notebooks/19_define_immuneclusters.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=\"#Assign-genes-to-specific-immune-celltypes\" data-toc-modified-id=\"Assign-genes-to-specific-immune-celltypes-5\">Assign genes to specific immune celltypes</a></span><ul class=\"toc-item\"><li><span><a href=\"#1--gene-regulation-atlas-of-28-immune-cell-types-(immunexut)\" data-toc-modified-id=\"1--gene-regulation-atlas-of-28-immune-cell-types-(immunexut)-5.1\">1- gene-regulation atlas of 28 immune cell types (immunexut)</a></span></li><li><span><a href=\"#2--blood-gene-expression-from-proteinatlas\" data-toc-modified-id=\"2--blood-gene-expression-from-proteinatlas-5.2\">2- blood gene expression from proteinatlas</a></span></li><li><span><a href=\"#3--MCPcounter\" data-toc-modified-id=\"3--MCPcounter-5.3\">3- MCPcounter</a></span></li><li><span><a href=\"#4--CIBERSORT\" data-toc-modified-id=\"4--CIBERSORT-5.4\">4- CIBERSORT</a></span></li><li><span><a href=\"#5--Quantiseq\" data-toc-modified-id=\"5--Quantiseq-5.5\">5- Quantiseq</a></span></li><li><span><a href=\"#6--EPIC\" data-toc-modified-id=\"6--EPIC-5.6\">6- EPIC</a></span></li><li><span><a href=\"#7--Xcell\" data-toc-modified-id=\"7--Xcell-5.7\">7- Xcell</a></span></li></ul></li><li><span><a href=\"#Compile-all-celltypes\" data-toc-modified-id=\"Compile-all-celltypes-6\">Compile all celltypes</a></span></li><li><span><a href=\"#Determine-clusters-with-CC\" data-toc-modified-id=\"Determine-clusters-with-CC-7\">Determine clusters with CC</a></span></li><li><span><a href=\"#Heatmaps\" data-toc-modified-id=\"Heatmaps-8\">Heatmaps</a></span><ul class=\"toc-item\"><li><span><a href=\"#Legends\" data-toc-modified-id=\"Legends-8.1\">Legends</a></span></li></ul></li><li><span><a href=\"#Ridge-plots-and-stats\" data-toc-modified-id=\"Ridge-plots-and-stats-9\">Ridge plots and stats</a></span></li><li><span><a href=\"#Heatmap-Cancer-type\" data-toc-modified-id=\"Heatmap-Cancer-type-10\">Heatmap Cancer type</a></span></li><li><span><a href=\"#Heatmap-CRI-cluster\" data-toc-modified-id=\"Heatmap-CRI-cluster-11\">Heatmap CRI cluster</a></span><ul class=\"toc-item\"><li><span><a href=\"#Legends\" data-toc-modified-id=\"Legends-11.1\">Legends</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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(ConsensusClusterPlus)\n",
    "library(GSVA)\n",
    "library(biomaRt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: grid\n",
      "\n",
      "========================================\n",
      "ComplexHeatmap version 2.6.2\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "  genomic data. Bioinformatics 2016.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
      "\n",
      "\n",
      "========================================\n",
      "circlize version 0.4.12\n",
      "CRAN page: https://cran.r-project.org/package=circlize\n",
      "Github page: https://github.com/jokergoo/circlize\n",
      "Documentation: https://jokergoo.github.io/circlize_book/book/\n",
      "\n",
      "If you use it in published research, please cite:\n",
      "Gu, Z. circlize implements and enhances circular visualization\n",
      "  in R. Bioinformatics 2014.\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(circlize))\n",
      "========================================\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:biomaRt’:\n",
      "\n",
      "    select\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:Biobase’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/Heatmap_functions.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/plotting_functions.R\")\n",
    "source(\"~/OneDrive - UHN/R_src/color_schemes.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 { (x - meds) / mads}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(paste0(datapath, \"exp_mat/IPD_ExprSet_log2_combat.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vars <- pData(IPD_Set_log2_combat)\n",
    "tpms <- exprs(IPD_Set_log2_combat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"TME_clusters/TME_immune_genes.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assign genes to specific immune celltypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- gene-regulation atlas of 28 immune cell types (immunexut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are average counts \n",
    "immunexut <- read.csv(paste0(datapath, \"TME_clusters/cell_specific_genes_in_immunediseases.csv\"), \n",
    "                      header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "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>7172</li><li>27</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 7172\n",
       "\\item 27\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 7172\n",
       "2. 27\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 7172   27"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(immunexut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     CL Mono       CM CD8         DN B       EM CD8  Fr. I nTreg Fr. II eTreg \n",
       "         437           12          145           61           87          395 \n",
       "   Fr. III T     Int Mono          LDG          mDC      Naïve B    Naïve CD4 \n",
       "           3          132          720          414          543          135 \n",
       "   Naïve CD8      NC Mono          Neu           NK          pDC  Plasmablast \n",
       "         223          430          654          486         1115          686 \n",
       "        SM B    TEMRA CD8          Tfh          Th1         Th17          Th2 \n",
       "          70          105           20            7           95           63 \n",
       "       USM B \n",
       "         134 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(immunexut$Cell.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp <- immunexut[,3:ncol(immunexut)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#median center\n",
    "immunexut_m <- (tmp - median(unlist(tmp)))/ mad(unlist(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(immunexut_m) <- immunexut$Gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 25</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Naïve.CD4</th><th scope=col>Th1</th><th scope=col>Th2</th><th scope=col>Th17</th><th scope=col>Tfh</th><th scope=col>Fr..I.nTreg</th><th scope=col>Fr..II.eTreg</th><th scope=col>Fr..III.T</th><th scope=col>Naïve.CD8</th><th scope=col>CM.CD8</th><th scope=col>⋯</th><th scope=col>SM.B</th><th scope=col>Plasmablast</th><th scope=col>DN.B</th><th scope=col>CL.Mono</th><th scope=col>Int.Mono</th><th scope=col>NC.Mono</th><th scope=col>mDC</th><th scope=col>pDC</th><th scope=col>Neu</th><th scope=col>LDG</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>AC000068.3</th><td>-0.15193307</td><td>-0.39134276</td><td>-0.34300042</td><td>-0.3660206</td><td>-0.34530244</td><td>-0.2992621</td><td>-0.40745688</td><td>-0.336094372</td><td>-0.2440137</td><td>-0.35911453</td><td>⋯</td><td>-0.40745688</td><td>-0.5432758</td><td>-0.40975889</td><td>-0.45119518</td><td>-0.5294637</td><td>-0.4673093</td><td>-0.54327583</td><td>-0.3959468</td><td>-0.5801081</td><td>-0.5087456</td></tr>\n",
       "\t<tr><th scope=row>AC005674.2</th><td> 5.10587203</td><td> 2.13857309</td><td> 2.70486909</td><td> 1.9705259</td><td> 3.01564128</td><td> 4.2518240</td><td> 1.01288715</td><td> 2.304318260</td><td> 3.5197828</td><td> 2.06951260</td><td>⋯</td><td> 1.26610893</td><td>-0.6169404</td><td> 0.69060487</td><td>-0.01841613</td><td> 0.1542351</td><td> 0.9783569</td><td> 0.04143629</td><td> 0.6629807</td><td> 0.6031283</td><td> 1.6620557</td></tr>\n",
       "\t<tr><th scope=row>AC005920.1</th><td> 1.40653193</td><td> 0.05294637</td><td>-0.08287258</td><td> 0.1404230</td><td> 0.08977863</td><td> 0.9852630</td><td>-0.29235606</td><td> 0.004604032</td><td> 1.0566255</td><td>-0.03222823</td><td>⋯</td><td>-0.66758471</td><td>-0.6721887</td><td>-0.66758471</td><td>-0.64686656</td><td>-0.6330545</td><td>-0.5916182</td><td>-0.60543027</td><td>-0.6744908</td><td>-0.6721887</td><td>-0.6537726</td></tr>\n",
       "\t<tr><th scope=row>AC006252.1</th><td> 1.11647788</td><td> 0.02762419</td><td> 0.13351694</td><td> 0.1611411</td><td> 0.25322179</td><td> 0.3591145</td><td>-0.06445645</td><td> 0.073664520</td><td> 0.8977863</td><td> 0.06215444</td><td>⋯</td><td>-0.02071815</td><td> 0.1358190</td><td> 0.05755041</td><td>-0.39594679</td><td>-0.5087456</td><td>-0.4788194</td><td>-0.46040325</td><td>-0.2463157</td><td>-0.5271617</td><td>-0.4834234</td></tr>\n",
       "\t<tr><th scope=row>AC009041.2</th><td> 7.35263988</td><td> 1.48940451</td><td> 4.08607883</td><td> 1.2615049</td><td> 2.03728438</td><td> 1.4778944</td><td>-0.65607463</td><td>-0.036832260</td><td> 6.5676523</td><td> 2.29511020</td><td>⋯</td><td>-0.63535648</td><td>-0.6744908</td><td>-0.65607463</td><td>-0.67449076</td><td>-0.6744908</td><td>-0.6744908</td><td>-0.67679278</td><td>-0.6307525</td><td>-0.6744908</td><td>-0.6261484</td></tr>\n",
       "\t<tr><th scope=row>AC011726.3</th><td> 0.06445645</td><td>-0.62614842</td><td>-0.56859801</td><td>-0.6445645</td><td>-0.58471213</td><td>-0.5893162</td><td>-0.67218874</td><td>-0.669886727</td><td>-0.3015641</td><td>-0.62845043</td><td>⋯</td><td>-0.66988673</td><td>-0.6744908</td><td>-0.66988673</td><td>-0.58701414</td><td>-0.6491686</td><td>-0.6698867</td><td>-0.66758471</td><td>-0.6744908</td><td>-0.6537726</td><td>-0.6491686</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 25\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Naïve.CD4 & Th1 & Th2 & Th17 & Tfh & Fr..I.nTreg & Fr..II.eTreg & Fr..III.T & Naïve.CD8 & CM.CD8 & ⋯ & SM.B & Plasmablast & DN.B & CL.Mono & Int.Mono & NC.Mono & mDC & pDC & Neu & LDG\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tAC000068.3 & -0.15193307 & -0.39134276 & -0.34300042 & -0.3660206 & -0.34530244 & -0.2992621 & -0.40745688 & -0.336094372 & -0.2440137 & -0.35911453 & ⋯ & -0.40745688 & -0.5432758 & -0.40975889 & -0.45119518 & -0.5294637 & -0.4673093 & -0.54327583 & -0.3959468 & -0.5801081 & -0.5087456\\\\\n",
       "\tAC005674.2 &  5.10587203 &  2.13857309 &  2.70486909 &  1.9705259 &  3.01564128 &  4.2518240 &  1.01288715 &  2.304318260 &  3.5197828 &  2.06951260 & ⋯ &  1.26610893 & -0.6169404 &  0.69060487 & -0.01841613 &  0.1542351 &  0.9783569 &  0.04143629 &  0.6629807 &  0.6031283 &  1.6620557\\\\\n",
       "\tAC005920.1 &  1.40653193 &  0.05294637 & -0.08287258 &  0.1404230 &  0.08977863 &  0.9852630 & -0.29235606 &  0.004604032 &  1.0566255 & -0.03222823 & ⋯ & -0.66758471 & -0.6721887 & -0.66758471 & -0.64686656 & -0.6330545 & -0.5916182 & -0.60543027 & -0.6744908 & -0.6721887 & -0.6537726\\\\\n",
       "\tAC006252.1 &  1.11647788 &  0.02762419 &  0.13351694 &  0.1611411 &  0.25322179 &  0.3591145 & -0.06445645 &  0.073664520 &  0.8977863 &  0.06215444 & ⋯ & -0.02071815 &  0.1358190 &  0.05755041 & -0.39594679 & -0.5087456 & -0.4788194 & -0.46040325 & -0.2463157 & -0.5271617 & -0.4834234\\\\\n",
       "\tAC009041.2 &  7.35263988 &  1.48940451 &  4.08607883 &  1.2615049 &  2.03728438 &  1.4778944 & -0.65607463 & -0.036832260 &  6.5676523 &  2.29511020 & ⋯ & -0.63535648 & -0.6744908 & -0.65607463 & -0.67449076 & -0.6744908 & -0.6744908 & -0.67679278 & -0.6307525 & -0.6744908 & -0.6261484\\\\\n",
       "\tAC011726.3 &  0.06445645 & -0.62614842 & -0.56859801 & -0.6445645 & -0.58471213 & -0.5893162 & -0.67218874 & -0.669886727 & -0.3015641 & -0.62845043 & ⋯ & -0.66988673 & -0.6744908 & -0.66988673 & -0.58701414 & -0.6491686 & -0.6698867 & -0.66758471 & -0.6744908 & -0.6537726 & -0.6491686\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 25\n",
       "\n",
       "| <!--/--> | Naïve.CD4 &lt;dbl&gt; | Th1 &lt;dbl&gt; | Th2 &lt;dbl&gt; | Th17 &lt;dbl&gt; | Tfh &lt;dbl&gt; | Fr..I.nTreg &lt;dbl&gt; | Fr..II.eTreg &lt;dbl&gt; | Fr..III.T &lt;dbl&gt; | Naïve.CD8 &lt;dbl&gt; | CM.CD8 &lt;dbl&gt; | ⋯ ⋯ | SM.B &lt;dbl&gt; | Plasmablast &lt;dbl&gt; | DN.B &lt;dbl&gt; | CL.Mono &lt;dbl&gt; | Int.Mono &lt;dbl&gt; | NC.Mono &lt;dbl&gt; | mDC &lt;dbl&gt; | pDC &lt;dbl&gt; | Neu &lt;dbl&gt; | LDG &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| AC000068.3 | -0.15193307 | -0.39134276 | -0.34300042 | -0.3660206 | -0.34530244 | -0.2992621 | -0.40745688 | -0.336094372 | -0.2440137 | -0.35911453 | ⋯ | -0.40745688 | -0.5432758 | -0.40975889 | -0.45119518 | -0.5294637 | -0.4673093 | -0.54327583 | -0.3959468 | -0.5801081 | -0.5087456 |\n",
       "| AC005674.2 |  5.10587203 |  2.13857309 |  2.70486909 |  1.9705259 |  3.01564128 |  4.2518240 |  1.01288715 |  2.304318260 |  3.5197828 |  2.06951260 | ⋯ |  1.26610893 | -0.6169404 |  0.69060487 | -0.01841613 |  0.1542351 |  0.9783569 |  0.04143629 |  0.6629807 |  0.6031283 |  1.6620557 |\n",
       "| AC005920.1 |  1.40653193 |  0.05294637 | -0.08287258 |  0.1404230 |  0.08977863 |  0.9852630 | -0.29235606 |  0.004604032 |  1.0566255 | -0.03222823 | ⋯ | -0.66758471 | -0.6721887 | -0.66758471 | -0.64686656 | -0.6330545 | -0.5916182 | -0.60543027 | -0.6744908 | -0.6721887 | -0.6537726 |\n",
       "| AC006252.1 |  1.11647788 |  0.02762419 |  0.13351694 |  0.1611411 |  0.25322179 |  0.3591145 | -0.06445645 |  0.073664520 |  0.8977863 |  0.06215444 | ⋯ | -0.02071815 |  0.1358190 |  0.05755041 | -0.39594679 | -0.5087456 | -0.4788194 | -0.46040325 | -0.2463157 | -0.5271617 | -0.4834234 |\n",
       "| AC009041.2 |  7.35263988 |  1.48940451 |  4.08607883 |  1.2615049 |  2.03728438 |  1.4778944 | -0.65607463 | -0.036832260 |  6.5676523 |  2.29511020 | ⋯ | -0.63535648 | -0.6744908 | -0.65607463 | -0.67449076 | -0.6744908 | -0.6744908 | -0.67679278 | -0.6307525 | -0.6744908 | -0.6261484 |\n",
       "| AC011726.3 |  0.06445645 | -0.62614842 | -0.56859801 | -0.6445645 | -0.58471213 | -0.5893162 | -0.67218874 | -0.669886727 | -0.3015641 | -0.62845043 | ⋯ | -0.66988673 | -0.6744908 | -0.66988673 | -0.58701414 | -0.6491686 | -0.6698867 | -0.66758471 | -0.6744908 | -0.6537726 | -0.6491686 |\n",
       "\n"
      ],
      "text/plain": [
       "           Naïve.CD4   Th1         Th2         Th17       Tfh        \n",
       "AC000068.3 -0.15193307 -0.39134276 -0.34300042 -0.3660206 -0.34530244\n",
       "AC005674.2  5.10587203  2.13857309  2.70486909  1.9705259  3.01564128\n",
       "AC005920.1  1.40653193  0.05294637 -0.08287258  0.1404230  0.08977863\n",
       "AC006252.1  1.11647788  0.02762419  0.13351694  0.1611411  0.25322179\n",
       "AC009041.2  7.35263988  1.48940451  4.08607883  1.2615049  2.03728438\n",
       "AC011726.3  0.06445645 -0.62614842 -0.56859801 -0.6445645 -0.58471213\n",
       "           Fr..I.nTreg Fr..II.eTreg Fr..III.T    Naïve.CD8  CM.CD8      ⋯\n",
       "AC000068.3 -0.2992621  -0.40745688  -0.336094372 -0.2440137 -0.35911453 ⋯\n",
       "AC005674.2  4.2518240   1.01288715   2.304318260  3.5197828  2.06951260 ⋯\n",
       "AC005920.1  0.9852630  -0.29235606   0.004604032  1.0566255 -0.03222823 ⋯\n",
       "AC006252.1  0.3591145  -0.06445645   0.073664520  0.8977863  0.06215444 ⋯\n",
       "AC009041.2  1.4778944  -0.65607463  -0.036832260  6.5676523  2.29511020 ⋯\n",
       "AC011726.3 -0.5893162  -0.67218874  -0.669886727 -0.3015641 -0.62845043 ⋯\n",
       "           SM.B        Plasmablast DN.B        CL.Mono     Int.Mono  \n",
       "AC000068.3 -0.40745688 -0.5432758  -0.40975889 -0.45119518 -0.5294637\n",
       "AC005674.2  1.26610893 -0.6169404   0.69060487 -0.01841613  0.1542351\n",
       "AC005920.1 -0.66758471 -0.6721887  -0.66758471 -0.64686656 -0.6330545\n",
       "AC006252.1 -0.02071815  0.1358190   0.05755041 -0.39594679 -0.5087456\n",
       "AC009041.2 -0.63535648 -0.6744908  -0.65607463 -0.67449076 -0.6744908\n",
       "AC011726.3 -0.66988673 -0.6744908  -0.66988673 -0.58701414 -0.6491686\n",
       "           NC.Mono    mDC         pDC        Neu        LDG       \n",
       "AC000068.3 -0.4673093 -0.54327583 -0.3959468 -0.5801081 -0.5087456\n",
       "AC005674.2  0.9783569  0.04143629  0.6629807  0.6031283  1.6620557\n",
       "AC005920.1 -0.5916182 -0.60543027 -0.6744908 -0.6721887 -0.6537726\n",
       "AC006252.1 -0.4788194 -0.46040325 -0.2463157 -0.5271617 -0.4834234\n",
       "AC009041.2 -0.6744908 -0.67679278 -0.6307525 -0.6744908 -0.6261484\n",
       "AC011726.3 -0.6698867 -0.66758471 -0.6744908 -0.6537726 -0.6491686"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(immunexut_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "    0.00     0.26     2.94    50.60    30.00 55587.39 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "   -0.677    -0.617     0.000    10.970     6.229 12795.631 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(unlist(immunexut_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\n/wO+vgbcE9m9/QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() + geom_histogram(aes(unlist(immunexut_m)), binwidth = 0.1) + myaxis + myplot +\n",
    "coord_cartesian(xlim = c(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "immunexut_expressed_genes <- rownames(immunexut_m)[rowSums(immunexut_m > quantile(unlist(immunexut_m),0.75)) >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>7172</li><li>25</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 7172\n",
       "\\item 25\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 7172\n",
       "2. 25\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 7172   25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(immunexut_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3896"
      ],
      "text/latex": [
       "3896"
      ],
      "text/markdown": [
       "3896"
      ],
      "text/plain": [
       "[1] 3896"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(immunexut_expressed_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get median scores for genes for all celltypes\n",
    "immunexut_genes_mcentr <- t(apply(immunexut[,3:ncol(immunexut)],1,mcentr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(immunexut_genes_mcentr) <- immunexut$Gene\n",
    "colnames(immunexut_genes_mcentr) <- colnames(immunexut)[3:ncol(immunexut)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 25 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Naïve.CD4</th><th scope=col>Th1</th><th scope=col>Th2</th><th scope=col>Th17</th><th scope=col>Tfh</th><th scope=col>Fr..I.nTreg</th><th scope=col>Fr..II.eTreg</th><th scope=col>Fr..III.T</th><th scope=col>Naïve.CD8</th><th scope=col>CM.CD8</th><th scope=col>⋯</th><th scope=col>SM.B</th><th scope=col>Plasmablast</th><th scope=col>DN.B</th><th scope=col>CL.Mono</th><th scope=col>Int.Mono</th><th scope=col>NC.Mono</th><th scope=col>mDC</th><th scope=col>pDC</th><th scope=col>Neu</th><th scope=col>LDG</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AC000068.3</th><td>  3.108523</td><td> 0.05865137</td><td> 0.6744908</td><td> 0.3812339</td><td> 0.6451651</td><td> 1.231679</td><td>-0.1466284</td><td> 0.7624678</td><td>  1.935495</td><td> 0.4692110</td><td>⋯</td><td>-0.14662843</td><td>-1.8768439</td><td>-0.1759541</td><td>-0.7038164</td><td>-1.7008897</td><td>-0.9090962</td><td>-1.8768439</td><td> 0.00000000</td><td>-2.3460548</td><td>-1.4369586</td></tr>\n",
       "\t<tr><th scope=row>AC005674.2</th><td>  3.676636</td><td> 0.83539869</td><td> 1.3776364</td><td> 0.6744908</td><td> 1.6752058</td><td> 2.858871</td><td>-0.2424640</td><td> 0.9941024</td><td>  2.157930</td><td> 0.7692721</td><td>⋯</td><td> 0.00000000</td><td>-1.8030505</td><td>-0.5510545</td><td>-1.2299537</td><td>-1.0646374</td><td>-0.2755273</td><td>-1.1726441</td><td>-0.57750516</td><td>-0.6348148</td><td> 0.3791255</td></tr>\n",
       "\t<tr><th scope=row>AC005920.1</th><td> 11.281189</td><td> 3.50470689</td><td> 2.7244137</td><td> 4.0072686</td><td> 3.7163118</td><td> 8.860957</td><td> 1.5209105</td><td> 3.2269754</td><td>  9.270942</td><td> 3.0153705</td><td>⋯</td><td>-0.63481483</td><td>-0.6612655</td><td>-0.6348148</td><td>-0.5157871</td><td>-0.4364352</td><td>-0.1983796</td><td>-0.2777315</td><td>-0.67449076</td><td>-0.6612655</td><td>-0.5554630</td></tr>\n",
       "\t<tr><th scope=row>AC006252.1</th><td>  5.028022</td><td> 0.19417158</td><td> 0.6642712</td><td> 0.7869059</td><td> 1.1956882</td><td> 1.665788</td><td>-0.2146107</td><td> 0.3985627</td><td>  4.057164</td><td> 0.3474649</td><td>⋯</td><td>-0.02043911</td><td> 0.6744908</td><td> 0.3270258</td><td>-1.6862269</td><td>-2.1869852</td><td>-2.0541309</td><td>-1.9723745</td><td>-1.02195570</td><td>-2.2687416</td><td>-2.0745701</td></tr>\n",
       "\t<tr><th scope=row>AC009041.2</th><td>111.323094</td><td>29.51700038</td><td>65.7467897</td><td>26.3372582</td><td>37.1612290</td><td>29.356407</td><td>-0.4175419</td><td> 8.2223635</td><td>100.370649</td><td>40.7585130</td><td>⋯</td><td>-0.12847443</td><td>-0.6744908</td><td>-0.4175419</td><td>-0.6744908</td><td>-0.6744908</td><td>-0.6744908</td><td>-0.7066094</td><td>-0.06423722</td><td>-0.6744908</td><td> 0.0000000</td></tr>\n",
       "\t<tr><th scope=row>AC011726.3</th><td> 26.305140</td><td> 1.01173614</td><td> 3.1195198</td><td> 0.3372454</td><td> 2.5293403</td><td> 2.360718</td><td>-0.6744908</td><td>-0.5901794</td><td> 12.899636</td><td> 0.9274248</td><td>⋯</td><td>-0.59017941</td><td>-0.7588021</td><td>-0.5901794</td><td> 2.4450290</td><td> 0.1686227</td><td>-0.5901794</td><td>-0.5058681</td><td>-0.75880210</td><td> 0.0000000</td><td> 0.1686227</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 25 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Naïve.CD4 & Th1 & Th2 & Th17 & Tfh & Fr..I.nTreg & Fr..II.eTreg & Fr..III.T & Naïve.CD8 & CM.CD8 & ⋯ & SM.B & Plasmablast & DN.B & CL.Mono & Int.Mono & NC.Mono & mDC & pDC & Neu & LDG\\\\\n",
       "\\hline\n",
       "\tAC000068.3 &   3.108523 &  0.05865137 &  0.6744908 &  0.3812339 &  0.6451651 &  1.231679 & -0.1466284 &  0.7624678 &   1.935495 &  0.4692110 & ⋯ & -0.14662843 & -1.8768439 & -0.1759541 & -0.7038164 & -1.7008897 & -0.9090962 & -1.8768439 &  0.00000000 & -2.3460548 & -1.4369586\\\\\n",
