                                                                         |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene    \n",
       "1 TSPAN6  \n",
       "2 TNMD    \n",
       "5 C1orf112\n",
       "6 FGR     \n",
       "7 CFH     \n",
       "9 GCLC    \n",
       "  Tissue                                                                             \n",
       "1 salivary gland: 88.4                                                               \n",
       "2 adipose tissue: 18.4;seminal vesicle: 25.0                                         \n",
       "5 lymphoid tissue: 24.6                                                              \n",
       "6 adipose tissue: 20.8;blood: 68.0;bone marrow: 73.6;lung: 30.9;lymphoid tissue: 54.3\n",
       "7 liver: 295.2                                                                       \n",
       "9 liver: 69.2                                                                        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gene_cells_tissuespecificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate rows\n",
    "gene_cells_tissuespecificity_sep <- separate_rows(gene_cells_tissuespecificity, Tissue,sep = \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seprate gene expression as a new column\n",
    "gene_cells_tissuespecificity_sep$NX <- gsub(\".*:\", \"\", gene_cells_tissuespecificity_sep$Tissue)\n",
    "gene_cells_tissuespecificity_sep$Tissue <- gsub(\":.*\", \"\", gene_cells_tissuespecificity_sep$Tissue)\n",
    "gene_cells_tissuespecificity_sep$NX <- as.numeric(gene_cells_tissuespecificity_sep$NX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group some blood related tissues - keep others the same as tissue\n",
    "gene_cells_tissuespecificity_sep$Tissuegroup <- NA\n",
    "gene_cells_tissuespecificity_sep$Tissuegroup[ grepl(\"blood\", gene_cells_tissuespecificity_sep$Tissue)] <- \"Blood\"\n",
    "gene_cells_tissuespecificity_sep$Tissuegroup[ grepl(\"lymphoid\", gene_cells_tissuespecificity_sep$Tissue)] <- \"Blood\"\n",
    "gene_cells_tissuespecificity_sep$Tissuegroup[ grepl(\"marrow\", gene_cells_tissuespecificity_sep$Tissue)] <- \"Blood\"\n",
    "gene_cells_tissuespecificity_sep$Tissuegroup[ is.na(gene_cells_tissuespecificity_sep$Tissuegroup)] <- \n",
    " gene_cells_tissuespecificity_sep$Tissue[ is.na(gene_cells_tissuespecificity_sep$Tissuegroup)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "   adipose tissue     adrenal gland             Blood             brain \n",
       "              212               228              3351              2587 \n",
       "           breast   cervix, uterine   ductus deferens     endometrium 1 \n",
       "              187               131               115                85 \n",
       "       epididymis         esophagus    fallopian tube       gallbladder \n",
       "              412               311               312               161 \n",
       "     heart muscle         intestine            kidney             liver \n",
       "              387               764               413               936 \n",
       "             lung             ovary          pancreas parathyroid gland \n",
       "              239               173               422               224 \n",
       "  pituitary gland          placenta          prostate            retina \n",
       "              353               494               120               310 \n",
       "   salivary gland   seminal vesicle   skeletal muscle            skin 1 \n",
       "              319               180               907               547 \n",
       "    smooth muscle         stomach 1            testis     thyroid gland \n",
       "              112               159              2274               199 \n",
       "           tongue   urinary bladder            vagina \n",
       "              375                99                91 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(gene_cells_tissuespecificity_sep$Tissuegroup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 10 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Gene</th><th scope=col>Tissue</th><th scope=col>NX</th><th scope=col>Tissuegroup</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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>TSPAN6  </td><td>salivary gland </td><td> 88.4</td><td>salivary gland </td></tr>\n",
       "\t<tr><td>TNMD    </td><td>adipose tissue </td><td> 18.4</td><td>adipose tissue </td></tr>\n",
       "\t<tr><td>TNMD    </td><td>seminal vesicle</td><td> 25.0</td><td>seminal vesicle</td></tr>\n",
       "\t<tr><td>C1orf112</td><td>lymphoid tissue</td><td> 24.6</td><td>Blood          </td></tr>\n",
       "\t<tr><td>FGR     </td><td>adipose tissue </td><td> 20.8</td><td>adipose tissue </td></tr>\n",
       "\t<tr><td>FGR     </td><td>blood          </td><td> 68.0</td><td>Blood          </td></tr>\n",
       "\t<tr><td>FGR     </td><td>bone marrow    </td><td> 73.6</td><td>Blood          </td></tr>\n",
       "\t<tr><td>FGR     </td><td>lung           </td><td> 30.9</td><td>lung           </td></tr>\n",
       "\t<tr><td>FGR     </td><td>lymphoid tissue</td><td> 54.3</td><td>Blood          </td></tr>\n",
       "\t<tr><td>CFH     </td><td>liver          </td><td>295.2</td><td>liver          </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 10 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Gene & Tissue & NX & Tissuegroup\\\\\n",
       " <chr> & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t TSPAN6   & salivary gland  &  88.4 & salivary gland \\\\\n",
       "\t TNMD     & adipose tissue  &  18.4 & adipose tissue \\\\\n",
       "\t TNMD     & seminal vesicle &  25.0 & seminal vesicle\\\\\n",
       "\t C1orf112 & lymphoid tissue &  24.6 & Blood          \\\\\n",
       "\t FGR      & adipose tissue  &  20.8 & adipose tissue \\\\\n",
       "\t FGR      & blood           &  68.0 & Blood          \\\\\n",
       "\t FGR      & bone marrow     &  73.6 & Blood          \\\\\n",
       "\t FGR      & lung            &  30.9 & lung           \\\\\n",
       "\t FGR      & lymphoid tissue &  54.3 & Blood          \\\\\n",
       "\t CFH      & liver           & 295.2 & liver          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 10 × 4\n",
       "\n",
       "| Gene &lt;chr&gt; | Tissue &lt;chr&gt; | NX &lt;dbl&gt; | Tissuegroup &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| TSPAN6   | salivary gland  |  88.4 | salivary gland  |\n",
       "| TNMD     | adipose tissue  |  18.4 | adipose tissue  |\n",
       "| TNMD     | seminal vesicle |  25.0 | seminal vesicle |\n",
       "| C1orf112 | lymphoid tissue |  24.6 | Blood           |\n",
       "| FGR      | adipose tissue  |  20.8 | adipose tissue  |\n",
       "| FGR      | blood           |  68.0 | Blood           |\n",
       "| FGR      | bone marrow     |  73.6 | Blood           |\n",
       "| FGR      | lung            |  30.9 | lung            |\n",
       "| FGR      | lymphoid tissue |  54.3 | Blood           |\n",
       "| CFH      | liver           | 295.2 | liver           |\n",
       "\n"
      ],
      "text/plain": [
       "   Gene     Tissue          NX    Tissuegroup    \n",
       "1  TSPAN6   salivary gland   88.4 salivary gland \n",
       "2  TNMD     adipose tissue   18.4 adipose tissue \n",
       "3  TNMD     seminal vesicle  25.0 seminal vesicle\n",
       "4  C1orf112 lymphoid tissue  24.6 Blood          \n",
       "5  FGR      adipose tissue   20.8 adipose tissue \n",
       "6  FGR      blood            68.0 Blood          \n",
       "7  FGR      bone marrow      73.6 Blood          \n",
       "8  FGR      lung             30.9 lung           \n",
       "9  FGR      lymphoid tissue  54.3 Blood          \n",
       "10 CFH      liver           295.2 liver          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gene_cells_tissuespecificity_sep,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rm genes if more than one tissuegroup\n",
    "gene_cells_tissuespecificity_sep_specific <- gene_cells_tissuespecificity_sep[0,]\n",
    "\n",
    "for(g in unique(gene_cells_tissuespecificity_sep$Gene)){\n",
    "    tmp <- gene_cells_tissuespecificity_sep[ gene_cells_tissuespecificity_sep$Gene == g,]\n",
    "    if(nrow(tmp) == 1){ \n",
    "    gene_cells_tissuespecificity_sep_specific <- rbind(gene_cells_tissuespecificity_sep_specific, tmp)\n",
    "    } else{\n",
    "    if(length(unique(tmp$Tissuegroup)) == 1){ \n",
    "    gene_cells_tissuespecificity_sep_specific <- rbind(gene_cells_tissuespecificity_sep_specific, tmp)\n",
    "    }}\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>18189</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 18189\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 18189\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 18189     4"
      ]
     },
     "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>7206</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 7206\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 7206\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 7206    4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gene_cells_tissuespecificity_sep)\n",
    "dim(gene_cells_tissuespecificity_sep_specific)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only blood related genes\n",
    "blood_specific <- gene_cells_tissuespecificity_sep_specific[ gene_cells_tissuespecificity_sep_specific$Tissuegroup == \"Blood\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>1791</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1791\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1791\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1791    4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(blood_specific)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_blood_genes <- blood_specific$Gene[!duplicated(blood_specific$Gene)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>'C1orf112'</li><li>'MCUB'</li><li>'MPO'</li><li>'ITGAL'</li><li>'ALDH3B1'</li><li>'CEACAM21'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'C1orf112'\n",
       "\\item 'MCUB'\n",
       "\\item 'MPO'\n",
       "\\item 'ITGAL'\n",
       "\\item 'ALDH3B1'\n",
       "\\item 'CEACAM21'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'C1orf112'\n",
       "2. 'MCUB'\n",
       "3. 'MPO'\n",
       "4. 'ITGAL'\n",
       "5. 'ALDH3B1'\n",
       "6. 'CEACAM21'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"C1orf112\" \"MCUB\"     \"MPO\"      \"ITGAL\"    \"ALDH3B1\"  \"CEACAM21\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pa_blood_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1257"
      ],
      "text/latex": [
       "1257"
      ],
      "text/markdown": [
       "1257"
      ],
      "text/plain": [
       "[1] 1257"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(pa_blood_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1257 genes are specific to blood related tissues according to pan-tissue analysis using protein atlas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4- ImmuneScore genes from ESTIMATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yoshihara suppl data 1\n",
    "estimate_sigs <- read.csv(file = paste0(datapath, \"TME_clusters/ESTIMATE_genesig.csv\"),\n",
    "                          header = T, stringsAsFactors = F, check.names = F, skip = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "immunescore_genes <- estimate_sigs$Gene[ estimate_sigs$Set == \"Immune141_UP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "141"
      ],
      "text/latex": [
       "141"
      ],
      "text/markdown": [
       "141"
      ],
      "text/plain": [
       "[1] 141"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(immunescore_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "141 genes are used in ESTIMATE ImmuneScore that we benchmarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile all immune genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp <- c(compendium_immune_genes, pa_sc_genes, pa_blood_genes, immunescore_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_genes <- tmp[!duplicated(tmp)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3041"
      ],
      "text/latex": [
       "3041"
      ],
      "text/markdown": [
       "3041"
      ],
      "text/plain": [
       "[1] 3041"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(immune_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter out genes expressed in pediatric tumour/non-immune cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- PDX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdx_mat1 <- read.table(paste0(datapath, \"exp_mat/PDX_2017Oct06.normalizedTPMs_perGencodeV26Gene.sleuth.txt\"), sep = \"\\t\",\n",
    "                             header = TRUE, check.names = F,\n",
    "                             stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>sample</th><th scope=col>target_id</th><th scope=col>scaled_reads_per_base</th><th scope=col>tpm</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>BT084_XP3_2   </td><td>7SK</td><td>0.0000000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>BT084_XP5_845 </td><td>7SK</td><td>0.0000000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Icb_984MB_XP11</td><td>7SK</td><td>0.0000000</td><td>0.00000000</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Med113FH_P0-4 </td><td>7SK</td><td>0.6526691</td><td>0.05560582</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Med113FH_P5-2 </td><td>7SK</td><td>0.6021602</td><td>0.04985232</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Med114FH_P5-3 </td><td>7SK</td><td>0.0000000</td><td>0.00000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & sample & target\\_id & scaled\\_reads\\_per\\_base & tpm\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & BT084\\_XP3\\_2    & 7SK & 0.0000000 & 0.00000000\\\\\n",
       "\t2 & BT084\\_XP5\\_845  & 7SK & 0.0000000 & 0.00000000\\\\\n",
       "\t3 & Icb\\_984MB\\_XP11 & 7SK & 0.0000000 & 0.00000000\\\\\n",
       "\t4 & Med113FH\\_P0-4  & 7SK & 0.6526691 & 0.05560582\\\\\n",
       "\t5 & Med113FH\\_P5-2  & 7SK & 0.6021602 & 0.04985232\\\\\n",
       "\t6 & Med114FH\\_P5-3  & 7SK & 0.0000000 & 0.00000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | sample &lt;chr&gt; | target_id &lt;chr&gt; | scaled_reads_per_base &lt;dbl&gt; | tpm &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | BT084_XP3_2    | 7SK | 0.0000000 | 0.00000000 |\n",
       "| 2 | BT084_XP5_845  | 7SK | 0.0000000 | 0.00000000 |\n",
       "| 3 | Icb_984MB_XP11 | 7SK | 0.0000000 | 0.00000000 |\n",
       "| 4 | Med113FH_P0-4  | 7SK | 0.6526691 | 0.05560582 |\n",
       "| 5 | Med113FH_P5-2  | 7SK | 0.6021602 | 0.04985232 |\n",
       "| 6 | Med114FH_P5-3  | 7SK | 0.0000000 | 0.00000000 |\n",
       "\n"
      ],
      "text/plain": [
       "  sample         target_id scaled_reads_per_base tpm       \n",
       "1 BT084_XP3_2    7SK       0.0000000             0.00000000\n",
       "2 BT084_XP5_845  7SK       0.0000000             0.00000000\n",
       "3 Icb_984MB_XP11 7SK       0.0000000             0.00000000\n",
       "4 Med113FH_P0-4  7SK       0.6526691             0.05560582\n",
       "5 Med113FH_P5-2  7SK       0.6021602             0.04985232\n",
       "6 Med114FH_P5-3  7SK       0.0000000             0.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pdx_mat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdx_mat1$scaled_reads_per_base <- NULL\n",
    "pdx_mat_tpm <- longtowidefx(pdx_mat1)\n",
    "rownames(pdx_mat_tpm) <- pdx_mat_tpm$target_id\n",
    "pdx_mat_tpm$target_id <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>51472</li><li>20</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 51472\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 51472\n",
       "2. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 51472    20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pdx_mat_tpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdx_mat <- read.table(paste0(datapath, \"exp_mat/RNAseq_itcc-P4_TPM_values_210726.tsv\"), sep = \"\\t\",\n",
    "                      header = TRUE, check.names = F, comment.char = \"\", stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>#chrom</th><th scope=col>chromStart</th><th scope=col>chromEnd</th><th scope=col>gene_id</th><th scope=col>score</th><th scope=col>strand</th><th scope=col>name</th><th scope=col>exonic_length</th><th scope=col>ITCC-P4_s01_EP0027_TP01_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0027_PP01_F01_R01_A03</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&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><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td> 11869</td><td> 14412</td><td>ENSG00000223972.4</td><td>.</td><td>+</td><td>DDX11L1     </td><td>1756</td><td> 0.00000000</td><td>  0.00000000</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td> 14363</td><td> 29806</td><td>ENSG00000227232.4</td><td>.</td><td>-</td><td>WASH7P      </td><td>2073</td><td>88.16388486</td><td>124.73545549</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td> 29554</td><td> 31109</td><td>ENSG00000243485.2</td><td>.</td><td>+</td><td>MIR1302-11  </td><td>1021</td><td> 0.18749550</td><td>  0.03864177</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1</td><td> 34554</td><td> 36081</td><td>ENSG00000237613.2</td><td>.</td><td>-</td><td>FAM138A     </td><td>1219</td><td> 0.00000000</td><td>  0.00000000</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td> 52473</td><td> 54936</td><td>ENSG00000268020.2</td><td>.</td><td>+</td><td>OR4G4P      </td><td> 947</td><td> 0.00000000</td><td>  0.00000000</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1</td><td> 62948</td><td> 63887</td><td>ENSG00000240361.1</td><td>.</td><td>+</td><td>OR4G11P     </td><td> 940</td><td> 0.00000000</td><td>  0.00000000</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>1</td><td> 69091</td><td> 70008</td><td>ENSG00000186092.4</td><td>.</td><td>+</td><td>OR4F5       </td><td> 918</td><td> 0.00000000</td><td>  0.00000000</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>1</td><td> 89295</td><td>133566</td><td>ENSG00000238009.2</td><td>.</td><td>-</td><td>RP11-34P13.7</td><td>3569</td><td> 0.14558801</td><td>  0.09948984</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>1</td><td> 89551</td><td> 91105</td><td>ENSG00000239945.1</td><td>.</td><td>-</td><td>RP11-34P13.8</td><td>1319</td><td> 0.00000000</td><td>  0.00000000</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>1</td><td>131025</td><td>134836</td><td>ENSG00000233750.3</td><td>.</td><td>+</td><td>CICP27      </td><td>3812</td><td> 0.02152221</td><td>  0.22769452</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & \\#chrom & chromStart & chromEnd & gene\\_id & score & strand & name & exonic\\_length & ITCC-P4\\_s01\\_EP0027\\_TP01\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0027\\_PP01\\_F01\\_R01\\_A03\\\\\n",
       "  & <chr> & <int> & <int> & <chr> & <chr> & <chr> & <chr> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 &  11869 &  14412 & ENSG00000223972.4 & . & + & DDX11L1      & 1756 &  0.00000000 &   0.00000000\\\\\n",
       "\t2 & 1 &  14363 &  29806 & ENSG00000227232.4 & . & - & WASH7P       & 2073 & 88.16388486 & 124.73545549\\\\\n",
       "\t3 & 1 &  29554 &  31109 & ENSG00000243485.2 & . & + & MIR1302-11   & 1021 &  0.18749550 &   0.03864177\\\\\n",
       "\t4 & 1 &  34554 &  36081 & ENSG00000237613.2 & . & - & FAM138A      & 1219 &  0.00000000 &   0.00000000\\\\\n",
       "\t5 & 1 &  52473 &  54936 & ENSG00000268020.2 & . & + & OR4G4P       &  947 &  0.00000000 &   0.00000000\\\\\n",
       "\t6 & 1 &  62948 &  63887 & ENSG00000240361.1 & . & + & OR4G11P      &  940 &  0.00000000 &   0.00000000\\\\\n",
       "\t7 & 1 &  69091 &  70008 & ENSG00000186092.4 & . & + & OR4F5        &  918 &  0.00000000 &   0.00000000\\\\\n",
       "\t8 & 1 &  89295 & 133566 & ENSG00000238009.2 & . & - & RP11-34P13.7 & 3569 &  0.14558801 &   0.09948984\\\\\n",
       "\t9 & 1 &  89551 &  91105 & ENSG00000239945.1 & . & - & RP11-34P13.8 & 1319 &  0.00000000 &   0.00000000\\\\\n",
       "\t10 & 1 & 131025 & 134836 & ENSG00000233750.3 & . & + & CICP27       & 3812 &  0.02152221 &   0.22769452\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | #chrom &lt;chr&gt; | chromStart &lt;int&gt; | chromEnd &lt;int&gt; | gene_id &lt;chr&gt; | score &lt;chr&gt; | strand &lt;chr&gt; | name &lt;chr&gt; | exonic_length &lt;int&gt; | ITCC-P4_s01_EP0027_TP01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0027_PP01_F01_R01_A03 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1 |  11869 |  14412 | ENSG00000223972.4 | . | + | DDX11L1      | 1756 |  0.00000000 |   0.00000000 |\n",
       "| 2 | 1 |  14363 |  29806 | ENSG00000227232.4 | . | - | WASH7P       | 2073 | 88.16388486 | 124.73545549 |\n",
       "| 3 | 1 |  29554 |  31109 | ENSG00000243485.2 | . | + | MIR1302-11   | 1021 |  0.18749550 |   0.03864177 |\n",
       "| 4 | 1 |  34554 |  36081 | ENSG00000237613.2 | . | - | FAM138A      | 1219 |  0.00000000 |   0.00000000 |\n",
       "| 5 | 1 |  52473 |  54936 | ENSG00000268020.2 | . | + | OR4G4P       |  947 |  0.00000000 |   0.00000000 |\n",
       "| 6 | 1 |  62948 |  63887 | ENSG00000240361.1 | . | + | OR4G11P      |  940 |  0.00000000 |   0.00000000 |\n",
       "| 7 | 1 |  69091 |  70008 | ENSG00000186092.4 | . | + | OR4F5        |  918 |  0.00000000 |   0.00000000 |\n",
       "| 8 | 1 |  89295 | 133566 | ENSG00000238009.2 | . | - | RP11-34P13.7 | 3569 |  0.14558801 |   0.09948984 |\n",
       "| 9 | 1 |  89551 |  91105 | ENSG00000239945.1 | . | - | RP11-34P13.8 | 1319 |  0.00000000 |   0.00000000 |\n",
       "| 10 | 1 | 131025 | 134836 | ENSG00000233750.3 | . | + | CICP27       | 3812 |  0.02152221 |   0.22769452 |\n",
       "\n"
      ],
      "text/plain": [
       "   #chrom chromStart chromEnd gene_id           score strand name        \n",
       "1  1       11869      14412   ENSG00000223972.4 .     +      DDX11L1     \n",
       "2  1       14363      29806   ENSG00000227232.4 .     -      WASH7P      \n",
       "3  1       29554      31109   ENSG00000243485.2 .     +      MIR1302-11  \n",
       "4  1       34554      36081   ENSG00000237613.2 .     -      FAM138A     \n",
       "5  1       52473      54936   ENSG00000268020.2 .     +      OR4G4P      \n",
       "6  1       62948      63887   ENSG00000240361.1 .     +      OR4G11P     \n",
       "7  1       69091      70008   ENSG00000186092.4 .     +      OR4F5       \n",
       "8  1       89295     133566   ENSG00000238009.2 .     -      RP11-34P13.7\n",
       "9  1       89551      91105   ENSG00000239945.1 .     -      RP11-34P13.8\n",
       "10 1      131025     134836   ENSG00000233750.3 .     +      CICP27      \n",
       "   exonic_length ITCC-P4_s01_EP0027_TP01_F01_R01_A03\n",
       "1  1756           0.00000000                        \n",
       "2  2073          88.16388486                        \n",
       "3  1021           0.18749550                        \n",
       "4  1219           0.00000000                        \n",
       "5   947           0.00000000                        \n",
       "6   940           0.00000000                        \n",
       "7   918           0.00000000                        \n",
       "8  3569           0.14558801                        \n",
       "9  1319           0.00000000                        \n",
       "10 3812           0.02152221                        \n",
       "   ITCC-P4_s01_EP0027_PP01_F01_R01_A03\n",
       "1    0.00000000                       \n",
       "2  124.73545549                       \n",
       "3    0.03864177                       \n",
       "4    0.00000000                       \n",
       "5    0.00000000                       \n",
       "6    0.00000000                       \n",
       "7    0.00000000                       \n",
       "8    0.09948984                       \n",
       "9    0.00000000                       \n",
       "10   0.22769452                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdx_mat[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>57820</li><li>175</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 57820\n",
       "\\item 175\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 57820\n",
       "2. 175\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 57820   175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pdx_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dedup\n",
    "pdx_mat <- pdx_mat[!duplicated(pdx_mat$name),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>55765</li><li>175</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 55765\n",
       "\\item 175\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 55765\n",
       "2. 175\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 55765   175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pdx_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(pdx_mat) <- pdx_mat$name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdx_mat <- pdx_mat[, grepl(\"ITCC\",colnames(pdx_mat))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# include only CNS and NBL pdxes, remove all others\n",
    "\n",
    "pdx_mat <- pdx_mat[,grep(paste(c(\"EP\", \"MB\", \"HG\", \"NB\", \"RT\"),collapse=\"|\"),colnames(pdx_mat), value=TRUE)]\n",
    "pdx_mat <- pdx_mat[,grep(paste(c(\"PP\", \"PT\", \"PR\", \"PM\"),collapse=\"|\"),colnames(pdx_mat), value=TRUE)]\n",
    "pdx_mat <- pdx_mat[,!colnames(pdx_mat) %in% c(\"ITCC.P4_s07_HG0068_PT01_F01_R01_A03\",\"ITCC.P4_s15_NB0537_PT03_F01_R01_03\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 61</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ITCC-P4_s01_EP0027_PP01_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0035_PP01_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0076_PR03_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0076_PP03_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0077_PP02_F01_R01</th><th scope=col>ITCC-P4_s01_EP0191_PR01_F01_R01_A03</th><th scope=col>ITCC-P4_s05_EP0191_PR04_F01_R01_03</th><th scope=col>ITCC-P4_s01_EP0233_PR01_F01_R01_A03</th><th scope=col>ITCC-P4_s05_EP0233_PR05_F01_R01_03</th><th scope=col>ITCC-P4_s01_EP0307_PM01_F01_R01_A03</th><th scope=col>⋯</th><th scope=col>ITCC-P4_s05_NB0179_PR07_F01_R01_03</th><th scope=col>ITCC-P4_s05_NB0211_PR07_F01_R01_03</th><th scope=col>ITCC-P4_s05_NB0219_PR05_F01_R01_03</th><th scope=col>ITCC-P4_s13_NB0241_PR09_F01_R01_03</th><th scope=col>ITCC-P4_s05_NB0453_PR01_F01_R01_03</th><th scope=col>ITCC-P4_s05_NB0457_PR01_F01_R01_03</th><th scope=col>ITCC-P4_s15_NB0537_PT03_F01_R01_03</th><th scope=col>ITCC-P4_s15_NB0542_PP03_F01_R01_03</th><th scope=col>ITCC-P4_s04_RT0414_PP01_F01_R01_03</th><th scope=col>ITCC-P4_s01_RT0423_PP01_F01_R01_A03</th></tr>\n",
       "\t<tr><th></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;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>⋯</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;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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>DDX11L1</th><td>  0.00000000</td><td> 0.0000</td><td>  0.0000000</td><td> 0.000000</td><td>  0.1919366</td><td> 0.00000</td><td> 0.09207969</td><td> 0.00000</td><td> 0.00000</td><td> 0.00000</td><td>⋯</td><td> 2.567243</td><td> 0.00000</td><td> 0.07145530</td><td> 0.6075527</td><td> 0.2759262</td><td> 0.03438562</td><td>0</td><td> 0.13485808</td><td> 0.01149935</td><td>  0.4206718</td></tr>\n",
       "\t<tr><th scope=row>WASH7P</th><td>124.73545549</td><td>33.2096</td><td>108.6258929</td><td>92.094845</td><td>106.4937876</td><td>42.33729</td><td>48.12538547</td><td>53.14819</td><td>29.14271</td><td>44.71067</td><td>⋯</td><td>37.367976</td><td>69.80682</td><td>51.38866711</td><td>86.5178254</td><td>67.0810769</td><td>20.41832507</td><td>0</td><td>34.65152333</td><td>40.02529931</td><td>130.3325659</td></tr>\n",
       "\t<tr><th scope=row>MIR1302-11</th><td>  0.03864177</td><td> 0.0000</td><td>  0.3462322</td><td> 0.294466</td><td>  0.0000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.00000</td><td> 0.00000</td><td> 0.00000</td><td>⋯</td><td> 0.000000</td><td> 0.00000</td><td> 0.06144736</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td>0</td><td> 0.07731335</td><td> 0.00000000</td><td>  0.0000000</td></tr>\n",
       "\t<tr><th scope=row>FAM138A</th><td>  0.00000000</td><td> 0.0000</td><td>  0.0000000</td><td> 0.000000</td><td>  0.0000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.00000</td><td> 0.00000</td><td> 0.00000</td><td>⋯</td><td> 0.000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td>0</td><td> 0.00000000</td><td> 0.00000000</td><td>  0.0000000</td></tr>\n",
       "\t<tr><th scope=row>OR4G4P</th><td>  0.00000000</td><td> 0.0000</td><td>  0.0000000</td><td> 0.000000</td><td>  0.0000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.00000</td><td> 0.00000</td><td> 0.00000</td><td>⋯</td><td> 0.000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td>0</td><td> 0.00000000</td><td> 0.00000000</td><td>  0.0000000</td></tr>\n",
       "\t<tr><th scope=row>OR4G11P</th><td>  0.00000000</td><td> 0.0000</td><td>  0.1253557</td><td> 0.000000</td><td>  0.0000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.00000</td><td> 0.00000</td><td> 0.00000</td><td>⋯</td><td> 0.000000</td><td> 0.00000</td><td> 0.00000000</td><td> 0.0000000</td><td> 0.0000000</td><td> 0.00000000</td><td>0</td><td> 0.00000000</td><td> 0.00000000</td><td>  0.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 61\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & ITCC-P4\\_s01\\_EP0027\\_PP01\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0035\\_PP01\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0076\\_PR03\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0076\\_PP03\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0077\\_PP02\\_F01\\_R01 & ITCC-P4\\_s01\\_EP0191\\_PR01\\_F01\\_R01\\_A03 & ITCC-P4\\_s05\\_EP0191\\_PR04\\_F01\\_R01\\_03 & ITCC-P4\\_s01\\_EP0233\\_PR01\\_F01\\_R01\\_A03 & ITCC-P4\\_s05\\_EP0233\\_PR05\\_F01\\_R01\\_03 & ITCC-P4\\_s01\\_EP0307\\_PM01\\_F01\\_R01\\_A03 & ⋯ & ITCC-P4\\_s05\\_NB0179\\_PR07\\_F01\\_R01\\_03 & ITCC-P4\\_s05\\_NB0211\\_PR07\\_F01\\_R01\\_03 & ITCC-P4\\_s05\\_NB0219\\_PR05\\_F01\\_R01\\_03 & ITCC-P4\\_s13\\_NB0241\\_PR09\\_F01\\_R01\\_03 & ITCC-P4\\_s05\\_NB0453\\_PR01\\_F01\\_R01\\_03 & ITCC-P4\\_s05\\_NB0457\\_PR01\\_F01\\_R01\\_03 & ITCC-P4\\_s15\\_NB0537\\_PT03\\_F01\\_R01\\_03 & ITCC-P4\\_s15\\_NB0542\\_PP03\\_F01\\_R01\\_03 & ITCC-P4\\_s04\\_RT0414\\_PP01\\_F01\\_R01\\_03 & ITCC-P4\\_s01\\_RT0423\\_PP01\\_F01\\_R01\\_A03\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tDDX11L1 &   0.00000000 &  0.0000 &   0.0000000 &  0.000000 &   0.1919366 &  0.00000 &  0.09207969 &  0.00000 &  0.00000 &  0.00000 & ⋯ &  2.567243 &  0.00000 &  0.07145530 &  0.6075527 &  0.2759262 &  0.03438562 & 0 &  0.13485808 &  0.01149935 &   0.4206718\\\\\n",
       "\tWASH7P & 124.73545549 & 33.2096 & 108.6258929 & 92.094845 & 106.4937876 & 42.33729 & 48.12538547 & 53.14819 & 29.14271 & 44.71067 & ⋯ & 37.367976 & 69.80682 & 51.38866711 & 86.5178254 & 67.0810769 & 20.41832507 & 0 & 34.65152333 & 40.02529931 & 130.3325659\\\\\n",
       "\tMIR1302-11 &   0.03864177 &  0.0000 &   0.3462322 &  0.294466 &   0.0000000 &  0.00000 &  0.00000000 &  0.00000 &  0.00000 &  0.00000 & ⋯ &  0.000000 &  0.00000 &  0.06144736 &  0.0000000 &  0.0000000 &  0.00000000 & 0 &  0.07731335 &  0.00000000 &   0.0000000\\\\\n",
       "\tFAM138A &   0.00000000 &  0.0000 &   0.0000000 &  0.000000 &   0.0000000 &  0.00000 &  0.00000000 &  0.00000 &  0.00000 &  0.00000 & ⋯ &  0.000000 &  0.00000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.00000000 & 0 &  0.00000000 &  0.00000000 &   0.0000000\\\\\n",
       "\tOR4G4P &   0.00000000 &  0.0000 &   0.0000000 &  0.000000 &   0.0000000 &  0.00000 &  0.00000000 &  0.00000 &  0.00000 &  0.00000 & ⋯ &  0.000000 &  0.00000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.00000000 & 0 &  0.00000000 &  0.00000000 &   0.0000000\\\\\n",
       "\tOR4G11P &   0.00000000 &  0.0000 &   0.1253557 &  0.000000 &   0.0000000 &  0.00000 &  0.00000000 &  0.00000 &  0.00000 &  0.00000 & ⋯ &  0.000000 &  0.00000 &  0.00000000 &  0.0000000 &  0.0000000 &  0.00000000 & 0 &  0.00000000 &  0.00000000 &   0.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 61\n",
       "\n",
       "| <!--/--> | ITCC-P4_s01_EP0027_PP01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0035_PP01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0076_PR03_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0076_PP03_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0077_PP02_F01_R01 &lt;dbl&gt; | ITCC-P4_s01_EP0191_PR01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s05_EP0191_PR04_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s01_EP0233_PR01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s05_EP0233_PR05_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s01_EP0307_PM01_F01_R01_A03 &lt;dbl&gt; | ⋯ ⋯ | ITCC-P4_s05_NB0179_PR07_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s05_NB0211_PR07_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s05_NB0219_PR05_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s13_NB0241_PR09_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s05_NB0453_PR01_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s05_NB0457_PR01_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s15_NB0537_PT03_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s15_NB0542_PP03_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s04_RT0414_PP01_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s01_RT0423_PP01_F01_R01_A03 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| DDX11L1 |   0.00000000 |  0.0000 |   0.0000000 |  0.000000 |   0.1919366 |  0.00000 |  0.09207969 |  0.00000 |  0.00000 |  0.00000 | ⋯ |  2.567243 |  0.00000 |  0.07145530 |  0.6075527 |  0.2759262 |  0.03438562 | 0 |  0.13485808 |  0.01149935 |   0.4206718 |\n",
       "| WASH7P | 124.73545549 | 33.2096 | 108.6258929 | 92.094845 | 106.4937876 | 42.33729 | 48.12538547 | 53.14819 | 29.14271 | 44.71067 | ⋯ | 37.367976 | 69.80682 | 51.38866711 | 86.5178254 | 67.0810769 | 20.41832507 | 0 | 34.65152333 | 40.02529931 | 130.3325659 |\n",
       "| MIR1302-11 |   0.03864177 |  0.0000 |   0.3462322 |  0.294466 |   0.0000000 |  0.00000 |  0.00000000 |  0.00000 |  0.00000 |  0.00000 | ⋯ |  0.000000 |  0.00000 |  0.06144736 |  0.0000000 |  0.0000000 |  0.00000000 | 0 |  0.07731335 |  0.00000000 |   0.0000000 |\n",
       "| FAM138A |   0.00000000 |  0.0000 |   0.0000000 |  0.000000 |   0.0000000 |  0.00000 |  0.00000000 |  0.00000 |  0.00000 |  0.00000 | ⋯ |  0.000000 |  0.00000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.00000000 | 0 |  0.00000000 |  0.00000000 |   0.0000000 |\n",
       "| OR4G4P |   0.00000000 |  0.0000 |   0.0000000 |  0.000000 |   0.0000000 |  0.00000 |  0.00000000 |  0.00000 |  0.00000 |  0.00000 | ⋯ |  0.000000 |  0.00000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.00000000 | 0 |  0.00000000 |  0.00000000 |   0.0000000 |\n",
       "| OR4G11P |   0.00000000 |  0.0000 |   0.1253557 |  0.000000 |   0.0000000 |  0.00000 |  0.00000000 |  0.00000 |  0.00000 |  0.00000 | ⋯ |  0.000000 |  0.00000 |  0.00000000 |  0.0000000 |  0.0000000 |  0.00000000 | 0 |  0.00000000 |  0.00000000 |   0.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "           ITCC-P4_s01_EP0027_PP01_F01_R01_A03\n",
       "DDX11L1      0.00000000                       \n",
       "WASH7P     124.73545549                       \n",
       "MIR1302-11   0.03864177                       \n",
       "FAM138A      0.00000000                       \n",
       "OR4G4P       0.00000000                       \n",
       "OR4G11P      0.00000000                       \n",
       "           ITCC-P4_s01_EP0035_PP01_F01_R01_A03\n",
       "DDX11L1     0.0000                            \n",
       "WASH7P     33.2096                            \n",
       "MIR1302-11  0.0000                            \n",
       "FAM138A     0.0000                            \n",
       "OR4G4P      0.0000                            \n",
       "OR4G11P     0.0000                            \n",
       "           ITCC-P4_s01_EP0076_PR03_F01_R01_A03\n",
       "DDX11L1      0.0000000                        \n",
       "WASH7P     108.6258929                        \n",
       "MIR1302-11   0.3462322                        \n",
       "FAM138A      0.0000000                        \n",
       "OR4G4P       0.0000000                        \n",
       "OR4G11P      0.1253557                        \n",
       "           ITCC-P4_s01_EP0076_PP03_F01_R01_A03 ITCC-P4_s01_EP0077_PP02_F01_R01\n",
       "DDX11L1     0.000000                             0.1919366                    \n",
       "WASH7P     92.094845                           106.4937876                    \n",
       "MIR1302-11  0.294466                             0.0000000                    \n",
       "FAM138A     0.000000                             0.0000000                    \n",
       "OR4G4P      0.000000                             0.0000000                    \n",
       "OR4G11P     0.000000                             0.0000000                    \n",
       "           ITCC-P4_s01_EP0191_PR01_F01_R01_A03\n",
       "DDX11L1     0.00000                           \n",
       "WASH7P     42.33729                           \n",
       "MIR1302-11  0.00000                           \n",
       "FAM138A     0.00000                           \n",
       "OR4G4P      0.00000                           \n",
       "OR4G11P     0.00000                           \n",
       "           ITCC-P4_s05_EP0191_PR04_F01_R01_03\n",
       "DDX11L1     0.09207969                       \n",
       "WASH7P     48.12538547                       \n",
       "MIR1302-11  0.00000000                       \n",
       "FAM138A     0.00000000                       \n",
       "OR4G4P      0.00000000                       \n",
       "OR4G11P     0.00000000                       \n",
       "           ITCC-P4_s01_EP0233_PR01_F01_R01_A03\n",
       "DDX11L1     0.00000                           \n",
       "WASH7P     53.14819                           \n",
       "MIR1302-11  0.00000                           \n",
       "FAM138A     0.00000                           \n",
       "OR4G4P      0.00000                           \n",
       "OR4G11P     0.00000                           \n",
       "           ITCC-P4_s05_EP0233_PR05_F01_R01_03\n",
       "DDX11L1     0.00000                          \n",
       "WASH7P     29.14271                          \n",
       "MIR1302-11  0.00000                          \n",
       "FAM138A     0.00000                          \n",
       "OR4G4P      0.00000                          \n",
       "OR4G11P     0.00000                          \n",
       "           ITCC-P4_s01_EP0307_PM01_F01_R01_A03 ⋯\n",
       "DDX11L1     0.00000                            ⋯\n",
       "WASH7P     44.71067                            ⋯\n",
       "MIR1302-11  0.00000                            ⋯\n",
       "FAM138A     0.00000                            ⋯\n",
       "OR4G4P      0.00000                            ⋯\n",
       "OR4G11P     0.00000                            ⋯\n",
       "           ITCC-P4_s05_NB0179_PR07_F01_R01_03\n",
       "DDX11L1     2.567243                         \n",
       "WASH7P     37.367976                         \n",
       "MIR1302-11  0.000000                         \n",
       "FAM138A     0.000000                         \n",
       "OR4G4P      0.000000                         \n",
       "OR4G11P     0.000000                         \n",
       "           ITCC-P4_s05_NB0211_PR07_F01_R01_03\n",
       "DDX11L1     0.00000                          \n",
       "WASH7P     69.80682                          \n",
       "MIR1302-11  0.00000                          \n",
       "FAM138A     0.00000                          \n",
       "OR4G4P      0.00000                          \n",
       "OR4G11P     0.00000                          \n",
       "           ITCC-P4_s05_NB0219_PR05_F01_R01_03\n",
       "DDX11L1     0.07145530                       \n",
       "WASH7P     51.38866711                       \n",
       "MIR1302-11  0.06144736                       \n",
       "FAM138A     0.00000000                       \n",
       "OR4G4P      0.00000000                       \n",
       "OR4G11P     0.00000000                       \n",
       "           ITCC-P4_s13_NB0241_PR09_F01_R01_03\n",
       "DDX11L1     0.6075527                        \n",
       "WASH7P     86.5178254                        \n",
       "MIR1302-11  0.0000000                        \n",
       "FAM138A     0.0000000                        \n",
       "OR4G4P      0.0000000                        \n",
       "OR4G11P     0.0000000                        \n",
       "           ITCC-P4_s05_NB0453_PR01_F01_R01_03\n",
       "DDX11L1     0.2759262                        \n",
       "WASH7P     67.0810769                        \n",
       "MIR1302-11  0.0000000                        \n",
       "FAM138A     0.0000000                        \n",
       "OR4G4P      0.0000000                        \n",
       "OR4G11P     0.0000000                        \n",
       "           ITCC-P4_s05_NB0457_PR01_F01_R01_03\n",
       "DDX11L1     0.03438562                       \n",
       "WASH7P     20.41832507                       \n",
       "MIR1302-11  0.00000000                       \n",
       "FAM138A     0.00000000                       \n",
       "OR4G4P      0.00000000                       \n",
       "OR4G11P     0.00000000                       \n",
       "           ITCC-P4_s15_NB0537_PT03_F01_R01_03\n",
       "DDX11L1    0                                 \n",
       "WASH7P     0                                 \n",
       "MIR1302-11 0                                 \n",
       "FAM138A    0                                 \n",
       "OR4G4P     0                                 \n",
       "OR4G11P    0                                 \n",
       "           ITCC-P4_s15_NB0542_PP03_F01_R01_03\n",
       "DDX11L1     0.13485808                       \n",
       "WASH7P     34.65152333                       \n",
       "MIR1302-11  0.07731335                       \n",
       "FAM138A     0.00000000                       \n",
       "OR4G4P      0.00000000                       \n",
       "OR4G11P     0.00000000                       \n",
       "           ITCC-P4_s04_RT0414_PP01_F01_R01_03\n",
       "DDX11L1     0.01149935                       \n",
       "WASH7P     40.02529931                       \n",
       "MIR1302-11  0.00000000                       \n",
       "FAM138A     0.00000000                       \n",
       "OR4G4P      0.00000000                       \n",
       "OR4G11P     0.00000000                       \n",
       "           ITCC-P4_s01_RT0423_PP01_F01_R01_A03\n",
       "DDX11L1      0.4206718                        \n",
       "WASH7P     130.3325659                        \n",
       "MIR1302-11   0.0000000                        \n",
       "FAM138A      0.0000000                        \n",
       "OR4G4P       0.0000000                        \n",
       "OR4G11P      0.0000000                        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pdx_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#match rownames and bind both pdx matrices\n",
    "pdx_mat <- pdx_mat[rownames(pdx_mat_tpm),]\n",
    "pdx_mat <- cbind(pdx_mat, pdx_mat_tpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>51472</li><li>81</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 51472\n",
       "\\item 81\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 51472\n",
       "2. 81\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 51472    81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pdx_mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset to protein_coding genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "hg38 <- useMart(biomart=\"ENSEMBL_MART_ENSEMBL\", host=\"www.ensembl.org\", \n",
    "                path=\"/biomart/martservice\", dataset=\"hsapiens_gene_ensembl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgncs <- rownames(pdx_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgnc_type <- getBM(filters=\"hgnc_symbol\",\n",
    "                   attributes=c(\"hgnc_symbol\",\"ensembl_gene_id\", \"entrezgene_id\", \"gene_biotype\"), \n",
    "                   values= hgncs,mart = hg38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "                         IG_C_gene                    IG_C_pseudogene \n",
       "                                23                                 10 \n",
       "                         IG_D_gene                          IG_J_gene \n",
       "                                64                                 24 \n",
       "                   IG_J_pseudogene                          IG_V_gene \n",
       "                                 6                                207 \n",
       "                   IG_V_pseudogene                             lncRNA \n",
       "                               274                               1699 \n",