       "\tAC005674.2 &   3.676636 &  0.83539869 &  1.3776364 &  0.6744908 &  1.6752058 &  2.858871 & -0.2424640 &  0.9941024 &   2.157930 &  0.7692721 & ⋯ &  0.00000000 & -1.8030505 & -0.5510545 & -1.2299537 & -1.0646374 & -0.2755273 & -1.1726441 & -0.57750516 & -0.6348148 &  0.3791255\\\\\n",
       "\tAC005920.1 &  11.281189 &  3.50470689 &  2.7244137 &  4.0072686 &  3.7163118 &  8.860957 &  1.5209105 &  3.2269754 &   9.270942 &  3.0153705 & ⋯ & -0.63481483 & -0.6612655 & -0.6348148 & -0.5157871 & -0.4364352 & -0.1983796 & -0.2777315 & -0.67449076 & -0.6612655 & -0.5554630\\\\\n",
       "\tAC006252.1 &   5.028022 &  0.19417158 &  0.6642712 &  0.7869059 &  1.1956882 &  1.665788 & -0.2146107 &  0.3985627 &   4.057164 &  0.3474649 & ⋯ & -0.02043911 &  0.6744908 &  0.3270258 & -1.6862269 & -2.1869852 & -2.0541309 & -1.9723745 & -1.02195570 & -2.2687416 & -2.0745701\\\\\n",
       "\tAC009041.2 & 111.323094 & 29.51700038 & 65.7467897 & 26.3372582 & 37.1612290 & 29.356407 & -0.4175419 &  8.2223635 & 100.370649 & 40.7585130 & ⋯ & -0.12847443 & -0.6744908 & -0.4175419 & -0.6744908 & -0.6744908 & -0.6744908 & -0.7066094 & -0.06423722 & -0.6744908 &  0.0000000\\\\\n",
       "\tAC011726.3 &  26.305140 &  1.01173614 &  3.1195198 &  0.3372454 &  2.5293403 &  2.360718 & -0.6744908 & -0.5901794 &  12.899636 &  0.9274248 & ⋯ & -0.59017941 & -0.7588021 & -0.5901794 &  2.4450290 &  0.1686227 & -0.5901794 & -0.5058681 & -0.75880210 &  0.0000000 &  0.1686227\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 25 of type dbl\n",
       "\n",
       "| <!--/--> | Naïve.CD4 | Th1 | Th2 | Th17 | Tfh | Fr..I.nTreg | Fr..II.eTreg | Fr..III.T | Naïve.CD8 | CM.CD8 | ⋯ | SM.B | Plasmablast | DN.B | CL.Mono | Int.Mono | NC.Mono | mDC | pDC | Neu | LDG |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| AC000068.3 |   3.108523 |  0.05865137 |  0.6744908 |  0.3812339 |  0.6451651 |  1.231679 | -0.1466284 |  0.7624678 |   1.935495 |  0.4692110 | ⋯ | -0.14662843 | -1.8768439 | -0.1759541 | -0.7038164 | -1.7008897 | -0.9090962 | -1.8768439 |  0.00000000 | -2.3460548 | -1.4369586 |\n",
       "| AC005674.2 |   3.676636 |  0.83539869 |  1.3776364 |  0.6744908 |  1.6752058 |  2.858871 | -0.2424640 |  0.9941024 |   2.157930 |  0.7692721 | ⋯ |  0.00000000 | -1.8030505 | -0.5510545 | -1.2299537 | -1.0646374 | -0.2755273 | -1.1726441 | -0.57750516 | -0.6348148 |  0.3791255 |\n",
       "| AC005920.1 |  11.281189 |  3.50470689 |  2.7244137 |  4.0072686 |  3.7163118 |  8.860957 |  1.5209105 |  3.2269754 |   9.270942 |  3.0153705 | ⋯ | -0.63481483 | -0.6612655 | -0.6348148 | -0.5157871 | -0.4364352 | -0.1983796 | -0.2777315 | -0.67449076 | -0.6612655 | -0.5554630 |\n",
       "| AC006252.1 |   5.028022 |  0.19417158 |  0.6642712 |  0.7869059 |  1.1956882 |  1.665788 | -0.2146107 |  0.3985627 |   4.057164 |  0.3474649 | ⋯ | -0.02043911 |  0.6744908 |  0.3270258 | -1.6862269 | -2.1869852 | -2.0541309 | -1.9723745 | -1.02195570 | -2.2687416 | -2.0745701 |\n",
       "| AC009041.2 | 111.323094 | 29.51700038 | 65.7467897 | 26.3372582 | 37.1612290 | 29.356407 | -0.4175419 |  8.2223635 | 100.370649 | 40.7585130 | ⋯ | -0.12847443 | -0.6744908 | -0.4175419 | -0.6744908 | -0.6744908 | -0.6744908 | -0.7066094 | -0.06423722 | -0.6744908 |  0.0000000 |\n",
       "| AC011726.3 |  26.305140 |  1.01173614 |  3.1195198 |  0.3372454 |  2.5293403 |  2.360718 | -0.6744908 | -0.5901794 |  12.899636 |  0.9274248 | ⋯ | -0.59017941 | -0.7588021 | -0.5901794 |  2.4450290 |  0.1686227 | -0.5901794 | -0.5058681 | -0.75880210 |  0.0000000 |  0.1686227 |\n",
       "\n"
      ],
      "text/plain": [
       "           Naïve.CD4  Th1         Th2        Th17       Tfh        Fr..I.nTreg\n",
       "AC000068.3   3.108523  0.05865137  0.6744908  0.3812339  0.6451651  1.231679  \n",
       "AC005674.2   3.676636  0.83539869  1.3776364  0.6744908  1.6752058  2.858871  \n",
       "AC005920.1  11.281189  3.50470689  2.7244137  4.0072686  3.7163118  8.860957  \n",
       "AC006252.1   5.028022  0.19417158  0.6642712  0.7869059  1.1956882  1.665788  \n",
       "AC009041.2 111.323094 29.51700038 65.7467897 26.3372582 37.1612290 29.356407  \n",
       "AC011726.3  26.305140  1.01173614  3.1195198  0.3372454  2.5293403  2.360718  \n",
       "           Fr..II.eTreg Fr..III.T  Naïve.CD8  CM.CD8     ⋯ SM.B       \n",
       "AC000068.3 -0.1466284    0.7624678   1.935495  0.4692110 ⋯ -0.14662843\n",
       "AC005674.2 -0.2424640    0.9941024   2.157930  0.7692721 ⋯  0.00000000\n",
       "AC005920.1  1.5209105    3.2269754   9.270942  3.0153705 ⋯ -0.63481483\n",
       "AC006252.1 -0.2146107    0.3985627   4.057164  0.3474649 ⋯ -0.02043911\n",
       "AC009041.2 -0.4175419    8.2223635 100.370649 40.7585130 ⋯ -0.12847443\n",
       "AC011726.3 -0.6744908   -0.5901794  12.899636  0.9274248 ⋯ -0.59017941\n",
       "           Plasmablast DN.B       CL.Mono    Int.Mono   NC.Mono    mDC       \n",
       "AC000068.3 -1.8768439  -0.1759541 -0.7038164 -1.7008897 -0.9090962 -1.8768439\n",
       "AC005674.2 -1.8030505  -0.5510545 -1.2299537 -1.0646374 -0.2755273 -1.1726441\n",
       "AC005920.1 -0.6612655  -0.6348148 -0.5157871 -0.4364352 -0.1983796 -0.2777315\n",
       "AC006252.1  0.6744908   0.3270258 -1.6862269 -2.1869852 -2.0541309 -1.9723745\n",
       "AC009041.2 -0.6744908  -0.4175419 -0.6744908 -0.6744908 -0.6744908 -0.7066094\n",
       "AC011726.3 -0.7588021  -0.5901794  2.4450290  0.1686227 -0.5901794 -0.5058681\n",
       "           pDC         Neu        LDG       \n",
       "AC000068.3  0.00000000 -2.3460548 -1.4369586\n",
       "AC005674.2 -0.57750516 -0.6348148  0.3791255\n",
       "AC005920.1 -0.67449076 -0.6612655 -0.5554630\n",
       "AC006252.1 -1.02195570 -2.2687416 -2.0745701\n",
       "AC009041.2 -0.06423722 -0.6744908  0.0000000\n",
       "AC011726.3 -0.75880210  0.0000000  0.1686227"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(immunexut_genes_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a list of tables with fold difference across celltype medians for each gene\n",
    "diff_gen_list_immunexut <- mclapply(rownames(immunexut_genes_mcentr), function(x){\n",
    "    mygen <- x\n",
    "    gen_df <- immunexut_genes_mcentr[mygen,]\n",
    "    #get z-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",
    "    return(tab_diff)    \n",
    "})\n",
    "\n",
    "names(diff_gen_list_immunexut) <- rownames(immunexut_genes_mcentr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 25 × 25 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Naïve.CD4</th><th scope=col>Th1</th><th scope=col>Th2</th><th scope=col>Th17</th><th scope=col>Tfh</th><th scope=col>Fr..I.nTreg</th><th scope=col>Fr..II.eTreg</th><th scope=col>Fr..III.T</th><th scope=col>Naïve.CD8</th><th scope=col>CM.CD8</th><th scope=col>⋯</th><th scope=col>SM.B</th><th scope=col>Plasmablast</th><th scope=col>DN.B</th><th scope=col>CL.Mono</th><th scope=col>Int.Mono</th><th scope=col>NC.Mono</th><th scope=col>mDC</th><th scope=col>pDC</th><th scope=col>Neu</th><th scope=col>LDG</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Naïve.CD4</th><td>   0.00000000</td><td>  -2.508263</td><td>   0.12646702</td><td>  -0.69556860</td><td>  -0.56910158</td><td>  -0.61125725</td><td>  -0.29508971</td><td>  -0.27401187</td><td>-1654.3994</td><td>-1554.2796</td><td>⋯</td><td>  -0.65341292</td><td>   0.10538918</td><td>  -0.8220356</td><td>   0.14754485</td><td>   0.10538918</td><td>2.107784e-01</td><td>   0.00000000</td><td>  -0.04215567</td><td>  -0.27401187</td><td> -11.382032</td></tr>\n",
       "\t<tr><th scope=row>Th1</th><td>   2.50826251</td><td>   0.000000</td><td>   2.63472953</td><td>   1.81269392</td><td>   1.93916093</td><td>   1.89700526</td><td>   2.21317280</td><td>   2.23425064</td><td>-1651.8911</td><td>-1551.7714</td><td>⋯</td><td>   1.85484959</td><td>   2.61365169</td><td>   1.6862269</td><td>   2.65580737</td><td>   2.61365169</td><td>2.719041e+00</td><td>   2.50826251</td><td>   2.46610684</td><td>   2.23425064</td><td>  -8.873769</td></tr>\n",
       "\t<tr><th scope=row>Th2</th><td>  -0.12646702</td><td>  -2.634730</td><td>   0.00000000</td><td>  -0.82203561</td><td>  -0.69556860</td><td>  -0.73772427</td><td>  -0.42155672</td><td>  -0.40047889</td><td>-1654.5258</td><td>-1554.4061</td><td>⋯</td><td>  -0.77987994</td><td>  -0.02107784</td><td>  -0.9485026</td><td>   0.02107784</td><td>  -0.02107784</td><td>8.431134e-02</td><td>  -0.12646702</td><td>  -0.16862269</td><td>  -0.40047889</td><td> -11.508499</td></tr>\n",
       "\t<tr><th scope=row>Th17</th><td>   0.69556860</td><td>  -1.812694</td><td>   0.82203561</td><td>   0.00000000</td><td>   0.12646702</td><td>   0.08431134</td><td>   0.40047889</td><td>   0.42155672</td><td>-1653.7038</td><td>-1553.5841</td><td>⋯</td><td>   0.04215567</td><td>   0.80095778</td><td>  -0.1264670</td><td>   0.84311345</td><td>   0.80095778</td><td>9.063470e-01</td><td>   0.69556860</td><td>   0.65341292</td><td>   0.42155672</td><td> -10.686463</td></tr>\n",
       "\t<tr><th scope=row>Tfh</th><td>   0.56910158</td><td>  -1.939161</td><td>   0.69556860</td><td>  -0.12646702</td><td>   0.00000000</td><td>  -0.04215567</td><td>   0.27401187</td><td>   0.29508971</td><td>-1653.8303</td><td>-1553.7105</td><td>⋯</td><td>  -0.08431134</td><td>   0.67449076</td><td>  -0.2529340</td><td>   0.71664643</td><td>   0.67449076</td><td>7.798799e-01</td><td>   0.56910158</td><td>   0.52694591</td><td>   0.29508971</td><td> -10.812930</td></tr>\n",
       "\t<tr><th scope=row>Fr..I.nTreg</th><td>   0.61125725</td><td>  -1.897005</td><td>   0.73772427</td><td>  -0.08431134</td><td>   0.04215567</td><td>   0.00000000</td><td>   0.31616754</td><td>   0.33724538</td><td>-1653.7881</td><td>-1553.6684</td><td>⋯</td><td>  -0.04215567</td><td>   0.71664643</td><td>  -0.2107784</td><td>   0.75880210</td><td>   0.71664643</td><td>8.220356e-01</td><td>   0.61125725</td><td>   0.56910158</td><td>   0.33724538</td><td> -10.770774</td></tr>\n",
       "\t<tr><th scope=row>Fr..II.eTreg</th><td>   0.29508971</td><td>  -2.213173</td><td>   0.42155672</td><td>  -0.40047889</td><td>  -0.27401187</td><td>  -0.31616754</td><td>   0.00000000</td><td>   0.02107784</td><td>-1654.1043</td><td>-1553.9846</td><td>⋯</td><td>  -0.35832322</td><td>   0.40047889</td><td>  -0.5269459</td><td>   0.44263456</td><td>   0.40047889</td><td>5.058681e-01</td><td>   0.29508971</td><td>   0.25293403</td><td>   0.02107784</td><td> -11.086942</td></tr>\n",
       "\t<tr><th scope=row>Fr..III.T</th><td>   0.27401187</td><td>  -2.234251</td><td>   0.40047889</td><td>  -0.42155672</td><td>  -0.29508971</td><td>  -0.33724538</td><td>  -0.02107784</td><td>   0.00000000</td><td>-1654.1254</td><td>-1554.0056</td><td>⋯</td><td>  -0.37940105</td><td>   0.37940105</td><td>  -0.5480237</td><td>   0.42155672</td><td>   0.37940105</td><td>4.847902e-01</td><td>   0.27401187</td><td>   0.23185620</td><td>   0.00000000</td><td> -11.108020</td></tr>\n",
       "\t<tr><th scope=row>Naïve.CD8</th><td>1654.39936598</td><td>1651.891103</td><td>1654.52583300</td><td>1653.70379738</td><td>1653.83026440</td><td>1653.78810873</td><td>1654.10427627</td><td>1654.12535411</td><td>    0.0000</td><td>  100.1197</td><td>⋯</td><td>1653.74595306</td><td>1654.50475516</td><td>1653.5773304</td><td>1654.54691083</td><td>1654.50475516</td><td>1.654610e+03</td><td>1654.39936598</td><td>1654.35721031</td><td>1654.12535411</td><td>1643.017334</td></tr>\n",
       "\t<tr><th scope=row>CM.CD8</th><td>1554.27964387</td><td>1551.771381</td><td>1554.40611089</td><td>1553.58407527</td><td>1553.71054229</td><td>1553.66838662</td><td>1553.98455416</td><td>1554.00563200</td><td> -100.1197</td><td>    0.0000</td><td>⋯</td><td>1553.62623095</td><td>1554.38503305</td><td>1553.4576083</td><td>1554.42718872</td><td>1554.38503305</td><td>1.554490e+03</td><td>1554.27964387</td><td>1554.23748820</td><td>1554.00563200</td><td>1542.897612</td></tr>\n",
       "\t<tr><th scope=row>EM.CD8</th><td>2061.18052745</td><td>2058.672265</td><td>2061.30699447</td><td>2060.48495886</td><td>2060.61142587</td><td>2060.56927020</td><td>2060.88543774</td><td>2060.90651558</td><td>  406.7812</td><td>  506.9009</td><td>⋯</td><td>2060.52711453</td><td>2061.28591663</td><td>2060.3584918</td><td>2061.32807231</td><td>2061.28591663</td><td>2.061391e+03</td><td>2061.18052745</td><td>2061.13837178</td><td>2060.90651558</td><td>2049.798496</td></tr>\n",
       "\t<tr><th scope=row>TEMRA.CD8</th><td>2513.25795899</td><td>2510.749696</td><td>2513.38442601</td><td>2512.56239040</td><td>2512.68885741</td><td>2512.64670174</td><td>2512.96286928</td><td>2512.98394712</td><td>  858.8586</td><td>  958.9783</td><td>⋯</td><td>2512.60454607</td><td>2513.36334817</td><td>2512.4359234</td><td>2513.40550384</td><td>2513.36334817</td><td>2.513469e+03</td><td>2513.25795899</td><td>2513.21580332</td><td>2512.98394712</td><td>2501.875927</td></tr>\n",
       "\t<tr><th scope=row>NK</th><td> 263.72588696</td><td> 261.217624</td><td> 263.85235397</td><td> 263.03031836</td><td> 263.15678538</td><td> 263.11462970</td><td> 263.43079725</td><td> 263.45187508</td><td>-1390.6735</td><td>-1290.5538</td><td>⋯</td><td> 263.07247403</td><td> 263.83127614</td><td> 262.9038513</td><td> 263.87343181</td><td> 263.83127614</td><td>2.639367e+02</td><td> 263.72588696</td><td> 263.68373128</td><td> 263.45187508</td><td> 252.343855</td></tr>\n",
       "\t<tr><th scope=row>Naïve.B</th><td>  -0.12646702</td><td>  -2.634730</td><td>   0.00000000</td><td>  -0.82203561</td><td>  -0.69556860</td><td>  -0.73772427</td><td>  -0.42155672</td><td>  -0.40047889</td><td>-1654.5258</td><td>-1554.4061</td><td>⋯</td><td>  -0.77987994</td><td>  -0.02107784</td><td>  -0.9485026</td><td>   0.02107784</td><td>  -0.02107784</td><td>8.431134e-02</td><td>  -0.12646702</td><td>  -0.16862269</td><td>  -0.40047889</td><td> -11.508499</td></tr>\n",
       "\t<tr><th scope=row>USM.B</th><td>   0.99065830</td><td>  -1.517604</td><td>   1.11712532</td><td>   0.29508971</td><td>   0.42155672</td><td>   0.37940105</td><td>   0.69556860</td><td>   0.71664643</td><td>-1653.4087</td><td>-1553.2890</td><td>⋯</td><td>   0.33724538</td><td>   1.09604748</td><td>   0.1686227</td><td>   1.13820316</td><td>   1.09604748</td><td>1.201437e+00</td><td>   0.99065830</td><td>   0.94850263</td><td>   0.71664643</td><td> -10.391373</td></tr>\n",
       "\t<tr><th scope=row>SM.B</th><td>   0.65341292</td><td>  -1.854850</td><td>   0.77987994</td><td>  -0.04215567</td><td>   0.08431134</td><td>   0.04215567</td><td>   0.35832322</td><td>   0.37940105</td><td>-1653.7460</td><td>-1553.6262</td><td>⋯</td><td>   0.00000000</td><td>   0.75880210</td><td>  -0.1686227</td><td>   0.80095778</td><td>   0.75880210</td><td>8.641913e-01</td><td>   0.65341292</td><td>   0.61125725</td><td>   0.37940105</td><td> -10.728619</td></tr>\n",
       "\t<tr><th scope=row>Plasmablast</th><td>  -0.10538918</td><td>  -2.613652</td><td>   0.02107784</td><td>  -0.80095778</td><td>  -0.67449076</td><td>  -0.71664643</td><td>  -0.40047889</td><td>  -0.37940105</td><td>-1654.5048</td><td>-1554.3850</td><td>⋯</td><td>  -0.75880210</td><td>   0.00000000</td><td>  -0.9274248</td><td>   0.04215567</td><td>   0.00000000</td><td>1.053892e-01</td><td>  -0.10538918</td><td>  -0.14754485</td><td>  -0.37940105</td><td> -11.487421</td></tr>\n",
       "\t<tr><th scope=row>DN.B</th><td>   0.82203561</td><td>  -1.686227</td><td>   0.94850263</td><td>   0.12646702</td><td>   0.25293403</td><td>   0.21077836</td><td>   0.52694591</td><td>   0.54802374</td><td>-1653.5773</td><td>-1553.4576</td><td>⋯</td><td>   0.16862269</td><td>   0.92742479</td><td>   0.0000000</td><td>   0.96958047</td><td>   0.92742479</td><td>1.032814e+00</td><td>   0.82203561</td><td>   0.77987994</td><td>   0.54802374</td><td> -10.559996</td></tr>\n",
       "\t<tr><th scope=row>CL.Mono</th><td>  -0.14754485</td><td>  -2.655807</td><td>  -0.02107784</td><td>  -0.84311345</td><td>  -0.71664643</td><td>  -0.75880210</td><td>  -0.44263456</td><td>  -0.42155672</td><td>-1654.5469</td><td>-1554.4272</td><td>⋯</td><td>  -0.80095778</td><td>  -0.04215567</td><td>  -0.9695805</td><td>   0.00000000</td><td>  -0.04215567</td><td>6.323351e-02</td><td>  -0.14754485</td><td>  -0.18970053</td><td>  -0.42155672</td><td> -11.529576</td></tr>\n",
       "\t<tr><th scope=row>Int.Mono</th><td>  -0.10538918</td><td>  -2.613652</td><td>   0.02107784</td><td>  -0.80095778</td><td>  -0.67449076</td><td>  -0.71664643</td><td>  -0.40047889</td><td>  -0.37940105</td><td>-1654.5048</td><td>-1554.3850</td><td>⋯</td><td>  -0.75880210</td><td>   0.00000000</td><td>  -0.9274248</td><td>   0.04215567</td><td>   0.00000000</td><td>1.053892e-01</td><td>  -0.10538918</td><td>  -0.14754485</td><td>  -0.37940105</td><td> -11.487421</td></tr>\n",
       "\t<tr><th scope=row>NC.Mono</th><td>  -0.21077836</td><td>  -2.719041</td><td>  -0.08431134</td><td>  -0.90634696</td><td>  -0.77987994</td><td>  -0.82203561</td><td>  -0.50586807</td><td>  -0.48479023</td><td>-1654.6101</td><td>-1554.4904</td><td>⋯</td><td>  -0.86419129</td><td>  -0.10538918</td><td>  -1.0328140</td><td>  -0.06323351</td><td>  -0.10538918</td><td>0.000000e+00</td><td>  -0.21077836</td><td>  -0.25293403</td><td>  -0.48479023</td><td> -11.592810</td></tr>\n",
       "\t<tr><th scope=row>mDC</th><td>   0.00000000</td><td>  -2.508263</td><td>   0.12646702</td><td>  -0.69556860</td><td>  -0.56910158</td><td>  -0.61125725</td><td>  -0.29508971</td><td>  -0.27401187</td><td>-1654.3994</td><td>-1554.2796</td><td>⋯</td><td>  -0.65341292</td><td>   0.10538918</td><td>  -0.8220356</td><td>   0.14754485</td><td>   0.10538918</td><td>2.107784e-01</td><td>   0.00000000</td><td>  -0.04215567</td><td>  -0.27401187</td><td> -11.382032</td></tr>\n",
       "\t<tr><th scope=row>pDC</th><td>   0.04215567</td><td>  -2.466107</td><td>   0.16862269</td><td>  -0.65341292</td><td>  -0.52694591</td><td>  -0.56910158</td><td>  -0.25293403</td><td>  -0.23185620</td><td>-1654.3572</td><td>-1554.2375</td><td>⋯</td><td>  -0.61125725</td><td>   0.14754485</td><td>  -0.7798799</td><td>   0.18970053</td><td>   0.14754485</td><td>2.529340e-01</td><td>   0.04215567</td><td>   0.00000000</td><td>  -0.23185620</td><td> -11.339876</td></tr>\n",
       "\t<tr><th scope=row>Neu</th><td>   0.27401187</td><td>  -2.234251</td><td>   0.40047889</td><td>  -0.42155672</td><td>  -0.29508971</td><td>  -0.33724538</td><td>  -0.02107784</td><td>   0.00000000</td><td>-1654.1254</td><td>-1554.0056</td><td>⋯</td><td>  -0.37940105</td><td>   0.37940105</td><td>  -0.5480237</td><td>   0.42155672</td><td>   0.37940105</td><td>4.847902e-01</td><td>   0.27401187</td><td>   0.23185620</td><td>   0.00000000</td><td> -11.108020</td></tr>\n",
       "\t<tr><th scope=row>LDG</th><td>  11.38203157</td><td>   8.873769</td><td>  11.50849858</td><td>  10.68646297</td><td>  10.81292999</td><td>  10.77077432</td><td>  11.08694186</td><td>  11.10801970</td><td>-1643.0173</td><td>-1542.8976</td><td>⋯</td><td>  10.72861864</td><td>  11.48742075</td><td>  10.5599960</td><td>  11.52957642</td><td>  11.48742075</td><td>1.159281e+01</td><td>  11.38203157</td><td>  11.33987589</td><td>  11.10801970</td><td>   0.000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 25 × 25 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Naïve.CD4 & Th1 & Th2 & Th17 & Tfh & Fr..I.nTreg & Fr..II.eTreg & Fr..III.T & Naïve.CD8 & CM.CD8 & ⋯ & SM.B & Plasmablast & DN.B & CL.Mono & Int.Mono & NC.Mono & mDC & pDC & Neu & LDG\\\\\n",
       "\\hline\n",
       "\tNaïve.CD4 &    0.00000000 &   -2.508263 &    0.12646702 &   -0.69556860 &   -0.56910158 &   -0.61125725 &   -0.29508971 &   -0.27401187 & -1654.3994 & -1554.2796 & ⋯ &   -0.65341292 &    0.10538918 &   -0.8220356 &    0.14754485 &    0.10538918 & 2.107784e-01 &    0.00000000 &   -0.04215567 &   -0.27401187 &  -11.382032\\\\\n",
       "\tTh1 &    2.50826251 &    0.000000 &    2.63472953 &    1.81269392 &    1.93916093 &    1.89700526 &    2.21317280 &    2.23425064 & -1651.8911 & -1551.7714 & ⋯ &    1.85484959 &    2.61365169 &    1.6862269 &    2.65580737 &    2.61365169 & 2.719041e+00 &    2.50826251 &    2.46610684 &    2.23425064 &   -8.873769\\\\\n",
       "\tTh2 &   -0.12646702 &   -2.634730 &    0.00000000 &   -0.82203561 &   -0.69556860 &   -0.73772427 &   -0.42155672 &   -0.40047889 & -1654.5258 & -1554.4061 & ⋯ &   -0.77987994 &   -0.02107784 &   -0.9485026 &    0.02107784 &   -0.02107784 & 8.431134e-02 &   -0.12646702 &   -0.16862269 &   -0.40047889 &  -11.508499\\\\\n",
       "\tTh17 &    0.69556860 &   -1.812694 &    0.82203561 &    0.00000000 &    0.12646702 &    0.08431134 &    0.40047889 &    0.42155672 & -1653.7038 & -1553.5841 & ⋯ &    0.04215567 &    0.80095778 &   -0.1264670 &    0.84311345 &    0.80095778 & 9.063470e-01 &    0.69556860 &    0.65341292 &    0.42155672 &  -10.686463\\\\\n",
       "\tTfh &    0.56910158 &   -1.939161 &    0.69556860 &   -0.12646702 &    0.00000000 &   -0.04215567 &    0.27401187 &    0.29508971 & -1653.8303 & -1553.7105 & ⋯ &   -0.08431134 &    0.67449076 &   -0.2529340 &    0.71664643 &    0.67449076 & 7.798799e-01 &    0.56910158 &    0.52694591 &    0.29508971 &  -10.812930\\\\\n",
       "\tFr..I.nTreg &    0.61125725 &   -1.897005 &    0.73772427 &   -0.08431134 &    0.04215567 &    0.00000000 &    0.31616754 &    0.33724538 & -1653.7881 & -1553.6684 & ⋯ &   -0.04215567 &    0.71664643 &   -0.2107784 &    0.75880210 &    0.71664643 & 8.220356e-01 &    0.61125725 &    0.56910158 &    0.33724538 &  -10.770774\\\\\n",
       "\tFr..II.eTreg &    0.29508971 &   -2.213173 &    0.42155672 &   -0.40047889 &   -0.27401187 &   -0.31616754 &    0.00000000 &    0.02107784 & -1654.1043 & -1553.9846 & ⋯ &   -0.35832322 &    0.40047889 &   -0.5269459 &    0.44263456 &    0.40047889 & 5.058681e-01 &    0.29508971 &    0.25293403 &    0.02107784 &  -11.086942\\\\\n",
       "\tFr..III.T &    0.27401187 &   -2.234251 &    0.40047889 &   -0.42155672 &   -0.29508971 &   -0.33724538 &   -0.02107784 &    0.00000000 & -1654.1254 & -1554.0056 & ⋯ &   -0.37940105 &    0.37940105 &   -0.5480237 &    0.42155672 &    0.37940105 & 4.847902e-01 &    0.27401187 &    0.23185620 &    0.00000000 &  -11.108020\\\\\n",
       "\tNaïve.CD8 & 1654.39936598 & 1651.891103 & 1654.52583300 & 1653.70379738 & 1653.83026440 & 1653.78810873 & 1654.10427627 & 1654.12535411 &     0.0000 &   100.1197 & ⋯ & 1653.74595306 & 1654.50475516 & 1653.5773304 & 1654.54691083 & 1654.50475516 & 1.654610e+03 & 1654.39936598 & 1654.35721031 & 1654.12535411 & 1643.017334\\\\\n",
       "\tCM.CD8 & 1554.27964387 & 1551.771381 & 1554.40611089 & 1553.58407527 & 1553.71054229 & 1553.66838662 & 1553.98455416 & 1554.00563200 &  -100.1197 &     0.0000 & ⋯ & 1553.62623095 & 1554.38503305 & 1553.4576083 & 1554.42718872 & 1554.38503305 & 1.554490e+03 & 1554.27964387 & 1554.23748820 & 1554.00563200 & 1542.897612\\\\\n",
       "\tEM.CD8 & 2061.18052745 & 2058.672265 & 2061.30699447 & 2060.48495886 & 2060.61142587 & 2060.56927020 & 2060.88543774 & 2060.90651558 &   406.7812 &   506.9009 & ⋯ & 2060.52711453 & 2061.28591663 & 2060.3584918 & 2061.32807231 & 2061.28591663 & 2.061391e+03 & 2061.18052745 & 2061.13837178 & 2060.90651558 & 2049.798496\\\\\n",
       "\tTEMRA.CD8 & 2513.25795899 & 2510.749696 & 2513.38442601 & 2512.56239040 & 2512.68885741 & 2512.64670174 & 2512.96286928 & 2512.98394712 &   858.8586 &   958.9783 & ⋯ & 2512.60454607 & 2513.36334817 & 2512.4359234 & 2513.40550384 & 2513.36334817 & 2.513469e+03 & 2513.25795899 & 2513.21580332 & 2512.98394712 & 2501.875927\\\\\n",
       "\tNK &  263.72588696 &  261.217624 &  263.85235397 &  263.03031836 &  263.15678538 &  263.11462970 &  263.43079725 &  263.45187508 & -1390.6735 & -1290.5538 & ⋯ &  263.07247403 &  263.83127614 &  262.9038513 &  263.87343181 &  263.83127614 & 2.639367e+02 &  263.72588696 &  263.68373128 &  263.45187508 &  252.343855\\\\\n",
       "\tNaïve.B &   -0.12646702 &   -2.634730 &    0.00000000 &   -0.82203561 &   -0.69556860 &   -0.73772427 &   -0.42155672 &   -0.40047889 & -1654.5258 & -1554.4061 & ⋯ &   -0.77987994 &   -0.02107784 &   -0.9485026 &    0.02107784 &   -0.02107784 & 8.431134e-02 &   -0.12646702 &   -0.16862269 &   -0.40047889 &  -11.508499\\\\\n",
       "\tUSM.B &    0.99065830 &   -1.517604 &    1.11712532 &    0.29508971 &    0.42155672 &    0.37940105 &    0.69556860 &    0.71664643 & -1653.4087 & -1553.2890 & ⋯ &    0.33724538 &    1.09604748 &    0.1686227 &    1.13820316 &    1.09604748 & 1.201437e+00 &    0.99065830 &    0.94850263 &    0.71664643 &  -10.391373\\\\\n",
       "\tSM.B &    0.65341292 &   -1.854850 &    0.77987994 &   -0.04215567 &    0.08431134 &    0.04215567 &    0.35832322 &    0.37940105 & -1653.7460 & -1553.6262 & ⋯ &    0.00000000 &    0.75880210 &   -0.1686227 &    0.80095778 &    0.75880210 & 8.641913e-01 &    0.65341292 &    0.61125725 &    0.37940105 &  -10.728619\\\\\n",
       "\tPlasmablast &   -0.10538918 &   -2.613652 &    0.02107784 &   -0.80095778 &   -0.67449076 &   -0.71664643 &   -0.40047889 &   -0.37940105 & -1654.5048 & -1554.3850 & ⋯ &   -0.75880210 &    0.00000000 &   -0.9274248 &    0.04215567 &    0.00000000 & 1.053892e-01 &   -0.10538918 &   -0.14754485 &   -0.37940105 &  -11.487421\\\\\n",
       "\tDN.B &    0.82203561 &   -1.686227 &    0.94850263 &    0.12646702 &    0.25293403 &    0.21077836 &    0.52694591 &    0.54802374 & -1653.5773 & -1553.4576 & ⋯ &    0.16862269 &    0.92742479 &    0.0000000 &    0.96958047 &    0.92742479 & 1.032814e+00 &    0.82203561 &    0.77987994 &    0.54802374 &  -10.559996\\\\\n",
       "\tCL.Mono &   -0.14754485 &   -2.655807 &   -0.02107784 &   -0.84311345 &   -0.71664643 &   -0.75880210 &   -0.44263456 &   -0.42155672 & -1654.5469 & -1554.4272 & ⋯ &   -0.80095778 &   -0.04215567 &   -0.9695805 &    0.00000000 &   -0.04215567 & 6.323351e-02 &   -0.14754485 &   -0.18970053 &   -0.42155672 &  -11.529576\\\\\n",
       "\tInt.Mono &   -0.10538918 &   -2.613652 &    0.02107784 &   -0.80095778 &   -0.67449076 &   -0.71664643 &   -0.40047889 &   -0.37940105 & -1654.5048 & -1554.3850 & ⋯ &   -0.75880210 &    0.00000000 &   -0.9274248 &    0.04215567 &    0.00000000 & 1.053892e-01 &   -0.10538918 &   -0.14754485 &   -0.37940105 &  -11.487421\\\\\n",
       "\tNC.Mono &   -0.21077836 &   -2.719041 &   -0.08431134 &   -0.90634696 &   -0.77987994 &   -0.82203561 &   -0.50586807 &   -0.48479023 & -1654.6101 & -1554.4904 & ⋯ &   -0.86419129 &   -0.10538918 &   -1.0328140 &   -0.06323351 &   -0.10538918 & 0.000000e+00 &   -0.21077836 &   -0.25293403 &   -0.48479023 &  -11.592810\\\\\n",
       "\tmDC &    0.00000000 &   -2.508263 &    0.12646702 &   -0.69556860 &   -0.56910158 &   -0.61125725 &   -0.29508971 &   -0.27401187 & -1654.3994 & -1554.2796 & ⋯ &   -0.65341292 &    0.10538918 &   -0.8220356 &    0.14754485 &    0.10538918 & 2.107784e-01 &    0.00000000 &   -0.04215567 &   -0.27401187 &  -11.382032\\\\\n",
       "\tpDC &    0.04215567 &   -2.466107 &    0.16862269 &   -0.65341292 &   -0.52694591 &   -0.56910158 &   -0.25293403 &   -0.23185620 & -1654.3572 & -1554.2375 & ⋯ &   -0.61125725 &    0.14754485 &   -0.7798799 &    0.18970053 &    0.14754485 & 2.529340e-01 &    0.04215567 &    0.00000000 &   -0.23185620 &  -11.339876\\\\\n",
       "\tNeu &    0.27401187 &   -2.234251 &    0.40047889 &   -0.42155672 &   -0.29508971 &   -0.33724538 &   -0.02107784 &    0.00000000 & -1654.1254 & -1554.0056 & ⋯ &   -0.37940105 &    0.37940105 &   -0.5480237 &    0.42155672 &    0.37940105 & 4.847902e-01 &    0.27401187 &    0.23185620 &    0.00000000 &  -11.108020\\\\\n",
       "\tLDG &   11.38203157 &    8.873769 &   11.50849858 &   10.68646297 &   10.81292999 &   10.77077432 &   11.08694186 &   11.10801970 & -1643.0173 & -1542.8976 & ⋯ &   10.72861864 &   11.48742075 &   10.5599960 &   11.52957642 &   11.48742075 & 1.159281e+01 &   11.38203157 &   11.33987589 &   11.10801970 &    0.000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 25 × 25 of type dbl\n",
       "\n",
       "| <!--/--> | Naïve.CD4 | Th1 | Th2 | Th17 | Tfh | Fr..I.nTreg | Fr..II.eTreg | Fr..III.T | Naïve.CD8 | CM.CD8 | ⋯ | SM.B | Plasmablast | DN.B | CL.Mono | Int.Mono | NC.Mono | mDC | pDC | Neu | LDG |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Naïve.CD4 |    0.00000000 |   -2.508263 |    0.12646702 |   -0.69556860 |   -0.56910158 |   -0.61125725 |   -0.29508971 |   -0.27401187 | -1654.3994 | -1554.2796 | ⋯ |   -0.65341292 |    0.10538918 |   -0.8220356 |    0.14754485 |    0.10538918 | 2.107784e-01 |    0.00000000 |   -0.04215567 |   -0.27401187 |  -11.382032 |\n",
       "| Th1 |    2.50826251 |    0.000000 |    2.63472953 |    1.81269392 |    1.93916093 |    1.89700526 |    2.21317280 |    2.23425064 | -1651.8911 | -1551.7714 | ⋯ |    1.85484959 |    2.61365169 |    1.6862269 |    2.65580737 |    2.61365169 | 2.719041e+00 |    2.50826251 |    2.46610684 |    2.23425064 |   -8.873769 |\n",
       "| Th2 |   -0.12646702 |   -2.634730 |    0.00000000 |   -0.82203561 |   -0.69556860 |   -0.73772427 |   -0.42155672 |   -0.40047889 | -1654.5258 | -1554.4061 | ⋯ |   -0.77987994 |   -0.02107784 |   -0.9485026 |    0.02107784 |   -0.02107784 | 8.431134e-02 |   -0.12646702 |   -0.16862269 |   -0.40047889 |  -11.508499 |\n",
       "| Th17 |    0.69556860 |   -1.812694 |    0.82203561 |    0.00000000 |    0.12646702 |    0.08431134 |    0.40047889 |    0.42155672 | -1653.7038 | -1553.5841 | ⋯ |    0.04215567 |    0.80095778 |   -0.1264670 |    0.84311345 |    0.80095778 | 9.063470e-01 |    0.69556860 |    0.65341292 |    0.42155672 |  -10.686463 |\n",
       "| Tfh |    0.56910158 |   -1.939161 |    0.69556860 |   -0.12646702 |    0.00000000 |   -0.04215567 |    0.27401187 |    0.29508971 | -1653.8303 | -1553.7105 | ⋯ |   -0.08431134 |    0.67449076 |   -0.2529340 |    0.71664643 |    0.67449076 | 7.798799e-01 |    0.56910158 |    0.52694591 |    0.29508971 |  -10.812930 |\n",
       "| Fr..I.nTreg |    0.61125725 |   -1.897005 |    0.73772427 |   -0.08431134 |    0.04215567 |    0.00000000 |    0.31616754 |    0.33724538 | -1653.7881 | -1553.6684 | ⋯ |   -0.04215567 |    0.71664643 |   -0.2107784 |    0.75880210 |    0.71664643 | 8.220356e-01 |    0.61125725 |    0.56910158 |    0.33724538 |  -10.770774 |\n",
       "| Fr..II.eTreg |    0.29508971 |   -2.213173 |    0.42155672 |   -0.40047889 |   -0.27401187 |   -0.31616754 |    0.00000000 |    0.02107784 | -1654.1043 | -1553.9846 | ⋯ |   -0.35832322 |    0.40047889 |   -0.5269459 |    0.44263456 |    0.40047889 | 5.058681e-01 |    0.29508971 |    0.25293403 |    0.02107784 |  -11.086942 |\n",
       "| Fr..III.T |    0.27401187 |   -2.234251 |    0.40047889 |   -0.42155672 |   -0.29508971 |   -0.33724538 |   -0.02107784 |    0.00000000 | -1654.1254 | -1554.0056 | ⋯ |   -0.37940105 |    0.37940105 |   -0.5480237 |    0.42155672 |    0.37940105 | 4.847902e-01 |    0.27401187 |    0.23185620 |    0.00000000 |  -11.108020 |\n",
       "| Naïve.CD8 | 1654.39936598 | 1651.891103 | 1654.52583300 | 1653.70379738 | 1653.83026440 | 1653.78810873 | 1654.10427627 | 1654.12535411 |     0.0000 |   100.1197 | ⋯ | 1653.74595306 | 1654.50475516 | 1653.5773304 | 1654.54691083 | 1654.50475516 | 1.654610e+03 | 1654.39936598 | 1654.35721031 | 1654.12535411 | 1643.017334 |\n",
       "| CM.CD8 | 1554.27964387 | 1551.771381 | 1554.40611089 | 1553.58407527 | 1553.71054229 | 1553.66838662 | 1553.98455416 | 1554.00563200 |  -100.1197 |     0.0000 | ⋯ | 1553.62623095 | 1554.38503305 | 1553.4576083 | 1554.42718872 | 1554.38503305 | 1.554490e+03 | 1554.27964387 | 1554.23748820 | 1554.00563200 | 1542.897612 |\n",
       "| EM.CD8 | 2061.18052745 | 2058.672265 | 2061.30699447 | 2060.48495886 | 2060.61142587 | 2060.56927020 | 2060.88543774 | 2060.90651558 |   406.7812 |   506.9009 | ⋯ | 2060.52711453 | 2061.28591663 | 2060.3584918 | 2061.32807231 | 2061.28591663 | 2.061391e+03 | 2061.18052745 | 2061.13837178 | 2060.90651558 | 2049.798496 |\n",
       "| TEMRA.CD8 | 2513.25795899 | 2510.749696 | 2513.38442601 | 2512.56239040 | 2512.68885741 | 2512.64670174 | 2512.96286928 | 2512.98394712 |   858.8586 |   958.9783 | ⋯ | 2512.60454607 | 2513.36334817 | 2512.4359234 | 2513.40550384 | 2513.36334817 | 2.513469e+03 | 2513.25795899 | 2513.21580332 | 2512.98394712 | 2501.875927 |\n",
       "| NK |  263.72588696 |  261.217624 |  263.85235397 |  263.03031836 |  263.15678538 |  263.11462970 |  263.43079725 |  263.45187508 | -1390.6735 | -1290.5538 | ⋯ |  263.07247403 |  263.83127614 |  262.9038513 |  263.87343181 |  263.83127614 | 2.639367e+02 |  263.72588696 |  263.68373128 |  263.45187508 |  252.343855 |\n",
       "| Naïve.B |   -0.12646702 |   -2.634730 |    0.00000000 |   -0.82203561 |   -0.69556860 |   -0.73772427 |   -0.42155672 |   -0.40047889 | -1654.5258 | -1554.4061 | ⋯ |   -0.77987994 |   -0.02107784 |   -0.9485026 |    0.02107784 |   -0.02107784 | 8.431134e-02 |   -0.12646702 |   -0.16862269 |   -0.40047889 |  -11.508499 |\n",
       "| USM.B |    0.99065830 |   -1.517604 |    1.11712532 |    0.29508971 |    0.42155672 |    0.37940105 |    0.69556860 |    0.71664643 | -1653.4087 | -1553.2890 | ⋯ |    0.33724538 |    1.09604748 |    0.1686227 |    1.13820316 |    1.09604748 | 1.201437e+00 |    0.99065830 |    0.94850263 |    0.71664643 |  -10.391373 |\n",
       "| SM.B |    0.65341292 |   -1.854850 |    0.77987994 |   -0.04215567 |    0.08431134 |    0.04215567 |    0.35832322 |    0.37940105 | -1653.7460 | -1553.6262 | ⋯ |    0.00000000 |    0.75880210 |   -0.1686227 |    0.80095778 |    0.75880210 | 8.641913e-01 |    0.65341292 |    0.61125725 |    0.37940105 |  -10.728619 |\n",
       "| Plasmablast |   -0.10538918 |   -2.613652 |    0.02107784 |   -0.80095778 |   -0.67449076 |   -0.71664643 |   -0.40047889 |   -0.37940105 | -1654.5048 | -1554.3850 | ⋯ |   -0.75880210 |    0.00000000 |   -0.9274248 |    0.04215567 |    0.00000000 | 1.053892e-01 |   -0.10538918 |   -0.14754485 |   -0.37940105 |  -11.487421 |\n",
       "| DN.B |    0.82203561 |   -1.686227 |    0.94850263 |    0.12646702 |    0.25293403 |    0.21077836 |    0.52694591 |    0.54802374 | -1653.5773 | -1553.4576 | ⋯ |    0.16862269 |    0.92742479 |    0.0000000 |    0.96958047 |    0.92742479 | 1.032814e+00 |    0.82203561 |    0.77987994 |    0.54802374 |  -10.559996 |\n",
       "| CL.Mono |   -0.14754485 |   -2.655807 |   -0.02107784 |   -0.84311345 |   -0.71664643 |   -0.75880210 |   -0.44263456 |   -0.42155672 | -1654.5469 | -1554.4272 | ⋯ |   -0.80095778 |   -0.04215567 |   -0.9695805 |    0.00000000 |   -0.04215567 | 6.323351e-02 |   -0.14754485 |   -0.18970053 |   -0.42155672 |  -11.529576 |\n",
       "| Int.Mono |   -0.10538918 |   -2.613652 |    0.02107784 |   -0.80095778 |   -0.67449076 |   -0.71664643 |   -0.40047889 |   -0.37940105 | -1654.5048 | -1554.3850 | ⋯ |   -0.75880210 |    0.00000000 |   -0.9274248 |    0.04215567 |    0.00000000 | 1.053892e-01 |   -0.10538918 |   -0.14754485 |   -0.37940105 |  -11.487421 |\n",
       "| NC.Mono |   -0.21077836 |   -2.719041 |   -0.08431134 |   -0.90634696 |   -0.77987994 |   -0.82203561 |   -0.50586807 |   -0.48479023 | -1654.6101 | -1554.4904 | ⋯ |   -0.86419129 |   -0.10538918 |   -1.0328140 |   -0.06323351 |   -0.10538918 | 0.000000e+00 |   -0.21077836 |   -0.25293403 |   -0.48479023 |  -11.592810 |\n",
       "| mDC |    0.00000000 |   -2.508263 |    0.12646702 |   -0.69556860 |   -0.56910158 |   -0.61125725 |   -0.29508971 |   -0.27401187 | -1654.3994 | -1554.2796 | ⋯ |   -0.65341292 |    0.10538918 |   -0.8220356 |    0.14754485 |    0.10538918 | 2.107784e-01 |    0.00000000 |   -0.04215567 |   -0.27401187 |  -11.382032 |\n",
       "| pDC |    0.04215567 |   -2.466107 |    0.16862269 |   -0.65341292 |   -0.52694591 |   -0.56910158 |   -0.25293403 |   -0.23185620 | -1654.3572 | -1554.2375 | ⋯ |   -0.61125725 |    0.14754485 |   -0.7798799 |    0.18970053 |    0.14754485 | 2.529340e-01 |    0.04215567 |    0.00000000 |   -0.23185620 |  -11.339876 |\n",
       "| Neu |    0.27401187 |   -2.234251 |    0.40047889 |   -0.42155672 |   -0.29508971 |   -0.33724538 |   -0.02107784 |    0.00000000 | -1654.1254 | -1554.0056 | ⋯ |   -0.37940105 |    0.37940105 |   -0.5480237 |    0.42155672 |    0.37940105 | 4.847902e-01 |    0.27401187 |    0.23185620 |    0.00000000 |  -11.108020 |\n",
       "| LDG |   11.38203157 |    8.873769 |   11.50849858 |   10.68646297 |   10.81292999 |   10.77077432 |   11.08694186 |   11.10801970 | -1643.0173 | -1542.8976 | ⋯ |   10.72861864 |   11.48742075 |   10.5599960 |   11.52957642 |   11.48742075 | 1.159281e+01 |   11.38203157 |   11.33987589 |   11.10801970 |    0.000000 |\n",
       "\n"
      ],
      "text/plain": [
       "             Naïve.CD4     Th1         Th2           Th17         \n",
       "Naïve.CD4       0.00000000   -2.508263    0.12646702   -0.69556860\n",
       "Th1             2.50826251    0.000000    2.63472953    1.81269392\n",
       "Th2            -0.12646702   -2.634730    0.00000000   -0.82203561\n",
       "Th17            0.69556860   -1.812694    0.82203561    0.00000000\n",
       "Tfh             0.56910158   -1.939161    0.69556860   -0.12646702\n",
       "Fr..I.nTreg     0.61125725   -1.897005    0.73772427   -0.08431134\n",
       "Fr..II.eTreg    0.29508971   -2.213173    0.42155672   -0.40047889\n",
       "Fr..III.T       0.27401187   -2.234251    0.40047889   -0.42155672\n",
       "Naïve.CD8    1654.39936598 1651.891103 1654.52583300 1653.70379738\n",
       "CM.CD8       1554.27964387 1551.771381 1554.40611089 1553.58407527\n",
       "EM.CD8       2061.18052745 2058.672265 2061.30699447 2060.48495886\n",
       "TEMRA.CD8    2513.25795899 2510.749696 2513.38442601 2512.56239040\n",
       "NK            263.72588696  261.217624  263.85235397  263.03031836\n",
       "Naïve.B        -0.12646702   -2.634730    0.00000000   -0.82203561\n",
       "USM.B           0.99065830   -1.517604    1.11712532    0.29508971\n",
       "SM.B            0.65341292   -1.854850    0.77987994   -0.04215567\n",
       "Plasmablast    -0.10538918   -2.613652    0.02107784   -0.80095778\n",
       "DN.B            0.82203561   -1.686227    0.94850263    0.12646702\n",
       "CL.Mono        -0.14754485   -2.655807   -0.02107784   -0.84311345\n",
       "Int.Mono       -0.10538918   -2.613652    0.02107784   -0.80095778\n",
       "NC.Mono        -0.21077836   -2.719041   -0.08431134   -0.90634696\n",
       "mDC             0.00000000   -2.508263    0.12646702   -0.69556860\n",
       "pDC             0.04215567   -2.466107    0.16862269   -0.65341292\n",
       "Neu             0.27401187   -2.234251    0.40047889   -0.42155672\n",
       "LDG            11.38203157    8.873769   11.50849858   10.68646297\n",
       "             Tfh           Fr..I.nTreg   Fr..II.eTreg  Fr..III.T     Naïve.CD8 \n",
       "Naïve.CD4      -0.56910158   -0.61125725   -0.29508971   -0.27401187 -1654.3994\n",
       "Th1             1.93916093    1.89700526    2.21317280    2.23425064 -1651.8911\n",
       "Th2            -0.69556860   -0.73772427   -0.42155672   -0.40047889 -1654.5258\n",
       "Th17            0.12646702    0.08431134    0.40047889    0.42155672 -1653.7038\n",
       "Tfh             0.00000000   -0.04215567    0.27401187    0.29508971 -1653.8303\n",
       "Fr..I.nTreg     0.04215567    0.00000000    0.31616754    0.33724538 -1653.7881\n",
       "Fr..II.eTreg   -0.27401187   -0.31616754    0.00000000    0.02107784 -1654.1043\n",
       "Fr..III.T      -0.29508971   -0.33724538   -0.02107784    0.00000000 -1654.1254\n",
       "Naïve.CD8    1653.83026440 1653.78810873 1654.10427627 1654.12535411     0.0000\n",
       "CM.CD8       1553.71054229 1553.66838662 1553.98455416 1554.00563200  -100.1197\n",
       "EM.CD8       2060.61142587 2060.56927020 2060.88543774 2060.90651558   406.7812\n",
       "TEMRA.CD8    2512.68885741 2512.64670174 2512.96286928 2512.98394712   858.8586\n",
       "NK            263.15678538  263.11462970  263.43079725  263.45187508 -1390.6735\n",
       "Naïve.B        -0.69556860   -0.73772427   -0.42155672   -0.40047889 -1654.5258\n",
       "USM.B           0.42155672    0.37940105    0.69556860    0.71664643 -1653.4087\n",
       "SM.B            0.08431134    0.04215567    0.35832322    0.37940105 -1653.7460\n",
       "Plasmablast    -0.67449076   -0.71664643   -0.40047889   -0.37940105 -1654.5048\n",
       "DN.B            0.25293403    0.21077836    0.52694591    0.54802374 -1653.5773\n",
       "CL.Mono        -0.71664643   -0.75880210   -0.44263456   -0.42155672 -1654.5469\n",
       "Int.Mono       -0.67449076   -0.71664643   -0.40047889   -0.37940105 -1654.5048\n",
       "NC.Mono        -0.77987994   -0.82203561   -0.50586807   -0.48479023 -1654.6101\n",
       "mDC            -0.56910158   -0.61125725   -0.29508971   -0.27401187 -1654.3994\n",
       "pDC            -0.52694591   -0.56910158   -0.25293403   -0.23185620 -1654.3572\n",
       "Neu            -0.29508971   -0.33724538   -0.02107784    0.00000000 -1654.1254\n",
       "LDG            10.81292999   10.77077432   11.08694186   11.10801970 -1643.0173\n",
       "             CM.CD8     ⋯ SM.B          Plasmablast   DN.B        \n",
       "Naïve.CD4    -1554.2796 ⋯   -0.65341292    0.10538918   -0.8220356\n",
       "Th1          -1551.7714 ⋯    1.85484959    2.61365169    1.6862269\n",
       "Th2          -1554.4061 ⋯   -0.77987994   -0.02107784   -0.9485026\n",
       "Th17         -1553.5841 ⋯    0.04215567    0.80095778   -0.1264670\n",
       "Tfh          -1553.7105 ⋯   -0.08431134    0.67449076   -0.2529340\n",
       "Fr..I.nTreg  -1553.6684 ⋯   -0.04215567    0.71664643   -0.2107784\n",
       "Fr..II.eTreg -1553.9846 ⋯   -0.35832322    0.40047889   -0.5269459\n",
       "Fr..III.T    -1554.0056 ⋯   -0.37940105    0.37940105   -0.5480237\n",
       "Naïve.CD8      100.1197 ⋯ 1653.74595306 1654.50475516 1653.5773304\n",
       "CM.CD8           0.0000 ⋯ 1553.62623095 1554.38503305 1553.4576083\n",
       "EM.CD8         506.9009 ⋯ 2060.52711453 2061.28591663 2060.3584918\n",
       "TEMRA.CD8      958.9783 ⋯ 2512.60454607 2513.36334817 2512.4359234\n",
       "NK           -1290.5538 ⋯  263.07247403  263.83127614  262.9038513\n",
       "Naïve.B      -1554.4061 ⋯   -0.77987994   -0.02107784   -0.9485026\n",
       "USM.B        -1553.2890 ⋯    0.33724538    1.09604748    0.1686227\n",
       "SM.B         -1553.6262 ⋯    0.00000000    0.75880210   -0.1686227\n",
       "Plasmablast  -1554.3850 ⋯   -0.75880210    0.00000000   -0.9274248\n",
       "DN.B         -1553.4576 ⋯    0.16862269    0.92742479    0.0000000\n",
       "CL.Mono      -1554.4272 ⋯   -0.80095778   -0.04215567   -0.9695805\n",
       "Int.Mono     -1554.3850 ⋯   -0.75880210    0.00000000   -0.9274248\n",
       "NC.Mono      -1554.4904 ⋯   -0.86419129   -0.10538918   -1.0328140\n",
       "mDC          -1554.2796 ⋯   -0.65341292    0.10538918   -0.8220356\n",
       "pDC          -1554.2375 ⋯   -0.61125725    0.14754485   -0.7798799\n",
       "Neu          -1554.0056 ⋯   -0.37940105    0.37940105   -0.5480237\n",
       "LDG          -1542.8976 ⋯   10.72861864   11.48742075   10.5599960\n",
       "             CL.Mono       Int.Mono      NC.Mono      mDC          \n",
       "Naïve.CD4       0.14754485    0.10538918 2.107784e-01    0.00000000\n",
       "Th1             2.65580737    2.61365169 2.719041e+00    2.50826251\n",
       "Th2             0.02107784   -0.02107784 8.431134e-02   -0.12646702\n",
       "Th17            0.84311345    0.80095778 9.063470e-01    0.69556860\n",