       "                             miRNA                           misc_RNA \n",
       "                                 9                               1021 \n",
       "            polymorphic_pseudogene               processed_pseudogene \n",
       "                                61                               4138 \n",
       "                    protein_coding                           ribozyme \n",
       "                             20558                                  5 \n",
       "                            scaRNA                             snoRNA \n",
       "                                 2                                  1 \n",
       "                             snRNA                                TEC \n",
       "                                 3                                  7 \n",
       "                         TR_C_gene                          TR_D_gene \n",
       "                                 6                                  3 \n",
       "                         TR_J_gene                    TR_J_pseudogene \n",
       "                                81                                  4 \n",
       "                         TR_V_gene                    TR_V_pseudogene \n",
       "                               126                                 45 \n",
       "  transcribed_processed_pseudogene     transcribed_unitary_pseudogene \n",
       "                               251                                 69 \n",
       "transcribed_unprocessed_pseudogene    translated_processed_pseudogene \n",
       "                               588                                  1 \n",
       " translated_unprocessed_pseudogene                 unitary_pseudogene \n",
       "                                 1                                 59 \n",
       "            unprocessed_pseudogene \n",
       "                              1535 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(hgnc_type$gene_biotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteincoding <- hgnc_type$hgnc_symbol[hgnc_type$gene_biotype == \"protein_coding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdx_mat_tpm_pc <- pdx_mat[proteincoding,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>51472</li><li>81</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 51472\n",
       "\\item 81\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 51472\n",
       "2. 81\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 51472    81"
      ]
     },
     "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>20558</li><li>81</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20558\n",
       "\\item 81\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20558\n",
       "2. 81\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20558    81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pdx_mat)\n",
    "dim(pdx_mat_tpm_pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#median center MAD scale\n",
    "pdx_mat_tpm_m <- (pdx_mat_tpm_pc - median(unlist(pdx_mat_tpm_pc)))/mad(unlist(pdx_mat_tpm_pc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "     0.00      0.17      8.07     60.79     38.12 211758.04 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(pdx_mat_tpm_pc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "   -0.674    -0.660     0.000     4.407     2.512 17702.256 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(pdx_mat_tpm_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QCgIMkGu6yPXhqxIIQQQq1j7xp6wW6Vi3hc2vaH3PbywMOyo93sESMmJXleAAAK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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(pdx_mat_tpm_m)), binwidth = 0.5) + myaxis + myplot +\n",
    "coord_cartesian(xlim = c(-1,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 81</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ITCC-P4_s01_EP0027_PP01_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0035_PP01_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0076_PR03_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0076_PP03_F01_R01_A03</th><th scope=col>ITCC-P4_s01_EP0077_PP02_F01_R01</th><th scope=col>ITCC-P4_s01_EP0191_PR01_F01_R01_A03</th><th scope=col>ITCC-P4_s05_EP0191_PR04_F01_R01_03</th><th scope=col>ITCC-P4_s01_EP0233_PR01_F01_R01_A03</th><th scope=col>ITCC-P4_s05_EP0233_PR05_F01_R01_03</th><th scope=col>ITCC-P4_s01_EP0307_PM01_F01_R01_A03</th><th scope=col>⋯</th><th scope=col>Med210FH_fP5_5043</th><th scope=col>Med2312FH_P5-2</th><th scope=col>Med411FH_P5-4</th><th scope=col>Med610FH_P4-2</th><th scope=col>Med813FH_P5-2</th><th scope=col>Med913FH_P1-2+3</th><th scope=col>NCH2194_XP1_1020</th><th scope=col>NCH2194_XP6_686</th><th scope=col>Neo113FH_P5-5</th><th scope=col>RCMB28_XP5_283</th></tr>\n",
       "\t<tr><th></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;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>⋯</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;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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A1BG</th><td>-0.5921572</td><td>-0.5836629</td><td>-0.3548150</td><td>-0.4901875</td><td>-0.6674572</td><td>-0.4697676</td><td>-0.44166461</td><td>-0.5083461</td><td>-0.59639219</td><td>-0.6714543</td><td>⋯</td><td> 1.2647239</td><td> 0.7400879</td><td> 2.5806388</td><td> 0.04548172</td><td> 2.4076761</td><td> 0.1735341</td><td> 3.9535341</td><td> 0.7499960</td><td> 0.7800400</td><td> 1.2543403</td></tr>\n",
       "\t<tr><th scope=row>A1CF</th><td>-0.6703692</td><td>-0.6728313</td><td>-0.6683359</td><td>-0.6712191</td><td>-0.6686225</td><td>-0.6592403</td><td>-0.66322977</td><td>-0.6633119</td><td>-0.66750939</td><td>-0.6668906</td><td>⋯</td><td>-0.6728263</td><td>-0.6728055</td><td>-0.6743808</td><td>-0.67449076</td><td>-0.6738131</td><td>-0.6730773</td><td>-0.6739310</td><td>-0.6743220</td><td>-0.6729747</td><td>-0.6732867</td></tr>\n",
       "\t<tr><th scope=row>A2M</th><td>-0.2870044</td><td>-0.4868648</td><td>-0.6035097</td><td>-0.6297066</td><td>-0.6391818</td><td> 0.3325836</td><td>-0.05303696</td><td>-0.6005020</td><td> 0.30565609</td><td>-0.6459099</td><td>⋯</td><td>-0.6741509</td><td>-0.6744908</td><td>-0.6744908</td><td>-0.58097049</td><td>-0.6741266</td><td>-0.6744908</td><td>-0.6737666</td><td>-0.6744908</td><td>-0.6722639</td><td>-0.6744908</td></tr>\n",
       "\t<tr><th scope=row>A2ML1</th><td> 2.0208616</td><td>-0.3802814</td><td> 4.0911310</td><td> 3.9687056</td><td>-0.6455542</td><td> 1.0240346</td><td> 4.36376765</td><td> 0.5808520</td><td> 0.60153885</td><td>-0.5862131</td><td>⋯</td><td>-0.6471392</td><td>-0.6534215</td><td>-0.6701000</td><td>-0.66495081</td><td>-0.6555912</td><td>-0.6400824</td><td>-0.6497869</td><td>-0.6558138</td><td>-0.6434327</td><td>-0.6281294</td></tr>\n",
       "\t<tr><th scope=row>A3GALT2</th><td>-0.5838270</td><td>-0.6644211</td><td>-0.5064180</td><td>-0.5732390</td><td>-0.5676603</td><td>-0.5495570</td><td>-0.52714454</td><td>-0.5930872</td><td>-0.56857916</td><td>-0.5822519</td><td>⋯</td><td>-0.6659630</td><td>-0.6664484</td><td>-0.6685383</td><td>-0.64757426</td><td>-0.6261245</td><td>-0.6276556</td><td>-0.6431204</td><td>-0.6059228</td><td>-0.6307213</td><td>-0.6505268</td></tr>\n",
       "\t<tr><th scope=row>A4GALT</th><td> 0.6300293</td><td>-0.4416608</td><td> 0.2560422</td><td> 0.2638807</td><td> 0.2589825</td><td>-0.1768687</td><td>-0.09806009</td><td>-0.0252056</td><td>-0.02905257</td><td>-0.5670277</td><td>⋯</td><td>-0.6691560</td><td>-0.6706098</td><td>-0.6712372</td><td>-0.67058317</td><td> 3.0426267</td><td>-0.6298772</td><td>-0.6744908</td><td>-0.6744908</td><td>-0.6576712</td><td>-0.6740484</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 81\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & ITCC-P4\\_s01\\_EP0027\\_PP01\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0035\\_PP01\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0076\\_PR03\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0076\\_PP03\\_F01\\_R01\\_A03 & ITCC-P4\\_s01\\_EP0077\\_PP02\\_F01\\_R01 & ITCC-P4\\_s01\\_EP0191\\_PR01\\_F01\\_R01\\_A03 & ITCC-P4\\_s05\\_EP0191\\_PR04\\_F01\\_R01\\_03 & ITCC-P4\\_s01\\_EP0233\\_PR01\\_F01\\_R01\\_A03 & ITCC-P4\\_s05\\_EP0233\\_PR05\\_F01\\_R01\\_03 & ITCC-P4\\_s01\\_EP0307\\_PM01\\_F01\\_R01\\_A03 & ⋯ & Med210FH\\_fP5\\_5043 & Med2312FH\\_P5-2 & Med411FH\\_P5-4 & Med610FH\\_P4-2 & Med813FH\\_P5-2 & Med913FH\\_P1-2+3 & NCH2194\\_XP1\\_1020 & NCH2194\\_XP6\\_686 & Neo113FH\\_P5-5 & RCMB28\\_XP5\\_283\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tA1BG & -0.5921572 & -0.5836629 & -0.3548150 & -0.4901875 & -0.6674572 & -0.4697676 & -0.44166461 & -0.5083461 & -0.59639219 & -0.6714543 & ⋯ &  1.2647239 &  0.7400879 &  2.5806388 &  0.04548172 &  2.4076761 &  0.1735341 &  3.9535341 &  0.7499960 &  0.7800400 &  1.2543403\\\\\n",
       "\tA1CF & -0.6703692 & -0.6728313 & -0.6683359 & -0.6712191 & -0.6686225 & -0.6592403 & -0.66322977 & -0.6633119 & -0.66750939 & -0.6668906 & ⋯ & -0.6728263 & -0.6728055 & -0.6743808 & -0.67449076 & -0.6738131 & -0.6730773 & -0.6739310 & -0.6743220 & -0.6729747 & -0.6732867\\\\\n",
       "\tA2M & -0.2870044 & -0.4868648 & -0.6035097 & -0.6297066 & -0.6391818 &  0.3325836 & -0.05303696 & -0.6005020 &  0.30565609 & -0.6459099 & ⋯ & -0.6741509 & -0.6744908 & -0.6744908 & -0.58097049 & -0.6741266 & -0.6744908 & -0.6737666 & -0.6744908 & -0.6722639 & -0.6744908\\\\\n",
       "\tA2ML1 &  2.0208616 & -0.3802814 &  4.0911310 &  3.9687056 & -0.6455542 &  1.0240346 &  4.36376765 &  0.5808520 &  0.60153885 & -0.5862131 & ⋯ & -0.6471392 & -0.6534215 & -0.6701000 & -0.66495081 & -0.6555912 & -0.6400824 & -0.6497869 & -0.6558138 & -0.6434327 & -0.6281294\\\\\n",
       "\tA3GALT2 & -0.5838270 & -0.6644211 & -0.5064180 & -0.5732390 & -0.5676603 & -0.5495570 & -0.52714454 & -0.5930872 & -0.56857916 & -0.5822519 & ⋯ & -0.6659630 & -0.6664484 & -0.6685383 & -0.64757426 & -0.6261245 & -0.6276556 & -0.6431204 & -0.6059228 & -0.6307213 & -0.6505268\\\\\n",
       "\tA4GALT &  0.6300293 & -0.4416608 &  0.2560422 &  0.2638807 &  0.2589825 & -0.1768687 & -0.09806009 & -0.0252056 & -0.02905257 & -0.5670277 & ⋯ & -0.6691560 & -0.6706098 & -0.6712372 & -0.67058317 &  3.0426267 & -0.6298772 & -0.6744908 & -0.6744908 & -0.6576712 & -0.6740484\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 81\n",
       "\n",
       "| <!--/--> | ITCC-P4_s01_EP0027_PP01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0035_PP01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0076_PR03_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0076_PP03_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s01_EP0077_PP02_F01_R01 &lt;dbl&gt; | ITCC-P4_s01_EP0191_PR01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s05_EP0191_PR04_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s01_EP0233_PR01_F01_R01_A03 &lt;dbl&gt; | ITCC-P4_s05_EP0233_PR05_F01_R01_03 &lt;dbl&gt; | ITCC-P4_s01_EP0307_PM01_F01_R01_A03 &lt;dbl&gt; | ⋯ ⋯ | Med210FH_fP5_5043 &lt;dbl&gt; | Med2312FH_P5-2 &lt;dbl&gt; | Med411FH_P5-4 &lt;dbl&gt; | Med610FH_P4-2 &lt;dbl&gt; | Med813FH_P5-2 &lt;dbl&gt; | Med913FH_P1-2+3 &lt;dbl&gt; | NCH2194_XP1_1020 &lt;dbl&gt; | NCH2194_XP6_686 &lt;dbl&gt; | Neo113FH_P5-5 &lt;dbl&gt; | RCMB28_XP5_283 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| A1BG | -0.5921572 | -0.5836629 | -0.3548150 | -0.4901875 | -0.6674572 | -0.4697676 | -0.44166461 | -0.5083461 | -0.59639219 | -0.6714543 | ⋯ |  1.2647239 |  0.7400879 |  2.5806388 |  0.04548172 |  2.4076761 |  0.1735341 |  3.9535341 |  0.7499960 |  0.7800400 |  1.2543403 |\n",
       "| A1CF | -0.6703692 | -0.6728313 | -0.6683359 | -0.6712191 | -0.6686225 | -0.6592403 | -0.66322977 | -0.6633119 | -0.66750939 | -0.6668906 | ⋯ | -0.6728263 | -0.6728055 | -0.6743808 | -0.67449076 | -0.6738131 | -0.6730773 | -0.6739310 | -0.6743220 | -0.6729747 | -0.6732867 |\n",
       "| A2M | -0.2870044 | -0.4868648 | -0.6035097 | -0.6297066 | -0.6391818 |  0.3325836 | -0.05303696 | -0.6005020 |  0.30565609 | -0.6459099 | ⋯ | -0.6741509 | -0.6744908 | -0.6744908 | -0.58097049 | -0.6741266 | -0.6744908 | -0.6737666 | -0.6744908 | -0.6722639 | -0.6744908 |\n",
       "| A2ML1 |  2.0208616 | -0.3802814 |  4.0911310 |  3.9687056 | -0.6455542 |  1.0240346 |  4.36376765 |  0.5808520 |  0.60153885 | -0.5862131 | ⋯ | -0.6471392 | -0.6534215 | -0.6701000 | -0.66495081 | -0.6555912 | -0.6400824 | -0.6497869 | -0.6558138 | -0.6434327 | -0.6281294 |\n",
       "| A3GALT2 | -0.5838270 | -0.6644211 | -0.5064180 | -0.5732390 | -0.5676603 | -0.5495570 | -0.52714454 | -0.5930872 | -0.56857916 | -0.5822519 | ⋯ | -0.6659630 | -0.6664484 | -0.6685383 | -0.64757426 | -0.6261245 | -0.6276556 | -0.6431204 | -0.6059228 | -0.6307213 | -0.6505268 |\n",
       "| A4GALT |  0.6300293 | -0.4416608 |  0.2560422 |  0.2638807 |  0.2589825 | -0.1768687 | -0.09806009 | -0.0252056 | -0.02905257 | -0.5670277 | ⋯ | -0.6691560 | -0.6706098 | -0.6712372 | -0.67058317 |  3.0426267 | -0.6298772 | -0.6744908 | -0.6744908 | -0.6576712 | -0.6740484 |\n",
       "\n"
      ],
      "text/plain": [
       "        ITCC-P4_s01_EP0027_PP01_F01_R01_A03 ITCC-P4_s01_EP0035_PP01_F01_R01_A03\n",
       "A1BG    -0.5921572                          -0.5836629                         \n",
       "A1CF    -0.6703692                          -0.6728313                         \n",
       "A2M     -0.2870044                          -0.4868648                         \n",
       "A2ML1    2.0208616                          -0.3802814                         \n",
       "A3GALT2 -0.5838270                          -0.6644211                         \n",
       "A4GALT   0.6300293                          -0.4416608                         \n",
       "        ITCC-P4_s01_EP0076_PR03_F01_R01_A03 ITCC-P4_s01_EP0076_PP03_F01_R01_A03\n",
       "A1BG    -0.3548150                          -0.4901875                         \n",
       "A1CF    -0.6683359                          -0.6712191                         \n",
       "A2M     -0.6035097                          -0.6297066                         \n",
       "A2ML1    4.0911310                           3.9687056                         \n",
       "A3GALT2 -0.5064180                          -0.5732390                         \n",
       "A4GALT   0.2560422                           0.2638807                         \n",
       "        ITCC-P4_s01_EP0077_PP02_F01_R01 ITCC-P4_s01_EP0191_PR01_F01_R01_A03\n",
       "A1BG    -0.6674572                      -0.4697676                         \n",
       "A1CF    -0.6686225                      -0.6592403                         \n",
       "A2M     -0.6391818                       0.3325836                         \n",
       "A2ML1   -0.6455542                       1.0240346                         \n",
       "A3GALT2 -0.5676603                      -0.5495570                         \n",
       "A4GALT   0.2589825                      -0.1768687                         \n",
       "        ITCC-P4_s05_EP0191_PR04_F01_R01_03 ITCC-P4_s01_EP0233_PR01_F01_R01_A03\n",
       "A1BG    -0.44166461                        -0.5083461                         \n",
       "A1CF    -0.66322977                        -0.6633119                         \n",
       "A2M     -0.05303696                        -0.6005020                         \n",
       "A2ML1    4.36376765                         0.5808520                         \n",
       "A3GALT2 -0.52714454                        -0.5930872                         \n",
       "A4GALT  -0.09806009                        -0.0252056                         \n",
       "        ITCC-P4_s05_EP0233_PR05_F01_R01_03 ITCC-P4_s01_EP0307_PM01_F01_R01_A03\n",
       "A1BG    -0.59639219                        -0.6714543                         \n",
       "A1CF    -0.66750939                        -0.6668906                         \n",
       "A2M      0.30565609                        -0.6459099                         \n",
       "A2ML1    0.60153885                        -0.5862131                         \n",
       "A3GALT2 -0.56857916                        -0.5822519                         \n",
       "A4GALT  -0.02905257                        -0.5670277                         \n",
       "        ⋯ Med210FH_fP5_5043 Med2312FH_P5-2 Med411FH_P5-4 Med610FH_P4-2\n",
       "A1BG    ⋯  1.2647239         0.7400879      2.5806388     0.04548172  \n",
       "A1CF    ⋯ -0.6728263        -0.6728055     -0.6743808    -0.67449076  \n",
       "A2M     ⋯ -0.6741509        -0.6744908     -0.6744908    -0.58097049  \n",
       "A2ML1   ⋯ -0.6471392        -0.6534215     -0.6701000    -0.66495081  \n",
       "A3GALT2 ⋯ -0.6659630        -0.6664484     -0.6685383    -0.64757426  \n",
       "A4GALT  ⋯ -0.6691560        -0.6706098     -0.6712372    -0.67058317  \n",
       "        Med813FH_P5-2 Med913FH_P1-2+3 NCH2194_XP1_1020 NCH2194_XP6_686\n",
       "A1BG     2.4076761     0.1735341       3.9535341        0.7499960     \n",
       "A1CF    -0.6738131    -0.6730773      -0.6739310       -0.6743220     \n",
       "A2M     -0.6741266    -0.6744908      -0.6737666       -0.6744908     \n",
       "A2ML1   -0.6555912    -0.6400824      -0.6497869       -0.6558138     \n",
       "A3GALT2 -0.6261245    -0.6276556      -0.6431204       -0.6059228     \n",
       "A4GALT   3.0426267    -0.6298772      -0.6744908       -0.6744908     \n",
       "        Neo113FH_P5-5 RCMB28_XP5_283\n",
       "A1BG     0.7800400     1.2543403    \n",
       "A1CF    -0.6729747    -0.6732867    \n",
       "A2M     -0.6722639    -0.6744908    \n",
       "A2ML1   -0.6434327    -0.6281294    \n",
       "A3GALT2 -0.6307213    -0.6505268    \n",
       "A4GALT  -0.6576712    -0.6740484    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(pdx_mat_tpm_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>20558</li><li>81</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20558\n",
       "\\item 81\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20558\n",
       "2. 81\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20558    81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(pdx_mat_tpm_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdx_expressed_genes <- rownames(pdx_mat_tpm_m)[rowSums(pdx_mat_tpm_m > quantile(unlist(pdx_mat_tpm_m),0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "13788"
      ],
      "text/latex": [
       "13788"
      ],
      "text/markdown": [
       "13788"
      ],
      "text/plain": [
       "[1] 13788"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(pdx_expressed_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13788 protein coding genes are expressed more than 75th percentile in at least one PDX "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- scRNA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Frank's NBL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"single_cell/NBL_original_annotation/NBL_tumor_cells.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_tum <- as.matrix(nbl_tum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_immune <- read.table(paste0(datapath, \"single_cell/NBL_original_annotation/NBL_immune_cells.txt\"), sep = \"\\t\",\n",
    "                         header = TRUE, check.names = F,stringsAsFactors = FALSE)\n",
    "\n",
    "nbl_immune <- as.matrix(nbl_immune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_endo <- read.table(paste0(datapath, \"single_cell/NBL_original_annotation/NBL_endothelial_cells.txt\"), sep = \"\\t\",\n",
    "                             header = TRUE, check.names = F,stringsAsFactors = FALSE)\n",
    "\n",
    "nbl_endo <- as.matrix(nbl_endo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_mesench <- read.table(paste0(datapath, \"single_cell/NBL_original_annotation/NBL_mesenchymal_cells.txt\"), sep = \"\\t\",\n",
    "                             header = TRUE, check.names = F,stringsAsFactors = FALSE)\n",
    "\n",
    "nbl_mesench <- as.matrix(nbl_mesench)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_schwann <- read.table(paste0(datapath, \"single_cell/NBL_original_annotation/NBL_schwann_cells.txt\"), sep = \"\\t\",\n",
    "                             header = TRUE, check.names = F,stringsAsFactors = FALSE)\n",
    "\n",
    "nbl_schwann <- as.matrix(nbl_schwann)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>26344</li><li>2407</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 26344\n",
       "\\item 2407\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 26344\n",
       "2. 2407\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 26344  2407"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(nbl_immune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get max counts for each cell annotation\n",
    "maxcount_tumor <- apply(nbl_tum, 1, max)\n",
    "maxcount_immune <- apply(nbl_immune, 1, max)\n",
    "maxcount_endo <- apply(nbl_endo, 1, max)\n",
    "maxcount_mesench <- apply(nbl_mesench, 1, max)\n",
    "maxcount_schwann <- apply(nbl_schwann, 1, max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl <- cbind.data.frame(maxcount_tumor, maxcount_immune, maxcount_endo, maxcount_mesench, maxcount_schwann)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_tumor</th><th scope=col>maxcount_immune</th><th scope=col>maxcount_endo</th><th scope=col>maxcount_mesench</th><th scope=col>maxcount_schwann</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>RP11-34P13.7</th><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>AL627309.1</th><td>2</td><td>3</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>AP006222.2</th><td>4</td><td>2</td><td>1</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>RP4-669L17.10</th><td>4</td><td>2</td><td>2</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>RP5-857K21.4</th><td>6</td><td>2</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>RP11-206L10.5</th><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & maxcount\\_tumor & maxcount\\_immune & maxcount\\_endo & maxcount\\_mesench & maxcount\\_schwann\\\\\n",
       "  & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\tRP11-34P13.7 & 2 & 1 & 1 & 0 & 1\\\\\n",
       "\tAL627309.1 & 2 & 3 & 0 & 1 & 0\\\\\n",
       "\tAP006222.2 & 4 & 2 & 1 & 1 & 1\\\\\n",
       "\tRP4-669L17.10 & 4 & 2 & 2 & 3 & 1\\\\\n",
       "\tRP5-857K21.4 & 6 & 2 & 1 & 2 & 1\\\\\n",
       "\tRP11-206L10.5 & 1 & 1 & 1 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | maxcount_tumor &lt;int&gt; | maxcount_immune &lt;int&gt; | maxcount_endo &lt;int&gt; | maxcount_mesench &lt;int&gt; | maxcount_schwann &lt;int&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| RP11-34P13.7 | 2 | 1 | 1 | 0 | 1 |\n",
       "| AL627309.1 | 2 | 3 | 0 | 1 | 0 |\n",
       "| AP006222.2 | 4 | 2 | 1 | 1 | 1 |\n",