       "Tfh             0.71664643    0.67449076 7.798799e-01    0.56910158\n",
       "Fr..I.nTreg     0.75880210    0.71664643 8.220356e-01    0.61125725\n",
       "Fr..II.eTreg    0.44263456    0.40047889 5.058681e-01    0.29508971\n",
       "Fr..III.T       0.42155672    0.37940105 4.847902e-01    0.27401187\n",
       "Naïve.CD8    1654.54691083 1654.50475516 1.654610e+03 1654.39936598\n",
       "CM.CD8       1554.42718872 1554.38503305 1.554490e+03 1554.27964387\n",
       "EM.CD8       2061.32807231 2061.28591663 2.061391e+03 2061.18052745\n",
       "TEMRA.CD8    2513.40550384 2513.36334817 2.513469e+03 2513.25795899\n",
       "NK            263.87343181  263.83127614 2.639367e+02  263.72588696\n",
       "Naïve.B         0.02107784   -0.02107784 8.431134e-02   -0.12646702\n",
       "USM.B           1.13820316    1.09604748 1.201437e+00    0.99065830\n",
       "SM.B            0.80095778    0.75880210 8.641913e-01    0.65341292\n",
       "Plasmablast     0.04215567    0.00000000 1.053892e-01   -0.10538918\n",
       "DN.B            0.96958047    0.92742479 1.032814e+00    0.82203561\n",
       "CL.Mono         0.00000000   -0.04215567 6.323351e-02   -0.14754485\n",
       "Int.Mono        0.04215567    0.00000000 1.053892e-01   -0.10538918\n",
       "NC.Mono        -0.06323351   -0.10538918 0.000000e+00   -0.21077836\n",
       "mDC             0.14754485    0.10538918 2.107784e-01    0.00000000\n",
       "pDC             0.18970053    0.14754485 2.529340e-01    0.04215567\n",
       "Neu             0.42155672    0.37940105 4.847902e-01    0.27401187\n",
       "LDG            11.52957642   11.48742075 1.159281e+01   11.38203157\n",
       "             pDC           Neu           LDG        \n",
       "Naïve.CD4      -0.04215567   -0.27401187  -11.382032\n",
       "Th1             2.46610684    2.23425064   -8.873769\n",
       "Th2            -0.16862269   -0.40047889  -11.508499\n",
       "Th17            0.65341292    0.42155672  -10.686463\n",
       "Tfh             0.52694591    0.29508971  -10.812930\n",
       "Fr..I.nTreg     0.56910158    0.33724538  -10.770774\n",
       "Fr..II.eTreg    0.25293403    0.02107784  -11.086942\n",
       "Fr..III.T       0.23185620    0.00000000  -11.108020\n",
       "Naïve.CD8    1654.35721031 1654.12535411 1643.017334\n",
       "CM.CD8       1554.23748820 1554.00563200 1542.897612\n",
       "EM.CD8       2061.13837178 2060.90651558 2049.798496\n",
       "TEMRA.CD8    2513.21580332 2512.98394712 2501.875927\n",
       "NK            263.68373128  263.45187508  252.343855\n",
       "Naïve.B        -0.16862269   -0.40047889  -11.508499\n",
       "USM.B           0.94850263    0.71664643  -10.391373\n",
       "SM.B            0.61125725    0.37940105  -10.728619\n",
       "Plasmablast    -0.14754485   -0.37940105  -11.487421\n",
       "DN.B            0.77987994    0.54802374  -10.559996\n",
       "CL.Mono        -0.18970053   -0.42155672  -11.529576\n",
       "Int.Mono       -0.14754485   -0.37940105  -11.487421\n",
       "NC.Mono        -0.25293403   -0.48479023  -11.592810\n",
       "mDC            -0.04215567   -0.27401187  -11.382032\n",
       "pDC             0.00000000   -0.23185620  -11.339876\n",
       "Neu             0.23185620    0.00000000  -11.108020\n",
       "LDG            11.33987589   11.10801970    0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#row value - col value\n",
    "diff_gen_list_immunexut[[\"CD8A\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select genes if any immune cells express >=2 z compared to all nonimmune cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_gen_list_immune_immunexut <- lapply(diff_gen_list_immunexut, function(x) x[ rowSums(x >= 2) == 24,,drop = F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rmeove empty lists\n",
    "diff_gen_list_immune_immunexut <- diff_gen_list_immune_immunexut[sapply(diff_gen_list_immune_immunexut, function(x) length(unlist(x))) >= 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 1 × 25 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Naïve.CD4</th><th scope=col>Th1</th><th scope=col>Th2</th><th scope=col>Th17</th><th scope=col>Tfh</th><th scope=col>Fr..I.nTreg</th><th scope=col>Fr..II.eTreg</th><th scope=col>Fr..III.T</th><th scope=col>Naïve.CD8</th><th scope=col>CM.CD8</th><th scope=col>⋯</th><th scope=col>SM.B</th><th scope=col>Plasmablast</th><th scope=col>DN.B</th><th scope=col>CL.Mono</th><th scope=col>Int.Mono</th><th scope=col>NC.Mono</th><th scope=col>mDC</th><th scope=col>pDC</th><th scope=col>Neu</th><th scope=col>LDG</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TEMRA.CD8</th><td>2513.258</td><td>2510.75</td><td>2513.384</td><td>2512.562</td><td>2512.689</td><td>2512.647</td><td>2512.963</td><td>2512.984</td><td>858.8586</td><td>958.9783</td><td>⋯</td><td>2512.605</td><td>2513.363</td><td>2512.436</td><td>2513.406</td><td>2513.363</td><td>2513.469</td><td>2513.258</td><td>2513.216</td><td>2512.984</td><td>2501.876</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 25 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Naïve.CD4 & Th1 & Th2 & Th17 & Tfh & Fr..I.nTreg & Fr..II.eTreg & Fr..III.T & Naïve.CD8 & CM.CD8 & ⋯ & SM.B & Plasmablast & DN.B & CL.Mono & Int.Mono & NC.Mono & mDC & pDC & Neu & LDG\\\\\n",
       "\\hline\n",
       "\tTEMRA.CD8 & 2513.258 & 2510.75 & 2513.384 & 2512.562 & 2512.689 & 2512.647 & 2512.963 & 2512.984 & 858.8586 & 958.9783 & ⋯ & 2512.605 & 2513.363 & 2512.436 & 2513.406 & 2513.363 & 2513.469 & 2513.258 & 2513.216 & 2512.984 & 2501.876\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 25 of type dbl\n",
       "\n",
       "| <!--/--> | Naïve.CD4 | Th1 | Th2 | Th17 | Tfh | Fr..I.nTreg | Fr..II.eTreg | Fr..III.T | Naïve.CD8 | CM.CD8 | ⋯ | SM.B | Plasmablast | DN.B | CL.Mono | Int.Mono | NC.Mono | mDC | pDC | Neu | LDG |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| TEMRA.CD8 | 2513.258 | 2510.75 | 2513.384 | 2512.562 | 2512.689 | 2512.647 | 2512.963 | 2512.984 | 858.8586 | 958.9783 | ⋯ | 2512.605 | 2513.363 | 2512.436 | 2513.406 | 2513.363 | 2513.469 | 2513.258 | 2513.216 | 2512.984 | 2501.876 |\n",
       "\n"
      ],
      "text/plain": [
       "          Naïve.CD4 Th1     Th2      Th17     Tfh      Fr..I.nTreg Fr..II.eTreg\n",
       "TEMRA.CD8 2513.258  2510.75 2513.384 2512.562 2512.689 2512.647    2512.963    \n",
       "          Fr..III.T Naïve.CD8 CM.CD8   ⋯ SM.B     Plasmablast DN.B     CL.Mono \n",
       "TEMRA.CD8 2512.984  858.8586  958.9783 ⋯ 2512.605 2513.363    2512.436 2513.406\n",
       "          Int.Mono NC.Mono  mDC      pDC      Neu      LDG     \n",
       "TEMRA.CD8 2513.363 2513.469 2513.258 2513.216 2512.984 2501.876"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_gen_list_immune_immunexut$CD8A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4655"
      ],
      "text/latex": [
       "4655"
      ],
      "text/markdown": [
       "4655"
      ],
      "text/plain": [
       "[1] 4655"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_immune_immunexut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select genes with z (of median expression across all celltypes) > 75th percentile\n",
    "\n",
    "diff_gen_list_immune_immunexut_expressed <- diff_gen_list_immune_immunexut[names(diff_gen_list_immune_immunexut) %in% immunexut_expressed_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2555"
      ],
      "text/latex": [
       "2555"
      ],
      "text/markdown": [
       "2555"
      ],
      "text/plain": [
       "[1] 2555"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(diff_gen_list_immune_immunexut_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add cell type as a variable\n",
    "diff_gen_list_immune_immunexut_expressed <- lapply(diff_gen_list_immune_immunexut_expressed, function(x){cbind.data.frame(x, rownames(x))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_immunexut_expressed <- do.call(\"rbind\", diff_gen_list_immune_immunexut_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(immune_immunexut_expressed)[ colnames(immune_immunexut_expressed) == \"rownames(x)\"] <- \"celltype\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "immune_immunexut_expressed$Gene <- rownames(immune_immunexut_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 27</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Naïve.CD4</th><th scope=col>Th1</th><th scope=col>Th2</th><th scope=col>Th17</th><th scope=col>Tfh</th><th scope=col>Fr..I.nTreg</th><th scope=col>Fr..II.eTreg</th><th scope=col>Fr..III.T</th><th scope=col>Naïve.CD8</th><th scope=col>CM.CD8</th><th scope=col>⋯</th><th scope=col>DN.B</th><th scope=col>CL.Mono</th><th scope=col>Int.Mono</th><th scope=col>NC.Mono</th><th scope=col>mDC</th><th scope=col>pDC</th><th scope=col>Neu</th><th scope=col>LDG</th><th scope=col>celltype</th><th scope=col>Gene</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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>AC009041.2</th><td>0</td><td>81.806094</td><td>45.576304</td><td>84.985836</td><td>74.161865</td><td>81.966687</td><td>111.74064</td><td>103.100730</td><td>10.952445</td><td>70.564581</td><td>⋯</td><td>111.740636</td><td>111.997585</td><td>111.997585</td><td>111.997585</td><td>112.029703</td><td>111.387331</td><td>111.99758</td><td>111.32309</td><td>Naïve.CD4</td><td>AC009041.2</td></tr>\n",
       "\t<tr><th scope=row>ADTRP</th><td>0</td><td>62.938741</td><td>61.167559</td><td>57.903230</td><td>40.840158</td><td> 7.409101</td><td> 50.88544</td><td> 52.965545</td><td>59.658965</td><td>68.411899</td><td>⋯</td><td> 69.941088</td><td> 69.961683</td><td> 69.874153</td><td> 69.590970</td><td> 69.956534</td><td> 69.750583</td><td> 69.96168</td><td> 69.51889</td><td>Naïve.CD4</td><td>ADTRP     </td></tr>\n",
       "\t<tr><th scope=row>AK5</th><td>0</td><td>41.412320</td><td>38.869737</td><td>45.784150</td><td>35.970486</td><td>14.743450</td><td> 48.66221</td><td> 46.094910</td><td>19.284786</td><td>41.977339</td><td>⋯</td><td> 49.237825</td><td> 49.251951</td><td> 49.304921</td><td> 49.297859</td><td> 47.006003</td><td> 49.085977</td><td> 49.29786</td><td> 48.98710</td><td>Naïve.CD4</td><td>AK5       </td></tr>\n",
       "\t<tr><th scope=row>ANKRD55</th><td>0</td><td>84.561156</td><td>29.469417</td><td>89.415824</td><td>53.567890</td><td>78.524048</td><td> 96.28564</td><td> 93.371171</td><td>98.159223</td><td>99.999500</td><td>⋯</td><td>101.206922</td><td>100.698972</td><td>101.048708</td><td>101.256884</td><td>100.882167</td><td>101.331828</td><td> 99.94121</td><td> 97.98436</td><td>Naïve.CD4</td><td>ANKRD55   </td></tr>\n",
       "\t<tr><th scope=row>B4GALT4</th><td>0</td><td> 6.397443</td><td> 7.130696</td><td> 7.636564</td><td> 7.863949</td><td> 7.545865</td><td>  8.29317</td><td>  8.104109</td><td> 7.911215</td><td> 7.429618</td><td>⋯</td><td>  7.244388</td><td>  8.862911</td><td>  9.095406</td><td>  8.882072</td><td>  7.269937</td><td>  7.319758</td><td> 10.33325</td><td> 10.06371</td><td>Naïve.CD4</td><td>B4GALT4   </td></tr>\n",
       "\t<tr><th scope=row>CHRM3-AS2</th><td>0</td><td>23.827609</td><td>24.303400</td><td>24.913469</td><td>20.609612</td><td> 8.939137</td><td> 29.97409</td><td> 27.707677</td><td>15.010330</td><td>28.034444</td><td>⋯</td><td> 32.050342</td><td> 32.052670</td><td> 32.041028</td><td> 31.989801</td><td> 32.053446</td><td> 32.057327</td><td> 32.04336</td><td> 31.85397</td><td>Naïve.CD4</td><td>CHRM3-AS2 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 27\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Naïve.CD4 & Th1 & Th2 & Th17 & Tfh & Fr..I.nTreg & Fr..II.eTreg & Fr..III.T & Naïve.CD8 & CM.CD8 & ⋯ & DN.B & CL.Mono & Int.Mono & NC.Mono & mDC & pDC & Neu & LDG & celltype & Gene\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tAC009041.2 & 0 & 81.806094 & 45.576304 & 84.985836 & 74.161865 & 81.966687 & 111.74064 & 103.100730 & 10.952445 & 70.564581 & ⋯ & 111.740636 & 111.997585 & 111.997585 & 111.997585 & 112.029703 & 111.387331 & 111.99758 & 111.32309 & Naïve.CD4 & AC009041.2\\\\\n",
       "\tADTRP & 0 & 62.938741 & 61.167559 & 57.903230 & 40.840158 &  7.409101 &  50.88544 &  52.965545 & 59.658965 & 68.411899 & ⋯ &  69.941088 &  69.961683 &  69.874153 &  69.590970 &  69.956534 &  69.750583 &  69.96168 &  69.51889 & Naïve.CD4 & ADTRP     \\\\\n",
       "\tAK5 & 0 & 41.412320 & 38.869737 & 45.784150 & 35.970486 & 14.743450 &  48.66221 &  46.094910 & 19.284786 & 41.977339 & ⋯ &  49.237825 &  49.251951 &  49.304921 &  49.297859 &  47.006003 &  49.085977 &  49.29786 &  48.98710 & Naïve.CD4 & AK5       \\\\\n",
       "\tANKRD55 & 0 & 84.561156 & 29.469417 & 89.415824 & 53.567890 & 78.524048 &  96.28564 &  93.371171 & 98.159223 & 99.999500 & ⋯ & 101.206922 & 100.698972 & 101.048708 & 101.256884 & 100.882167 & 101.331828 &  99.94121 &  97.98436 & Naïve.CD4 & ANKRD55   \\\\\n",
       "\tB4GALT4 & 0 &  6.397443 &  7.130696 &  7.636564 &  7.863949 &  7.545865 &   8.29317 &   8.104109 &  7.911215 &  7.429618 & ⋯ &   7.244388 &   8.862911 &   9.095406 &   8.882072 &   7.269937 &   7.319758 &  10.33325 &  10.06371 & Naïve.CD4 & B4GALT4   \\\\\n",
       "\tCHRM3-AS2 & 0 & 23.827609 & 24.303400 & 24.913469 & 20.609612 &  8.939137 &  29.97409 &  27.707677 & 15.010330 & 28.034444 & ⋯ &  32.050342 &  32.052670 &  32.041028 &  31.989801 &  32.053446 &  32.057327 &  32.04336 &  31.85397 & Naïve.CD4 & CHRM3-AS2 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 27\n",
       "\n",
       "| <!--/--> | Naïve.CD4 &lt;dbl&gt; | Th1 &lt;dbl&gt; | Th2 &lt;dbl&gt; | Th17 &lt;dbl&gt; | Tfh &lt;dbl&gt; | Fr..I.nTreg &lt;dbl&gt; | Fr..II.eTreg &lt;dbl&gt; | Fr..III.T &lt;dbl&gt; | Naïve.CD8 &lt;dbl&gt; | CM.CD8 &lt;dbl&gt; | ⋯ ⋯ | DN.B &lt;dbl&gt; | CL.Mono &lt;dbl&gt; | Int.Mono &lt;dbl&gt; | NC.Mono &lt;dbl&gt; | mDC &lt;dbl&gt; | pDC &lt;dbl&gt; | Neu &lt;dbl&gt; | LDG &lt;dbl&gt; | celltype &lt;chr&gt; | Gene &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| AC009041.2 | 0 | 81.806094 | 45.576304 | 84.985836 | 74.161865 | 81.966687 | 111.74064 | 103.100730 | 10.952445 | 70.564581 | ⋯ | 111.740636 | 111.997585 | 111.997585 | 111.997585 | 112.029703 | 111.387331 | 111.99758 | 111.32309 | Naïve.CD4 | AC009041.2 |\n",
       "| ADTRP | 0 | 62.938741 | 61.167559 | 57.903230 | 40.840158 |  7.409101 |  50.88544 |  52.965545 | 59.658965 | 68.411899 | ⋯ |  69.941088 |  69.961683 |  69.874153 |  69.590970 |  69.956534 |  69.750583 |  69.96168 |  69.51889 | Naïve.CD4 | ADTRP      |\n",
       "| AK5 | 0 | 41.412320 | 38.869737 | 45.784150 | 35.970486 | 14.743450 |  48.66221 |  46.094910 | 19.284786 | 41.977339 | ⋯ |  49.237825 |  49.251951 |  49.304921 |  49.297859 |  47.006003 |  49.085977 |  49.29786 |  48.98710 | Naïve.CD4 | AK5        |\n",
       "| ANKRD55 | 0 | 84.561156 | 29.469417 | 89.415824 | 53.567890 | 78.524048 |  96.28564 |  93.371171 | 98.159223 | 99.999500 | ⋯ | 101.206922 | 100.698972 | 101.048708 | 101.256884 | 100.882167 | 101.331828 |  99.94121 |  97.98436 | Naïve.CD4 | ANKRD55    |\n",
       "| B4GALT4 | 0 |  6.397443 |  7.130696 |  7.636564 |  7.863949 |  7.545865 |   8.29317 |   8.104109 |  7.911215 |  7.429618 | ⋯ |   7.244388 |   8.862911 |   9.095406 |   8.882072 |   7.269937 |   7.319758 |  10.33325 |  10.06371 | Naïve.CD4 | B4GALT4    |\n",
       "| CHRM3-AS2 | 0 | 23.827609 | 24.303400 | 24.913469 | 20.609612 |  8.939137 |  29.97409 |  27.707677 | 15.010330 | 28.034444 | ⋯ |  32.050342 |  32.052670 |  32.041028 |  31.989801 |  32.053446 |  32.057327 |  32.04336 |  31.85397 | Naïve.CD4 | CHRM3-AS2  |\n",
       "\n"
      ],
      "text/plain": [
       "           Naïve.CD4 Th1       Th2       Th17      Tfh       Fr..I.nTreg\n",
       "AC009041.2 0         81.806094 45.576304 84.985836 74.161865 81.966687  \n",
       "ADTRP      0         62.938741 61.167559 57.903230 40.840158  7.409101  \n",
       "AK5        0         41.412320 38.869737 45.784150 35.970486 14.743450  \n",
       "ANKRD55    0         84.561156 29.469417 89.415824 53.567890 78.524048  \n",
       "B4GALT4    0          6.397443  7.130696  7.636564  7.863949  7.545865  \n",
       "CHRM3-AS2  0         23.827609 24.303400 24.913469 20.609612  8.939137  \n",
       "           Fr..II.eTreg Fr..III.T  Naïve.CD8 CM.CD8    ⋯ DN.B       CL.Mono   \n",
       "AC009041.2 111.74064    103.100730 10.952445 70.564581 ⋯ 111.740636 111.997585\n",
       "ADTRP       50.88544     52.965545 59.658965 68.411899 ⋯  69.941088  69.961683\n",
       "AK5         48.66221     46.094910 19.284786 41.977339 ⋯  49.237825  49.251951\n",
       "ANKRD55     96.28564     93.371171 98.159223 99.999500 ⋯ 101.206922 100.698972\n",
       "B4GALT4      8.29317      8.104109  7.911215  7.429618 ⋯   7.244388   8.862911\n",
       "CHRM3-AS2   29.97409     27.707677 15.010330 28.034444 ⋯  32.050342  32.052670\n",
       "           Int.Mono   NC.Mono    mDC        pDC        Neu       LDG      \n",
       "AC009041.2 111.997585 111.997585 112.029703 111.387331 111.99758 111.32309\n",
       "ADTRP       69.874153  69.590970  69.956534  69.750583  69.96168  69.51889\n",
       "AK5         49.304921  49.297859  47.006003  49.085977  49.29786  48.98710\n",
       "ANKRD55    101.048708 101.256884 100.882167 101.331828  99.94121  97.98436\n",
       "B4GALT4      9.095406   8.882072   7.269937   7.319758  10.33325  10.06371\n",
       "CHRM3-AS2   32.041028  31.989801  32.053446  32.057327  32.04336  31.85397\n",
       "           celltype  Gene      \n",
       "AC009041.2 Naïve.CD4 AC009041.2\n",
       "ADTRP      Naïve.CD4 ADTRP     \n",
       "AK5        Naïve.CD4 AK5       \n",
       "ANKRD55    Naïve.CD4 ANKRD55   \n",
       "B4GALT4    Naïve.CD4 B4GALT4   \n",
       "CHRM3-AS2  Naïve.CD4 CHRM3-AS2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(immune_immunexut_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>2555</li><li>27</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2555\n",
       "\\item 27\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2555\n",
       "2. 27\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2555   27"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(immune_immunexut_expressed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     CL.Mono       CM.CD8         DN.B       EM.CD8  Fr..I.nTreg Fr..II.eTreg \n",
       "         176            1           28           11           20           99 \n",
       "    Int.Mono          LDG          mDC      Naïve.B    Naïve.CD4    Naïve.CD8 \n",
       "          40          210          153          178           20           30 \n",
       "     NC.Mono          Neu           NK          pDC  Plasmablast         SM.B \n",
       "         158          242          173          532          385           13 \n",
       "   TEMRA.CD8          Tfh          Th1         Th17          Th2        USM.B \n",
       "          29            1            1           21           10           24 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(immune_immunexut_expressed$celltype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group some immunexut cell types together\n",
    "immune_immunexut_expressed$Cellgroup <- NA\n",
    "immune_immunexut_expressed$Cellgroup[ grepl(\"CD8\", immune_immunexut_expressed$celltype)] <- \"T_cells\"\n",
    "immune_immunexut_expressed$Cellgroup[ immune_immunexut_expressed$celltype %in% \n",
    "                              c(\"Fr..I.nTreg\", \"Fr..II.eTreg\", \"Th17\", \"Th2\",\"Th1\", \"Naïve.CD4\", \"Tfh\")] <- \"T_cells\"\n",
    "immune_immunexut_expressed$Cellgroup[ immune_immunexut_expressed$celltype %in% \n",
    "                              c(\"Naïve.B\", \"DN.B\", \"SM.B\", \"USM.B\", \"Plasmablast\")] <- \"B_cells\"\n",
    "immune_immunexut_expressed$Cellgroup[ immune_immunexut_expressed$celltype %in% \n",
    "                              c(\"CL.Mono\", \"Int.Mono\", \"NC.Mono\")] <- \"Monocytes\"\n",
    "immune_immunexut_expressed$Cellgroup[ immune_immunexut_expressed$celltype %in% \n",
    "                              c(\"LDG\",  \"Neu\")] <- \"Granulocytes\"\n",
    "immune_immunexut_expressed$Cellgroup[ immune_immunexut_expressed$celltype %in% \n",
    "                              c(\"pDC\",  \"mDC\")] <- \"DC\"\n",
    "immune_immunexut_expressed$Cellgroup[ immune_immunexut_expressed$celltype == \"NK\"] <- \"NK_cells\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     B_cells           DC Granulocytes    Monocytes     NK_cells      T_cells \n",
       "         628          685          452          374          173          243 \n",
       "        <NA> \n",
       "           0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(immune_immunexut_expressed$Cellgroup, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- blood gene expression from proteinatlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteinatlas <- read.csv(paste0(datapath, \"TME_clusters/proteinatlas_v20.1.csv\"), header = TRUE, stringsAsFactors = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>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": 44,
   "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteinatlas <- proteinatlas[,! colnames(proteinatlas) %in% rmcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_cells_blood <- as.data.frame(proteinatlas$Gene)\n",
    "gene_cells_blood$Celltype <- proteinatlas$RNA.blood.cell.specific.NX\n",
    "colnames(gene_cells_blood) <- c(\"Gene\", \"Celltype\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>2</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 19670\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 19670\n",
       "2. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 19670     2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gene_cells_blood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Celltype</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>naive CD4 T-cell: 6.6</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>TNMD    </td><td>                     </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>DPM1    </td><td>                     </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>SCYL3   </td><td>                     </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>C1orf112</td><td>                     </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>FGR     </td><td>                     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Gene & Celltype\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & TSPAN6   & naive CD4 T-cell: 6.6\\\\\n",
       "\t2 & TNMD     &                      \\\\\n",
       "\t3 & DPM1     &                      \\\\\n",
       "\t4 & SCYL3    &                      \\\\\n",
       "\t5 & C1orf112 &                      \\\\\n",
       "\t6 & FGR      &                      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | TSPAN6   | naive CD4 T-cell: 6.6 |\n",
       "| 2 | TNMD     | <!----> |\n",
       "| 3 | DPM1     | <!----> |\n",
       "| 4 | SCYL3    | <!----> |\n",
       "| 5 | C1orf112 | <!----> |\n",
       "| 6 | FGR      | <!----> |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene     Celltype             \n",
       "1 TSPAN6   naive CD4 T-cell: 6.6\n",
       "2 TNMD                          \n",
       "3 DPM1                          \n",
       "4 SCYL3                         \n",
       "5 C1orf112                      \n",
       "6 FGR                           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gene_cells_blood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rmeoves non specific genes\n",
    "gene_cells_blood <- gene_cells_blood[ gene_cells_blood$Celltype != \"\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>5934</li><li>2</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5934\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5934\n",
       "2. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5934    2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gene_cells_blood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Celltype</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>naive CD4 T-cell: 6.6                                      </td></tr>\n",
       "\t<tr><th scope=row>7</th><td>CFH   </td><td>MAIT T-cell: 16.5                                          </td></tr>\n",
       "\t<tr><th scope=row>9</th><td>GCLC  </td><td>NK-cell: 8.0                                               </td></tr>\n",
       "\t<tr><th scope=row>24</th><td>HS3ST1</td><td>memory B-cell: 7.3;naive B-cell: 15.8;plasmacytoid DC: 22.3</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>AOC1  </td><td>eosinophil: 20.3                                           </td></tr>\n",
       "\t<tr><th scope=row>26</th><td>WNT16 </td><td>memory B-cell: 13.1;naive B-cell: 11.8                     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Gene & Celltype\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & TSPAN6 & naive CD4 T-cell: 6.6                                      \\\\\n",
       "\t7 & CFH    & MAIT T-cell: 16.5                                          \\\\\n",
       "\t9 & GCLC   & NK-cell: 8.0                                               \\\\\n",
       "\t24 & HS3ST1 & memory B-cell: 7.3;naive B-cell: 15.8;plasmacytoid DC: 22.3\\\\\n",
       "\t25 & AOC1   & eosinophil: 20.3                                           \\\\\n",
       "\t26 & WNT16  & memory B-cell: 13.1;naive B-cell: 11.8                     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | TSPAN6 | naive CD4 T-cell: 6.6                                       |\n",
       "| 7 | CFH    | MAIT T-cell: 16.5                                           |\n",
       "| 9 | GCLC   | NK-cell: 8.0                                                |\n",
       "| 24 | HS3ST1 | memory B-cell: 7.3;naive B-cell: 15.8;plasmacytoid DC: 22.3 |\n",
       "| 25 | AOC1   | eosinophil: 20.3                                            |\n",
       "| 26 | WNT16  | memory B-cell: 13.1;naive B-cell: 11.8                      |\n",
       "\n"
      ],
      "text/plain": [
       "   Gene   Celltype                                                   \n",
       "1  TSPAN6 naive CD4 T-cell: 6.6                                      \n",
       "7  CFH    MAIT T-cell: 16.5                                          \n",
       "9  GCLC   NK-cell: 8.0                                               \n",
       "24 HS3ST1 memory B-cell: 7.3;naive B-cell: 15.8;plasmacytoid DC: 22.3\n",
       "25 AOC1   eosinophil: 20.3                                           \n",
       "26 WNT16  memory B-cell: 13.1;naive B-cell: 11.8                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gene_cells_blood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate rows\n",
    "gene_cells_blood_sep <- tidyr::separate_rows(gene_cells_blood, Celltype,sep = \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seprate gene expression as a new column\n",
    "\n",
    "gene_cells_blood_sep$NX <- gsub(\".*:\", \"\", gene_cells_blood_sep$Celltype)\n",
    "gene_cells_blood_sep$Celltype <- gsub(\":.*\", \"\", gene_cells_blood_sep$Celltype)\n",
    "\n",
    "gene_cells_blood_sep$NX <- as.numeric(gene_cells_blood_sep$NX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "              basophil     classical monocyte             eosinophil \n",
       "                   926                    496                    679 \n",
       "              gdT-cell  intermediate monocyte            MAIT T-cell \n",
       "                   338                    517                    358 \n",
       "         memory B-cell      memory CD4 T-cell      memory CD8 T-cell \n",
       "                   518                    246                    253 \n",
       "            myeloid DC           naive B-cell       naive CD4 T-cell \n",
       "                   527                    519                    351 \n",
       "      naive CD8 T-cell             neutrophil                NK-cell \n",
       "                   311                   1461                    472 \n",
       "non-classical monocyte        plasmacytoid DC                  T-reg \n",
       "                   498                    818                    442 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(gene_cells_blood_sep$Celltype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group some atlas cell types together\n",
    "gene_cells_blood_sep$Cellgroup <- NA\n",
    "\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"CD8\", gene_cells_blood_sep$Celltype)] <- \"T_cells\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"B-cell\", gene_cells_blood_sep$Celltype)] <- \"B_cells\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"NK-cell\", gene_cells_blood_sep$Celltype)] <- \"NK_cells\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"monocyte\", gene_cells_blood_sep$Celltype)] <- \"Monocytes\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"myeloid DC\", gene_cells_blood_sep$Celltype)] <- \"DC\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"plasmacytoid DC\", gene_cells_blood_sep$Celltype)] <- \"DC\"\n",
    "gene_cells_blood_sep$Cellgroup[gene_cells_blood_sep$Celltype == \"neutrophil\"] <- \"Granulocytes\"\n",
    "gene_cells_blood_sep$Cellgroup[gene_cells_blood_sep$Celltype == \"basophil\"] <- \"Granulocytes\"\n",
    "gene_cells_blood_sep$Cellgroup[gene_cells_blood_sep$Celltype == \"eosinophil\"] <- \"Granulocytes\"\n",
    "gene_cells_blood_sep$Cellgroup[ gene_cells_blood_sep$Celltype %in% c(\"memory CD4 T-cell\", \"naive CD4 T-cell\")] <- \"T_cells\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"T-reg\", gene_cells_blood_sep$Celltype)] <- \"T_cells\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"gdT-cell\", gene_cells_blood_sep$Celltype)] <- \"T_cells\"\n",
    "gene_cells_blood_sep$Cellgroup[ grepl(\"MAIT T-cell\", gene_cells_blood_sep$Celltype)] <- \"T_cells\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Gene</th><th scope=col>Celltype</th><th scope=col>NX</th><th scope=col>Cellgroup</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>naive CD4 T-cell</td><td> 6.6</td><td>T_cells </td></tr>\n",
       "\t<tr><td>CFH   </td><td>MAIT T-cell     </td><td>16.5</td><td>T_cells </td></tr>\n",
       "\t<tr><td>GCLC  </td><td>NK-cell         </td><td> 8.0</td><td>NK_cells</td></tr>\n",
       "\t<tr><td>HS3ST1</td><td>memory B-cell   </td><td> 7.3</td><td>B_cells </td></tr>\n",
       "\t<tr><td>HS3ST1</td><td>naive B-cell    </td><td>15.8</td><td>B_cells </td></tr>\n",
       "\t<tr><td>HS3ST1</td><td>plasmacytoid DC </td><td>22.3</td><td>DC      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Gene & Celltype & NX & Cellgroup\\\\\n",
       " <chr> & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t TSPAN6 & naive CD4 T-cell &  6.6 & T\\_cells \\\\\n",
       "\t CFH    & MAIT T-cell      & 16.5 & T\\_cells \\\\\n",
       "\t GCLC   & NK-cell          &  8.0 & NK\\_cells\\\\\n",
       "\t HS3ST1 & memory B-cell    &  7.3 & B\\_cells \\\\\n",
       "\t HS3ST1 & naive B-cell     & 15.8 & B\\_cells \\\\\n",
       "\t HS3ST1 & plasmacytoid DC  & 22.3 & DC      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| Gene &lt;chr&gt; | Celltype &lt;chr&gt; | NX &lt;dbl&gt; | Cellgroup &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| TSPAN6 | naive CD4 T-cell |  6.6 | T_cells  |\n",
       "| CFH    | MAIT T-cell      | 16.5 | T_cells  |\n",
       "| GCLC   | NK-cell          |  8.0 | NK_cells |\n",
       "| HS3ST1 | memory B-cell    |  7.3 | B_cells  |\n",
       "| HS3ST1 | naive B-cell     | 15.8 | B_cells  |\n",
       "| HS3ST1 | plasmacytoid DC  | 22.3 | DC       |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene   Celltype         NX   Cellgroup\n",
       "1 TSPAN6 naive CD4 T-cell  6.6 T_cells  \n",
       "2 CFH    MAIT T-cell      16.5 T_cells  \n",
       "3 GCLC   NK-cell           8.0 NK_cells \n",
       "4 HS3ST1 memory B-cell     7.3 B_cells  \n",
       "5 HS3ST1 naive B-cell     15.8 B_cells  \n",
       "6 HS3ST1 plasmacytoid DC  22.3 DC       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gene_cells_blood_sep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rm genes if more than one cellgroup\n",
    "gene_cells_blood_sep_specific <- gene_cells_blood_sep[0,]\n",
    "\n",
    "for(g in unique(gene_cells_blood_sep$Gene)){\n",
    "    tmp <- gene_cells_blood_sep[ gene_cells_blood_sep$Gene == g,]\n",
    "    if(nrow(tmp) == 1){ \n",
    "    gene_cells_blood_sep_specific <- rbind(gene_cells_blood_sep_specific, tmp)\n",
    "    } else{\n",
    "    if(length(unique(tmp$Cellgroup)) == 1){ \n",
    "    gene_cells_blood_sep_specific <- rbind(gene_cells_blood_sep_specific, tmp)\n",
    "    }}\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>9730</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 9730\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 9730\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 9730    4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gene_cells_blood_sep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>5955</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5955\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5955\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5955    4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(gene_cells_blood_sep_specific)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "              basophil     classical monocyte             eosinophil \n",
       "                   752                    119                    477 \n",
       "              gdT-cell  intermediate monocyte            MAIT T-cell \n",
       "                   196                    195                    227 \n",
       "         memory B-cell      memory CD4 T-cell      memory CD8 T-cell \n",
       "                   311                    134                    136 \n",
       "            myeloid DC           naive B-cell       naive CD4 T-cell \n",
       "                   166                    315                    235 \n",
       "      naive CD8 T-cell             neutrophil                NK-cell \n",
       "                   184                   1185                    245 \n",
       "non-classical monocyte        plasmacytoid DC                  T-reg \n",
       "                   236                    539                    303 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(gene_cells_blood_sep_specific$Celltype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- MCPcounter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcp_sig <- read.csv(file = paste0(datapath, \"TME_clusters/MCPCounter_genesig.csv\"), \n",
    "                       header = T, stringsAsFactors = F, check.names = T, skip =  1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcp_sig <- mcp_sig[,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some probes map to the same gene\n",
    "mcp_sig <- mcp_sig[!duplicated(mcp_sig$Symbol),]\n",
    "# remove those probesets that map to two genes\n",
    "mcp_sig <- mcp_sig[!grepl(\"///\", mcp_sig$Symbol),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Probeset</th><th scope=col>Symbol</th><th scope=col>Population</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1557733_a_at</td><td>CHRM3-AS2</td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1558971_at  </td><td>THEMIS   </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1566448_at  </td><td>CD6      </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>204777_s_at </td><td>MAL      </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>206485_at   </td><td>CD5      </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>206545_at   </td><td>CD28     </td><td>T cells</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Probeset & Symbol & Population\\\\\n",
       "  & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 1557733\\_a\\_at & CHRM3-AS2 & T cells\\\\\n",
       "\t2 & 1558971\\_at   & THEMIS    & T cells\\\\\n",
       "\t4 & 1566448\\_at   & CD6       & T cells\\\\\n",
       "\t5 & 204777\\_s\\_at  & MAL       & T cells\\\\\n",
       "\t6 & 206485\\_at    & CD5       & T cells\\\\\n",
       "\t7 & 206545\\_at    & CD28      & T cells\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | Probeset &lt;chr&gt; | Symbol &lt;chr&gt; | Population &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 1557733_a_at | CHRM3-AS2 | T cells |\n",
       "| 2 | 1558971_at   | THEMIS    | T cells |\n",
       "| 4 | 1566448_at   | CD6       | T cells |\n",
       "| 5 | 204777_s_at  | MAL       | T cells |\n",
       "| 6 | 206485_at    | CD5       | T cells |\n",
       "| 7 | 206545_at    | CD28      | T cells |\n",
       "\n"
      ],
      "text/plain": [
       "  Probeset     Symbol    Population\n",
       "1 1557733_a_at CHRM3-AS2 T cells   \n",
       "2 1558971_at   THEMIS    T cells   \n",
       "4 1566448_at   CD6       T cells   \n",
       "5 204777_s_at  MAL       T cells   \n",
       "6 206485_at    CD5       T cells   \n",
       "7 206545_at    CD28      T cells   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mcp_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "              B lineage   Cytotoxic lymphocytes       Endothelial cells \n",
       "                     42                       7                      86 \n",
       "            Fibroblasts       Monocytic lineage Myeloid dendritic cells \n",
       "                     63                       7                       6 \n",
       "            Neutrophils                NK cells                 T cells \n",
       "                     53                       9                      16 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mcp_sig$Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Some recoding and cleanup\n",
    "\n",
    "mcp_sig$Cellgroup <- NA\n",
    "\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"B lineage\"] <- \"B_cells\"\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"Cytotoxic lymphocytes\"] <- \"T_cells\"\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"Monocytic lineage\"] <- \"Monocytes\"\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"Neutrophils\"] <- \"Granulocytes\"\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"NK cells\"] <- \"NK_cells\"\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"T cells\"] <- \"T_cells\"\n",
    "mcp_sig$Cellgroup[ mcp_sig$Population == \"Myeloid dendritic cells\"] <- \"DC\"\n",
    "\n",
    "mcp_sig <- mcp_sig[!is.na(mcp_sig$Cellgroup),] #removes endothe and fibroblasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mcp_sig)[ colnames(mcp_sig) == \"Symbol\"] <- \"Gene\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcp_sig <- mcp_sig[!is.na(mcp_sig$Gene),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4- CIBERSORT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm22 <- read.table(file = paste0(datapath, \"TME_clusters/LM22.txt\"), sep = \"\\t\", row.names = 1,\n",
    "                   header = T, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ciber_celltypes <- colnames(lm22)[apply(lm22,1,which.max)]\n",
    "names(ciber_celltypes) <- rownames(lm22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>ABCB4</dt><dd>'B.cells.naive'</dd><dt>ABCB9</dt><dd>'Plasma.cells'</dd><dt>ACAP1</dt><dd>'T.cells.regulatory..Tregs.'</dd><dt>ACHE</dt><dd>'Macrophages.M1'</dd><dt>ACP5</dt><dd>'Dendritic.cells.resting'</dd><dt>ADAM28</dt><dd>'B.cells.naive'</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[ABCB4] 'B.cells.naive'\n",
       "\\item[ABCB9] 'Plasma.cells'\n",
       "\\item[ACAP1] 'T.cells.regulatory..Tregs.'\n",
       "\\item[ACHE] 'Macrophages.M1'\n",
       "\\item[ACP5] 'Dendritic.cells.resting'\n",
       "\\item[ADAM28] 'B.cells.naive'\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "ABCB4\n",
       ":   'B.cells.naive'ABCB9\n",
       ":   'Plasma.cells'ACAP1\n",
       ":   'T.cells.regulatory..Tregs.'ACHE\n",
       ":   'Macrophages.M1'ACP5\n",
       ":   'Dendritic.cells.resting'ADAM28\n",
       ":   'B.cells.naive'\n",
       "\n"
      ],
      "text/plain": [
       "                       ABCB4                        ABCB9 \n",
       "             \"B.cells.naive\"               \"Plasma.cells\" \n",
       "                       ACAP1                         ACHE \n",
       "\"T.cells.regulatory..Tregs.\"             \"Macrophages.M1\" \n",
       "                        ACP5                       ADAM28 \n",
       "   \"Dendritic.cells.resting\"              \"B.cells.naive\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(ciber_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm22_celltypes <- as.data.frame(ciber_celltypes)\n",
    "lm22_celltypes$Gene <- rownames(lm22_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "              B.cells.memory                B.cells.naive \n",
       "                          15                           37 \n",
       "   Dendritic.cells.activated      Dendritic.cells.resting \n",
       "                          20                           22 \n",
       "                 Eosinophils               Macrophages.M0 \n",
       "                          37                           15 \n",
       "              Macrophages.M1               Macrophages.M2 \n",
       "                          38                           23 \n",
       "        Mast.cells.activated           Mast.cells.resting \n",
       "                          24                           22 \n",
       "                   Monocytes                  Neutrophils \n",
       "                          13                           47 \n",
       "          NK.cells.activated             NK.cells.resting \n",
       "                          24                           22 \n",
       "                Plasma.cells T.cells.CD4.memory.activated \n",
       "                          39                           13 \n",
       "  T.cells.CD4.memory.resting            T.cells.CD4.naive \n",
       "                          16                           25 \n",
       "                 T.cells.CD8    T.cells.follicular.helper \n",
       "                           8                           26 \n",
       "         T.cells.gamma.delta   T.cells.regulatory..Tregs. \n",
       "                          17                           44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(lm22_celltypes$ciber_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#some recoding\n",
    "lm22_celltypes$Cellgroup <- NA\n",
    "\n",
    "lm22_celltypes$Cellgroup[ grepl(\"B.cells\",lm22_celltypes$ciber_celltypes)] <- \"B_cells\"\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"Plasma.cells\"] <- \"B_cells\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[ grepl(\"Macrophages\",lm22_celltypes$ciber_celltypes)] <- \"Macrophages\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[ grepl(\"NK.cells\",lm22_celltypes$ciber_celltypes)] <- \"NK_cells\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[ grepl(\"Dendritic.cells\",lm22_celltypes$ciber_celltypes)] <- \"DC\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"Neutrophils\"] <- \"Granulocytes\"\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"Eosinophils\"] <- \"Granulocytes\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"T.cells.CD8\"] <- \"T_cells\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"Monocytes\"] <- \"Monocytes\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[ lm22_celltypes$ciber_celltypes %in% c(\"T.cells.CD4.memory.resting\",\"T.cells.CD4.memory.activated\", \n",