       "| RP4-669L17.10 | 4 | 2 | 2 | 3 | 1 |\n",
       "| RP5-857K21.4 | 6 | 2 | 1 | 2 | 1 |\n",
       "| RP11-206L10.5 | 1 | 1 | 1 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "              maxcount_tumor maxcount_immune maxcount_endo maxcount_mesench\n",
       "RP11-34P13.7  2              1               1             0               \n",
       "AL627309.1    2              3               0             1               \n",
       "AP006222.2    4              2               1             1               \n",
       "RP4-669L17.10 4              2               2             3               \n",
       "RP5-857K21.4  6              2               1             2               \n",
       "RP11-206L10.5 1              1               1             0               \n",
       "              maxcount_schwann\n",
       "RP11-34P13.7  1               \n",
       "AL627309.1    0               \n",
       "AP006222.2    1               \n",
       "RP4-669L17.10 1               \n",
       "RP5-857K21.4  1               \n",
       "RP11-206L10.5 0               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(nbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.4826"
      ],
      "text/latex": [
       "1.4826"
      ],
      "text/markdown": [
       "1.4826"
      ],
      "text/plain": [
       "[1] 1.4826"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mad(unlist(nbl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_m <- (nbl - median(unlist(nbl)))/ mad(unlist(nbl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "  -0.6745    0.0000    0.0000    0.7823    0.6745 1607.9860 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(nbl_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "   0.00    1.00    1.00    2.16    2.00 2385.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(nbl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SrKJgBAZDJlxW7+0z+/4NGvkyGE0PjU\nhx7/5T6VS6TrvDitZs0KD0cJhYUlb58tGh9Zk7QTTzzxwAMPDCEsXLjwzDPPXLt27foXAwAZ\nIiOCXXLmvy74+TPzQggh7HHBo4+eXfGPytaTyMqqE8LK1If8/PwQapV/QUFBQXFdt27dGJuk\nZWdnp36ll5ubm/qxHQCQoTJgK3bN5/f0/fWIJSGEsEPLKx9/4JRGVWhS+odoeXl5FY5fvrzk\nTJD69etH2QQAiMy2H+xy7rngpgkrQwih5sE3/fuu48t9x0KZGjUqiYPfffddheMXLFhQXDdr\n1izKJgBAZLb9YDd7+vT0ARz5H/+pfe3EhjW8dEzxFa9eWKfkixMHLQ8hhBYtWxYf9jZ37twK\n71pqTL3mzXdOVZE1AQAis+0Hu00jq1WrPYrqeVOnLqxg+OJp04qXuNq2PSTOJgBAZLaXYBcO\nPuKI4hM/Jr//QfmPfiYnTZpcVLfq2LH4cN/ImgAAcdn2g91xDy1IboRnehVfcfJjeSV/H9Ev\n9aO8Ol1P6FQtPWDx6yMmJjdwq2KTRo1elC6zu3dvU/z3yJoAAHHZ9oPdptKgR8/ja6TrucMG\njSr7zVlr3hw8dGa6bn7+BR1LvYk1siYAQFS2n2AXdj/vN2cVPUs6b/Dv/zatcIPD8j+967p/\nzUrV1Q+77JL26wShyJoAADHZjoJdqHvSjde0T5/InD/x5j7XjVu0/pD8mc9efNot769Ofcq+\n+PYr9427CQAQke0p2IXEAdcOvPnQ9EMHqz76a7f2J9/8+Jtfzl9ZsHrZvJx3n77zwg7t+jwx\nI/UkQmLvSx/9c9e6sTcBAOKRWOe1oxns2d6JPs+lypMfy3ulX+0yxiVnP3/BcWc9Pr2gjO9T\nEk1OfGDs8P6ty3gLa2RNUnJzc1PvFhs5cmS3bt3Kbbh59e3bdyvevVKGDh26tacAACW2qxW7\nEEJI7Nlz8PgxA07bp6zkF2pkH3/D8A9eLi8GRdYEAIhDNCt2lVWQO+GpR4Y89/q4yZ99PW9x\nXqhdb5emrdt26HLqORed27n5Rm5ZRtXEil0VWLEDYJuy3QY71ifYVYFgB8A2ZbvbigUAiJVg\nBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBA\nJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDs\nAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCI\nhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoId\nAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACR\nEOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrAD\nAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERCsAMAiIRgBwAQCcEOACAS\ngh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYA\nAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERC\nsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACRyLxgl5/77hO3X/WzLu1bZjfMqlW9Ru2d\ndt5tr1aHdTvz8t/9/eVPF63dyDZrFkx5/q5fndW13b5NGmbVrFErq+HuLdt3PvOqO4ZNnFew\nsXOJrAkAkNkSyWRya89hoxXmjvq/Sy4Z8No3+WUOqd3sxOsffOh3P21Ws5w+yz586IoLrh/y\nybINf11r75NvGvjwzV2yq5U3mciahJCbm5udnR1CGDlyZLdu3cofvFn17dt3K969UoYOHbq1\npwAAJTJnxS45a2jfI0/6Q3mpLoSw6usRt53c7qS/frSqrBGLxl57bKdflBmDQgirZ776hxOO\n6DVo2poyh0TWBACIQqYEu8L/3dH7oqe+SW+0Jhodfv4fHhv90Ve5i/JW5y365qPXB97Uo0Wd\nosGL/nvNyb8etWIDbZIzHz67190fLi9q077fgKfHfzF78cqV38/6fMKzd/Q7fJdECCGEtbOH\nX3ry1W8t39BcImsCAMQiQ7Ziv320e/OLR+WFEEKovv/lL7z+tx57VV9vTN5nD53dtf/wual/\nzw6H/N/UKTe3WnfIvEE99rvw1dTi1g5New8Z8+Q5Ldbdsy3IeezM4y9+YXYyhBASLa5+e+rd\nR9WIukkxW7FVYCsWgG1KZqzY/e+he9KpLlRvf+u/N5DqQgh19r/8yUEXZqc/FX70zPPT1h2w\ndsLtt75atGXZrP/gQevHoBBCjRYXPnpf74apD8mchwY8tTDqJgBARDIi2E1/4YVP02XWaTf+\n+uANpLr0t90uOatZ0YeP33tvnR/arXjpgUHfpOvaP731tuOyNtykQc/f9m+drle+dv/AnIib\nAAAxyYRgl/fuxE/SZeKYE7vvWN7Y1q1Ldl+//XZ+qW/yX3/m5aXpOuv0i/o0LLtJ2zNOb54u\nk++/9EputE0AgKhkQrCbkZNTmC6bHnhgvXLHrl69urhOJBIlXyTfem1E0b7lDp1OKj8eHtql\nc4OieuKo0UUBKrYmAEBcMiHYLVmypKhs0qRJuUOTX3xR/MO6mk2blhr85cSJi4rqAzt02Knc\nNol27doW1WsmTfo40iYAQFwyIdgd9de5ybSJ1zUvb+SKkYOenZuuaxzTuWPJwwSrp0z5rKiu\n1aZNiwpu2ah168ZF9bc5OcujbAIARCYTgt1GWj19yIWXDpqX/rT7edf23bXky1lffVX8srE9\nmzZNhAqkDv5ImTFjRpRNAIDIlPmAaYYoXL10wbe5MyaPfH7wQw+9ODX9s7O6h1w99O6TSj8m\nOmfOnOK6dMopy667lqTC4q3gyJqkzJ07d/bs2SGEhQsX1qtXb9myst9hAQBs2zI32I2+pEG3\nR5b84M877Nz+gv976J7+h9Vf58+l00xWVhkng5RSp07xeyzCihUromySMmLEiAcffDBVt2zZ\n8qOPPqqwGwCwbYpoKzal9k47JRfOyl1/2Skvr+RMu9q1a1fcptSYVatWRdkEAIhM5q7YlWHl\n12MH3TL2yUf7/OOVJy4+oOjpiXVenFaz5g/e0PADhYWFxXXR+MiapJ177rm9e/cOIXz77bf7\n77//2rVr178YAMgQmbti13Xg4mSycE3eskW50z9888WHb724c7PiZamCr565tPulLy0o+pzI\nyirZi8zPz6+we0FBQXFdt27dGJuk1axZs169evXq1dtxxx2lOgDIaJkb7EIIISSq1d6xwW77\ntO102qV/GDjmy2mv/Kpd0VG9yTlDfnnruKI8U/qHaHl5eRU2Xr685EyQ+vXrR9kEAIhMhge7\nddXc8+R7h/+1S3Hmmf34oyPTya5Ro0bFw7777rsKWy1YULzaF5o1axZlEwAgMlEFuxBC2Ov8\nX5xS/H6tZRMm/C9VtWjZsviwt7lz5/7wuvWUGlOvefOdo2wCAERm2w92r1+4UyKt0WVvVDy+\nVvv2bYo/FK9mZbVqtUfRH+dNnbqwgiaLp00rXuJq2/aQEGUTACAy236w23nn4gWmhTNnbsTp\nufXq1SuuS36LdvARRxQ/WjH5/Q/Kf0ggOWnS5KK6VceOxYf7RtYEAIjLth/s9tt//+JJTvnw\nw4ovmDdvXnG9++67p6s6XU/oVC1dL359xMTkD64rZdKo0YvSZXb37iULgJE1AQDisu0Hu/qd\nOhXvHc4fMWJyeWNDCOGb1177tKjes2PH5kV1gx49j6+RrucOGzSq7DNC1rw5eOjMdN38/As6\nlnoTa2RNAICobPvBLux39rmHF01z2r8GPLugvMHfPn3dncXLV0179/5JyVe7n/ebs4qeJZ03\n+Pd/m1a4/sUhhBDyP73run/NStXVD7vskvbrBKHImgAAMcmAYBeaX3xd76Lf2S147tKf3T7p\nh6+IDSGEgpnDr+ze7+mi4LfTqX+86cjS/766J914Tfv0qzbyJ97c57pxi9ZvkT/z2YtPu+X9\n1alP2RfffuW+642IrAkAEJFMCHahfp977z+lKNotfuO3HQ7sfMVfhr31v2/mL121Jn/FojlT\nxz3/wDWnHnTw6Q98nD6sN7Hnzx5+8Nz1HhNIHHDtwJsPTT90sOqjv3Zrf/LNj7/55fyVBauX\nzct59+k7L+zQrs8TM1JPIiT2vvTRP3etG9YXWRMAIB6JdV47ug1b+NbvTuwx4P2NeCo2hJrN\nz7jv5WGXt6m1ge+Ss5+/4LizHp9esIHvSiSanPjA2OH9W5fxFtbImqTk5uZmZ2eHEEaOHNmt\nW7dyG25effv23Yp3r5ShQ4du7SkAQImMWLELIYSdO/1pzFuP/Pyo3aqXO6zWHkf//KHxU57f\ncKoLIST27Dl4/JgBp+1Te8Pfh1Aj+/gbhn/wcnkxKLImAEAcMmbFrsjK6f995tlXR415a1LO\n3AXfL1ycF+rWb9iwUXbLtkd0OKbzqT879bBda1TcJYSC3AlPPTLkudfHTf7s63mL80Lters0\nbd22Q5dTz7no3M7NN3LLMqomVuyqwIodANuUjAt2bC6CXRUIdgBsUzJmKxYAgPIJdgAAkRDs\nAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASFQ22I27rWvKbeMqfa8fcy0AABWo\nXsnx3348ZsyYEEJocHllb5Wc91GVrwUAoCJbcCt23rx5W+5mAADbnfJX7BbN+GD6wnX+MmNR\nyVcffLDRt8lfOO31e26fkPpQq3btykwRAICNUX6wG3P94X2e2/BX/73h8MOreM9mzZpV8UoA\nAMq05Z+KTbTu3evALX5XAIDobdlgt0PDg/r+/embD01s0bsCAGwXyt+KbXXqNdfsvc5fvnz5\n7pe/DCGE/U655pT9NvYuiZo77bLHXvscdPxJnZpnVWGWAABUqPxgd9D5f/nLun95dmY62B10\n/l/+0nuzTQsAgMry5gkAgEhU9oDi1mfccEOLEEI4qPVmmA0AAFVW2WB34Dl33LFZJgIAwI9j\nKxYAIBKVXbErbdWcyW+MGffB1Onf5H6/bGXe6oLC5MZdeNQNL17f4UfcGACAH6pasEsufO/v\nV15yy78/WVxYpZueW6W7AgBQjqoEu2+f73f4mUNmrdnkkwEAoOoq/xu7uYP7XSTVAQBscyq9\nYjdt8P0jlhR9yNqvx8UX/uykYw9qtusujerX2tiUWKteZe8KAEBFKhvsZg8fPjlVJbJ7DR73\n5Hn71NrkcwIAoAoquxX7zTffpIo6p/35X1IdAMC2o7LBbsGCBani8O7dG27y2QAAUGWVDXYN\nGjRYrwAAYJtQ2WC31157pYrvv/9+k08GAICqq2ywa96t274hhBA+HDt26aafDgAAVVXpc+wO\nO/ucVokQwsoX7rz38yq9dwIAgM2h8gcUH3LdvRftEUIonPLH0y9/Zf6mnxIAAFVR+WAXdjzx\nrid+dWDtEAq++FevDqfdMWb26k0/LwAAKqmyBxR///nbny0I1Xvd+qt3+t35wYr8GS/d1PXV\nuw7ofMKxh7TYY9eGO9auUS1RYZMWJ11x4r5Vmy8AAGWobLB74+aOfZ5b729rF04dNWzqqI1v\n0usZwQ4AYFOrwlYsAADbIsEOACASld2K7Xjzf/5zyY+96W7tfmwHAADWV9lg16TtiSdulokA\nAPDj2IoFAIiEYAcAEAnBDgAgEpX9jd3MMQNHf/Vjb9q86yVd9v6xTQAAWEdlg90H/7j00vUP\nKK60Xs8IdgAAm5qtWACASAh2AACRqOxWbIdrn3nmrArGJNfmr8pbuXTBvDkzP//kg/Fvvf/V\n0sIQQgi19+1116C/9Gxer2GV5goAQDkqG+z2OLJ370pekp/7zuABt/zfw2/MWjX9uav65O/0\nzgsXVLIF26a+fftu7SkAACW2wFZszd2PvvRv/53y2jXt6oSQnPfy5efdm/qRabkAACAASURB\nVJPc/LcFANjObLHf2O3c5S8v//nYmiGEVeP/9MdX87bUfQEAthdb8uGJPS655qz6IYSw6Jkn\nXyvYgjcGANgebNGnYmu3bdsqhBDCyokTP9mSNwYA2A5s2eNOdtlll1Qxb968LXpjAID4bdlg\nN2vWrFRRrVq1LXpjAID4bclgt+ajIUM/TpW77777FrwxAMD2YIsFu+WfDrqw9x2fpj7UPfjg\nFlvqxgAA24nKHlA8fcQD/8mpxPjC/OWLFsz58t1R/xn3xaI16T9mnXr2KXUreWMAAMpX2WD3\n4cArr3zux91yx2Nu+3+nZ/24HgAArG/LPjwRQr22vxj679/s59EJAIBNbYsFux2y9upwzv+9\n/NHEB0/ZI7GlbgoAsB2p7FbsT371+OOnb/zwRKJajTo7Nti1acs2BzRvWKOSNwMAYONVNtg1\n7XjuuR03y0wAAPhRtvRv7AAA2EwEOwCASFR2K3YDClfM+XjCuHFvT/xk5rcLFy5cuKyg5k4N\nGjbYuUnzNocedthPjjy89S61fvxdAAAo348Kdvlz3n7sLwPu+td/pq8oe1C1nQ88qe9Fl115\nWY/9HEoMALD5VHkrdu3s167reGDHy+8tN9WFENYu/PSVB64+5cDW3W98flpeVW8HAEAFqhbs\nkrOGnf2TU/7y3uKNv6Rg1qg7ex1xwu3vLa3SHQEAqEBVtmJXf/in0y9+Jrcw/XGHevt1OeP0\n7sf+5KD9mu/VuH5W7WoFK5cv+X729C8/++jdsaNf/+/73yxPpoYuGvfbLj3qffjmL1s4oxgA\nYBOrQrD7+sEr/29yak91h8ZHXXHfwD/13X/HHw47sP0xJ/7sot/8Kbk8Z/QT9/y/Wx96Z35h\nCGH5uJuueOSMEZdk/7h5AwCwnkpvxa4Zc8ft7+SHEEJoeMLf33nrvg2mulISO7bodvnfx332\n3+vb1w4hhLDs9dvumViFqQIAUJ7KBrvk2y8M/y6EEELdHvcMvXy/aht5XWLnY+948a7jU9Fu\n1vDhkyt5XwAAKlDZYDftrbdyQwgh1O9z2Zk7V+rSxJ4XXnFGanVv2htvzKnkjQEAKF9lg93c\nuXNTxf7t2tWu7M2yDjts//XaAACwiVQ22C1dmj6uZJdddqn83Zo0aZIq5s+fX/mrAQAoR2WD\nXePGjVPFd999V/m7LVy4MFXUrFmz8lcDAFCOyga7PfbYI1V8Mn788srebPWkSf9LVbvttltl\nLwYAoFyVDXbNunZtGUIIYcXwe/85o7CC0euaP+yhF5aEEELIateuZSVvDABA+Sp9jl37Pr33\nCSGEsHr8b3tdM3r+xma7vKl/O//qV1M/0Kt1wmknVPrJCwAAylX5d8Ueeu3tfRqFEELIn3Lv\nCYd0uXrIxG/XlHvFmm8n/PPnxxx51euLQgghJPa5/NqeO1V+qgAAlKcKrxTb+Wd//dvQcecM\nn5cMoXDe2HsuOPLBG9t3P/WUE487/JA2rfdu0mCnrFph9fKl38/N+d9H77/52gvPvjJhdl7R\n1YnsC+//Q4dam/LfAABAqFKwC2HPs58a/X2P464cvSD1eXXupJf/Oenlf1Z8ZaMT/jbyHyc3\nqMpNAQAoV+W3YkMIIdRqc8Wrk177Xdc9Nj4YJnY+/Mp/v/PiL9s46AQAYHOoYrALIdRsetIf\nR/7vw2fvuPi45nXLHVmtYZtTr/7HuE/H339mK89MAABsJlXaii2WqH9grxsG9rru3llTJox/\n9933pnw5Z8GiRYuWrCisXa9hg4aN92p96JFHHdXxqLa719lE8wUAoAw/Ltil7bDjXod2O/PQ\nbmduimYAAFRJ1bdi15GfX7DhL9YUlPEFAACb1o8Jdqu+eXvoHb884+g2e9bLOmf4hse8eE79\nJgcc0/Oqu5//aIGIBwCwGVUx2K2dPeKWn+6/X8dzbnrwxfFT5yxbkyxrZGHe/M/eeeFv1/Zq\nu/fBfe8dv6DMgQAA/ChVCnZzX+x37Cl//M/M1ZW6asXnw35zfKdfvDpftgMA2AyqEOy+HXL5\nRU/MKHmLWO3dDup2bvdWGx68X+eeh+9ecsZJ/mf/PPPch7+u/E0BAKhApYNdctK9/+/l1Etf\nQ/XdO139xMffzv145OM/P2jDww/u/9x7s7/9/NnrOzZOpP6yYtSNNw5fXtX5AgBQhkoHu/FP\nPDkjVe3V99nJY+8+56B6iYruUa9Vrzvf+Oiln7eoFkIIYfFz//j3/MreFwCA8lU22M0aP35W\nCCGE6t1vuee03SrKdCWqZfd44LEr9g4hhFAwduTY/EreGACA8lU22M2YkV6va3fCCbtW8toa\nx5x/dvMQQgirJ0+eWsmLAQAoX2WD3ZIlS1LF7rvvXvm7tWqVfsbiu+++q/zVAACUo7LBbscd\nd0wVVYpmhYWFqWL16sodlQIAQEUqG+yys7NTxdRJk/IqfbcvvvgyVTRp0qTSFwMAUJ7KBrv9\nOnXaLYQQwpJhdzz4VeWOGl79xmPDZqbu2rLlPpW8MQAA5atssNvh2DN7p5JdwfibTrrkhbmF\nG3lhYe5L/S9+6JtUk05nnNaokjcukVw5693n7rv+3BOOOvSAffZolFWzVlaDJnvte2CHHhdd\nf9cT42Zt3CbvmgVTnr/rV2d1bbdvk4ZZNWvUymq4e8v2nc+86o5hE+dt9EttI2sCAGS2RDJZ\n2Td85T5yUutLRixNXb5zu3N/d8f/u7xb87pln3yyZv4HQ/983XX3jJ2fSoGN+zz3xdM9d67K\ndFd8NuyGflc++N73ZU+65p7H/vyuh/9y1n61yhyy7MOHrrjg+iGfLNvw17X2PvmmgQ/f3CW7\nWnlTiaxJCLm5uamN9pEjR3br1q38wSl9+/bdmGERGzp06NaeAgCUqEKwC8kZj550xMWvLyhp\nktXsiO7dOh3Z/qCWzffabeed6taqtjZv+ZIFs6d99tF7b/znlTFTvi1ZRmtwypD/vXRedhXm\n+t0rlx/d+5/TNmZFbueOt//ntRt/suMGvlo09toup9z9YQXvvqi252kD//tsv5bVN/x1ZE1C\nCIJdlQh2AGxTqvCu2JDY56LnR97VqUHxH5Irvn73hYF/vuEX5/U88bijftK+7SFt2x95TOce\nZ1123e2PvFY61dXv+McRQ6uU6go+v6/3OSWpLqvVKdfe/+LEL775bvnq1cu/nzll1GO//9kB\n9YpGLxx3U8/LXlrwgy7JmQ+f3as4BiUate834OnxX8xevHLl97M+n/DsHf0O3yW18rh29vBL\nT776rQ3mpciaAACxqMqKXcrqr1665cLL7n5z3kb+yi7R8NCf3zP47gsOzKrK3b65t2OL37yd\n+rVYommvh0cMuWT/uj+Y0vRh/bqe+++Z6RntdvF/Zgw8sU6pAfMG9djvwldTW5Y7NO09ZMyT\n57SouU6LgpzHzjz+4hdmJ0MIIdHi6ren3n1UjXXvElmTYlbsqsCKHQDblKqs2KXUan7qn9+Y\nOvmpARcdt/cPItY6qjU84JRfPzhu6nsPVTHVhfD+3+9Np7qQaHHFk4M3kOpCCLX2PXvwszfu\nX/Rrv3mDBzw2p9TXayfcfuurRT9Ea9Z/8KD1Y1AIoUaLCx+9r3fD1IdkzkMDnlq47oDImgAA\nEal6sAshhETDQ3520yNvTP921oejn/7ngOsuv+CsXqee2OXYo485rnuPnmddeNWt9z85YtI3\nc/730j2/OHq3Cn68X44pw4d/nS6rd7/hlmPKjoc121/z665FN1rzzguvlByjvOKlBwZ9k65r\n//TW244ro0uDnr/t3zpdr3zt/oE5pb+MrAkAEJMfF+yKmuy4Z9sufX5+05//MWjYs8P/M3rs\n2+PeeP3l54Y9et8frux7wqHZdSpuUa6l7733RVF9+E9/uku5g3fu2LFNUV04efKUojr/9Wde\nXpqus06/qE/Dslu0PeP05uky+f5Lr+SWfBNZEwAgKpsk2G1uOTnFy0wNDzywokcvGjduXFwv\nWrAg/YO75FuvjSjat9yh00ndN/TAbLFDu3QufjRk4qjRRQEqtiYAQFwyItgtXFj8y7Bdd921\notGl32KbVa9e+l/45cSJi4r+emCHDjuV2yLRrl3bonrNpEkfF9WRNQEA4pIRwa7rwMXJtM//\neGAFg2e99tqnxR/222+/VLF6ypTPiv5Yq02bFhU0adS6dfGy37c5OcujbAIARCYjgl0lfP/i\nTX+ZWHyAy349erRMVbO++mpt0V/3bNq07NdkpKUO/kiZMWNGlE0AgMiU9yqCjLNs0h2n93vy\n26KPO550Q/+D0vWcOSUHn5ROOWUpveW7ZMmSKJukfPzxxx9++GEIYdmyZbvtttv8+fMr7AYA\nbJuiCXYr/zfkqjP7P/K/FUV/aND5rvv6NSn6VDrNZGVVfJpenTolj/KuWLEiyiYpH3zwwYMP\nPpiq99hjjwULfvi+DgAgM8QQ7Arm/PfuK37+hxenl7y6LOvg655/9vKWJRvNeXmriuvatWtX\n2LP0mFWrVkXZJKVWrVr16tULIRQWFq63mAcAZJYMD3Zrct+4/7or/vDk1GUlf6u1zxl/eWbQ\nFYfWKzVunRen1az5gzc0/EBhYcmb0orGR9Yk7ZxzzjnnnHNCqVeKAQAZKnMfniiY88bdZx/S\nqvM1pVNd/UMvGfjeh8+vm+pCCImsrJK9yPz8/Iq7FxQU13Xr1o2xCQAQm4wMdoXz37777Lat\nO1/771KZrt5Bfe9587OJ/7r44HobuKT0D9Hy8vIqvMXy5SVngtSvXz/KJgBAZDIu2OVPf+7q\nY9oce+2/pxZHlR0aHXbx38Z9+eGTv+60e1lby40aNSquS59gXJbSzxA0a9YsyiYAQGQyK9gt\nHHdb9yN63zOh6D1hofrux/36scnT3ht4xTFNqpV3ZYuWLYsPe5s7d26Fdyo1pl7z5jtH2QQA\niEwGBbsV797S/YRb3/w+/THR+Ij+gyd99sY9/Q5pWOEBvSGrVas9iup5U6cuLG9sCGHxtGnF\nS1xt2x4SZxMAIDIZE+y+e+6SU/84qejXZPV/cvXwD9/++/kHb/TPxQ4+4ojiEz8mv//B2vLG\nhuSkSZOL6lYdOxYf7htZEwAgLhkS7FaMvulX/y76KVmDY+/87xt3n7JnpY5qqdP1hE5Fm7WL\nXx8xMVne4EmjRi9Kl9ndu7eJtQkAEJfMCHbzHr9jSNE7tPY8f8jz1x9a+RM7GvToeXyNdD13\n2KBRZZ8RsubNwUNnpuvm51/QsdRGb2RNAICoZESw++bxx/6bPodtp1PuvPuUqv34f/fzfnNW\n0bOk8wb//m/TCjc4LP/Tu67716xUXf2wyy5pv04QiqwJABCTTAh2uS8Nfz+91bhzn1/8rHFV\n+9Q96cZr2qf3b/Mn3tznunGL1h+SP/PZi0+75f30u8myL779yn3jbgIARCQDgl3+2+OKcl1Y\n+OhPayQqYc9fv12qU+KAawfefGj6oYNVH/21W/uTb378zS/nryxYvWxezrtP33lhh3Z9npiR\nehIhsfelj/656w+3fCNrAgDEI7HOa0e3SVNubt3uT19U7do9fjVu9r3HlP5LcvbzFxx31uPT\nC8q6JIQQQqLJiQ+MHd6/dRlvYY2sSUrxu2JHjhzZrVu3chum9e3bd2OGRWzo0KFbewoAUCID\nVuy++WbWJuyW2LPn4PFjBpy2T+2yRtTIPv6G4R+8XF4MiqwJABCHDFix2zwKcic89ciQ514f\nN/mzr+ctzgu16+3StHXbDl1OPeeiczs338gty6iaWLGrAit2AGxTtttgx/oEuyoQ7ADYpmTA\nViwAABtDsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAH\nABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAk\nBDsAgEgIdgAAkRDsAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwA\nACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiE\nYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0A\nQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ\n7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERCsAMA\niIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKC\nHQBAJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAA\nkRDsAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASGR+sJtxZ7tEIpFIJBpfPrYS\nl61ZMOX5u351Vtd2+zZpmFWzRq2shru3bN/5zKvuGDZxXsF22gQAyGyJZDK5tefwo8x/qOtu\nvxiTDCE0uuyNBQ8dtzHXLPvwoSsuuH7IJ8s2/HWtvU++aeDDN3fJrrYdNQkhNzc3Ozs7hDBy\n5Mhu3bqVPzilb9++GzMsYkOHDt3aUwCAEpm+Yjdz0KNvVC6ZLhp77bGdflFmDAohrJ756h9O\nOKLXoGlrtpcmAEAUMjvYLXj2xrveL6zEBcmZD5/d6+4Pl6c+JRq17zfg6fFfzF68cuX3sz6f\n8Owd/Q7fJRFCCGHt7OGXnnz1W8u3gyYAQCwydiu2cPEnw3533mUPfrSi6C8bsRU7b1CP/S58\nNbW4tUPT3kPGPHlOi5rrjCjIeezM4y9+YXYyhBASLa5+e+rdR9WIukkxW7FVYCsWgG1KBq3Y\nJfOXzJ02ZdzLj993a/9eRzbf85BzS6W6jbF2wu23vlq0Zdms/+BB68egEEKNFhc+el/vhuk7\n5jw04KmFUTcBACKSQcHu1Yua7LFfu06nnv/r2/7x/MRvVlR2pXHFSw8M+iZd1/7prbcdl7Xh\ncQ16/rZ/63S98rX7B+ZE3AQAiEkGBbsfKf/1Z15emq6zTr+oT8Oyh7Y94/Tm6TL5/kuv5Ebb\nBACIynYT7JJvvTaiaN9yh04ndd+xvMGHduncoKieOGp0UYCKrQkAEJcMCnY9nliVXF/e4ydv\n5NVfTpy4qKg+sEOHncodnGjXrm1RvWbSpI8jbQIAxCWDgt2PsnrKlM+K6lpt2rSoYHij1q0b\nF9Xf5uQsj7IJABCZ7SXYzfrqq7VF9Z5NmyYqGp86+CNlxowZUTYBACJTfWtPYAuZM2dOcV06\n5ZRl1113La6XLFkSZZOUd95558033wwhrFy5slmzZrNnz66wGwCwbdpegl3pNJOVVcbJIKXU\nqVOnuF6xYkWUTVK++OKL559/PlU3bty4dGQEADLL9hLs8vJWFde1a9eucHzpMatWrYqySUrj\nxo3333//EEJBQcHHH3uwAgAy2HYS7NZ5cVrNmj94Q8MPFBaWvIK2aHxkTdJOPfXUU089NZR6\npRgAkKG2k4cnEllZJXuR+fn5FV5QUFBQXNetWzfGJgBAbLaTYLfOD9Hy8vIqHL98ecmZIPXr\n14+yCQAQme0l2DVq1Ki4/u677yocv2DBguK6WbNmUTYBACKzvQS7Fi1bFh/2Nnfu3ArHlxpT\nr3nznaNsAgBEZnsJdlmtWu1RVM+bOnVhBcMXT5tWvMTVtu0hcTYBACKzvQS7cPARRxSf+DH5\n/Q/Wljc2JCdNmlxUt+rYsfhw38iaAABx2W6CXZ2uJ3Sqlq4Xvz5iYrK8wZNGjV6ULrO7d28T\naxMAIC7bTbALDXr0PL5Gup47bNCoss8IWfPm4KEz03Xz8y/oWOpNrJE1AQCisv0Eu7D7eb85\nq+hZ0nmDf/+3aYUbHJb/6V3X/WtWqq5+2GWXtF8nCEXWBACIyXYU7ELdk268pn36VRv5E2/u\nc924ResPyZ/57MWn3fL+6tSn7Itvv3LfuJsAABHZnoJdSBxw7cCbD00/dLDqo792a3/yzY+/\n+eX8lQWrl83LeffpOy/s0K7PEzNSTyIk9r700T93/eFLGiJrAgDEI7HOa0czzqonetQ579VU\n3eiyNxY8dFxFVyRnP3/BcWc9Pr2g3FGJJic+MHZ4/9ZlvIU1siYpxe+KHTlyZLdu3cptmNa3\nb9+NGRaxoUOHbu0pAECJ7WrFLoQQEnv2HDx+zIDT9qld1oga2cffMPyDl8uLQZE1AQDikOEr\ndlVXkDvhqUeGPPf6uMmffT1vcV6oXW+Xpq3bduhy6jkXndu5+UZuWUbVxIpdFVixA2Cbst0G\nO9Yn2FWBYAfANmW724oFAIiVYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERC\nsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4A\nIBKCHQBAJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgI\ndgAAkRDsAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCSqb+0JQAbr27fv5mg7dOjQzdEW\ngOhZsQMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhDdPwDZn\nc7zQwtssALYHVuwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ7AAA\nIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDsAAAiIdgBAERCsAMAiIRg\nBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBA\nJAQ7AIBICHYAAJEQ7AAAIiHYAQBEQrADAIiEYAcAEAnBDgAgEoIdAEAkBDsAgEgIdgAAkRDs\nAAAiIdgBAERCsAMAiIRgBwAQCcEOACASgh0AQCQEOwCASAh2AACREOwAACJRfWtPAMhIffv2\n3Uydhw4dupk6A0TPih0AQCSs2MF2YfMtsAGw7bBiBwAQCcEOACASgl0c1iyY8vxdvzqra7t9\nmzTMqlmjVlbD3Vu273zmVXcMmzivYGtPDgDYMvzGLvMt+/ChKy64fsgny0r9rWDxvJzJ83Im\nv/H03/7w25NvGvjwzV2yq221GQIAW4QVuwy3aOy1x3b6xbqpbl2rZ776hxOO6DVo2potOC0A\nYCsQ7DJZcubDZ/e6+8PlqU+JRu37DXh6/BezF69c+f2szyc8e0e/w3dJhBBCWDt7+KUnX/3W\n8q04VwBgsxPsMti8wVdc+/rCVL1D096Pvzv+sZv6dNhvj/p16uy8Z6sje93w2DvjHzljz1S2\nWzPtgYtvHe/3dgAQMb+xy1hrJ9x+66tFO7DN+g8edE6LmuuPqdHiwkfv+8/YXs8sCiEkcx4a\n8NTvXjl35y07T9jaNtMZft6QAWyDrNhlqhUvPTDom3Rd+6e33nZc1obHNej52/6t0/XK1+4f\nmLMlJgcAbA2CXYbKf/2Zl5em66zTL+rTsOyhbc84vXm6TL7/0iu5m3tqAMBWYis2MyXfem1E\n0TbsDp1O6r5jeYMP7dK5wR2PLA4hhDBx1Oilvz6v3uaeH1Sdt58BVJkVu8z05cSJi4rqAzt0\n2KncwYl27doW1WsmTfp4M84LANiKrNhlpNVTpnxWVNdq06ZFBcMbtW7dOIxdEEII4ducnOXh\nmHJX+ICNkEEri57zgO2HYJeRZn311dqies+mTRMVjc/Ozg4hFezCjBkzQjh4880NiN7mC7Uy\nKPxIgl1GmjNnTnGdnZ1d4fhdd921uF6yZMlmmROwrcqgxcVNzmE3bG8Eu4xUOpxlZZVx0Ekp\nderUKa5XrFhR+qsRI0a89NJLIYTVq1e3bNlyxowZm26aAJWTKRk0U+ZJBtlU/7cg2GWkvLxV\nxXXt2rUrHF96zKpVq0p/NXfu3Pfeey9V16tXL5GocF8XANjEKvV/C7vuuuu99967wa8Eu0yU\nTCZLPtSs+YMXTvxAYWFhWeNbtWrVs2fPEMLKlSufeOKJdVpXxGYEAGxTBLtMlMjKqhPCytSH\n/Pz8EGqVf0FBQclLYuvWrVv6q6OPPvroo48OIeTm5v7pT3/a1FMFALYc59hlpNK/q8vLy6tw\n/PLly4vr+vXrb5Y5AQBbm2CXkRo1alRcf/fddxWOX7BgQXHdrFmzzTInAGBrE+wyUouWLYuf\ncZg7d26F40uNqde8+c6baVYAwNYl2GWkrFat9iiq502durCC4YunTStesWvb9pDNNi0AYKsS\n7DLTwUccUXyAyeT3P1hb3tiQnDRpclHdqmPHXcsbCwBkLsEuM9XpekKnaul68esjJpZ7Rsmk\nUaMXpcvs7t3bbN6ZAQBbjWCXoRr06Hl8jXQ9d9igUflljlzz5uChM9N18/Mv6OgAYgCIlWCX\nqXY/7zdnFT0aO2/w7/82rXCDw/I/veu6f81K1dUPu+yS9nIdAERLsMtYdU+68Zr26QOm8yfe\n3Oe6cYvWH5I/89mLT7vl/dWpT9kX337lvltyhgDAliXYZa7EAdcOvPnQ9DMUqz76a7f2J9/8\n+Jtfzl9ZsHrZvJx3n77zwg7t+jwxI/VgRWLvSx/9c9e6ZXcDADJeolLvBmVbk5z9/AXHnfX4\n9IJyRyWanPjA2OH9W5f7Utnc3Nzs7OwQwsiRI7t167YpZwkAbBFW7DJbYs+eg8ePGXDaPrXL\nGlEj+/gbhn/wcgWpDgCIQPWtPQF+rMSuHW968fN+E556ZMhzr4+b/NnX8xbnhdr1dmnaum2H\nLqeec9G5nZvbgQWA7YKtWNJsxQJAprMVCwAQCcEOACASgh0AQCQEfeGecQAAH+BJREFUOwCA\nSAh2AACREOwAACIh2AEAREKwAwCIhGAHABAJwQ4AIBKCHQBAJAQ7AIBICHYAAJEQ7AAAIlF9\na0+Abc5TTz01efLkrT0LAGADGjRocNlll5X1bSKZTG7J2bDNWrRo0WWXXfbSSy+tXr16a89l\nozRs2DCEsGLFivz8/K09F8pUvXr1nXbaKYSwZMmSwsLCrT0dylSnTp3atWuvXbt26dKlW3su\nlKdevXrVqlVbtWpVXl7e1p4LZUokEg0aNAghLF++vKCgYNM232effaZPn17mrQU7SvvJT37y\n/fffb+1ZbBTBLiMUB7vFixf7r822TLDLFIJdRtiswW6vvfYaO3Zsmbf2n1oy1E9+8pPCwsJb\nb731lFNO2dpzoUzvvfde//79Qwiv/f/27jugibOPA/gT9pClLHGx3YBKraIWRAouXKhFUdGi\ndVato+JqcVSraK17771p8bVa98SCCtYBWhERUKbsDcn7B7kQIAkBktxd+H7+Orjnkod7+OV+\neZ6757l82dTUlO7qgFhbt249fPiwtbX1mTNn6K4LSOLn5/f69euxY8fOmzeP7rqAWFlZWR4e\nHoSQzZs39+rVS5FvjYcnAAAAAJQEEjsAAAAAJYGhWGCr8PBwQoiVlZWJiQnddQGxcnJyYmJi\nCCFOTk4aGhp0VwfESkhI+PTpk5aWloODA911AUlevHhRUFBgbm7eunVruusCYpWVlVXML2Fv\nb19xs53CILEDAAAAUBIYigUAAABQEkjsAAAAAJQEEjsAAAAAJYHEDgAAAEBJILEDAPnJDTty\n4l9Mjw8gGwgoqB0SOwCQk9ywFV5e/n7ugzfiUgTQYAgokAqmOwEAecgNW+HlFRSWSwghHIeg\nZ1E/d+bQXScA1kJAgbTU6K4AQN0UfYq6c+efl3GpeTwd45Z2Tj17d7driv9jhqlyEbIYsvvs\nYlyEmAsxxXgIKDahP6B4ACyRF3N+2fCOBtVvH1A36+bz465biSV01w/4ch4G9dTjtw7HYsie\n18V01wjEQEyxAQKKNRgSUEjsgBUKnu8dY6cl6SuKrt3QlaGxhXRXtNHDRYgtEFOsgIBiCwYF\nFBI7YL6CiLV9hFba4zQxt7GzszHTqTkW0cTpuxNvcCGiDS5CbIGYYgUEFFswK6CQ2AHTJZzw\nMRN84+k0adfd9/k8Ho/H4xYkhp9d4+tgVC109Jxm/JFQRnOlGyNchFgDMcUGCCjWYFpAIbED\nZks57m3EDwZD9+Dn+TUKcDMf757UWa9K2Ki2HnEgpoiG2jZeuAixB2KKBRBQ7MG8gEJiB4wW\nubQ9Pw6MR5xJFVus+P2fc50NqsSNsfvvUQUKrGljhosQmyCmGA8BxSYMDCgkdsBkUYG2/BCw\nnPeQK7lsYcxhP3sNoajhmA8++LZUMRVtxOp0ESr+9OSPbYGThnn0dLCzsmnv4PyV9+Rl20Ke\npZcrrL6NHWKK4RBQ7MLEgEJiBwyWvf9rKgBGnJDiSys39ercLjrC3d1W/qEp8q9nY5Z79VsL\n6nSr9d76QWzBvOizgf0tNYkoHIPOvuvvptbyqQgygJhiOAQUuzAyoJDYAYPFBXfj//OrjDwl\n5RRAWfeXfdlEKGwM3Le+xW3f8pR7d15bVepse2x/J6KvoCw+ZJaDrshLUCX1NsP3iLg/BWQK\nMcV4CCg2YWRAIbEDBvu8x53617dc8EjqwzJvL+wiNJ2QhuOycNyjIk8l/67toc0/25rOK55W\nO9tJ58a1Ep6xU1XXzKpD5w42zfVqTMZuNvRYPAaR5AkxxQIIKPZgZEAhsQMmuzPdhPrPt5oX\nXofvNMkh/taVH31qjqueo4dBnrjx+wY2pb642n5/M1uwp+jJyi7UNUrF+Ispm/+KzqDuKSnN\neHl50wSnKnMBaDouf4oFD+QIMcUGCCjWYGJAIbEDJis6+43gbgS9kac+1+HQkpcb+ugLoka7\nz+Y4eVUSeDwej5d+aUIr6oJiPvp8Mo/H4/HKY37pps5vgs7TQkVO3VSW8MdMJ6FhJY0ewa9x\nc5DcIKZYAgHFDkwMKCR2wGgFof6V03m3mnwtty4HfzpbOWsksZh5C2NH8pX/cFEH/kWHf29Q\n7jlf/q0kxoMOx0u4uGTdmC04lBBDv1AsdSA3iCm2QECxAgMDCokdMFvZ4wU2gv97jvW0azl1\nOTojZJwpdbCO9+G6fJmCeih9uaE31VWg6bzi0fFvKgaNNDz3JNdyaNmLFU6CK5H2iBPZtZSH\nekNMsQYCig2YF1BI7IDp0o4Pr+ytJibDjkj6olrDx91fU/3k6oOO4sNN7hIPD6VuOVExNKyY\nj0vH52xe7UcW/hXQjGpmPf9QDB7JD2KKPRBQLMC0gBJ+tAaAiYzHbFj5leCOkbSQqcODwvOk\nPrr5xIXjjCs2S+/cfsiTefWgqhYTDh0LsFQhhBBuVlYuIYSQtl271jY1AyFEy2uirzl/Ozcy\n8q28agiIKRZBQDEDNyf+Wdida39dvfPkTXJeedWdTAsoJHbAeBzr2QfWVUZN4dOVA4ZufVks\n5dEafQf04z9WnhcTkySH+kFVhp5bzyzpLDy9urGxsTQHcrr37kUNHiUmJsq+ZkBBTLEIAope\n2f8e+XFE1xamlk4ubp4D+7s5t23etLnjwJmbbySWUGUYFlBI7IAFODYzTx4YbS74+fPNOV+P\nPvhfiYRDKqm1bt2cv5mfny+H2kF12l8EnQvuW7nmdWFhoVTHaZiYUGspamlpSSwKNeSGrXB3\nmXk5Vbov/Igp+tStpQgCijbliZcC+7Tr5h98MTJZOEsrTfv3rx1zPew7jz3wit8UzAooGQzn\nAihC3oMlwjM6Eo75gB3PpVlB+e4M6t7UfjvT5F5N4Pt0cqQZIYQYuQY/leKGIB6Px8s/NJg/\nwyrHYxfuyq8LwQKjmh1n/C9F2ht8EFOKV7+W4iGgFC7v6ZbBFrX2fem7rI8SPHLMlIBCYgcs\nkhI62b7K3OpGPZdcT67ls/Hd+q780i3nhymmnsDj8Xi87BvTvnCX+iLE430+NYx/BzKn16Z4\nedZM+STu7CcYB6pTxoCYUrB6txQCSpEKn677qvJ5CF1rj4Blm/YePXZox5q5Pk7GqsIRo9I6\n4GqW4EBGBBQSO2CV8g9nJ7YVvtuEqLXyXn8vTWzc5Fyd0ppfss0Pj7C4jmJxudJftfLuzLbn\nt5SBz9lMOdZKKZU9W96uMirqkjEgphSr/i2FgFKU5PNjqKFR0uyrpX8nVlm7g5v5dPsoK6Hs\njdNh6ZPKqaIZEFBI7IBtuGnXFjgLr6BMiKq567zjzzJrBE7x671DqB5uXc89CXRUF6RR/Gav\nNzWpg7brlreYmqHukrf3VRcKirpkDIgphWpAS0kLAdUAyce9qRmH9foEvxA5ZzA36dRoi8o2\nNJl6S/gU0x1QSOyAjXIfbxneRr1K3BCVpg4+C3eEhL1Jzs7PTX8XHhI8sQt15zAx+npXHD7b\n6Ff08uSWMzFVx5KK4i8t7k2ti0maDdj7Hp1A9fLf2m5VQ6JOGQNiSnEa1lLCEFAyV3Jjegsq\nXRt5NlV8wYyTQysHa22WRlbbTWdAIbEDtsoM3zLcWpNIQcdpwZ2s2l8Q5Kzo5Y5BphxCNFv2\n8lu06cDx0ycPBC+a0KtF5aiFdpeF99BS9ZW6w73G/37dMgbElGI0vKV4PB4CSi7yz/rwZwvm\nOK1+JbFoyUU/wZPKmv6hIkrQFVBI7IDN8l6fWz7ERkdCvKi1GrDuH3y00Y+6CInXtPfyu+l0\nV5PFikLGGYo6r3XMGBBTcieTlkJAyQX3ymQqWbNa/KSWwm/WUM88EM7oM2L6RekIKCR2wHrF\nSQ8PBU1ys9ar+imnatRh8I9HI3EBYobsiC2jbMR9d1Vr3nfh+bdFdNeR1eK39hbbNVDn3iDE\nlBzJpqUQUHKRsr0vdRK/3FTbDW/xG3tShZvNvCWppIIDisPjYUEYUA7FaTFPn7yI/ZhVoqZv\nbuvQ3bmdsUbtR4Hi5MaEbP01ePuphx/5k31ytM069R05aebsyQPt9SQfCxKlnvDp6Hchnf+T\njuP8HdMSfp51Jl6w9JFmxxkXbm4bKLmPpwbElMzJsqUQUDKXusPdbOYtQgghRtNvfBYxZi7k\n0QLLnhvjCSGE9Nyc9HC2haTChBDFBZSsM0UAAIlKMt9FPrgfFvHsbZrIB86grhKPDhMs9050\nHBfcSOfxeGVxJ0e3EZ5wSx5PX0LdyKWlEFCyIzQUS1rOuFkiqWzOvv5U9m06466iaigNJHYA\nACxWHrPJRbd6rlABuR2joKVYIO/MCMH9cBpfrIsuE1/01lRqyV71IUfyFVfF2mGtWAAAFlNp\n0au/kyEhhOg4Lgi9Eexe2SWkaul74vYxoYyh+OWOEe6z6rBKKcgQWooFdL2njaOGVEsilvsu\nCxe3dmvM/fvUiLqzq6ukpyMUj+7MEgAAGiY3fOXXrsI9QMLQG8QgaCnmSzzgWXmDonqfzXEi\nS6Xt7EcNxFoveSrxBYvi/kuU0PMne3h4AgBAyZW/PzXWbVyDn6UAuUNLMUHi4SEOE0Mzibr9\npNO39g+3EHHyS875Go06XUAIISZTb6XschPXQMWvtg3t+8Nr9+NVemTlC0OxAABKDiN9bIGW\nYoKW/scuzOvaWWxWRwj559btgootXQ9PF4lZ3fdXU8venxrrNu1qtnyqWwMSOwAA5YeMgS3Q\nUgyg77YhPFJsVkdI9O3bKRVbKq5f9xM9ZQmV1RFCCNHrPvFbFwOR5WQPiR0AQKOAjIEt0FL0\n46iqih/+znr4MJq/6fDVV6LytapZXc+gq1d/7qmwqQWR2AEANBbIGNgCLcVkTx4/5reDqYuL\nTY3dtGZ1BIkdAECjgoyBLdBSTJUeHU3NdGJra1ttJ91ZHUFiBwDQ2IjKGCYGXs6js04gClqK\nkdLS0qhNc3Nz4T0MyOoIEjsAgEaoasag1zPoj82DmtBcJxAFLcU8JSUl1GZ5uWBmGoZkdYQQ\nNTreFAAAaKZq6XviNiFuMxIm/Y+uKxBIAy3FMIaGhtRmfHw8ITaEQVkdIQQTFAMANF7lWVmF\nhoboAmI+tBRjlIf6Nx1yJIcQQlQ99qVcC2jCoKyOILEDAAAAkF72sWGm4/8oIYQQ1W4/n/F/\nNH02Y7I6gsQOAAAAoA4KQ/1aDDmRWe23jMjqCB6eAAAAAKgD7UGLZneumj8xJasjSOwAAAAA\n6kLFYf4GfwvBjwzK6ggSOwAAAIC60fPcuMu/OSFMy+oI7rEDAAAAqLv8+ws9lzXZEMqkrI4g\nsQMAAACoDx6Px+Fw6K5FNUjsAAAAAJQE7rEDAAAAUBJI7AAAAACUBBI7AAAAACWBxA4AAABA\nSSCxAwAAAFASSOwAAAAAlAQSOwAAAAAlgcQOAAAAQEkgsQMAAABQEkjsAAAAAJQEEjsAAMqL\noHYcPtvAx1V2XZnchNrltDqGpvpVEbe9ny6Hw+FYLQgrle0r35/bkvpbBx8rqu+rSDiZ7JF5\nzteIw+Gotv3xUb1PBIBCIbEDAKDZowWWVApkKW0KlLDvu8U3CwjRGxwU2FNdvvVrxIxGrl7k\nrEa4b34LCHpSQndtAKSAxA4AgHU+HZy+8HouIZx2c1ePM6a7NkrNds7agOaElL/aGPBLZBnd\ntQGoFRI7AACW+Xx+3sL/ZRFC9If/NM9Rle7qKDltj6WL+mgQUvbs18nBL8vprg5ALZDYAQBI\nof++PB5f1LJ2dNYk/9biH05lEEKI/ffLvjGksyqNRKupQQEWhJCSpyunbHrLo7s6ABIhsQMA\nYBFu1LrZ+xIIIUR7YOAPXfAZrgha7gvndFclhBSF/Txtdzzd1QGQBB8KAADs8engjxtfcAkh\nxHzi/LHN6K5Oo2E1df4IPUIIKbixbEloLt3VARAPiR0A0Kzo2DDqmVCO27ZkSUXP+QpK9t+X\nVWVX5XQk5nPvV/yKm/EsZOtif0/ntm3MDLQ0dZu1tO3kMmTGr8cffaz7843STHdSlv70TPBc\nv/7Odi1MDLQ1NJs0s7Bq19V9zNxfD918V1Cj+ONA24oX7LlR0AkUv+4L6m08dmVVP6Ls7tqV\n1ypeqNPU2e6aEuspy/PAy3wRsiXQr18Xu1bGTTS19E1bt3X2nLhk26VX2VK+guwJtQj1z1CS\neHf/Un+vbjamelrqGtr6ZjbdPCcsOfDgk/BDD0Xxdw6unu7ds4NVcyNtTd1mLWwd+oycvznk\nZaakQVaDEdPHmBFCCMk4tWL7a/n9WQANxQMAoFXh0aGCTyTXrZ8kFT37jaCk197MKrv+CtDl\n7zGbc4/H+/x452RnI46YDz5D5zkX40prvv7zn9tSRWwWRYh5feK4KrrGkdz0sN99bLXFf9aq\nt3Sff+FtifAxEYtsJH0699uZWe1N0o8M1KnYx3H57b3oUySL83BvTguqxKCjhXlRB6Z+0UxM\nL4CqSc/ph18WiKqJhJMpE0It4rU3k5f7bKevvZboWup98eP1dB6Px029/esIca2k3tr794gs\n8e/HDZtvyS/aavbDMtn/QQAygR47AFA2vOS/Zrr2mb7vsdgumKzHm0cN+ilcZlPOZlyf08d1\n7vm3heKLlCbe3DjSdfSRt/WfTPjjyf1X+P1+X4z0aVNreVmch+wHQZ5ffbs7IoMren95WthO\n/96eKx7R13NHCCFZ9wP79pp+6o2YvyQ3Yv1w310RV2a7ewVeENdKpR9C5w6aciFN3Htwvhzi\nXdFnRxKO7L2KSe2AoZDYAYCSydw/ceiO54WEqDR19F118l7Mx6yCkuLclDd3jy72aE596JW9\nWj95fbRM3jD99OTRW6MrLvS67b5Zfepe9MfsotLSwuzU91HXjq2Z4Gxc8a7cpJDJo9a+oGbM\ncP71bcU37LD5giytTWXf1vVpVZ94TTx5/C4/veo4YEDrWqslk/Nwf9e6hzmEEKJjP2zJ/qtR\nHzLyi/LT459d3bXAs5Ua9U73g4Z8ezK11hrJze1lI9c9ziNEt53P8qO3o1Nyi4pykl9e3zml\niz5VJPf6zB6Dtr0oJoTo2g1fdujmy485RYWZCa9uH1r4dWtqUDv17Iylf4tL2Ti9vAc1rdjM\nunjqCjI7YCi6ugoBACrIeii2gmb7KReTyqu/QFnsNrfKclaLn1TdXa+h2MilgoMsp10XNZZX\nGn/Ux4Iqozf6THa1/aITu2pStvehCpnPui2mkGzOg9BQLCGEEA07/xP/FVd/DW7arfkOGoJC\nRqNOp1fZr8ChWEII4bQaeeBVYbUyGSG+plX+FGLSf/vzvGqluEkH++sJ2mf8/0QM0lfIPzSY\nmjVQ3y+0xhkBYAL02AGAEmo6ZO+NPcMsanzCqVrPWBkgyFri7t1LavBbvfnzT+pW+g4TZvcz\nEFFErfW4zUv78Nf9yr0UcqMeCxiU3LsXQW07OTlKeZQszoPBgB3XDo6x1aj+e46x2/pza1yo\n32ee33iAvolAOO0XnD06qX31e+yaDpniI5zZmUzYf2pGJ91qpTgW42cMpzK73LCwl+LeRcfZ\nuT1/M+f27acNrDOAXCCxAwClo+oStH18c9H7OC4D+guSr9jY2Aa/WWzsO2pTS0vMzfuEtBg0\niErGCt68qUc6+eThQ+oGsjaOjtJNSyyL86DSY/mugDZinr5QsZv1yySqL5IbfuCQ2JRIztTd\n5y34UtTJ53To0L7yJ5tvf/AWlXkTVVtbK2o7Pi5O7OOx7bt2pR69SHrwABPaARMhsQMAZaPS\nd4JfS7F7Vdu0EXRVZWRkNPjdysoE/W9RZ068EtcZJzTGGrGo9gcfqiuIjv4geKk20h0vi/Og\n5jl5oqTb+TTd/H0FtYm5fz9dqprJ3Jfe3qai9xgYVGZyBq6u4ro6tbUFz8qW5uSIfQhGxdKy\nFbUdFRmJRSiAgZDYAYCyaeXo2FTC7mbNBPP6lhQWinnaU3rt2gluJeNGBfUfuvbv9zJ72rbS\nu3eCfkGNFi2MpTpGFueho6trLbMg9+jvJUidHkc8lqpmsmbu5GQmZpe6urpg29LKStzEL8JK\nS8U/udyqlSCxK4qN/ShtBQEUB4kdACgbMzNxV3lCCCEqKpUffFxugxM7u/HfuQkGAcsSLi/x\nsmvZ0fPbpVtO33yeUiirPp2EhARq08TUVJr0RCbnQcfBQeJce4QQTufOnajtrMTEPKmqJmO1\n/KUUfX392gsRwuOJbzVDc3PBtNDx8RiLBQZCYgcAykbCrW7yYDnj4O4RLVUrf1GW8erawTVz\nfPs5mBuadHAb/f3qfZejUoob8h7c3FzByhVS/3kyOA9GTZvWmkSamZsLymRl1VgtQxH09PRq\nL1Q1la0voTHbnJycBr8cgMwhsQMAZcPhSNejJbP3s5xw9vG1NT5t9Wq8b0lG9J2z25ZPGdTF\nwtTea8amq/Ucps3PzxdsC6UWtdSr4edBmoyJo6cneMw0L4+WHjtVVdXaC8mGrq7gjy3Iz8dN\ndsA8SOwAgDVKSpg6KayKWd/F514lRP+1a8kEj04mIhZx5eb89/fOef07dp92Kanuw7/Co4OS\nRgplrby8vPZCRUWC3sgmTZrIszoMIHT2VdXUFPsNAkAaSOwAgDUKCgpqL0QfFYO2/af+cvja\n85TMT1HXTmxaMmVID2v9qn1JBc93+47dJn4+DTH0DAwEn9aFhRJWLpMxaXrginJzBQ8bGBpK\nNxELexUVCfpchTrvAJgDiR0AsEZycjLdVZAKR9vc0WPM3F/2/BEWm5H++sahFeOoVcUIIfl3\nN+wIr+srGhkJHj1VZHqbkpRU62TKQg/sNrO0VPYeO6HETvm7J4GVkNgBAM2kvhWMGxPzn3yr\nIgdqhvbu/j8dfRR5eITgyc2Ee/fe1/FlTExMqM30lJQGP8orLe6LF7Wtp1v4/Plbartbt65y\nrhDdClJTBWl169a1L9gLoHBI7ACAZsIzjUkc+ot6GMa4odibM0w5FdR9zkq4A1C15bhAf8FM\nvmlpaXV8n7adO1NnqezDB8XNn/bfrVuSl8kou3nlOjUS275XL0kT5ymDpI+Cc69lbS1mVQ8A\nOiGxAwCaqRgaCh69TEoSm0aUPjh6+p24nbSxtrbmb5U9e/ZKYlEDA8EsanW/FU2jS5eO1HZc\nXFwdj64/3oN9+15JuCMw549DF6kZTrqM8+ugkErRKD5esABI23bt8OwEMBASOwCgm52dHbWZ\nfO3aC9GFkvYu3/VB9C46Wbq5WfI3Y4/svyN+yQLyOSzsDX/TwMnJUniX0KQimZ8/i06j7Lt1\nowqlRkYqrsuO+/y3ZSfF9S+WRgavupBdsa3hPmVCbZMZs96Hf/+l0ljj3r3bSiwLQA8kdgBA\nNytXV8E6Ta+3Lz2cVCO1+XxzgfeCW3JYqavhuo0f34HfcZOw+7vvL30SmZcVxx6bsuQSf1YQ\ns1Fj3dWF97auXKcqJ2TTpn8+5ZfVeBXOVwO8dPjbkU+eKOwuO0JyLn43YOm9mhMPcz9emvnN\n2mcVNVFpNy94iviFaZUENyrqOX9TvXefL9FhB0yExA4AaNc94DtHalaQrD8n9xz804mHb1Pz\nigszE1+Hh26d7uY0cGNkISGaNjaMyx04HeeuHsN/sKH0ze6hHZ3Hrzp85WlcSlZhaXFO8rvn\n989vmO7ZwXH8BX4vm/7AtUH9qk50p+fWz5n6ME67Mr+HRRN1FQ7HY1eVZEpnwIj+/JmJ8+7e\njZTn31RJU1OTEJL/ZI1HJ7dpm0PDoxM+FxbnpcU9Cd08y63r0L3/VUxzp24/d9fSrmqKqRON\nIm7d5t8Dquk51EvKeaIBFEv5AxEAmK/jjwcXn+ux+lkJIYSUJVxe5Xd5VfUyHPOB2w95Heoz\nJ1Hx9ZOo6fAdR2c89t7xppQQws18euynicd+ElNWvd30M0cntaj+a+uAwNG/jjyVLvF9dAaP\nHKB14UIRIeR9aOiL4G6dJBaXCctZe/wfzVryILck6c7uuXd2iyxkNnzflWDXRjD1x8srV/gr\n9moN8huh7DP2AVuhxw4AGECjS9ClPxb2NhEzuKVi4jL73IOQAGsixUIIimfgte3h3ysHtJK4\nNCtH1370pvthO7xEPThq6LP/3snAUT1sTZtoiP1Y1hseMIp/8OuQkJgGVVlaWl0X/3V787DW\n6qJ3q1n0W345/PQEq8ZwLXl57hz/pDcbFTBUX3JhALpwFLk4DQCAJOVpTy8eOXwy5OazuE/J\naTnlWkbNrTt/6eY9dsrEwR0MGH9HU1nq43OHT1+9Fxbx/F1KZlZ2XplaEwOjZhZ2Tt1d3AaP\nG+/dwbCB+Q8vItC++7q3hBBi+2Pkm3VOijonBbFX9m4/dPF62It3ydllWs2aW7Sy/3LAqLHj\nR39tp8/4hpEJ7j8/WPf4PZ4QQjr9/O+/QZ0bx58N7IPEDgCAPVIOell/+3cBIcRk8pUPe70k\ndhKC7OSeH9Ny5KkcQkjT0RdiTw/HQCwwVWPoPgcAUBZm49fO66RCCCFpRzccTqG7Oo3Gh32/\nncshhBB1l2VrkNUBkyGxAwBgEbWuS3Z/b8shhBRfD94UUetKroxybiRHBnpseK/gehffXr/x\nYRkhRKXdD1tmKf1sfcBuSOwAAFhF2+WXPTPbcAghsdtXHJP8JC3IwqcDq/YnEUJIy6nbfuom\n5jkSAIbAPXYAAKyT9/fUjl57PhBis/Cf6PXdkWvIUcn9uXZ9Nn8gxNzvwqtjw43org+AZOix\nAwBgnSaewXsntSSExG4L3Ct2eV2QgcTdS3d9IIQYj9z2O7I6YAEkdgAALKTv+dueb1sQUnhr\nVdDVfLpro7QK/l655m4xIUbDN2/1Maa7NgBSwFAsAAAAgJJAjx0AAACAkkBiBwAAAKAkkNgB\nAAAAKAkkdgAAAABKAokdAAAAgJJAYgcAAACgJJDYAQAAACgJJHYAAAAASgKJHQAAAICS+D8r\n+tB16r5eKAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(nbl_m)), binwidth = 1) + myaxis + myplot  +\n",
    "coord_cartesian(xlim = c(-1,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_expressed_genes <- rownames(nbl_m)[rowSums(nbl_m > quantile(unlist(nbl), 0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "8825"
      ],
      "text/latex": [
       "8825"
      ],
      "text/markdown": [
       "8825"
      ],
      "text/plain": [
       "[1] 8825"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(nbl_expressed_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>26344</li><li>5</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 26344\n",
       "\\item 5\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 26344\n",
       "2. 5\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 26344     5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(nbl_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median center genes across cell types.\n",
    "nbl_mcentr <- t(apply(nbl,1,mcentr))\n",
    "colnames(nbl_mcentr) <- colnames(nbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a list of tables of differences for each gene\n",
    "diff_gen_list_nbl <- mclapply(rownames(nbl_mcentr), function(x){\n",
    "    mygen <- x\n",
    "    gen_df <- nbl_mcentr[mygen,]\n",
    "    #get ratio differences in counts across cell types\n",
    "    gen_tab_ratio <- outer(gen_df, gen_df, \"-\")\n",
    "    \n",
    "    return(gen_tab_ratio[c(\"maxcount_tumor\", \"maxcount_endo\",\n",
    "                          \"maxcount_mesench\", \"maxcount_schwann\"), \"maxcount_immune\", drop = F])    \n",
    "})\n",
    "\n",
    "names(diff_gen_list_nbl) <- rownames(nbl_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#genes with score diff > 2 in nonimmune vs immune\n",
    "diff_gen_list_nbl_nonimmune <- lapply(diff_gen_list_nbl, function(x) x[ rowSums(x >= 2) == 1,, drop = F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rmeove empty lists\n",
    "diff_gen_list_nbl_nonimmune <- diff_gen_list_nbl_nonimmune[sapply(diff_gen_list_nbl_nonimmune, function(x) length(unlist(x)) >= 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$FGB</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 1 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_immune</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>maxcount_mesench</th><td>2.023472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$CALCA</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 1 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_immune</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>maxcount_tumor</th><td>2.023472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$SAA2</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 1 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_immune</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>maxcount_mesench</th><td>2.023472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$APOC3</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 1 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_immune</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>maxcount_endo</th><td>2.023472</td></tr>\n",
       "\t<tr><th scope=row>maxcount_mesench</th><td>2.023472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$TRIM43</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 1 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_immune</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>maxcount_tumor</th><td>10.79185</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$LHX9</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 1 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcount_immune</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>maxcount_tumor</th><td>2.023472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$FGB] A matrix: 1 × 1 of type dbl\n",
       "\\begin{tabular}{r|l}\n",
       "  & maxcount\\_immune\\\\\n",
       "\\hline\n",
       "\tmaxcount\\_mesench & 2.023472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$CALCA] A matrix: 1 × 1 of type dbl\n",
       "\\begin{tabular}{r|l}\n",
       "  & maxcount\\_immune\\\\\n",
       "\\hline\n",
       "\tmaxcount\\_tumor & 2.023472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$SAA2] A matrix: 1 × 1 of type dbl\n",
       "\\begin{tabular}{r|l}\n",
       "  & maxcount\\_immune\\\\\n",
       "\\hline\n",
       "\tmaxcount\\_mesench & 2.023472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$APOC3] A matrix: 2 × 1 of type dbl\n",
       "\\begin{tabular}{r|l}\n",
       "  & maxcount\\_immune\\\\\n",
       "\\hline\n",
       "\tmaxcount\\_endo & 2.023472\\\\\n",
       "\tmaxcount\\_mesench & 2.023472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$TRIM43] A matrix: 1 × 1 of type dbl\n",
       "\\begin{tabular}{r|l}\n",
       "  & maxcount\\_immune\\\\\n",
       "\\hline\n",
       "\tmaxcount\\_tumor & 10.79185\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$LHX9] A matrix: 1 × 1 of type dbl\n",
       "\\begin{tabular}{r|l}\n",
       "  & maxcount\\_immune\\\\\n",
       "\\hline\n",
       "\tmaxcount\\_tumor & 2.023472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$FGB\n",
       ":   \n",
       "A matrix: 1 × 1 of type dbl\n",
       "\n",
       "| <!--/--> | maxcount_immune |\n",
       "|---|---|\n",
       "| maxcount_mesench | 2.023472 |\n",
       "\n",
       "\n",
       "$CALCA\n",
       ":   \n",
       "A matrix: 1 × 1 of type dbl\n",
       "\n",
       "| <!--/--> | maxcount_immune |\n",
       "|---|---|\n",
       "| maxcount_tumor | 2.023472 |\n",
       "\n",
       "\n",
       "$SAA2\n",
       ":   \n",
       "A matrix: 1 × 1 of type dbl\n",
       "\n",
       "| <!--/--> | maxcount_immune |\n",
       "|---|---|\n",
       "| maxcount_mesench | 2.023472 |\n",
       "\n",
       "\n",
       "$APOC3\n",
       ":   \n",
       "A matrix: 2 × 1 of type dbl\n",
       "\n",
       "| <!--/--> | maxcount_immune |\n",
       "|---|---|\n",
       "| maxcount_endo | 2.023472 |\n",
       "| maxcount_mesench | 2.023472 |\n",
       "\n",
       "\n",
       "$TRIM43\n",
       ":   \n",
       "A matrix: 1 × 1 of type dbl\n",
       "\n",
       "| <!--/--> | maxcount_immune |\n",
       "|---|---|\n",
       "| maxcount_tumor | 10.79185 |\n",
       "\n",
       "\n",
       "$LHX9\n",
       ":   \n",
       "A matrix: 1 × 1 of type dbl\n",
       "\n",
       "| <!--/--> | maxcount_immune |\n",
       "|---|---|\n",
       "| maxcount_tumor | 2.023472 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$FGB\n",
       "                 maxcount_immune\n",
       "maxcount_mesench        2.023472\n",
       "\n",
       "$CALCA\n",
       "               maxcount_immune\n",
       "maxcount_tumor        2.023472\n",
       "\n",
       "$SAA2\n",
       "                 maxcount_immune\n",
       "maxcount_mesench        2.023472\n",
       "\n",
       "$APOC3\n",
       "                 maxcount_immune\n",
       "maxcount_endo           2.023472\n",
       "maxcount_mesench        2.023472\n",
       "\n",
       "$TRIM43\n",
       "               maxcount_immune\n",
       "maxcount_tumor        10.79185\n",
       "\n",
       "$LHX9\n",
       "               maxcount_immune\n",
       "maxcount_tumor        2.023472\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(diff_gen_list_nbl_nonimmune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select genes with score (of expression across all celltypes) > 0\n",
    "nbl_nonimmune_genes_expressed <- diff_gen_list_nbl_nonimmune[names(diff_gen_list_nbl_nonimmune) %in% \n",
    "                                                             nbl_expressed_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3054"
      ],
      "text/latex": [
       "3054"
      ],
      "text/markdown": [
       "3054"
      ],
      "text/plain": [
       "[1] 3054"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "3036"
      ],
      "text/latex": [
       "3036"
      ],
      "text/markdown": [
       "3036"
      ],
      "text/plain": [
       "[1] 3036"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_nbl_nonimmune)\n",
    "length(nbl_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbl_nonimmune_genes_expressed <- names(nbl_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3036"
      ],
      "text/latex": [
       "3036"
      ],
      "text/markdown": [
       "3036"
      ],
      "text/plain": [
       "[1] 3036"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(nbl_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbl_nonimmune_genes_expressed[ nbl_nonimmune_genes_expressed == \"CD8A\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3036 genes are expressed > 75th percentile expression of all genes in at least one cell type AND are expressed >2 difference in at least one nonimmune celltype compared to immune celltype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Gojo's EPN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_files <- list.files(paste0(datapath,\"single_cell/\"), pattern = \"gojo\", full.names = T, recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojofiles <- lapply(gojo_files, \n",
    "                    function(f){read.table(f, header = TRUE, sep = \"\\t\", stringsAsFactors = F)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "names(gojofiles) <- gsub(\".*gojo\", \"gojo\", gojo_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bind all tumour clusters together\n",
    "gojofiles_tum <- as.data.frame(do.call(\"cbind\", gojofiles[5:25]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>23686</li><li>5426</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 23686\n",
       "\\item 5426\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 23686\n",
       "2. 5426\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 23686  5426"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gojofiles_tum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_data <- list()\n",
    "\n",
    "gojo_data$Tumor <- gojofiles_tum\n",
    "gojo_data$Endothelial <- gojofiles[[\"gojo_Endothelial-cell.txt\"]]\n",
    "gojo_data$Microglia <- gojofiles[[\"gojo_Microglia.txt\"]]\n",
    "gojo_data$Oligodend <- gojofiles[[\"gojo_Oligodendrocyte.txt\"]]\n",
    "gojo_data$OPC <- gojofiles[[\"gojo_OPC.txt\"]]\n",
    "gojo_data$T_cells <- gojofiles[[\"gojo_T-cell.txt\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_maxcounts <- lapply(gojo_data, function(x){apply(x, 1, max)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_maxcounts_all <- as.data.frame(do.call(\"cbind\", gojo_maxcounts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Tumor</th><th scope=col>Endothelial</th><th scope=col>Microglia</th><th scope=col>Oligodend</th><th scope=col>OPC</th><th scope=col>T_cells</th></tr>\n",
       "\t<tr><th></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;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ZXDC</th><td> 809.50</td><td>562.61</td><td>336.66</td><td>162.93</td><td> 481.09</td><td> 91.02</td></tr>\n",
       "\t<tr><th scope=row>ZYG11A</th><td> 164.97</td><td> 15.78</td><td> 29.18</td><td>  6.85</td><td>  11.17</td><td> 11.00</td></tr>\n",
       "\t<tr><th scope=row>ZYG11B</th><td>1786.92</td><td>139.13</td><td>130.99</td><td>187.98</td><td> 339.48</td><td> 47.00</td></tr>\n",
       "\t<tr><th scope=row>ZYX</th><td> 880.00</td><td>298.00</td><td>714.00</td><td> 97.00</td><td>1156.00</td><td>142.00</td></tr>\n",
       "\t<tr><th scope=row>ZZEF1</th><td> 893.99</td><td>335.00</td><td>631.02</td><td>292.00</td><td> 178.00</td><td>369.00</td></tr>\n",
       "\t<tr><th scope=row>ZZZ3</th><td>2244.02</td><td>403.00</td><td>128.00</td><td>256.00</td><td> 286.00</td><td>131.00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & Tumor & Endothelial & Microglia & Oligodend & OPC & T\\_cells\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tZXDC &  809.50 & 562.61 & 336.66 & 162.93 &  481.09 &  91.02\\\\\n",
       "\tZYG11A &  164.97 &  15.78 &  29.18 &   6.85 &   11.17 &  11.00\\\\\n",
       "\tZYG11B & 1786.92 & 139.13 & 130.99 & 187.98 &  339.48 &  47.00\\\\\n",
       "\tZYX &  880.00 & 298.00 & 714.00 &  97.00 & 1156.00 & 142.00\\\\\n",
       "\tZZEF1 &  893.99 & 335.00 & 631.02 & 292.00 &  178.00 & 369.00\\\\\n",
       "\tZZZ3 & 2244.02 & 403.00 & 128.00 & 256.00 &  286.00 & 131.00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| <!--/--> | Tumor &lt;dbl&gt; | Endothelial &lt;dbl&gt; | Microglia &lt;dbl&gt; | Oligodend &lt;dbl&gt; | OPC &lt;dbl&gt; | T_cells &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| ZXDC |  809.50 | 562.61 | 336.66 | 162.93 |  481.09 |  91.02 |\n",
       "| ZYG11A |  164.97 |  15.78 |  29.18 |   6.85 |   11.17 |  11.00 |\n",
       "| ZYG11B | 1786.92 | 139.13 | 130.99 | 187.98 |  339.48 |  47.00 |\n",
       "| ZYX |  880.00 | 298.00 | 714.00 |  97.00 | 1156.00 | 142.00 |\n",
       "| ZZEF1 |  893.99 | 335.00 | 631.02 | 292.00 |  178.00 | 369.00 |\n",
       "| ZZZ3 | 2244.02 | 403.00 | 128.00 | 256.00 |  286.00 | 131.00 |\n",
       "\n"
      ],
      "text/plain": [
       "       Tumor   Endothelial Microglia Oligodend OPC     T_cells\n",
       "ZXDC    809.50 562.61      336.66    162.93     481.09  91.02 \n",
       "ZYG11A  164.97  15.78       29.18      6.85      11.17  11.00 \n",
       "ZYG11B 1786.92 139.13      130.99    187.98     339.48  47.00 \n",
       "ZYX     880.00 298.00      714.00     97.00    1156.00 142.00 \n",
       "ZZEF1   893.99 335.00      631.02    292.00     178.00 369.00 \n",
       "ZZZ3   2244.02 403.00      128.00    256.00     286.00 131.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(gojo_maxcounts_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "259.508577288975"
      ],
      "text/latex": [
       "259.508577288975"
      ],
      "text/markdown": [
       "259.508577288975"
      ],
      "text/plain": [
       "[1] 259.5086"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(unlist(gojo_maxcounts_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "75.6126"
      ],
      "text/latex": [
       "75.6126"
      ],
      "text/markdown": [
       "75.6126"
      ],
      "text/plain": [
       "[1] 75.6126"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mad(unlist(gojo_maxcounts_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_maxcounts_m <- (gojo_maxcounts_all - median(unlist(gojo_maxcounts_all)))/ mad(unlist(gojo_maxcounts_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "  -0.674   -0.661    0.000    2.758    2.156 3213.472 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(gojo_maxcounts_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Tumor</th><th scope=col>Endothelial</th><th scope=col>Microglia</th><th scope=col>Oligodend</th><th scope=col>OPC</th><th scope=col>T_cells</th></tr>\n",
       "\t<tr><th></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;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A1BG</th><td>  1.6531636</td><td> -0.1587037</td><td>-0.42320989</td><td>-0.4232099</td><td>-0.58191360</td><td>-0.22483025</td></tr>\n",