    "                                                                \"T.cells.CD4.naive\")] <- \"T_cells\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"T.cells.regulatory..Tregs.\"] <- \"T_cells\"\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"T.cells.follicular.helper\"] <- \"T_cells\"\n",
    "\n",
    "lm22_celltypes$Cellgroup[lm22_celltypes$ciber_celltypes == \"T.cells.gamma.delta\"] <- \"T_cells\"\n",
    "\n",
    "\n",
    "lm22_celltypes$Cellgroup[ lm22_celltypes$ciber_celltypes %in% c(\"Mast.cells.activated\", \"Mast.cells.resting\")] <- \"Myeloid_cells\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ciber_celltypes</th><th scope=col>Gene</th><th scope=col>Cellgroup</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ABCB4</th><td>B.cells.naive             </td><td>ABCB4 </td><td>B_cells    </td></tr>\n",
       "\t<tr><th scope=row>ABCB9</th><td>Plasma.cells              </td><td>ABCB9 </td><td>B_cells    </td></tr>\n",
       "\t<tr><th scope=row>ACAP1</th><td>T.cells.regulatory..Tregs.</td><td>ACAP1 </td><td>T_cells    </td></tr>\n",
       "\t<tr><th scope=row>ACHE</th><td>Macrophages.M1            </td><td>ACHE  </td><td>Macrophages</td></tr>\n",
       "\t<tr><th scope=row>ACP5</th><td>Dendritic.cells.resting   </td><td>ACP5  </td><td>DC         </td></tr>\n",
       "\t<tr><th scope=row>ADAM28</th><td>B.cells.naive             </td><td>ADAM28</td><td>B_cells    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & ciber\\_celltypes & Gene & Cellgroup\\\\\n",
       "  & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tABCB4 & B.cells.naive              & ABCB4  & B\\_cells    \\\\\n",
       "\tABCB9 & Plasma.cells               & ABCB9  & B\\_cells    \\\\\n",
       "\tACAP1 & T.cells.regulatory..Tregs. & ACAP1  & T\\_cells    \\\\\n",
       "\tACHE & Macrophages.M1             & ACHE   & Macrophages\\\\\n",
       "\tACP5 & Dendritic.cells.resting    & ACP5   & DC         \\\\\n",
       "\tADAM28 & B.cells.naive              & ADAM28 & B\\_cells    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | ciber_celltypes &lt;chr&gt; | Gene &lt;chr&gt; | Cellgroup &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| ABCB4 | B.cells.naive              | ABCB4  | B_cells     |\n",
       "| ABCB9 | Plasma.cells               | ABCB9  | B_cells     |\n",
       "| ACAP1 | T.cells.regulatory..Tregs. | ACAP1  | T_cells     |\n",
       "| ACHE | Macrophages.M1             | ACHE   | Macrophages |\n",
       "| ACP5 | Dendritic.cells.resting    | ACP5   | DC          |\n",
       "| ADAM28 | B.cells.naive              | ADAM28 | B_cells     |\n",
       "\n"
      ],
      "text/plain": [
       "       ciber_celltypes            Gene   Cellgroup  \n",
       "ABCB4  B.cells.naive              ABCB4  B_cells    \n",
       "ABCB9  Plasma.cells               ABCB9  B_cells    \n",
       "ACAP1  T.cells.regulatory..Tregs. ACAP1  T_cells    \n",
       "ACHE   Macrophages.M1             ACHE   Macrophages\n",
       "ACP5   Dendritic.cells.resting    ACP5   DC         \n",
       "ADAM28 B.cells.naive              ADAM28 B_cells    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(lm22_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "      B_cells            DC  Granulocytes   Macrophages     Monocytes \n",
       "           91            42            84            76            13 \n",
       "Myeloid_cells      NK_cells       T_cells          <NA> \n",
       "           46            46           149             0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(lm22_celltypes$Cellgroup, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5- Quantiseq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "quanti_sig <- read.csv(file = paste0(datapath, \"TME_clusters/quantiseq_genesig.csv\"), row.names = 1,\n",
    "                       header = T, stringsAsFactors = F, check.names = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantiseq_celltypes <- colnames(quanti_sig)[apply(quanti_sig,1,which.max)]\n",
    "names(quantiseq_celltypes) <- rownames(quanti_sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>ABCB4</dt><dd>'B.cells'</dd><dt>ADAM28</dt><dd>'B.cells'</dd><dt>ADAM6</dt><dd>'B.cells'</dd><dt>AFF3</dt><dd>'B.cells'</dd><dt>AKAP2</dt><dd>'B.cells'</dd><dt>ARHGAP24</dt><dd>'B.cells'</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[ABCB4] 'B.cells'\n",
       "\\item[ADAM28] 'B.cells'\n",
       "\\item[ADAM6] 'B.cells'\n",
       "\\item[AFF3] 'B.cells'\n",
       "\\item[AKAP2] 'B.cells'\n",
       "\\item[ARHGAP24] 'B.cells'\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "ABCB4\n",
       ":   'B.cells'ADAM28\n",
       ":   'B.cells'ADAM6\n",
       ":   'B.cells'AFF3\n",
       ":   'B.cells'AKAP2\n",
       ":   'B.cells'ARHGAP24\n",
       ":   'B.cells'\n",
       "\n"
      ],
      "text/plain": [
       "    ABCB4    ADAM28     ADAM6      AFF3     AKAP2  ARHGAP24 \n",
       "\"B.cells\" \"B.cells\" \"B.cells\" \"B.cells\" \"B.cells\" \"B.cells\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(quantiseq_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantiseq_celltypes <- as.data.frame(quantiseq_celltypes)\n",
    "quantiseq_celltypes$Gene <- rownames(quantiseq_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>quantiseq_celltypes</th><th scope=col>Gene</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>ABCB4</th><td>B.cells</td><td>ABCB4   </td></tr>\n",
       "\t<tr><th scope=row>ADAM28</th><td>B.cells</td><td>ADAM28  </td></tr>\n",
       "\t<tr><th scope=row>ADAM6</th><td>B.cells</td><td>ADAM6   </td></tr>\n",
       "\t<tr><th scope=row>AFF3</th><td>B.cells</td><td>AFF3    </td></tr>\n",
       "\t<tr><th scope=row>AKAP2</th><td>B.cells</td><td>AKAP2   </td></tr>\n",
       "\t<tr><th scope=row>ARHGAP24</th><td>B.cells</td><td>ARHGAP24</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & quantiseq\\_celltypes & Gene\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tABCB4 & B.cells & ABCB4   \\\\\n",
       "\tADAM28 & B.cells & ADAM28  \\\\\n",
       "\tADAM6 & B.cells & ADAM6   \\\\\n",
       "\tAFF3 & B.cells & AFF3    \\\\\n",
       "\tAKAP2 & B.cells & AKAP2   \\\\\n",
       "\tARHGAP24 & B.cells & ARHGAP24\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | quantiseq_celltypes &lt;chr&gt; | Gene &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| ABCB4 | B.cells | ABCB4    |\n",
       "| ADAM28 | B.cells | ADAM28   |\n",
       "| ADAM6 | B.cells | ADAM6    |\n",
       "| AFF3 | B.cells | AFF3     |\n",
       "| AKAP2 | B.cells | AKAP2    |\n",
       "| ARHGAP24 | B.cells | ARHGAP24 |\n",
       "\n"
      ],
      "text/plain": [
       "         quantiseq_celltypes Gene    \n",
       "ABCB4    B.cells             ABCB4   \n",
       "ADAM28   B.cells             ADAM28  \n",
       "ADAM6    B.cells             ADAM6   \n",
       "AFF3     B.cells             AFF3    \n",
       "AKAP2    B.cells             AKAP2   \n",
       "ARHGAP24 B.cells             ARHGAP24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(quantiseq_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        B.cells Dendritic.cells  Macrophages.M1  Macrophages.M2       Monocytes \n",
       "             30               9              19              18              11 \n",
       "    Neutrophils        NK.cells     T.cells.CD4     T.cells.CD8           Tregs \n",
       "             30              30               5               4              14 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(quantiseq_celltypes$quantiseq_celltypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#some recoding\n",
    "quantiseq_celltypes$Cellgroup <- NA\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"B.cells\",quantiseq_celltypes$quantiseq_celltypes)] <- \"B_cells\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"Dendritic\",quantiseq_celltypes$quantiseq_celltypes)] <- \"DC\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"Macrophages\",quantiseq_celltypes$quantiseq_celltypes)] <- \"Macrophages\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"Monocytes\",quantiseq_celltypes$quantiseq_celltypes)] <- \"Monocytes\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"Neutrophils\",quantiseq_celltypes$quantiseq_celltypes)] <- \"Granulocytes\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"NK\",quantiseq_celltypes$quantiseq_celltypes)] <- \"NK_cells\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"CD4\",quantiseq_celltypes$quantiseq_celltypes)] <- \"T_cells\"\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"Tregs\",quantiseq_celltypes$quantiseq_celltypes)] <- \"T_cells\"\n",
    "\n",
    "quantiseq_celltypes$Cellgroup[ grepl(\"CD8\",quantiseq_celltypes$quantiseq_celltypes)] <- \"T_cells\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     B_cells           DC Granulocytes  Macrophages    Monocytes     NK_cells \n",
       "          30            9           30           37           11           30 \n",
       "     T_cells         <NA> \n",
       "          23            0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(quantiseq_celltypes$Cellgroup, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>quantiseq_celltypes</th><th scope=col>Gene</th><th scope=col>Cellgroup</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ABCB4</th><td>B.cells</td><td>ABCB4   </td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>ADAM28</th><td>B.cells</td><td>ADAM28  </td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>ADAM6</th><td>B.cells</td><td>ADAM6   </td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>AFF3</th><td>B.cells</td><td>AFF3    </td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>AKAP2</th><td>B.cells</td><td>AKAP2   </td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>ARHGAP24</th><td>B.cells</td><td>ARHGAP24</td><td>B_cells</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & quantiseq\\_celltypes & Gene & Cellgroup\\\\\n",
       "  & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tABCB4 & B.cells & ABCB4    & B\\_cells\\\\\n",
       "\tADAM28 & B.cells & ADAM28   & B\\_cells\\\\\n",
       "\tADAM6 & B.cells & ADAM6    & B\\_cells\\\\\n",
       "\tAFF3 & B.cells & AFF3     & B\\_cells\\\\\n",
       "\tAKAP2 & B.cells & AKAP2    & B\\_cells\\\\\n",
       "\tARHGAP24 & B.cells & ARHGAP24 & B\\_cells\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | quantiseq_celltypes &lt;chr&gt; | Gene &lt;chr&gt; | Cellgroup &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| ABCB4 | B.cells | ABCB4    | B_cells |\n",
       "| ADAM28 | B.cells | ADAM28   | B_cells |\n",
       "| ADAM6 | B.cells | ADAM6    | B_cells |\n",
       "| AFF3 | B.cells | AFF3     | B_cells |\n",
       "| AKAP2 | B.cells | AKAP2    | B_cells |\n",
       "| ARHGAP24 | B.cells | ARHGAP24 | B_cells |\n",
       "\n"
      ],
      "text/plain": [
       "         quantiseq_celltypes Gene     Cellgroup\n",
       "ABCB4    B.cells             ABCB4    B_cells  \n",
       "ADAM28   B.cells             ADAM28   B_cells  \n",
       "ADAM6    B.cells             ADAM6    B_cells  \n",
       "AFF3     B.cells             AFF3     B_cells  \n",
       "AKAP2    B.cells             AKAP2    B_cells  \n",
       "ARHGAP24 B.cells             ARHGAP24 B_cells  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(quantiseq_celltypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6- EPIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "epic_sig <- read.csv(file = paste0(datapath, \"TME_clusters/EPIC_genesig.csv\"), row.names = 1,\n",
    "                       header = T, stringsAsFactors = F, check.names = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Genes.markers</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>B cells</th><td>BANK1, CD79A, CD79B, FCER2, FCRL2, FCRL5, MS4A1, PAX5, POU2AF1, STAP1, TCL1A                                                                  </td></tr>\n",
       "\t<tr><th scope=row>CAFs</th><td>ADAM33, CLDN11, COL1A1, COL3A1, COL14A1, CRISPLD2, CXCL14, DPT, F3, FBLN1, ISLR, LUM, MEG3, MFAP5, PRELP, PTGIS, SFRP2, SFRP4, SYNPO2, TMEM119</td></tr>\n",
       "\t<tr><th scope=row>CD4 T cells</th><td>ANKRD55, DGKA, FOXP3, GCNT4, IL2RA, MDS2, RCAN3, TBC1D4, TRAT1                                                                                </td></tr>\n",
       "\t<tr><th scope=row>CD8 T cells</th><td>CD8B, HAUS3, JAKMIP1, NAA16, TSPYL1                                                                                                           </td></tr>\n",
       "\t<tr><th scope=row>Endothelial cells</th><td>CDH5, CLDN5, CLEC14A, CXorf36, ECSCR, F2RL3, FLT1, FLT4, GPR4, GPR182, KDR, MMRN1, MMRN2, MYCT1, PTPRB, RHOJ, SLCO2A1, SOX18, STAB2, VWF      </td></tr>\n",
       "\t<tr><th scope=row>Macrophages</th><td>APOC1, C1QC, CD14, CD163, CD300C, CD300E, CSF1R, F13A1, FPR3, HAMP, IL1B, LILRB4, MS4A6A, MSR1, SIGLEC1, VSIG4                                </td></tr>\n",
       "\t<tr><th scope=row>Monocytes</th><td>CD33, CD300C, CD300E, CECR1, CLEC6A, CPVL, EGR2, EREG, MS4A6A, NAGA, SLC37A2                                                                  </td></tr>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>CEACAM3, CNTNAP3, CXCR1, CYP4F3, FFAR2, HIST1H2BC, HIST1H3D, KY, MMP25, PGLYRP1, SLC12A1, TAS2R40                                             </td></tr>\n",
       "\t<tr><th scope=row>NK cells</th><td>CD160, CLIC3, FGFBP2, GNLY, GNPTAB, KLRF1, NCR1, NMUR1, S1PR5, SH2D1B                                                                         </td></tr>\n",
       "\t<tr><th scope=row>T cells</th><td>BCL11B, CD5, CD28, IL7R, ITK, THEMIS, UBASH3A                                                                                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & Genes.markers\\\\\n",
       "  & <chr>\\\\\n",
       "\\hline\n",
       "\tB cells & BANK1, CD79A, CD79B, FCER2, FCRL2, FCRL5, MS4A1, PAX5, POU2AF1, STAP1, TCL1A                                                                  \\\\\n",
       "\tCAFs & ADAM33, CLDN11, COL1A1, COL3A1, COL14A1, CRISPLD2, CXCL14, DPT, F3, FBLN1, ISLR, LUM, MEG3, MFAP5, PRELP, PTGIS, SFRP2, SFRP4, SYNPO2, TMEM119\\\\\n",
       "\tCD4 T cells & ANKRD55, DGKA, FOXP3, GCNT4, IL2RA, MDS2, RCAN3, TBC1D4, TRAT1                                                                                \\\\\n",
       "\tCD8 T cells & CD8B, HAUS3, JAKMIP1, NAA16, TSPYL1                                                                                                           \\\\\n",
       "\tEndothelial cells & CDH5, CLDN5, CLEC14A, CXorf36, ECSCR, F2RL3, FLT1, FLT4, GPR4, GPR182, KDR, MMRN1, MMRN2, MYCT1, PTPRB, RHOJ, SLCO2A1, SOX18, STAB2, VWF      \\\\\n",
       "\tMacrophages & APOC1, C1QC, CD14, CD163, CD300C, CD300E, CSF1R, F13A1, FPR3, HAMP, IL1B, LILRB4, MS4A6A, MSR1, SIGLEC1, VSIG4                                \\\\\n",
       "\tMonocytes & CD33, CD300C, CD300E, CECR1, CLEC6A, CPVL, EGR2, EREG, MS4A6A, NAGA, SLC37A2                                                                  \\\\\n",
       "\tNeutrophils & CEACAM3, CNTNAP3, CXCR1, CYP4F3, FFAR2, HIST1H2BC, HIST1H3D, KY, MMP25, PGLYRP1, SLC12A1, TAS2R40                                             \\\\\n",
       "\tNK cells & CD160, CLIC3, FGFBP2, GNLY, GNPTAB, KLRF1, NCR1, NMUR1, S1PR5, SH2D1B                                                                         \\\\\n",
       "\tT cells & BCL11B, CD5, CD28, IL7R, ITK, THEMIS, UBASH3A                                                                                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 1\n",
       "\n",
       "| <!--/--> | Genes.markers &lt;chr&gt; |\n",
       "|---|---|\n",
       "| B cells | BANK1, CD79A, CD79B, FCER2, FCRL2, FCRL5, MS4A1, PAX5, POU2AF1, STAP1, TCL1A                                                                   |\n",
       "| CAFs | ADAM33, CLDN11, COL1A1, COL3A1, COL14A1, CRISPLD2, CXCL14, DPT, F3, FBLN1, ISLR, LUM, MEG3, MFAP5, PRELP, PTGIS, SFRP2, SFRP4, SYNPO2, TMEM119 |\n",
       "| CD4 T cells | ANKRD55, DGKA, FOXP3, GCNT4, IL2RA, MDS2, RCAN3, TBC1D4, TRAT1                                                                                 |\n",
       "| CD8 T cells | CD8B, HAUS3, JAKMIP1, NAA16, TSPYL1                                                                                                            |\n",
       "| Endothelial cells | CDH5, CLDN5, CLEC14A, CXorf36, ECSCR, F2RL3, FLT1, FLT4, GPR4, GPR182, KDR, MMRN1, MMRN2, MYCT1, PTPRB, RHOJ, SLCO2A1, SOX18, STAB2, VWF       |\n",
       "| Macrophages | APOC1, C1QC, CD14, CD163, CD300C, CD300E, CSF1R, F13A1, FPR3, HAMP, IL1B, LILRB4, MS4A6A, MSR1, SIGLEC1, VSIG4                                 |\n",
       "| Monocytes | CD33, CD300C, CD300E, CECR1, CLEC6A, CPVL, EGR2, EREG, MS4A6A, NAGA, SLC37A2                                                                   |\n",
       "| Neutrophils | CEACAM3, CNTNAP3, CXCR1, CYP4F3, FFAR2, HIST1H2BC, HIST1H3D, KY, MMP25, PGLYRP1, SLC12A1, TAS2R40                                              |\n",
       "| NK cells | CD160, CLIC3, FGFBP2, GNLY, GNPTAB, KLRF1, NCR1, NMUR1, S1PR5, SH2D1B                                                                          |\n",
       "| T cells | BCL11B, CD5, CD28, IL7R, ITK, THEMIS, UBASH3A                                                                                                  |\n",
       "\n"
      ],
      "text/plain": [
       "                  Genes.markers                                                                                                                                 \n",
       "B cells           BANK1, CD79A, CD79B, FCER2, FCRL2, FCRL5, MS4A1, PAX5, POU2AF1, STAP1, TCL1A                                                                  \n",
       "CAFs              ADAM33, CLDN11, COL1A1, COL3A1, COL14A1, CRISPLD2, CXCL14, DPT, F3, FBLN1, ISLR, LUM, MEG3, MFAP5, PRELP, PTGIS, SFRP2, SFRP4, SYNPO2, TMEM119\n",
       "CD4 T cells       ANKRD55, DGKA, FOXP3, GCNT4, IL2RA, MDS2, RCAN3, TBC1D4, TRAT1                                                                                \n",
       "CD8 T cells       CD8B, HAUS3, JAKMIP1, NAA16, TSPYL1                                                                                                           \n",
       "Endothelial cells CDH5, CLDN5, CLEC14A, CXorf36, ECSCR, F2RL3, FLT1, FLT4, GPR4, GPR182, KDR, MMRN1, MMRN2, MYCT1, PTPRB, RHOJ, SLCO2A1, SOX18, STAB2, VWF      \n",
       "Macrophages       APOC1, C1QC, CD14, CD163, CD300C, CD300E, CSF1R, F13A1, FPR3, HAMP, IL1B, LILRB4, MS4A6A, MSR1, SIGLEC1, VSIG4                                \n",
       "Monocytes         CD33, CD300C, CD300E, CECR1, CLEC6A, CPVL, EGR2, EREG, MS4A6A, NAGA, SLC37A2                                                                  \n",
       "Neutrophils       CEACAM3, CNTNAP3, CXCR1, CYP4F3, FFAR2, HIST1H2BC, HIST1H3D, KY, MMP25, PGLYRP1, SLC12A1, TAS2R40                                             \n",
       "NK cells          CD160, CLIC3, FGFBP2, GNLY, GNPTAB, KLRF1, NCR1, NMUR1, S1PR5, SH2D1B                                                                         \n",
       "T cells           BCL11B, CD5, CD28, IL7R, ITK, THEMIS, UBASH3A                                                                                                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epic_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "genesets_epic <- data.frame(Gene=character(), Celltype=character(),stringsAsFactors=FALSE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:10){\n",
    "    cell <- rownames(epic_sig)[i] \n",
    "    genes <- unlist(strsplit(epic_sig$Genes.markers[i], \",\"))\n",
    "    # trim leading white space\n",
    "    genes_df <- as.data.frame(trimws(genes))\n",
    "    genes_df$Celltype <- cell\n",
    "    colnames(genes_df)[1] <- \"Gene\"\n",
    "    genesets_epic <- rbind(genesets_epic, genes_df)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Celltype</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>116</th><td>CD5    </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>117</th><td>CD28   </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>118</th><td>IL7R   </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>119</th><td>ITK    </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>120</th><td>THEMIS </td><td>T cells</td></tr>\n",
       "\t<tr><th scope=row>121</th><td>UBASH3A</td><td>T cells</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Gene & Celltype\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t116 & CD5     & T cells\\\\\n",
       "\t117 & CD28    & T cells\\\\\n",
       "\t118 & IL7R    & T cells\\\\\n",
       "\t119 & ITK     & T cells\\\\\n",
       "\t120 & THEMIS  & T cells\\\\\n",
       "\t121 & UBASH3A & T cells\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 116 | CD5     | T cells |\n",
       "| 117 | CD28    | T cells |\n",
       "| 118 | IL7R    | T cells |\n",
       "| 119 | ITK     | T cells |\n",
       "| 120 | THEMIS  | T cells |\n",
       "| 121 | UBASH3A | T cells |\n",
       "\n"
      ],
      "text/plain": [
       "    Gene    Celltype\n",
       "116 CD5     T cells \n",
       "117 CD28    T cells \n",
       "118 IL7R    T cells \n",
       "119 ITK     T cells \n",
       "120 THEMIS  T cells \n",
       "121 UBASH3A T cells "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(genesets_epic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "          B cells              CAFs       CD4 T cells       CD8 T cells \n",
       "               11                20                 9                 5 \n",
       "Endothelial cells       Macrophages         Monocytes       Neutrophils \n",
       "               20                16                11                12 \n",
       "         NK cells           T cells              <NA> \n",
       "               10                 7                 0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(genesets_epic$Celltype, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "genesets_epic$Cellgroup <- NA\n",
    "\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"CD4 T cells\"] <- \"T_cells\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"CD8 T cells\"] <- \"T_cells\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"NK cells\"] <- \"NK_cells\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"Monocytes\"] <- \"Monocytes\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"Neutrophils\"] <- \"Granulocytes\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"Macrophages\"] <- \"Macrophages\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"B cells\"] <- \"B_cells\"\n",
    "genesets_epic$Cellgroup[ genesets_epic$Celltype == \"T cells\"] <- \"T_cells\"\n",
    "genesets_epic <- genesets_epic[ !is.na(genesets_epic$Cellgroup),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Gene</th><th scope=col>Celltype</th><th scope=col>Cellgroup</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>BANK1</td><td>B cells</td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>CD79A</td><td>B cells</td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>CD79B</td><td>B cells</td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>FCER2</td><td>B cells</td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>FCRL2</td><td>B cells</td><td>B_cells</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>FCRL5</td><td>B cells</td><td>B_cells</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Gene & Celltype & Cellgroup\\\\\n",
       "  & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & BANK1 & B cells & B\\_cells\\\\\n",
       "\t2 & CD79A & B cells & B\\_cells\\\\\n",
       "\t3 & CD79B & B cells & B\\_cells\\\\\n",
       "\t4 & FCER2 & B cells & B\\_cells\\\\\n",
       "\t5 & FCRL2 & B cells & B\\_cells\\\\\n",
       "\t6 & FCRL5 & B cells & B\\_cells\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; | Cellgroup &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | BANK1 | B cells | B_cells |\n",
       "| 2 | CD79A | B cells | B_cells |\n",
       "| 3 | CD79B | B cells | B_cells |\n",
       "| 4 | FCER2 | B cells | B_cells |\n",
       "| 5 | FCRL2 | B cells | B_cells |\n",
       "| 6 | FCRL5 | B cells | B_cells |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene  Celltype Cellgroup\n",
       "1 BANK1 B cells  B_cells  \n",
       "2 CD79A B cells  B_cells  \n",
       "3 CD79B B cells  B_cells  \n",
       "4 FCER2 B cells  B_cells  \n",
       "5 FCRL2 B cells  B_cells  \n",
       "6 FCRL5 B cells  B_cells  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(genesets_epic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     B_cells Granulocytes  Macrophages    Monocytes     NK_cells      T_cells \n",
       "          11           12           16           11           10           21 \n",
       "        <NA> \n",
       "           0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(genesets_epic$Cellgroup, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7- Xcell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcell <- read.csv(file = paste0(datapath, \"TME_clusters/xcell_paper_489celltypesignatures.csv\"), row.names = 2,\n",
    "                       header = F, stringsAsFactors = F, check.names = F, skip = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>489</li><li>202</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 489\n",
       "\\item 202\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 489\n",
       "2. 202\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 489 202"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(xcell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcell$V1 <- NULL # weird rownames when I write.csv from read.excel function\n",
    "xcell$V3 <- NULL # number of genes for each signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "removethem <- c(\"Adipocytes\", \"Astrocytes\", \"Chondrocytes\", \"Platelets\", \"NKT\", \n",
    "                \"Endothelial\", \"Epithelial\", \"Mesangial\", \"Melanocytes\", \n",
    "                \"Skeletal\", \"Smooth\", \"Preadipocytes\", \"Erythrocytes\",\n",
    "                \"Sebocytes\", \"Osteoblast\", \"Myocytes\", \"Fibroblasts\",\n",
    "                \"Hepatocytes\", \"HSC\", \"Keratinocytes\", \"Megakaryocytes\",\n",
    "                \"Neurons\", \"Pericytes\", \"MPP\", \"MEP\", \"GMP\", \"CLP\", \"CMP\", \"MSC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_rows <- rownames(xcell)[grep(paste(removethem,collapse=\"|\"), rownames(xcell))]\n",
    "xcell <- xcell[!rownames(xcell) %in% remove_rows,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(xcell) <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>264</li><li>200</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 264\n",
       "\\item 200\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 264\n",
       "2. 200\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 264 200"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(xcell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 200</caption>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>aDC_HPCA_1</th><td>C1QA</td><td>C1QB   </td><td>CD80   </td><td>IL12B </td><td>CCL13   </td><td>CCL17</td><td>CCL19</td><td>CCL22 </td><td>NA   </td><td>NA   </td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>aDC_HPCA_2</th><td>C1QA</td><td>C1QB   </td><td>CD80   </td><td>FPR3  </td><td>HLA-DQA1</td><td>IL12B</td><td>CCL13</td><td>CCL17 </td><td>CCL19</td><td>CCL22</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>aDC_HPCA_3</th><td>C1QA</td><td>C1QB   </td><td>CD80   </td><td>FPR3  </td><td>HLA-DQA1</td><td>IL12B</td><td>CCL13</td><td>CCL17 </td><td>CCL19</td><td>CCL22</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>aDC_IRIS_1</th><td>CD80</td><td>IL3RA  </td><td>IL12B  </td><td>CXCL9 </td><td>PTGIR   </td><td>CCL8 </td><td>CCL13</td><td>CCL17 </td><td>CCL19</td><td>CCL23</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>aDC_IRIS_2</th><td>ACHE</td><td>ADPRH  </td><td>ALOX15B</td><td>ANXA5 </td><td>XIAP    </td><td>ARF3 </td><td>RHOG </td><td>ATP1B3</td><td>BLVRA</td><td>C1QA </td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>aDC_IRIS_3</th><td>ACHE</td><td>ALOX15B</td><td>RHOG   </td><td>ATP1B3</td><td>BLVRA   </td><td>C1QB </td><td>CD80 </td><td>CD86  </td><td>EIF5 </td><td>ENO1 </td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 200\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "\taDC\\_HPCA\\_1 & C1QA & C1QB    & CD80    & IL12B  & CCL13    & CCL17 & CCL19 & CCL22  & NA    & NA    & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\taDC\\_HPCA\\_2 & C1QA & C1QB    & CD80    & FPR3   & HLA-DQA1 & IL12B & CCL13 & CCL17  & CCL19 & CCL22 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\taDC\\_HPCA\\_3 & C1QA & C1QB    & CD80    & FPR3   & HLA-DQA1 & IL12B & CCL13 & CCL17  & CCL19 & CCL22 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\taDC\\_IRIS\\_1 & CD80 & IL3RA   & IL12B   & CXCL9  & PTGIR    & CCL8  & CCL13 & CCL17  & CCL19 & CCL23 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\taDC\\_IRIS\\_2 & ACHE & ADPRH   & ALOX15B & ANXA5  & XIAP     & ARF3  & RHOG  & ATP1B3 & BLVRA & C1QA  & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\taDC\\_IRIS\\_3 & ACHE & ALOX15B & RHOG    & ATP1B3 & BLVRA    & C1QB  & CD80  & CD86   & EIF5  & ENO1  & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 200\n",
       "\n",
       "| aDC_HPCA_1 | C1QA | C1QB    | CD80    | IL12B  | CCL13    | CCL17 | CCL19 | CCL22  | NA    | NA    | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| aDC_HPCA_2 | C1QA | C1QB    | CD80    | FPR3   | HLA-DQA1 | IL12B | CCL13 | CCL17  | CCL19 | CCL22 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| aDC_HPCA_3 | C1QA | C1QB    | CD80    | FPR3   | HLA-DQA1 | IL12B | CCL13 | CCL17  | CCL19 | CCL22 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| aDC_IRIS_1 | CD80 | IL3RA   | IL12B   | CXCL9  | PTGIR    | CCL8  | CCL13 | CCL17  | CCL19 | CCL23 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| aDC_IRIS_2 | ACHE | ADPRH   | ALOX15B | ANXA5  | XIAP     | ARF3  | RHOG  | ATP1B3 | BLVRA | C1QA  | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| aDC_IRIS_3 | ACHE | ALOX15B | RHOG    | ATP1B3 | BLVRA    | C1QB  | CD80  | CD86   | EIF5  | ENO1  | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "           [,1] [,2]    [,3]    [,4]   [,5]     [,6]  [,7]  [,8]   [,9]  [,10]\n",
       "aDC_HPCA_1 C1QA C1QB    CD80    IL12B  CCL13    CCL17 CCL19 CCL22  NA    NA   \n",
       "aDC_HPCA_2 C1QA C1QB    CD80    FPR3   HLA-DQA1 IL12B CCL13 CCL17  CCL19 CCL22\n",
       "aDC_HPCA_3 C1QA C1QB    CD80    FPR3   HLA-DQA1 IL12B CCL13 CCL17  CCL19 CCL22\n",
       "aDC_IRIS_1 CD80 IL3RA   IL12B   CXCL9  PTGIR    CCL8  CCL13 CCL17  CCL19 CCL23\n",
       "aDC_IRIS_2 ACHE ADPRH   ALOX15B ANXA5  XIAP     ARF3  RHOG  ATP1B3 BLVRA C1QA \n",
       "aDC_IRIS_3 ACHE ALOX15B RHOG    ATP1B3 BLVRA    C1QB  CD80  CD86   EIF5  ENO1 \n",
       "           [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]\n",
       "aDC_HPCA_1 ⋯     NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   \n",
       "aDC_HPCA_2 ⋯     NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   \n",
       "aDC_HPCA_3 ⋯     NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   \n",
       "aDC_IRIS_1 ⋯     NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   \n",
       "aDC_IRIS_2 ⋯     NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   \n",
       "aDC_IRIS_3 ⋯     NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(xcell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "genesets_xcell <- data.frame(Gene=character(), \n",
    "                             Celltype=character(), stringsAsFactors=FALSE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(xcell)){\n",
    "    cell <- rownames(xcell)[i] \n",
    "    genes <- unlist(xcell[i,1:ncol(xcell)])\n",
    "    genes <- genes[!is.na(genes)]\n",
    "    genes_df <- as.data.frame(genes)\n",
    "    genes_df$Celltype <- cell\n",
    "    colnames(genes_df)[1] <- \"Gene\"\n",
    "    genesets_xcell <- rbind(genesets_xcell, genes_df)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "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>Celltype</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>C1QA </td><td>aDC_HPCA_1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>C1QB </td><td>aDC_HPCA_1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>CD80 </td><td>aDC_HPCA_1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>IL12B</td><td>aDC_HPCA_1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>CCL13</td><td>aDC_HPCA_1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>CCL17</td><td>aDC_HPCA_1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Gene & Celltype\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & C1QA  & aDC\\_HPCA\\_1\\\\\n",
       "\t2 & C1QB  & aDC\\_HPCA\\_1\\\\\n",
       "\t3 & CD80  & aDC\\_HPCA\\_1\\\\\n",
       "\t4 & IL12B & aDC\\_HPCA\\_1\\\\\n",
       "\t5 & CCL13 & aDC\\_HPCA\\_1\\\\\n",
       "\t6 & CCL17 & aDC\\_HPCA\\_1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | C1QA  | aDC_HPCA_1 |\n",
       "| 2 | C1QB  | aDC_HPCA_1 |\n",
       "| 3 | CD80  | aDC_HPCA_1 |\n",
       "| 4 | IL12B | aDC_HPCA_1 |\n",
       "| 5 | CCL13 | aDC_HPCA_1 |\n",
       "| 6 | CCL17 | aDC_HPCA_1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene  Celltype  \n",
       "1 C1QA  aDC_HPCA_1\n",
       "2 C1QB  aDC_HPCA_1\n",
       "3 CD80  aDC_HPCA_1\n",
       "4 IL12B aDC_HPCA_1\n",
       "5 CCL13 aDC_HPCA_1\n",
       "6 CCL17 aDC_HPCA_1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(genesets_xcell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "                                 aDC_HPCA_1 \n",
       "                                          8 \n",
       "                                 aDC_HPCA_2 \n",
       "                                         10 \n",
       "                                 aDC_HPCA_3 \n",
       "                                         10 \n",
       "                                 aDC_IRIS_1 \n",
       "                                         19 \n",
       "                                 aDC_IRIS_2 \n",
       "                                        173 \n",
       "                                 aDC_IRIS_3 \n",
       "                                         98 \n",
       "                           B-cells_FANTOM_1 \n",
       "                                         16 \n",
       "                           B-cells_FANTOM_2 \n",
       "                                         12 \n",
       "                           B-cells_FANTOM_3 \n",
       "                                         21 \n",
       "                             B-cells_HPCA_1 \n",
       "                                         44 \n",
       "                             B-cells_HPCA_2 \n",
       "                                         10 \n",
       "                             B-cells_HPCA_3 \n",
       "                                          8 \n",
       "                      B-cells_NOVERSHTERN_1 \n",
       "                                         65 \n",
       "                      B-cells_NOVERSHTERN_2 \n",
       "                                         76 \n",
       "                      B-cells_NOVERSHTERN_3 \n",
       "                                         55 \n",
       "                         Basophils_FANTOM_1 \n",
       "                                         45 \n",
       "                         Basophils_FANTOM_2 \n",
       "                                        197 \n",
       "                         Basophils_FANTOM_3 \n",
       "                                          8 \n",
       "                    Basophils_NOVERSHTERN_1 \n",
       "                                         42 \n",
       "                    Basophils_NOVERSHTERN_2 \n",
       "                                         11 \n",
       "                    Basophils_NOVERSHTERN_3 \n",
       "                                          8 \n",
       "               CD4+ memory T-cells_FANTOM_1 \n",
       "                                        200 \n",
       "               CD4+ memory T-cells_FANTOM_2 \n",
       "                                         11 \n",
       "               CD4+ memory T-cells_FANTOM_3 \n",
       "                                        139 \n",
       "                 CD4+ memory T-cells_IRIS_1 \n",
       "                                         13 \n",
       "                 CD4+ memory T-cells_IRIS_2 \n",
       "                                         15 \n",
       "                 CD4+ memory T-cells_IRIS_3 \n",
       "                                         21 \n",
       "                CD4+ naive T-cells_FANTOM_1 \n",
       "                                         15 \n",
       "                CD4+ naive T-cells_FANTOM_2 \n",
       "                                         56 \n",
       "                CD4+ naive T-cells_FANTOM_3 \n",
       "                                          9 \n",
       "                  CD4+ naive T-cells_HPCA_1 \n",
       "                                         35 \n",
       "                  CD4+ naive T-cells_HPCA_2 \n",
       "                                         20 \n",
       "                  CD4+ naive T-cells_HPCA_3 \n",
       "                                         24 \n",
       "                  CD4+ naive T-cells_IRIS_1 \n",
       "                                         17 \n",
       "                  CD4+ naive T-cells_IRIS_2 \n",
       "                                         12 \n",
       "                  CD4+ naive T-cells_IRIS_3 \n",
       "                                         47 \n",
       "           CD4+ naive T-cells_NOVERSHTERN_1 \n",
       "                                         39 \n",
       "           CD4+ naive T-cells_NOVERSHTERN_2 \n",
       "                                          9 \n",
       "           CD4+ naive T-cells_NOVERSHTERN_3 \n",
       "                                         15 \n",
       "                   CD4+ T-cells_BLUEPRINT_1 \n",
       "                                         34 \n",
       "                   CD4+ T-cells_BLUEPRINT_2 \n",
       "                                         67 \n",
       "                   CD4+ T-cells_BLUEPRINT_3 \n",
       "                                         29 \n",
       "                      CD4+ T-cells_FANTOM_1 \n",
       "                                         26 \n",
       "                      CD4+ T-cells_FANTOM_2 \n",
       "                                         13 \n",
       "                      CD4+ T-cells_FANTOM_3 \n",
       "                                         77 \n",
       "                        CD4+ T-cells_HPCA_1 \n",
       "                                         37 \n",
       "                        CD4+ T-cells_HPCA_2 \n",
       "                                         30 \n",
       "                        CD4+ T-cells_HPCA_3 \n",
       "                                         35 \n",
       "                       CD4+ Tcm_BLUEPRINT_1 \n",
       "                                         43 \n",
       "                       CD4+ Tcm_BLUEPRINT_2 \n",
       "                                         39 \n",
       "                       CD4+ Tcm_BLUEPRINT_3 \n",
       "                                         87 \n",
       "                            CD4+ Tcm_HPCA_1 \n",
       "                                         56 \n",
       "                            CD4+ Tcm_HPCA_2 \n",
       "                                         77 \n",
       "                            CD4+ Tcm_HPCA_3 \n",
       "                                         37 \n",
       "                     CD4+ Tcm_NOVERSHTERN_1 \n",
       "                                         22 \n",
       "                     CD4+ Tcm_NOVERSHTERN_2 \n",
       "                                         19 \n",
       "                     CD4+ Tcm_NOVERSHTERN_3 \n",
       "                                         58 \n",
       "                       CD4+ Tem_BLUEPRINT_1 \n",
       "                                         10 \n",
       "                       CD4+ Tem_BLUEPRINT_2 \n",
       "                                         10 \n",
       "                       CD4+ Tem_BLUEPRINT_3 \n",
       "                                         22 \n",
       "                            CD4+ Tem_HPCA_1 \n",
       "                                        139 \n",
       "                            CD4+ Tem_HPCA_2 \n",
       "                                         53 \n",
       "                            CD4+ Tem_HPCA_3 \n",
       "                                         10 \n",
       "                     CD4+ Tem_NOVERSHTERN_1 \n",
       "                                         44 \n",
       "                     CD4+ Tem_NOVERSHTERN_2 \n",
       "                                         15 \n",
       "                     CD4+ Tem_NOVERSHTERN_3 \n",
       "                                         15 \n",
       "                  CD8+ naive T-cells_HPCA_1 \n",
       "                                         62 \n",
       "                  CD8+ naive T-cells_HPCA_2 \n",
       "                                         28 \n",
       "                  CD8+ naive T-cells_HPCA_3 \n",
       "                                         29 \n",
       "                   CD8+ T-cells_BLUEPRINT_1 \n",
       "                                         30 \n",
       "                   CD8+ T-cells_BLUEPRINT_2 \n",
       "                                         52 \n",
       "                   CD8+ T-cells_BLUEPRINT_3 \n",
       "                                         21 \n",
       "                      CD8+ T-cells_FANTOM_1 \n",
       "                                         52 \n",
       "                      CD8+ T-cells_FANTOM_2 \n",
       "                                         12 \n",
       "                      CD8+ T-cells_FANTOM_3 \n",
       "                                         40 \n",
       "                        CD8+ T-cells_HPCA_1 \n",
       "                                         11 \n",
       "                        CD8+ T-cells_HPCA_2 \n",
       "                                         22 \n",
       "                        CD8+ T-cells_HPCA_3 \n",
       "                                         25 \n",
       "                        CD8+ T-cells_IRIS_1 \n",
       "                                         10 \n",
       "                        CD8+ T-cells_IRIS_2 \n",
       "                                          9 \n",
       "                        CD8+ T-cells_IRIS_3 \n",
       "                                         17 \n",
       "                 CD8+ T-cells_NOVERSHTERN_1 \n",
       "                                         30 \n",
       "                 CD8+ T-cells_NOVERSHTERN_2 \n",
       "                                         11 \n",
       "                 CD8+ T-cells_NOVERSHTERN_3 \n",
       "                                         40 \n",
       "                       CD8+ Tcm_BLUEPRINT_1 \n",
       "                                          9 \n",