       "\t<tr><th scope=row>A1BG-AS1</th><td>  3.3274877</td><td> -0.3835340</td><td>-0.03147624</td><td> 0.3437258</td><td> 0.01177053</td><td>-0.16663889</td></tr>\n",
       "\t<tr><th scope=row>A1CF</th><td>  0.3570833</td><td> -0.5951389</td><td>-0.64804014</td><td>-0.6612655</td><td>-0.62158952</td><td>-0.66126545</td></tr>\n",
       "\t<tr><th scope=row>A2M</th><td>143.5144407</td><td>377.1532787</td><td>82.76490955</td><td>-0.4496605</td><td> 2.22185191</td><td> 0.05290124</td></tr>\n",
       "\t<tr><th scope=row>A2M-AS1</th><td>  4.2850001</td><td>  0.3570833</td><td> 0.96544756</td><td>-0.5951389</td><td>-0.26450618</td><td>-0.67449076</td></tr>\n",
       "\t<tr><th scope=row>A2ML1</th><td>  7.2474693</td><td> -0.6083642</td><td>-0.64804014</td><td>-0.6480401</td><td>-0.63481483</td><td>-0.58191360</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & Tumor & Endothelial & Microglia & Oligodend & OPC & T\\_cells\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tA1BG &   1.6531636 &  -0.1587037 & -0.42320989 & -0.4232099 & -0.58191360 & -0.22483025\\\\\n",
       "\tA1BG-AS1 &   3.3274877 &  -0.3835340 & -0.03147624 &  0.3437258 &  0.01177053 & -0.16663889\\\\\n",
       "\tA1CF &   0.3570833 &  -0.5951389 & -0.64804014 & -0.6612655 & -0.62158952 & -0.66126545\\\\\n",
       "\tA2M & 143.5144407 & 377.1532787 & 82.76490955 & -0.4496605 &  2.22185191 &  0.05290124\\\\\n",
       "\tA2M-AS1 &   4.2850001 &   0.3570833 &  0.96544756 & -0.5951389 & -0.26450618 & -0.67449076\\\\\n",
       "\tA2ML1 &   7.2474693 &  -0.6083642 & -0.64804014 & -0.6480401 & -0.63481483 & -0.58191360\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| <!--/--> | Tumor &lt;dbl&gt; | Endothelial &lt;dbl&gt; | Microglia &lt;dbl&gt; | Oligodend &lt;dbl&gt; | OPC &lt;dbl&gt; | T_cells &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| A1BG |   1.6531636 |  -0.1587037 | -0.42320989 | -0.4232099 | -0.58191360 | -0.22483025 |\n",
       "| A1BG-AS1 |   3.3274877 |  -0.3835340 | -0.03147624 |  0.3437258 |  0.01177053 | -0.16663889 |\n",
       "| A1CF |   0.3570833 |  -0.5951389 | -0.64804014 | -0.6612655 | -0.62158952 | -0.66126545 |\n",
       "| A2M | 143.5144407 | 377.1532787 | 82.76490955 | -0.4496605 |  2.22185191 |  0.05290124 |\n",
       "| A2M-AS1 |   4.2850001 |   0.3570833 |  0.96544756 | -0.5951389 | -0.26450618 | -0.67449076 |\n",
       "| A2ML1 |   7.2474693 |  -0.6083642 | -0.64804014 | -0.6480401 | -0.63481483 | -0.58191360 |\n",
       "\n"
      ],
      "text/plain": [
       "         Tumor       Endothelial Microglia   Oligodend  OPC         T_cells    \n",
       "A1BG       1.6531636  -0.1587037 -0.42320989 -0.4232099 -0.58191360 -0.22483025\n",
       "A1BG-AS1   3.3274877  -0.3835340 -0.03147624  0.3437258  0.01177053 -0.16663889\n",
       "A1CF       0.3570833  -0.5951389 -0.64804014 -0.6612655 -0.62158952 -0.66126545\n",
       "A2M      143.5144407 377.1532787 82.76490955 -0.4496605  2.22185191  0.05290124\n",
       "A2M-AS1    4.2850001   0.3570833  0.96544756 -0.5951389 -0.26450618 -0.67449076\n",
       "A2ML1      7.2474693  -0.6083642 -0.64804014 -0.6480401 -0.63481483 -0.58191360"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gojo_maxcounts_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "     0.0      1.0     51.0    259.5    214.0 243030.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(gojo_maxcounts_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vxlPbdgxwAAbKp0wS570cS7Lzzrqic/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\npg07tXPBJb8zWh4xdEb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GDJOC9GyHA6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\nAICKQGAHAAAAoCIQ2AEAAACoiP8DB1VWjiJF3ycAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(gojo_maxcounts_m)), binwidth = 1) + myplot + myaxis +\n",
    "coord_cartesian(xlim = c(-1,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_expressed_genes <- rownames(gojo_maxcounts_m)[rowSums(gojo_maxcounts_m > quantile(unlist(gojo_maxcounts_m), 0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#median center genes\n",
    "gojo_mcentr <- t(apply(gojo_maxcounts_all,1,mcentr))\n",
    "\n",
    "colnames(gojo_mcentr) <- colnames(gojo_maxcounts_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 6 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Tumor</th><th scope=col>Endothelial</th><th scope=col>Microglia</th><th scope=col>Oligodend</th><th scope=col>OPC</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A1BG</th><td> 10.083637</td><td> 0.8431134</td><td>-0.50586807</td><td>-0.5058681</td><td>-1.31525698</td><td> 0.5058681</td></tr>\n",
       "\t<tr><th scope=row>A1BG-AS1</th><td>  8.821165</td><td>-0.9877034</td><td>-0.05715431</td><td> 0.9345691</td><td> 0.05715431</td><td>-0.4144124</td></tr>\n",
       "\t<tr><th scope=row>A1CF</th><td> 25.293403</td><td> 1.0117361</td><td>-0.33724538</td><td>-0.6744908</td><td> 0.33724538</td><td>-0.6744908</td></tr>\n",
       "\t<tr><th scope=row>A2M</th><td>  1.596040</td><td> 5.2873202</td><td> 0.63625332</td><td>-0.6784608</td><td>-0.63625332</td><td>-0.6705208</td></tr>\n",
       "\t<tr><th scope=row>A2M-AS1</th><td>  4.197559</td><td> 0.3077773</td><td> 0.91023510</td><td>-0.6352000</td><td>-0.30777734</td><td>-0.7137815</td></tr>\n",
       "\t<tr><th scope=row>A2ML1</th><td>200.661001</td><td> 0.3372454</td><td>-0.67449076</td><td>-0.6744908</td><td>-0.33724538</td><td> 1.0117361</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 6 of type dbl\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & Tumor & Endothelial & Microglia & Oligodend & OPC & T\\_cells\\\\\n",
       "\\hline\n",
       "\tA1BG &  10.083637 &  0.8431134 & -0.50586807 & -0.5058681 & -1.31525698 &  0.5058681\\\\\n",
       "\tA1BG-AS1 &   8.821165 & -0.9877034 & -0.05715431 &  0.9345691 &  0.05715431 & -0.4144124\\\\\n",
       "\tA1CF &  25.293403 &  1.0117361 & -0.33724538 & -0.6744908 &  0.33724538 & -0.6744908\\\\\n",
       "\tA2M &   1.596040 &  5.2873202 &  0.63625332 & -0.6784608 & -0.63625332 & -0.6705208\\\\\n",
       "\tA2M-AS1 &   4.197559 &  0.3077773 &  0.91023510 & -0.6352000 & -0.30777734 & -0.7137815\\\\\n",
       "\tA2ML1 & 200.661001 &  0.3372454 & -0.67449076 & -0.6744908 & -0.33724538 &  1.0117361\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 6 of type dbl\n",
       "\n",
       "| <!--/--> | Tumor | Endothelial | Microglia | Oligodend | OPC | T_cells |\n",
       "|---|---|---|---|---|---|---|\n",
       "| A1BG |  10.083637 |  0.8431134 | -0.50586807 | -0.5058681 | -1.31525698 |  0.5058681 |\n",
       "| A1BG-AS1 |   8.821165 | -0.9877034 | -0.05715431 |  0.9345691 |  0.05715431 | -0.4144124 |\n",
       "| A1CF |  25.293403 |  1.0117361 | -0.33724538 | -0.6744908 |  0.33724538 | -0.6744908 |\n",
       "| A2M |   1.596040 |  5.2873202 |  0.63625332 | -0.6784608 | -0.63625332 | -0.6705208 |\n",
       "| A2M-AS1 |   4.197559 |  0.3077773 |  0.91023510 | -0.6352000 | -0.30777734 | -0.7137815 |\n",
       "| A2ML1 | 200.661001 |  0.3372454 | -0.67449076 | -0.6744908 | -0.33724538 |  1.0117361 |\n",
       "\n"
      ],
      "text/plain": [
       "         Tumor      Endothelial Microglia   Oligodend  OPC         T_cells   \n",
       "A1BG      10.083637  0.8431134  -0.50586807 -0.5058681 -1.31525698  0.5058681\n",
       "A1BG-AS1   8.821165 -0.9877034  -0.05715431  0.9345691  0.05715431 -0.4144124\n",
       "A1CF      25.293403  1.0117361  -0.33724538 -0.6744908  0.33724538 -0.6744908\n",
       "A2M        1.596040  5.2873202   0.63625332 -0.6784608 -0.63625332 -0.6705208\n",
       "A2M-AS1    4.197559  0.3077773   0.91023510 -0.6352000 -0.30777734 -0.7137815\n",
       "A2ML1    200.661001  0.3372454  -0.67449076 -0.6744908 -0.33724538  1.0117361"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gojo_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a list of tables with score differences for each gene\n",
    "diff_gen_list_gojo <- mclapply(rownames(gojo_mcentr), function(x){\n",
    "    mygen <- x\n",
    "    gen_df <- gojo_mcentr[mygen,]\n",
    "    #get differences in counts across cell types\n",
    "    gen_tab_ratio <- outer(gen_df, gen_df, \"-\")\n",
    "    \n",
    "    return(gen_tab_ratio[c(\"Tumor\", \"Endothelial\",\n",
    "                          \"Oligodend\", \"OPC\"), c(\"Microglia\", \"T_cells\"), drop = F])    \n",
    "})\n",
    "\n",
    "names(diff_gen_list_gojo) <- rownames(gojo_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#genes with z score diff > 2 in nonimmune vs immune\n",
    "diff_gen_list_gojo_nonimmune <- lapply(diff_gen_list_gojo, function(x) x[ rowSums(x >= 2) >= 1,, drop = F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rmeove empty lists\n",
    "diff_gen_list_gojo_nonimmune <- diff_gen_list_gojo_nonimmune[sapply(diff_gen_list_gojo_nonimmune, \n",
    "                                                                  function(x) length(unlist(x)) >= 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$A1BG</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>10.5895</td><td>9.577769</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$`A1BG-AS1`</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>8.878319</td><td>9.235577</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$A1CF</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>25.63065</td><td>25.96789</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$A2M</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>0.959787</td><td>2.266561</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td>4.651067</td><td>5.957841</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$`A2M-AS1`</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>3.287324</td><td>4.91134</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$A2ML1</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Microglia</th><th scope=col>T_cells</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>201.3355</td><td>199.6493</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$A1BG] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Microglia & T\\_cells\\\\\n",
       "\\hline\n",
       "\tTumor & 10.5895 & 9.577769\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$`A1BG-AS1`] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Microglia & T\\_cells\\\\\n",
       "\\hline\n",
       "\tTumor & 8.878319 & 9.235577\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$A1CF] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Microglia & T\\_cells\\\\\n",
       "\\hline\n",
       "\tTumor & 25.63065 & 25.96789\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$A2M] A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Microglia & T\\_cells\\\\\n",
       "\\hline\n",
       "\tTumor & 0.959787 & 2.266561\\\\\n",
       "\tEndothelial & 4.651067 & 5.957841\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$`A2M-AS1`] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Microglia & T\\_cells\\\\\n",
       "\\hline\n",
       "\tTumor & 3.287324 & 4.91134\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$A2ML1] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Microglia & T\\_cells\\\\\n",
       "\\hline\n",
       "\tTumor & 201.3355 & 199.6493\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$A1BG\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Microglia | T_cells |\n",
       "|---|---|---|\n",
       "| Tumor | 10.5895 | 9.577769 |\n",
       "\n",
       "\n",
       "$`A1BG-AS1`\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Microglia | T_cells |\n",
       "|---|---|---|\n",
       "| Tumor | 8.878319 | 9.235577 |\n",
       "\n",
       "\n",
       "$A1CF\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Microglia | T_cells |\n",
       "|---|---|---|\n",
       "| Tumor | 25.63065 | 25.96789 |\n",
       "\n",
       "\n",
       "$A2M\n",
       ":   \n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Microglia | T_cells |\n",
       "|---|---|---|\n",
       "| Tumor | 0.959787 | 2.266561 |\n",
       "| Endothelial | 4.651067 | 5.957841 |\n",
       "\n",
       "\n",
       "$`A2M-AS1`\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Microglia | T_cells |\n",
       "|---|---|---|\n",
       "| Tumor | 3.287324 | 4.91134 |\n",
       "\n",
       "\n",
       "$A2ML1\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Microglia | T_cells |\n",
       "|---|---|---|\n",
       "| Tumor | 201.3355 | 199.6493 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$A1BG\n",
       "      Microglia  T_cells\n",
       "Tumor   10.5895 9.577769\n",
       "\n",
       "$`A1BG-AS1`\n",
       "      Microglia  T_cells\n",
       "Tumor  8.878319 9.235577\n",
       "\n",
       "$A1CF\n",
       "      Microglia  T_cells\n",
       "Tumor  25.63065 25.96789\n",
       "\n",
       "$A2M\n",
       "            Microglia  T_cells\n",
       "Tumor        0.959787 2.266561\n",
       "Endothelial  4.651067 5.957841\n",
       "\n",
       "$`A2M-AS1`\n",
       "      Microglia T_cells\n",
       "Tumor  3.287324 4.91134\n",
       "\n",
       "$A2ML1\n",
       "      Microglia  T_cells\n",
       "Tumor  201.3355 199.6493\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(diff_gen_list_gojo_nonimmune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16573"
      ],
      "text/latex": [
       "16573"
      ],
      "text/markdown": [
       "16573"
      ],
      "text/plain": [
       "[1] 16573"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_gojo_nonimmune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select expressed genes\n",
    "\n",
    "gojo_nonimmune_genes_expressed <- diff_gen_list_gojo_nonimmune[names(diff_gen_list_gojo_nonimmune) %in% gojo_expressed_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>'A1BG-AS1'</li><li>'A2M'</li><li>'A2M-AS1'</li><li>'A2ML1'</li><li>'A4GALT'</li><li>'AAAS'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'A1BG-AS1'\n",
       "\\item 'A2M'\n",
       "\\item 'A2M-AS1'\n",
       "\\item 'A2ML1'\n",
       "\\item 'A4GALT'\n",
       "\\item 'AAAS'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'A1BG-AS1'\n",
       "2. 'A2M'\n",
       "3. 'A2M-AS1'\n",
       "4. 'A2ML1'\n",
       "5. 'A4GALT'\n",
       "6. 'AAAS'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"A1BG-AS1\" \"A2M\"      \"A2M-AS1\"  \"A2ML1\"    \"A4GALT\"   \"AAAS\"    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gojo_expressed_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16573"
      ],
      "text/latex": [
       "16573"
      ],
      "text/markdown": [
       "16573"
      ],
      "text/plain": [
       "[1] 16573"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "12109"
      ],
      "text/latex": [
       "12109"
      ],
      "text/markdown": [
       "12109"
      ],
      "text/plain": [
       "[1] 12109"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_gojo_nonimmune)\n",
    "length(gojo_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "gojo_nonimmune_genes_expressed <- names(gojo_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12109 genes are expressed > 75th percentile in at least one cell type AND their expression is >2 mad differnet in at least one nonimmune and at least one immune"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Gillen's EPN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_files <- list.files(paste0(datapath,\"single_cell/\"), pattern = \"gillen\", full.names = T, recursive = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove doublet file\n",
    "gillen_files <- gillen_files[!grepl(\"Doublets\", gillen_files)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillenfiles <- lapply(gillen_files, \n",
    "                    function(f){read.table(f, header = TRUE, sep = \"\\t\", stringsAsFactors = F)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "names(gillenfiles) <- gsub(\".*gillen\", \"gillen\", gillen_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>'gillen_1.txt'</li><li>'gillen_10.txt'</li><li>'gillen_11.txt'</li><li>'gillen_12.txt'</li><li>'gillen_13.txt'</li><li>'gillen_14.txt'</li><li>'gillen_15.txt'</li><li>'gillen_2.txt'</li><li>'gillen_3.txt'</li><li>'gillen_4.txt'</li><li>'gillen_5.txt'</li><li>'gillen_6.txt'</li><li>'gillen_7.txt'</li><li>'gillen_8.txt'</li><li>'gillen_9.txt'</li><li>'gillen_Lymphocytes.txt'</li><li>'gillen_Myeloid.txt'</li><li>'gillen_Oligodendrocytes.txt'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'gillen\\_1.txt'\n",
       "\\item 'gillen\\_10.txt'\n",
       "\\item 'gillen\\_11.txt'\n",
       "\\item 'gillen\\_12.txt'\n",
       "\\item 'gillen\\_13.txt'\n",
       "\\item 'gillen\\_14.txt'\n",
       "\\item 'gillen\\_15.txt'\n",
       "\\item 'gillen\\_2.txt'\n",
       "\\item 'gillen\\_3.txt'\n",
       "\\item 'gillen\\_4.txt'\n",
       "\\item 'gillen\\_5.txt'\n",
       "\\item 'gillen\\_6.txt'\n",
       "\\item 'gillen\\_7.txt'\n",
       "\\item 'gillen\\_8.txt'\n",
       "\\item 'gillen\\_9.txt'\n",
       "\\item 'gillen\\_Lymphocytes.txt'\n",
       "\\item 'gillen\\_Myeloid.txt'\n",
       "\\item 'gillen\\_Oligodendrocytes.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'gillen_1.txt'\n",
       "2. 'gillen_10.txt'\n",
       "3. 'gillen_11.txt'\n",
       "4. 'gillen_12.txt'\n",
       "5. 'gillen_13.txt'\n",
       "6. 'gillen_14.txt'\n",
       "7. 'gillen_15.txt'\n",
       "8. 'gillen_2.txt'\n",
       "9. 'gillen_3.txt'\n",
       "10. 'gillen_4.txt'\n",
       "11. 'gillen_5.txt'\n",
       "12. 'gillen_6.txt'\n",
       "13. 'gillen_7.txt'\n",
       "14. 'gillen_8.txt'\n",
       "15. 'gillen_9.txt'\n",
       "16. 'gillen_Lymphocytes.txt'\n",
       "17. 'gillen_Myeloid.txt'\n",
       "18. 'gillen_Oligodendrocytes.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"gillen_1.txt\"                \"gillen_10.txt\"              \n",
       " [3] \"gillen_11.txt\"               \"gillen_12.txt\"              \n",
       " [5] \"gillen_13.txt\"               \"gillen_14.txt\"              \n",
       " [7] \"gillen_15.txt\"               \"gillen_2.txt\"               \n",
       " [9] \"gillen_3.txt\"                \"gillen_4.txt\"               \n",
       "[11] \"gillen_5.txt\"                \"gillen_6.txt\"               \n",
       "[13] \"gillen_7.txt\"                \"gillen_8.txt\"               \n",
       "[15] \"gillen_9.txt\"                \"gillen_Lymphocytes.txt\"     \n",
       "[17] \"gillen_Myeloid.txt\"          \"gillen_Oligodendrocytes.txt\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(gillenfiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bind all tumour clusters together\n",
    "gillenfiles_tum <- as.data.frame(do.call(\"cbind\", gillenfiles[1:15]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>23580</li><li>11200</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 23580\n",
       "\\item 11200\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 23580\n",
       "2. 11200\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 23580 11200"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gillenfiles_tum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_data <- list()\n",
    "gillen_data$Tumor <- gillenfiles_tum\n",
    "gillen_data$Lymphocytes <- gillenfiles[[\"gillen_Lymphocytes.txt\"]]\n",
    "gillen_data$Myeloid <- gillenfiles[[\"gillen_Myeloid.txt\"]]\n",
    "gillen_data$Oligodend <- gillenfiles[[\"gillen_Oligodendrocytes.txt\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$Tumor</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>23580</li><li>11200</li></ol>\n",
       "</dd>\n",
       "\t<dt>$Lymphocytes</dt>\n",
       "\t\t<dd><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>23580</li><li>1675</li></ol>\n",
       "</dd>\n",
       "\t<dt>$Myeloid</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>23580</li><li>5097</li></ol>\n",
       "</dd>\n",
       "\t<dt>$Oligodend</dt>\n",
       "\t\t<dd><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>23580</li><li>484</li></ol>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$Tumor] \\begin{enumerate*}\n",
       "\\item 23580\n",
       "\\item 11200\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$Lymphocytes] \\begin{enumerate*}\n",
       "\\item 23580\n",
       "\\item 1675\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$Myeloid] \\begin{enumerate*}\n",
       "\\item 23580\n",
       "\\item 5097\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$Oligodend] \\begin{enumerate*}\n",
       "\\item 23580\n",
       "\\item 484\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$Tumor\n",
       ":   1. 23580\n",
       "2. 11200\n",
       "\n",
       "\n",
       "\n",
       "$Lymphocytes\n",
       ":   1. 23580\n",
       "2. 1675\n",
       "\n",
       "\n",
       "\n",
       "$Myeloid\n",
       ":   1. 23580\n",
       "2. 5097\n",
       "\n",
       "\n",
       "\n",
       "$Oligodend\n",
       ":   1. 23580\n",
       "2. 484\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$Tumor\n",
       "[1] 23580 11200\n",
       "\n",
       "$Lymphocytes\n",
       "[1] 23580  1675\n",
       "\n",
       "$Myeloid\n",
       "[1] 23580  5097\n",
       "\n",
       "$Oligodend\n",
       "[1] 23580   484\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lapply(gillen_data, dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_maxcounts <- lapply(gillen_data, function(x){apply(x, 1, max)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_maxcounts_all <- as.data.frame(do.call(\"cbind\", gillen_maxcounts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Tumor</th><th scope=col>Lymphocytes</th><th scope=col>Myeloid</th><th scope=col>Oligodend</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AL627309.1</th><td>2</td><td>1</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>AL669831.5</th><td>2</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>FAM87B</th><td>2</td><td>1</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>LINC00115</th><td>3</td><td>2</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>FAM41C</th><td>2</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>AL645608.3</th><td>2</td><td>1</td><td>1</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Tumor & Lymphocytes & Myeloid & Oligodend\\\\\n",
       "  & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\tAL627309.1 & 2 & 1 & 1 & 1\\\\\n",
       "\tAL669831.5 & 2 & 1 & 2 & 1\\\\\n",
       "\tFAM87B & 2 & 1 & 1 & 1\\\\\n",
       "\tLINC00115 & 3 & 2 & 2 & 1\\\\\n",
       "\tFAM41C & 2 & 1 & 2 & 1\\\\\n",
       "\tAL645608.3 & 2 & 1 & 1 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | Tumor &lt;int&gt; | Lymphocytes &lt;int&gt; | Myeloid &lt;int&gt; | Oligodend &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| AL627309.1 | 2 | 1 | 1 | 1 |\n",
       "| AL669831.5 | 2 | 1 | 2 | 1 |\n",
       "| FAM87B | 2 | 1 | 1 | 1 |\n",
       "| LINC00115 | 3 | 2 | 2 | 1 |\n",
       "| FAM41C | 2 | 1 | 2 | 1 |\n",
       "| AL645608.3 | 2 | 1 | 1 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "           Tumor Lymphocytes Myeloid Oligodend\n",
       "AL627309.1 2     1           1       1        \n",
       "AL669831.5 2     1           2       1        \n",
       "FAM87B     2     1           1       1        \n",
       "LINC00115  3     2           2       1        \n",
       "FAM41C     2     1           2       1        \n",
       "AL645608.3 2     1           1       1        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gillen_maxcounts_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_maxcounts_m <- (gillen_maxcounts_all - median(unlist(gillen_maxcounts_all)))/mad(unlist(gillen_maxcounts_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "    0.000     1.000     2.000     7.536     5.000 23355.000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(gillen_maxcounts_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REnndjn0F/s17bFng3rZteovG7ViqU/z/z+u28++/DtN0b/7+OfVsQKqi4ee/0R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\n/B92Rp0WXpjtYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(gillen_maxcounts_m)), binwidth = 1) + myaxis + myplot +\n",
    "coord_cartesian(xlim = c(-2,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>23580</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 23580\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 23580\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 23580     4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gillen_maxcounts_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_expressed_genes <- rownames(gillen_maxcounts_m)[rowSums(gillen_maxcounts_m >= quantile(unlist(gillen_maxcounts_m), 0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "11649"
      ],
      "text/latex": [
       "11649"
      ],
      "text/markdown": [
       "11649"
      ],
      "text/plain": [
       "[1] 11649"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(gillen_expressed_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median center\n",
    "gillen_mcentr <- t(apply(gillen_maxcounts_all,1,mcentr))\n",
    "\n",
    "colnames(gillen_mcentr) <- colnames(gillen_maxcounts_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Tumor</th><th scope=col>Lymphocytes</th><th scope=col>Myeloid</th><th scope=col>Oligodend</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AL627309.1</th><td>0.0000000</td><td> 0.0000000</td><td>0.0000000</td><td> 0.0000000</td></tr>\n",
       "\t<tr><th scope=row>AL669831.5</th><td>0.6744908</td><td>-0.6744908</td><td>0.6744908</td><td>-0.6744908</td></tr>\n",
       "\t<tr><th scope=row>FAM87B</th><td>0.0000000</td><td> 0.0000000</td><td>0.0000000</td><td> 0.0000000</td></tr>\n",
       "\t<tr><th scope=row>LINC00115</th><td>1.3489815</td><td> 0.0000000</td><td>0.0000000</td><td>-1.3489815</td></tr>\n",
       "\t<tr><th scope=row>FAM41C</th><td>0.6744908</td><td>-0.6744908</td><td>0.6744908</td><td>-0.6744908</td></tr>\n",
       "\t<tr><th scope=row>AL645608.3</th><td>0.0000000</td><td> 0.0000000</td><td>0.0000000</td><td> 0.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Tumor & Lymphocytes & Myeloid & Oligodend\\\\\n",
       "\\hline\n",
       "\tAL627309.1 & 0.0000000 &  0.0000000 & 0.0000000 &  0.0000000\\\\\n",
       "\tAL669831.5 & 0.6744908 & -0.6744908 & 0.6744908 & -0.6744908\\\\\n",
       "\tFAM87B & 0.0000000 &  0.0000000 & 0.0000000 &  0.0000000\\\\\n",
       "\tLINC00115 & 1.3489815 &  0.0000000 & 0.0000000 & -1.3489815\\\\\n",
       "\tFAM41C & 0.6744908 & -0.6744908 & 0.6744908 & -0.6744908\\\\\n",
       "\tAL645608.3 & 0.0000000 &  0.0000000 & 0.0000000 &  0.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | Tumor | Lymphocytes | Myeloid | Oligodend |\n",
       "|---|---|---|---|---|\n",
       "| AL627309.1 | 0.0000000 |  0.0000000 | 0.0000000 |  0.0000000 |\n",
       "| AL669831.5 | 0.6744908 | -0.6744908 | 0.6744908 | -0.6744908 |\n",
       "| FAM87B | 0.0000000 |  0.0000000 | 0.0000000 |  0.0000000 |\n",
       "| LINC00115 | 1.3489815 |  0.0000000 | 0.0000000 | -1.3489815 |\n",
       "| FAM41C | 0.6744908 | -0.6744908 | 0.6744908 | -0.6744908 |\n",
       "| AL645608.3 | 0.0000000 |  0.0000000 | 0.0000000 |  0.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "           Tumor     Lymphocytes Myeloid   Oligodend \n",
       "AL627309.1 0.0000000  0.0000000  0.0000000  0.0000000\n",
       "AL669831.5 0.6744908 -0.6744908  0.6744908 -0.6744908\n",
       "FAM87B     0.0000000  0.0000000  0.0000000  0.0000000\n",
       "LINC00115  1.3489815  0.0000000  0.0000000 -1.3489815\n",
       "FAM41C     0.6744908 -0.6744908  0.6744908 -0.6744908\n",
       "AL645608.3 0.0000000  0.0000000  0.0000000  0.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gillen_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a list of tables with score differences \n",
    "diff_gen_list_gillen <- mclapply(rownames(gillen_mcentr), function(x){\n",
    "    mygen <- x\n",
    "    gen_df <- gillen_mcentr[mygen,]\n",
    "    #get ratio differences in counts across cell types\n",
    "    gen_tab_ratio <- outer(gen_df, gen_df, \"-\")\n",
    "    \n",
    "    return(gen_tab_ratio[c(\"Tumor\", \"Oligodend\"), c(\"Myeloid\", \"Lymphocytes\"), drop = F])    \n",
    "})\n",
    "\n",
    "names(diff_gen_list_gillen) <- rownames(gillen_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#genes with z score diff > 2 in nonimmune vs immune\n",
    "diff_gen_list_gillen_nonimmune <- lapply(diff_gen_list_gillen, function(x) x[ rowSums(x >= 2) >= 1,, drop = F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rmeove empty lists\n",
    "diff_gen_list_gillen_nonimmune <- diff_gen_list_gillen_nonimmune[sapply(diff_gen_list_gillen_nonimmune, \n",
    "                                                                  function(x) length(unlist(x)) >= 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$NOC2L</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Myeloid</th><th scope=col>Lymphocytes</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>2.023472</td><td>2.023472</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$AL645608.8</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Myeloid</th><th scope=col>Lymphocytes</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>2.023472</td><td>3.372454</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$HES4</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Myeloid</th><th scope=col>Lymphocytes</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>2.151079</td><td>1.203146</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$ISG15</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Myeloid</th><th scope=col>Lymphocytes</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>17.15134</td><td>17.15134</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$AGRN</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Myeloid</th><th scope=col>Lymphocytes</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>6.744908</td><td> 0.000000</td></tr>\n",
       "\t<tr><th scope=row>Oligodend</th><td>5.395926</td><td>-1.348982</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$TTLL10</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Myeloid</th><th scope=col>Lymphocytes</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Tumor</th><td>5.395926</td><td>4.046945</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$NOC2L] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Myeloid & Lymphocytes\\\\\n",
       "\\hline\n",
       "\tTumor & 2.023472 & 2.023472\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$AL645608.8] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Myeloid & Lymphocytes\\\\\n",
       "\\hline\n",
       "\tTumor & 2.023472 & 3.372454\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$HES4] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Myeloid & Lymphocytes\\\\\n",
       "\\hline\n",
       "\tTumor & 2.151079 & 1.203146\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$ISG15] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Myeloid & Lymphocytes\\\\\n",
       "\\hline\n",
       "\tTumor & 17.15134 & 17.15134\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$AGRN] A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Myeloid & Lymphocytes\\\\\n",
       "\\hline\n",
       "\tTumor & 6.744908 &  0.000000\\\\\n",
       "\tOligodend & 5.395926 & -1.348982\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$TTLL10] A matrix: 1 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Myeloid & Lymphocytes\\\\\n",
       "\\hline\n",
       "\tTumor & 5.395926 & 4.046945\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$NOC2L\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Myeloid | Lymphocytes |\n",
       "|---|---|---|\n",
       "| Tumor | 2.023472 | 2.023472 |\n",
       "\n",
       "\n",
       "$AL645608.8\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Myeloid | Lymphocytes |\n",
       "|---|---|---|\n",
       "| Tumor | 2.023472 | 3.372454 |\n",
       "\n",
       "\n",
       "$HES4\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Myeloid | Lymphocytes |\n",
       "|---|---|---|\n",
       "| Tumor | 2.151079 | 1.203146 |\n",
       "\n",
       "\n",
       "$ISG15\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Myeloid | Lymphocytes |\n",
       "|---|---|---|\n",
       "| Tumor | 17.15134 | 17.15134 |\n",
       "\n",
       "\n",
       "$AGRN\n",
       ":   \n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Myeloid | Lymphocytes |\n",
       "|---|---|---|\n",
       "| Tumor | 6.744908 |  0.000000 |\n",
       "| Oligodend | 5.395926 | -1.348982 |\n",
       "\n",
       "\n",
       "$TTLL10\n",
       ":   \n",
       "A matrix: 1 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | Myeloid | Lymphocytes |\n",
       "|---|---|---|\n",
       "| Tumor | 5.395926 | 4.046945 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$NOC2L\n",
       "       Myeloid Lymphocytes\n",
       "Tumor 2.023472    2.023472\n",
       "\n",
       "$AL645608.8\n",
       "       Myeloid Lymphocytes\n",
       "Tumor 2.023472    3.372454\n",
       "\n",
       "$HES4\n",
       "       Myeloid Lymphocytes\n",
       "Tumor 2.151079    1.203146\n",
       "\n",
       "$ISG15\n",
       "       Myeloid Lymphocytes\n",
       "Tumor 17.15134    17.15134\n",
       "\n",
       "$AGRN\n",
       "           Myeloid Lymphocytes\n",
       "Tumor     6.744908    0.000000\n",
       "Oligodend 5.395926   -1.348982\n",
       "\n",
       "$TTLL10\n",
       "       Myeloid Lymphocytes\n",
       "Tumor 5.395926    4.046945\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(diff_gen_list_gillen_nonimmune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select genes with score (of median expression across all celltypes) > 0 \n",
    "gillen_nonimmune_genes_expressed <- diff_gen_list_gillen_nonimmune[names(diff_gen_list_gillen_nonimmune) %in%\n",
    "                                                                   gillen_expressed_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "7961"
      ],
      "text/latex": [
       "7961"
      ],
      "text/markdown": [
       "7961"
      ],
      "text/plain": [
       "[1] 7961"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "5040"
      ],
      "text/latex": [
       "5040"
      ],
      "text/markdown": [
       "5040"
      ],
      "text/plain": [
       "[1] 5040"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_gillen_nonimmune)\n",
    "length(gillen_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "gillen_nonimmune_genes_expressed <- names(gillen_nonimmune_genes_expressed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5040 genes are expressed > 75th percentile in at least one cell type AND their expression is >2 mad differnet in at least one nonimmune and at least one immune"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- Cell lines from CBTN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_RNA_manifest_genes <- read.csv(paste0(manifestpath, \"CBTTC_RSEM-manifest.csv\"), header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_celllines <- read.csv(paste0(manifestpath,\"cell_line_RNAalqids_CBTTC.csv\"), \n",
    "                             header = TRUE, stringsAsFactors = F)\n",
    "\n",
    "CBTN_celllines$X <- NULL"
   ]
  },
  {
   "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>33</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 33\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 33\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 33  4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTN_celllines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>case_id</th><th scope=col>patient_id</th><th scope=col>clinical_event</th><th scope=col>cell_lines_rnaalq_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>C216603</td><td>PT_4347ZBEX</td><td>7316-913 </td><td>739931</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>C22878 </td><td>PT_TGQY3X3W</td><td>7316-24  </td><td>739935</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>C28290 </td><td>PT_3AWKWXEV</td><td>7316-85  </td><td>740005</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>C337143</td><td>PT_C2D4JXS1</td><td>7316-1746</td><td>739942</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>C377856</td><td>PT_Z4PJA6KT</td><td>7316-1763</td><td>739939</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>C56826 </td><td>PT_Y5KY6KN9</td><td>7316-195 </td><td>739937</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & case\\_id & patient\\_id & clinical\\_event & cell\\_lines\\_rnaalq\\_id\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & C216603 & PT\\_4347ZBEX & 7316-913  & 739931\\\\\n",
       "\t2 & C22878  & PT\\_TGQY3X3W & 7316-24   & 739935\\\\\n",
       "\t3 & C28290  & PT\\_3AWKWXEV & 7316-85   & 740005\\\\\n",
       "\t4 & C337143 & PT\\_C2D4JXS1 & 7316-1746 & 739942\\\\\n",
       "\t5 & C377856 & PT\\_Z4PJA6KT & 7316-1763 & 739939\\\\\n",
       "\t6 & C56826  & PT\\_Y5KY6KN9 & 7316-195  & 739937\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | case_id &lt;chr&gt; | patient_id &lt;chr&gt; | clinical_event &lt;chr&gt; | cell_lines_rnaalq_id &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | C216603 | PT_4347ZBEX | 7316-913  | 739931 |\n",
       "| 2 | C22878  | PT_TGQY3X3W | 7316-24   | 739935 |\n",
       "| 3 | C28290  | PT_3AWKWXEV | 7316-85   | 740005 |\n",
       "| 4 | C337143 | PT_C2D4JXS1 | 7316-1746 | 739942 |\n",
       "| 5 | C377856 | PT_Z4PJA6KT | 7316-1763 | 739939 |\n",
       "| 6 | C56826  | PT_Y5KY6KN9 | 7316-195  | 739937 |\n",
       "\n"
      ],
      "text/plain": [
       "  case_id patient_id  clinical_event cell_lines_rnaalq_id\n",
       "1 C216603 PT_4347ZBEX 7316-913       739931              \n",
       "2 C22878  PT_TGQY3X3W 7316-24        739935              \n",
       "3 C28290  PT_3AWKWXEV 7316-85        740005              \n",
       "4 C337143 PT_C2D4JXS1 7316-1746      739942              \n",
       "5 C377856 PT_Z4PJA6KT 7316-1763      739939              \n",
       "6 C56826  PT_Y5KY6KN9 7316-195       739937              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(CBTN_celllines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "celllines_manifest <- CBTN_RNA_manifest_genes[CBTN_RNA_manifest_genes$aliquot_id %in% CBTN_celllines$cell_lines_rnaalq_id,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>22</li><li>20</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 22\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 22\n",
       "2. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 22 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(celllines_manifest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "                                      Ependymoma \n",
       "                                               1 \n",
       "High-grade glioma/astrocytoma (WHO grade III/IV) \n",
       "                                              18 \n",
       "                                 Medulloblastoma \n",
       "                                               3 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(celllines_manifest$disease_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "find cell line files using KF_CBTTC_tpm_rsem.txt file from cavatica (before colname cleanup)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_tpm_all <- read.table(paste0(datapath,\"exp_mat/KF_CBTTC_tpm_rsem_matrix.txt\"), sep = \"\\t\", check.names = F,\n",
    "                             header = TRUE, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some cleanup\n",
    "colnames(CBTN_tpm_all) <- gsub(\".*_RSEM/\", \"\", colnames(CBTN_tpm_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col></th><th scope=col>00245bb9-b207-4648-b26e-bf16ec38103a.rsem.genes.results</th><th scope=col>0071cbef-6d2d-4328-b95b-f1076b66851a.rsem.genes.results</th><th scope=col>0080e250-aef6-4501-8f0e-42b483252440.rsem.genes.results</th><th scope=col>00a72866-565d-4e01-a666-e13ea722e7e3.rsem.genes.results</th><th scope=col>00d202e7-8c8b-4696-9d4b-8221e6251421.rsem.genes.results</th><th scope=col>00d8fca5-287f-40bb-a2bc-56bfd41fdccc.rsem.genes.results</th><th scope=col>010d6dff-c341-4d11-aa68-433e06bda14c.rsem.genes.results</th><th scope=col>01a28736-f93b-4e46-be14-dc604b48bf23.rsem.genes.results</th><th scope=col>01a81e81-dc1b-4387-997b-c9f956d9ac8f.rsem.genes.results</th></tr>\n",
       "\t<tr><th></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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ENSG00000000003.14_TSPAN6  </td><td>10.99</td><td> 8.66</td><td>6.15</td><td> 2.40</td><td>0.73</td><td> 1.50</td><td> 1.88</td><td>29.41</td><td>4.62</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ENSG00000000005.5_TNMD     </td><td> 0.18</td><td> 0.02</td><td>0.03</td><td> 0.02</td><td>0.02</td><td> 0.01</td><td> 0.03</td><td> 0.33</td><td>0.03</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ENSG00000000419.12_DPM1    </td><td> 4.95</td><td> 6.15</td><td>5.09</td><td> 3.35</td><td>1.09</td><td> 3.68</td><td> 3.27</td><td> 4.89</td><td>4.75</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ENSG00000000457.13_SCYL3   </td><td> 1.50</td><td> 1.75</td><td>1.71</td><td> 0.90</td><td>0.44</td><td> 1.44</td><td> 0.89</td><td> 1.52</td><td>1.61</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ENSG00000000460.16_C1orf112</td><td> 1.70</td><td> 1.12</td><td>2.78</td><td> 0.34</td><td>0.43</td><td> 0.71</td><td> 0.57</td><td> 1.00</td><td>1.64</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ENSG00000000938.12_FGR     </td><td> 1.27</td><td> 0.90</td><td>0.18</td><td> 1.12</td><td>0.36</td><td> 1.32</td><td> 1.05</td><td> 0.93</td><td>1.01</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>ENSG00000000971.15_CFH     </td><td> 2.04</td><td> 5.77</td><td>2.02</td><td> 7.50</td><td>5.57</td><td> 1.02</td><td>13.23</td><td> 2.75</td><td>2.32</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>ENSG00000001036.13_FUCA2   </td><td> 7.86</td><td> 7.15</td><td>3.33</td><td> 4.47</td><td>1.40</td><td> 1.88</td><td> 8.94</td><td>10.26</td><td>5.99</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>ENSG00000001084.10_GCLC    </td><td>14.26</td><td>13.19</td><td>7.39</td><td>11.16</td><td>1.20</td><td>15.90</td><td> 3.18</td><td>16.94</td><td>8.09</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>ENSG00000001167.14_NFYA    </td><td> 3.12</td><td> 4.19</td><td>9.64</td><td> 2.07</td><td>1.14</td><td> 5.44</td><td> 2.35</td><td> 6.90</td><td>4.68</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  &  & 00245bb9-b207-4648-b26e-bf16ec38103a.rsem.genes.results & 0071cbef-6d2d-4328-b95b-f1076b66851a.rsem.genes.results & 0080e250-aef6-4501-8f0e-42b483252440.rsem.genes.results & 00a72866-565d-4e01-a666-e13ea722e7e3.rsem.genes.results & 00d202e7-8c8b-4696-9d4b-8221e6251421.rsem.genes.results & 00d8fca5-287f-40bb-a2bc-56bfd41fdccc.rsem.genes.results & 010d6dff-c341-4d11-aa68-433e06bda14c.rsem.genes.results & 01a28736-f93b-4e46-be14-dc604b48bf23.rsem.genes.results & 01a81e81-dc1b-4387-997b-c9f956d9ac8f.rsem.genes.results\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & ENSG00000000003.14\\_TSPAN6   & 10.99 &  8.66 & 6.15 &  2.40 & 0.73 &  1.50 &  1.88 & 29.41 & 4.62\\\\\n",
       "\t2 & ENSG00000000005.5\\_TNMD      &  0.18 &  0.02 & 0.03 &  0.02 & 0.02 &  0.01 &  0.03 &  0.33 & 0.03\\\\\n",
       "\t3 & ENSG00000000419.12\\_DPM1     &  4.95 &  6.15 & 5.09 &  3.35 & 1.09 &  3.68 &  3.27 &  4.89 & 4.75\\\\\n",
       "\t4 & ENSG00000000457.13\\_SCYL3    &  1.50 &  1.75 & 1.71 &  0.90 & 0.44 &  1.44 &  0.89 &  1.52 & 1.61\\\\\n",
       "\t5 & ENSG00000000460.16\\_C1orf112 &  1.70 &  1.12 & 2.78 &  0.34 & 0.43 &  0.71 &  0.57 &  1.00 & 1.64\\\\\n",
       "\t6 & ENSG00000000938.12\\_FGR      &  1.27 &  0.90 & 0.18 &  1.12 & 0.36 &  1.32 &  1.05 &  0.93 & 1.01\\\\\n",
       "\t7 & ENSG00000000971.15\\_CFH      &  2.04 &  5.77 & 2.02 &  7.50 & 5.57 &  1.02 & 13.23 &  2.75 & 2.32\\\\\n",
       "\t8 & ENSG00000001036.13\\_FUCA2    &  7.86 &  7.15 & 3.33 &  4.47 & 1.40 &  1.88 &  8.94 & 10.26 & 5.99\\\\\n",
       "\t9 & ENSG00000001084.10\\_GCLC     & 14.26 & 13.19 & 7.39 & 11.16 & 1.20 & 15.90 &  3.18 & 16.94 & 8.09\\\\\n",
       "\t10 & ENSG00000001167.14\\_NFYA     &  3.12 &  4.19 & 9.64 &  2.07 & 1.14 &  5.44 &  2.35 &  6.90 & 4.68\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | <!----> &lt;chr&gt; | 00245bb9-b207-4648-b26e-bf16ec38103a.rsem.genes.results &lt;dbl&gt; | 0071cbef-6d2d-4328-b95b-f1076b66851a.rsem.genes.results &lt;dbl&gt; | 0080e250-aef6-4501-8f0e-42b483252440.rsem.genes.results &lt;dbl&gt; | 00a72866-565d-4e01-a666-e13ea722e7e3.rsem.genes.results &lt;dbl&gt; | 00d202e7-8c8b-4696-9d4b-8221e6251421.rsem.genes.results &lt;dbl&gt; | 00d8fca5-287f-40bb-a2bc-56bfd41fdccc.rsem.genes.results &lt;dbl&gt; | 010d6dff-c341-4d11-aa68-433e06bda14c.rsem.genes.results &lt;dbl&gt; | 01a28736-f93b-4e46-be14-dc604b48bf23.rsem.genes.results &lt;dbl&gt; | 01a81e81-dc1b-4387-997b-c9f956d9ac8f.rsem.genes.results &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | ENSG00000000003.14_TSPAN6   | 10.99 |  8.66 | 6.15 |  2.40 | 0.73 |  1.50 |  1.88 | 29.41 | 4.62 |\n",
       "| 2 | ENSG00000000005.5_TNMD      |  0.18 |  0.02 | 0.03 |  0.02 | 0.02 |  0.01 |  0.03 |  0.33 | 0.03 |\n",
       "| 3 | ENSG00000000419.12_DPM1     |  4.95 |  6.15 | 5.09 |  3.35 | 1.09 |  3.68 |  3.27 |  4.89 | 4.75 |\n",
       "| 4 | ENSG00000000457.13_SCYL3    |  1.50 |  1.75 | 1.71 |  0.90 | 0.44 |  1.44 |  0.89 |  1.52 | 1.61 |\n",
       "| 5 | ENSG00000000460.16_C1orf112 |  1.70 |  1.12 | 2.78 |  0.34 | 0.43 |  0.71 |  0.57 |  1.00 | 1.64 |\n",
       "| 6 | ENSG00000000938.12_FGR      |  1.27 |  0.90 | 0.18 |  1.12 | 0.36 |  1.32 |  1.05 |  0.93 | 1.01 |\n",
       "| 7 | ENSG00000000971.15_CFH      |  2.04 |  5.77 | 2.02 |  7.50 | 5.57 |  1.02 | 13.23 |  2.75 | 2.32 |\n",
       "| 8 | ENSG00000001036.13_FUCA2    |  7.86 |  7.15 | 3.33 |  4.47 | 1.40 |  1.88 |  8.94 | 10.26 | 5.99 |\n",
       "| 9 | ENSG00000001084.10_GCLC     | 14.26 | 13.19 | 7.39 | 11.16 | 1.20 | 15.90 |  3.18 | 16.94 | 8.09 |\n",
       "| 10 | ENSG00000001167.14_NFYA     |  3.12 |  4.19 | 9.64 |  2.07 | 1.14 |  5.44 |  2.35 |  6.90 | 4.68 |\n",
       "\n"
      ],
      "text/plain": [
       "                              \n",
       "1  ENSG00000000003.14_TSPAN6  \n",
       "2  ENSG00000000005.5_TNMD     \n",
       "3  ENSG00000000419.12_DPM1    \n",
       "4  ENSG00000000457.13_SCYL3   \n",
       "5  ENSG00000000460.16_C1orf112\n",
       "6  ENSG00000000938.12_FGR     \n",
       "7  ENSG00000000971.15_CFH     \n",
       "8  ENSG00000001036.13_FUCA2   \n",
       "9  ENSG00000001084.10_GCLC    \n",
       "10 ENSG00000001167.14_NFYA    \n",
       "   00245bb9-b207-4648-b26e-bf16ec38103a.rsem.genes.results\n",
       "1  10.99                                                  \n",
       "2   0.18                                                  \n",
       "3   4.95                                                  \n",
       "4   1.50                                                  \n",
       "5   1.70                                                  \n",
       "6   1.27                                                  \n",
       "7   2.04                                                  \n",
       "8   7.86                                                  \n",
       "9  14.26                                                  \n",
       "10  3.12                                                  \n",
       "   0071cbef-6d2d-4328-b95b-f1076b66851a.rsem.genes.results\n",
       "1   8.66                                                  \n",
       "2   0.02                                                  \n",
       "3   6.15                                                  \n",
       "4   1.75                                                  \n",
       "5   1.12                                                  \n",
       "6   0.90                                                  \n",
       "7   5.77                                                  \n",
       "8   7.15                                                  \n",
       "9  13.19                                                  \n",
       "10  4.19                                                  \n",
       "   0080e250-aef6-4501-8f0e-42b483252440.rsem.genes.results\n",
       "1  6.15                                                   \n",
       "2  0.03                                                   \n",
       "3  5.09                                                   \n",
       "4  1.71                                                   \n",
       "5  2.78                                                   \n",
       "6  0.18                                                   \n",
       "7  2.02                                                   \n",
       "8  3.33                                                   \n",
       "9  7.39                                                   \n",
       "10 9.64                                                   \n",
       "   00a72866-565d-4e01-a666-e13ea722e7e3.rsem.genes.results\n",
       "1   2.40                                                  \n",
       "2   0.02                                                  \n",
       "3   3.35                                                  \n",
       "4   0.90                                                  \n",
       "5   0.34                                                  \n",
       "6   1.12                                                  \n",
       "7   7.50                                                  \n",
       "8   4.47                                                  \n",
       "9  11.16                                                  \n",
       "10  2.07                                                  \n",
       "   00d202e7-8c8b-4696-9d4b-8221e6251421.rsem.genes.results\n",
       "1  0.73                                                   \n",
       "2  0.02                                                   \n",
       "3  1.09                                                   \n",
       "4  0.44                                                   \n",
       "5  0.43                                                   \n",
       "6  0.36                                                   \n",
       "7  5.57                                                   \n",
       "8  1.40                                                   \n",
       "9  1.20                                                   \n",
       "10 1.14                                                   \n",
       "   00d8fca5-287f-40bb-a2bc-56bfd41fdccc.rsem.genes.results\n",
       "1   1.50                                                  \n",
       "2   0.01                                                  \n",
       "3   3.68                                                  \n",
       "4   1.44                                                  \n",
       "5   0.71                                                  \n",
       "6   1.32                                                  \n",
       "7   1.02                                                  \n",
       "8   1.88                                                  \n",
       "9  15.90                                                  \n",
       "10  5.44                                                  \n",
       "   010d6dff-c341-4d11-aa68-433e06bda14c.rsem.genes.results\n",
       "1   1.88                                                  \n",
       "2   0.03                                                  \n",
       "3   3.27                                                  \n",
       "4   0.89                                                  \n",
       "5   0.57                                                  \n",
       "6   1.05                                                  \n",
       "7  13.23                                                  \n",
       "8   8.94                                                  \n",
       "9   3.18                                                  \n",
       "10  2.35                                                  \n",
       "   01a28736-f93b-4e46-be14-dc604b48bf23.rsem.genes.results\n",
       "1  29.41                                                  \n",
       "2   0.33                                                  \n",
       "3   4.89                                                  \n",
       "4   1.52                                                  \n",
       "5   1.00                                                  \n",
       "6   0.93                                                  \n",
       "7   2.75                                                  \n",
       "8  10.26                                                  \n",
       "9  16.94                                                  \n",
       "10  6.90                                                  \n",
       "   01a81e81-dc1b-4387-997b-c9f956d9ac8f.rsem.genes.results\n",
       "1  4.62                                                   \n",
       "2  0.03                                                   \n",
       "3  4.75                                                   \n",
       "4  1.61                                                   \n",
       "5  1.64                                                   \n",
       "6  1.01                                                   \n",
       "7  2.32                                                   \n",
       "8  5.99                                                   \n",
       "9  8.09                                                   \n",
       "10 4.68                                                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CBTN_tpm_all[1:10,1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_tpm_lines <- CBTN_tpm_all[, colnames(CBTN_tpm_all) %in% celllines_manifest$name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>58347</li><li>22</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 58347\n",
       "\\item 22\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 58347\n",
       "2. 22\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 58347    22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTN_tpm_lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleanup rownames\n",
    "\n",
    "CBTN_tpm_lines$Gene <- gsub(\".*_\", \"\", CBTN_tpm_all[,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>58347</li><li>23</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 58347\n",
       "\\item 23\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 58347\n",
       "2. 23\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 58347    23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTN_tpm_lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_tpm_lines_dedup <- CBTN_tpm_lines[ !duplicated(CBTN_tpm_lines$Gene),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>56505</li><li>23</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 56505\n",
       "\\item 23\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 56505\n",
       "2. 23\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 56505    23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTN_tpm_lines_dedup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(CBTN_tpm_lines_dedup) <- CBTN_tpm_lines_dedup$Gene\n",
    "CBTN_tpm_lines_dedup$Gene <- NULL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset to protein coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "hg38 <- useMart(biomart=\"ENSEMBL_MART_ENSEMBL\", host=\"www.ensembl.org\", \n",
    "                path=\"/biomart/martservice\", dataset=\"hsapiens_gene_ensembl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgncs <- rownames(CBTN_tpm_lines_dedup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgnc_type <- getBM(filters=\"hgnc_symbol\",\n",
    "                   attributes=c(\"hgnc_symbol\",\"ensembl_gene_id\", \"entrezgene_id\", \"gene_biotype\"), \n",
    "                   values= hgncs,mart=hg38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "                         IG_C_gene                    IG_C_pseudogene \n",
       "                                23                                 11 \n",
       "                         IG_D_gene                          IG_J_gene \n",
       "                                64                                 24 \n",
       "                   IG_J_pseudogene                          IG_V_gene \n",
       "                                 6                                207 \n",
       "                   IG_V_pseudogene                             lncRNA \n",
       "                               274                               3842 \n",
       "                             miRNA                           misc_RNA \n",
       "                              1887                               1059 \n",
       "                           Mt_rRNA                            Mt_tRNA \n",
       "                                 2                                 22 \n",
       "            polymorphic_pseudogene               processed_pseudogene \n",
       "                                71                               5138 \n",
       "                    protein_coding                           ribozyme \n",
       "                             21371                                  5 \n",
       "                              rRNA                    rRNA_pseudogene \n",
       "                                35                                505 \n",
       "                            scaRNA                              scRNA \n",
       "                                22                                  1 \n",
       "                            snoRNA                              snRNA \n",
       "                               474                               1860 \n",
       "                               TEC                          TR_C_gene \n",
       "                                16                                  6 \n",
       "                         TR_D_gene                          TR_J_gene \n",
       "                                 3                                 81 \n",
       "                   TR_J_pseudogene                          TR_V_gene \n",
       "                                 4                                130 \n",
       "                   TR_V_pseudogene   transcribed_processed_pseudogene \n",
       "                                45                                305 \n",
       "    transcribed_unitary_pseudogene transcribed_unprocessed_pseudogene \n",
       "                                92                                725 \n",
       " translated_unprocessed_pseudogene                 unitary_pseudogene \n",
       "                                 1                                 64 \n",
       "            unprocessed_pseudogene                          vault_RNA \n",
       "                              1753                                  1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(hgnc_type$gene_biotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteincoding <- hgnc_type$hgnc_symbol[hgnc_type$gene_biotype == \"protein_coding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_tpm_lines_dedup_pc <- CBTN_tpm_lines_dedup[proteincoding,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>56505</li><li>22</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 56505\n",
       "\\item 22\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 56505\n",
       "2. 22\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 56505    22"
      ]
     },
     "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>21371</li><li>22</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 21371\n",
       "\\item 22\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 21371\n",
       "2. 22\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 21371    22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTN_tpm_lines_dedup)\n",
    "dim(CBTN_tpm_lines_dedup_pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBTN_tpm_lines_dedup_m <- (CBTN_tpm_lines_dedup_pc - median(unlist(CBTN_tpm_lines_dedup_pc)))/\n",
    "mad(unlist(CBTN_tpm_lines_dedup_pc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "   0.00    0.03    1.98   11.83    8.04 8749.22 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(CBTN_tpm_lines_dedup_pc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rHLzly5cOb0Sa8999Dvrxh4VKt9Djsv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N\nXQPTjr1c53odvRcTHTzHEl/bUvBi3cQt74VW0kKgM+bA7uHEhMePw7P+edYQ5xYRQtV63GRw\njUWRneqhqqrK69814RY/jY662XtOcjLR8ckLGMJzxWg1HPyIedzfvzmzdfVh/lTkJiQQJU/j\nm0Ha2rzG+5nIDWZSIgKYMSVGqEd75Orl82sKGrHxA9JDtQnJ+nif0eSgN6FbXNuo1npvaVbM\n46AD69yH2rZsbuHksedOHedpCgo4Y0sUU6yyBgacfjLXIJ/YUDJ1PXh73wA8wVjUvl23xTqb\n12Ky346BxKxL3D53rzdiXv1HhqYm32ETRU1Nwksg9xfChawsf1+zjZ262Tu3OBOUwkKuE8Lq\nwfyp4JYhdGQD78EgJnKDmZSIAGZMiRHq0R5VVIg9tYUFBZRjxyDsAHFSWtpAG2Eko+uwKvhT\nUsytw6sn9++gw2NBR2Xe17uHlgxq33XO9Z+CD1pxj7lTjb+3s7QkWpfsd+9+CPwihFJ9+xDd\ncu059+oQQ3VYBtOX/kV0h3Pv3n0pdJRCoe9+eGsfvBKviNk9c0ukmI6QpUZBgf9hBlxrbHDP\npwBNiovrLrJqrm0StflTUVLCGQykI3F5r8tiIjeYSYlA0GsRpNOUuNoJWTk5yuIAYQeIk2od\n5YaHjFqbQbO9T4V+TM9OjQw9u2e1+/DuJs2q99AKPx5xG39AYK8RqmpqhPEVFZFPH6p26UKs\n5UYfXr0SvLYuCXv6hvjD2ppsb5YgKHTq1IH4I/17/Z2HzhuW8Vz/jXZ43V3+fuvMnZ8qKJ8Q\nC/n5fFf6lOTnE62RSJ3bSCLq6pxNmVYbeZ1JQU5ewJDaEYrQ/On+DhrfDEkYJnKDmZQIBL0W\nQTpNiUurcw3e8QSEHSBO0tIaiWNZZT3r/uMWex+9Eh6Xlfn5/skNE7mOFSp4svOgoPuuWBoa\nRGVDWZlZOTkROz+Lbly4Juj+ibL71+8SutGke3cdAZ/niSbXAhbR7umgB8ti8bF1nfBefOnb\nDe57vzW4XQE0XPFwrYlWatWK73QTwI0G1/xceno6gzEzb/5UcP8OfovkycMwkRvMpEQg6LUI\n0mlKXMIOP1ONDBB2APPQXiJTGRv7VbRJEQFy6haOU/4JfBFxahSxHyopLOyHgNHo6BASKzM9\nnXwyh9XDdRThWbTw+skgwRzx5v138j/CKYn5X3/ZCpZKElJTOLvtZbS1G0C1KdtuxbFVHfET\nLYuf/zP7YLxYE1SbH9+/85mzK4uKIrxLWFpaijpBEkaTdu0I32XpUVEicljNkPlToNC+vTl+\n/fv9+2Q+wVOjonitpmMiN5hJCWK+RZBKUyrMyCAGAAwNDamCgrADRAC3/x7K4ZzI5+ENbir2\ngUdz9no0edcgivUesvoTPacQVeevX7/Ig/KkTceOeC6VJyZSeCWS7T13dkf8j9KbXv88ob/v\nE/t2aOd/eAHI2E6bbCVgKnmTff9+BPGHTZcuchRh6w15m9XHllriNVrhg1WzA/i1RPVLdmjo\nW8oA5fdvhuJfnGbXrmaiT5JkYdurFzGO8eYJXzP5sM4SX3pqugb/nuvL/KmwtrMjJtpe3rpF\n7Xc76+ZN3sOFTOQGMylhvkWQSlP6yelNK5mYkB4jghACYQeIAhl1dWIH1c+fpMftlD0LvPCd\n7KbYMDExYV+Vv3//iTKomhqxCYx7NQo9FGxtCUdL8fGUo0sdFq4fTQyKJR6dv+ElPTlc+e3Q\n3C2v8Y0EepO3LmwrYCJ5UvJy27bb+Kpq2S5/jTamDF5/KHT14j5CNjRUzLs6avL9v+AIirV/\nuddOXMYHVlQHD+1dvzvBJQBZe2fiQPWC64GXKX1clD7zD4xhX+sNHIj3eOrL/KmQ7esyFI+6\n/MHxf39QhE0MPEFyUi0TucFMSkTQIkijKSUkEEcVtWnblvongbADRIC5OTGCnxYaGsU70E//\ndYcTed8SJ0Z9+xqxL+P+Pf6YYpvX7/Bw/BBCNRsbI+5bXL4Bsn//5r3ay6JTJzxQRkQEpSNx\nDded24nzMSs+bh82+shnvvvP8p+tGrH4Hu7uSMvFZ7MT9YJbWhREHRrr4hOD/6QWU7zmNBRd\nhxBSstt8dC7D5ztRQqegOXw9sPokWUEXhq1feREfEmk1acYg/vv+gBqoui6egTs4K7qyYfMr\n0iG3sneb5x1mL8JiWXnMt8e3RDBi/sLSdPi8KfjvKH+5Y90VsqGyzOCV3s/I0shAbjCUEhG0\nCNJnSokfPuDaXLtXrzaUYUHYAaLA2N6ecCD52W/NqZ+1WrzfD5Y5L3tYz0f10KPTpEm4h5Gk\nI7MWXE/l2VyXxJ12X32dPW6lO2a8ozz3XUOOA828kD17XqYWlNeKhdVnsBPupCPi7VvqNSOt\nZ/4bMMEA76Vl3vLoZb/w9CfSOY2CjwFTuw7cEc2uaWXM5pw9OZHysEgqsLLC7LT4yNDAHXMd\n2v7P42o6+7fIm88N2DWkQZxfS9DUYduh6eTnjDINnYLmovD24uGreZxcWRR9cKzrPnx1kXKf\nNSscGsT0dmNDrufyTcPY+3qwr7vHTD2XyGMQqfiT/1jnze/Zd/Rn7Pq7PecmE+YvNLJ9PL2H\n4IOAKaenj9v7vrat573ZMmLGeYrFcwzkBkMpEUGLIG2mVBkZ+ZF9Kd+rdzd+Y5AC7YIGAJpE\nbbLmuAWQMxiy7syzr+n5xYW/k2JfXvWdY29Q5ZxC0dSUaISd/LOrRXFrBjHAZL8/leplvw7Z\nE+9yCSzjvvVxPdG1MeWc2l0zfutNMdx3si6P5+wdldH438SNJ2+9/Z6WXVhanJsa9yEs2GfO\nABPO6FezIQHJNZMUt7Nz7U5Tv0PVf2HB2VG4o1+jZW/45mnp12MjuB3iI3ld25Hzt54OffPp\ne8rvwpKi7JRvH8OvHV45tmsLrj6qgvnE03GlPGMMX1RnsYcULWdfTankm2iceJ9OnGeHBhbR\nfCzJpxvxFOmhZzX5fXV8zWN+Wi0Np51UgeBX0GG1s1jRwMFj37XXscm/C4vy0mKfnPYa3ZbL\nQ3VT+z0x5TXeQs8Wys65IhrB4rnytKbRMUKdjI4Ze8cwLPPqRH1Os6dmPWFz4IOopMw/JSX5\nv+Lf3jiyYqgp51wLOfP5ob9rRMCA+TNByplRXFvPlcyHrTh88+33tNzi4ry0z09OrXMxrapD\nZc3aWeCyUgS5wVRKhG8RmDElRhCDPb5YjCtjxaEn+BotCDtANJS8W2vNZwScpTfkWNi+XmTf\ntPiEHYbl3PawoNcHl28793YWjzRlB7vVOl6nprDD8s6OwmvVNus/8slRDMOwioxHmwe1pO/q\nXEar99oHmaTR1VHYKZsMXnXuYy6N9HKoT2GHYRkXxmhVS7LIhB2/guZqjSynrhyux6enrWS1\n4HZG7ZeAsOOGWthhWMmXwL9MaJivvPG40994NfzCmz8jFLzb6chvw7lm/30fL0zAfTiKJDcY\nSonQLQIzpsQI9W+PUV74+milUWf5V70wFQuIBgVbr+tXlvfSIbE+GR27hcHPQmaYoAboSRYh\nNacDz+9uHGxAeWQlS8Vi7J6n4QedeFV56q7Hw855julu1rypAqmVqY6cMYb98OeQkFj+6ZLR\nsV9z88Orf1e6tGvGp15TNh3ieS4y7vEmBy3qgPxhyato6Bmad+jSf9zirSduRv6IubnFrUPD\nmoKtjs7Y/buH8zkmnCFoFTRCCCH5NjODnpx170jmgUq1/dSj4U99nRjxNCjNKJhPPBf+YNf4\njuSfKKtZe7c9j16emWDKq5ckvPkzQhPbpdefnp3fVYv3d8VSt5l9+tm1hR2o5ZLQucFQSphs\nEaTOlKKDg9nNg9aYGS40ql4a6hQA6kp5xtugnQtH9epg2kpLRUFeqVlzY5t+bov3Xo3OoT+L\nJz7K0l+f27FsqnPP9kYttNWU5WXlldW0W5pY2Y+auco3JDq7Qug3VL5aiW/FN1sRIUCeVOZ8\nvn9696rpzj2tzFvraagoyCupNTcwa9+pt/P0NfuDnn7LEckcBCAgXMMM1lu/YhiGlaY8C1g3\nbXAXc71mSnKyCqpa+u17u87bduF9lvCfE8BNeca74D3LxjvYmLXSUpGXU2ii3tzEus+ImWsP\n3/hEp/4RvfnTouJXxMUd84d3a2ugraKg2Ey3dZtOAyatPnQ9WrAPRtjcYCYlQrQI0mtKFS8W\n4zvCOqz/QKesWBjFMZUAAIia9BNOJtPvFiKEdGbeTvR3ohwlABobTxfr995X5d/BeuvXSE+J\n8KgFAPWP1JpS/qVx+qPP5yGENMdejrswkoZrHZiKBQCxojtp65IOMggh9Ctw5ykmD0ICAAAA\nGjeJx3YH5yGEkLzd2i10VB0CYQcA4kbuf6uPVLnVLbnns+c1mY9PgBGCR7MYoPvOH+L+IcwC\n2SI8kIcNmcZaOiWPdux6Xo4Qkmn7t+98U5pPgbADAHGjbOd9dF5rFkIozm/DaRGdcQkAAAA0\nKlIDNh3/iRBC+rMP/NOJtrdEiXDdBwCNHBWHrUfdrzodTfxzY/OOVxN2dGXW3ylAMDoYVhXz\nALJFeCAPGzKNsnRKn27f9qAYIaQ3wde7nwAHB8GIHQA0BJoO9PGfpo8Qijvg6U96mCIAAAAg\nFSQfWXM4ESGkPfrA3pECeW8CYQcADYNmA3cfnd4KoaKHm7zuFIg7NQAAAIDYKLy7ccuTEoQ0\nRu7b71rLCTo1MBULAA0F9cHHk7Hj4k4FAAAAIGaaDDyaih2t27Pgxw4AAAAAAEBCgKlYAAAA\nAAAACQGEHQAAAAAAgIQAwg4AAAAAAEBCAGEHAAAAAAAgIYCwAwAAAAAAkBBA2AEAAAAAAEgI\nIOwAAAAAAAAkBBB2AAAAAAAAEsL/AZEO5Jogf0bEAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(CBTN_tpm_lines_dedup_m)), binwidth = 0.5) + myaxis + myplot  +\n",
    "coord_cartesian(xlim = c(-1,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines_expressed_genes <- rownames(CBTN_tpm_lines_dedup_m)[rowSums(CBTN_tpm_lines_dedup_m > quantile(unlist(CBTN_tpm_lines_dedup_m), 0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>21371</li><li>22</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 21371\n",
       "\\item 22\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 21371\n",
       "2. 22\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 21371    22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "11195"
      ],
      "text/latex": [
       "11195"
      ],
      "text/markdown": [
       "11195"
      ],
      "text/plain": [
       "[1] 11195"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(CBTN_tpm_lines_dedup_m)\n",
    "length(lines_expressed_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11195 genes are expressed more than 75th percentile in at least one cellline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bind all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonimmune_genes <- c(nbl_nonimmune_genes_expressed,gojo_nonimmune_genes_expressed,\n",
    "                     gillen_nonimmune_genes_expressed, pdx_expressed_genes, lines_expressed_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "45168"
      ],
      "text/latex": [
       "45168"
      ],
      "text/markdown": [
       "45168"
      ],
      "text/plain": [
       "[1] 45168"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(nonimmune_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_genes_nontumour <- immune_genes[!immune_genes %in% nonimmune_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3041"
      ],
      "text/latex": [
       "3041"
      ],
      "text/markdown": [
       "3041"
      ],
      "text/plain": [
       "[1] 3041"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(immune_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "791"
      ],
      "text/latex": [
       "791"
      ],
      "text/markdown": [
       "791"
      ],
      "text/plain": [
       "[1] 791"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(immune_genes_nontumour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(immune_genes_nontumour, file = paste0(datapath, \"TME_clusters/TME_immune_genes.RData\"))"
   ]
  }
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