       "                       CD8+ Tcm_BLUEPRINT_2 \n",
       "                                         40 \n",
       "                       CD8+ Tcm_BLUEPRINT_3 \n",
       "                                         16 \n",
       "                            CD8+ Tcm_HPCA_1 \n",
       "                                         23 \n",
       "                            CD8+ Tcm_HPCA_2 \n",
       "                                         10 \n",
       "                            CD8+ Tcm_HPCA_3 \n",
       "                                         53 \n",
       "                     CD8+ Tcm_NOVERSHTERN_1 \n",
       "                                         30 \n",
       "                     CD8+ Tcm_NOVERSHTERN_2 \n",
       "                                         24 \n",
       "                     CD8+ Tcm_NOVERSHTERN_3 \n",
       "                                         10 \n",
       "                       CD8+ Tem_BLUEPRINT_1 \n",
       "                                         10 \n",
       "                       CD8+ Tem_BLUEPRINT_2 \n",
       "                                        144 \n",
       "                       CD8+ Tem_BLUEPRINT_3 \n",
       "                                         64 \n",
       "                            CD8+ Tem_HPCA_1 \n",
       "                                        168 \n",
       "                            CD8+ Tem_HPCA_2 \n",
       "                                        162 \n",
       "                            CD8+ Tem_HPCA_3 \n",
       "                                         41 \n",
       "                     CD8+ Tem_NOVERSHTERN_1 \n",
       "                                         25 \n",
       "                     CD8+ Tem_NOVERSHTERN_2 \n",
       "                                         20 \n",
       "                     CD8+ Tem_NOVERSHTERN_3 \n",
       "                                         22 \n",
       "                                 cDC_HPCA_1 \n",
       "                                         14 \n",
       "                                 cDC_HPCA_2 \n",
       "                                         12 \n",
       "                                 cDC_HPCA_3 \n",
       "                                         11 \n",
       "                          cDC_NOVERSHTERN_1 \n",
       "                                         13 \n",
       "                          cDC_NOVERSHTERN_2 \n",
       "                                         12 \n",
       "                          cDC_NOVERSHTERN_3 \n",
       "                                         20 \n",
       "  Class-switched memory B-cells_BLUEPRINT_1 \n",
       "                                         12 \n",
       "  Class-switched memory B-cells_BLUEPRINT_2 \n",
       "                                         28 \n",
       "  Class-switched memory B-cells_BLUEPRINT_3 \n",
       "                                         20 \n",
       "Class-switched memory B-cells_NOVERSHTERN_1 \n",
       "                                         16 \n",
       "Class-switched memory B-cells_NOVERSHTERN_2 \n",
       "                                         14 \n",
       "Class-switched memory B-cells_NOVERSHTERN_3 \n",
       "                                         14 \n",
       "                             DC_BLUEPRINT_1 \n",
       "                                          8 \n",
       "                             DC_BLUEPRINT_2 \n",
       "                                          9 \n",
       "                             DC_BLUEPRINT_3 \n",
       "                                          8 \n",
       "                                DC_FANTOM_1 \n",
       "                                         14 \n",
       "                                DC_FANTOM_2 \n",
       "                                         63 \n",
       "                                DC_FANTOM_3 \n",
       "                                         21 \n",
       "                                  DC_HPCA_1 \n",
       "                                         11 \n",
       "                                  DC_HPCA_2 \n",
       "                                         11 \n",
       "                                  DC_HPCA_3 \n",
       "                                          8 \n",
       "                                  DC_IRIS_1 \n",
       "                                          8 \n",
       "                                  DC_IRIS_2 \n",
       "                                         31 \n",
       "                                  DC_IRIS_3 \n",
       "                                         17 \n",
       "                    Eosinophils_BLUEPRINT_1 \n",
       "                                         18 \n",
       "                    Eosinophils_BLUEPRINT_2 \n",
       "                                         15 \n",
       "                    Eosinophils_BLUEPRINT_3 \n",
       "                                         46 \n",
       "                       Eosinophils_FANTOM_1 \n",
       "                                         38 \n",
       "                       Eosinophils_FANTOM_2 \n",
       "                                         34 \n",
       "                       Eosinophils_FANTOM_3 \n",
       "                                         44 \n",
       "                  Eosinophils_NOVERSHTERN_1 \n",
       "                                         16 \n",
       "                  Eosinophils_NOVERSHTERN_2 \n",
       "                                         13 \n",
       "                  Eosinophils_NOVERSHTERN_3 \n",
       "                                         15 \n",
       "                                 iDC_HPCA_1 \n",
       "                                         11 \n",
       "                                 iDC_HPCA_2 \n",
       "                                         10 \n",
       "                                 iDC_HPCA_3 \n",
       "                                          8 \n",
       "                 Macrophages M1_BLUEPRINT_1 \n",
       "                                         10 \n",
       "                 Macrophages M1_BLUEPRINT_2 \n",
       "                                          9 \n",
       "                 Macrophages M1_BLUEPRINT_3 \n",
       "                                        106 \n",
       "                    Macrophages M1_FANTOM_1 \n",
       "                                         55 \n",
       "                    Macrophages M1_FANTOM_2 \n",
       "                                         89 \n",
       "                    Macrophages M1_FANTOM_3 \n",
       "                                         82 \n",
       "                 Macrophages M2_BLUEPRINT_1 \n",
       "                                         96 \n",
       "                 Macrophages M2_BLUEPRINT_2 \n",
       "                                         10 \n",
       "                 Macrophages M2_BLUEPRINT_3 \n",
       "                                         38 \n",
       "                      Macrophages M2_HPCA_1 \n",
       "                                         75 \n",
       "                      Macrophages M2_HPCA_2 \n",
       "                                         15 \n",
       "                      Macrophages M2_HPCA_3 \n",
       "                                          8 \n",
       "                    Macrophages_BLUEPRINT_1 \n",
       "                                         69 \n",
       "                    Macrophages_BLUEPRINT_2 \n",
       "                                        111 \n",
       "                    Macrophages_BLUEPRINT_3 \n",
       "                                         85 \n",
       "                       Macrophages_FANTOM_1 \n",
       "                                         17 \n",
       "                       Macrophages_FANTOM_2 \n",
       "                                         35 \n",
       "                       Macrophages_FANTOM_3 \n",
       "                                         57 \n",
       "                         Macrophages_HPCA_1 \n",
       "                                        123 \n",
       "                         Macrophages_HPCA_2 \n",
       "                                         78 \n",
       "                         Macrophages_HPCA_3 \n",
       "                                        114 \n",
       "                         Macrophages_IRIS_1 \n",
       "                                         37 \n",
       "                         Macrophages_IRIS_2 \n",
       "                                         79 \n",
       "                         Macrophages_IRIS_3 \n",
       "                                         17 \n",
       "                        Mast cells_FANTOM_1 \n",
       "                                        125 \n",
       "                        Mast cells_FANTOM_2 \n",
       "                                         99 \n",
       "                        Mast cells_FANTOM_3 \n",
       "                                         68 \n",
       "                 Memory B-cells_BLUEPRINT_1 \n",
       "                                        144 \n",
       "                 Memory B-cells_BLUEPRINT_2 \n",
       "                                         67 \n",
       "                 Memory B-cells_BLUEPRINT_3 \n",
       "                                         70 \n",
       "                      Memory B-cells_HPCA_1 \n",
       "                                         19 \n",
       "                      Memory B-cells_HPCA_2 \n",
       "                                         39 \n",
       "                      Memory B-cells_HPCA_3 \n",
       "                                         14 \n",
       "                      Memory B-cells_IRIS_1 \n",
       "                                         14 \n",
       "                      Memory B-cells_IRIS_2 \n",
       "                                         19 \n",
       "                      Memory B-cells_IRIS_3 \n",
       "                                         12 \n",
       "                      Monocytes_BLUEPRINT_1 \n",
       "                                         10 \n",
       "                      Monocytes_BLUEPRINT_2 \n",
       "                                         17 \n",
       "                      Monocytes_BLUEPRINT_3 \n",
       "                                         12 \n",
       "                         Monocytes_FANTOM_1 \n",
       "                                         80 \n",
       "                         Monocytes_FANTOM_2 \n",
       "                                        179 \n",
       "                         Monocytes_FANTOM_3 \n",
       "                                        163 \n",
       "                           Monocytes_HPCA_1 \n",
       "                                         19 \n",
       "                           Monocytes_HPCA_2 \n",
       "                                         15 \n",
       "                           Monocytes_HPCA_3 \n",
       "                                        134 \n",
       "                           Monocytes_IRIS_1 \n",
       "                                         18 \n",
       "                           Monocytes_IRIS_2 \n",
       "                                         10 \n",
       "                           Monocytes_IRIS_3 \n",
       "                                         35 \n",
       "                    Monocytes_NOVERSHTERN_1 \n",
       "                                         91 \n",
       "                    Monocytes_NOVERSHTERN_2 \n",
       "                                         14 \n",
       "                    Monocytes_NOVERSHTERN_3 \n",
       "                                         13 \n",
       "                  naive B-cells_BLUEPRINT_1 \n",
       "                                          8 \n",
       "                  naive B-cells_BLUEPRINT_2 \n",
       "                                         18 \n",
       "                  naive B-cells_BLUEPRINT_3 \n",
       "                                          9 \n",
       "                       naive B-cells_HPCA_1 \n",
       "                                         28 \n",
       "                       naive B-cells_HPCA_2 \n",
       "                                         29 \n",
       "                       naive B-cells_HPCA_3 \n",
       "                                         38 \n",
       "                naive B-cells_NOVERSHTERN_1 \n",
       "                                         47 \n",
       "                naive B-cells_NOVERSHTERN_2 \n",
       "                                         38 \n",
       "                naive B-cells_NOVERSHTERN_3 \n",
       "                                         69 \n",
       "                    Neutrophils_BLUEPRINT_1 \n",
       "                                          9 \n",
       "                    Neutrophils_BLUEPRINT_2 \n",
       "                                          8 \n",
       "                    Neutrophils_BLUEPRINT_3 \n",
       "                                          8 \n",
       "                       Neutrophils_FANTOM_1 \n",
       "                                         42 \n",
       "                       Neutrophils_FANTOM_2 \n",
       "                                         60 \n",
       "                       Neutrophils_FANTOM_3 \n",
       "                                         36 \n",
       "                         Neutrophils_HPCA_1 \n",
       "                                          9 \n",
       "                         Neutrophils_HPCA_2 \n",
       "                                          9 \n",
       "                         Neutrophils_HPCA_3 \n",
       "                                         12 \n",
       "                         Neutrophils_IRIS_1 \n",
       "                                          8 \n",
       "                         Neutrophils_IRIS_2 \n",
       "                                         15 \n",
       "                         Neutrophils_IRIS_3 \n",
       "                                         17 \n",
       "                       NK cells_BLUEPRINT_1 \n",
       "                                         13 \n",
       "                       NK cells_BLUEPRINT_2 \n",
       "                                         36 \n",
       "                       NK cells_BLUEPRINT_3 \n",
       "                                         25 \n",
       "                          NK cells_FANTOM_1 \n",
       "                                         15 \n",
       "                          NK cells_FANTOM_2 \n",
       "                                          8 \n",
       "                          NK cells_FANTOM_3 \n",
       "                                          8 \n",
       "                            NK cells_HPCA_1 \n",
       "                                         51 \n",
       "                            NK cells_HPCA_2 \n",
       "                                         37 \n",
       "                            NK cells_HPCA_3 \n",
       "                                         39 \n",
       "                            NK cells_IRIS_1 \n",
       "                                         13 \n",
       "                            NK cells_IRIS_2 \n",
       "                                         19 \n",
       "                            NK cells_IRIS_3 \n",
       "                                          8 \n",
       "                     NK cells_NOVERSHTERN_1 \n",
       "                                          8 \n",
       "                     NK cells_NOVERSHTERN_2 \n",
       "                                          9 \n",
       "                     NK cells_NOVERSHTERN_3 \n",
       "                                          9 \n",
       "                               pDC_FANTOM_1 \n",
       "                                         12 \n",
       "                               pDC_FANTOM_2 \n",
       "                                         11 \n",
       "                               pDC_FANTOM_3 \n",
       "                                         25 \n",
       "                          pDC_NOVERSHTERN_1 \n",
       "                                          9 \n",
       "                          pDC_NOVERSHTERN_2 \n",
       "                                         10 \n",
       "                          pDC_NOVERSHTERN_3 \n",
       "                                         13 \n",
       "                   Plasma cells_BLUEPRINT_1 \n",
       "                                          9 \n",
       "                   Plasma cells_BLUEPRINT_2 \n",
       "                                        134 \n",
       "                   Plasma cells_BLUEPRINT_3 \n",
       "                                        177 \n",
       "                        Plasma cells_HPCA_1 \n",
       "                                         22 \n",
       "                        Plasma cells_HPCA_2 \n",
       "                                         38 \n",
       "                        Plasma cells_HPCA_3 \n",
       "                                         17 \n",
       "                        Plasma cells_IRIS_1 \n",
       "                                          9 \n",
       "                        Plasma cells_IRIS_2 \n",
       "                                         41 \n",
       "                        Plasma cells_IRIS_3 \n",
       "                                         19 \n",
       "                         pro B-cells_HPCA_1 \n",
       "                                         55 \n",
       "                         pro B-cells_HPCA_2 \n",
       "                                        156 \n",
       "                         pro B-cells_HPCA_3 \n",
       "                                         51 \n",
       "                  pro B-cells_NOVERSHTERN_1 \n",
       "                                         60 \n",
       "                  pro B-cells_NOVERSHTERN_2 \n",
       "                                         13 \n",
       "                  pro B-cells_NOVERSHTERN_3 \n",
       "                                         15 \n",
       "                           Tgd cells_HPCA_1 \n",
       "                                        153 \n",
       "                           Tgd cells_HPCA_2 \n",
       "                                        164 \n",
       "                           Tgd cells_HPCA_3 \n",
       "                                        183 \n",
       "                           Th1 cells_IRIS_1 \n",
       "                                         24 \n",
       "                           Th1 cells_IRIS_2 \n",
       "                                         10 \n",
       "                           Th1 cells_IRIS_3 \n",
       "                                         17 \n",
       "                           Th2 cells_IRIS_1 \n",
       "                                          8 \n",
       "                           Th2 cells_IRIS_2 \n",
       "                                          9 \n",
       "                           Th2 cells_IRIS_3 \n",
       "                                         16 \n",
       "                          Tregs_BLUEPRINT_1 \n",
       "                                         22 \n",
       "                          Tregs_BLUEPRINT_2 \n",
       "                                          9 \n",
       "                          Tregs_BLUEPRINT_3 \n",
       "                                         15 \n",
       "                             Tregs_FANTOM_1 \n",
       "                                          9 \n",
       "                             Tregs_FANTOM_2 \n",
       "                                         15 \n",
       "                             Tregs_FANTOM_3 \n",
       "                                         21 \n",
       "                               Tregs_HPCA_1 \n",
       "                                         12 \n",
       "                               Tregs_HPCA_2 \n",
       "                                         10 \n",
       "                               Tregs_HPCA_3 \n",
       "                                         11 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(genesets_xcell$Celltype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "genesets_xcell$Cellgroup <- NA\n",
    "\n",
    "genesets_xcell$Cellgroup[ grepl(\"aDC_\",genesets_xcell$Celltype)] <- \"DC\" #activated dc\n",
    "genesets_xcell$Cellgroup[ grepl(\"cDC_\",genesets_xcell$Celltype)] <- \"DC\" #conventional dc\n",
    "genesets_xcell$Cellgroup[ grepl(\"DC_\",genesets_xcell$Celltype)] <- \"DC\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"iDC_\",genesets_xcell$Celltype)] <- \"DC\" #immature dc\n",
    "genesets_xcell$Cellgroup[ grepl(\"pDC_\",genesets_xcell$Celltype)] <- \"DC\" #plasmacytoid dc\n",
    "\n",
    "genesets_xcell$Cellgroup[ grepl(\"B-cells\",genesets_xcell$Celltype)] <- \"B_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Plasma cells\",genesets_xcell$Celltype)] <- \"B_cells\"\n",
    "\n",
    "genesets_xcell$Cellgroup[ grepl(\"Basophils\",genesets_xcell$Celltype)] <- \"Granulocytes\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Eosinophils\",genesets_xcell$Celltype)] <- \"Granulocytes\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Neutrophils\",genesets_xcell$Celltype)] <- \"Granulocytes\"\n",
    "\n",
    "genesets_xcell$Cellgroup[ grepl(\"Tregs\",genesets_xcell$Celltype)] <- \"T_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Th2\",genesets_xcell$Celltype)] <- \"T_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Th1\",genesets_xcell$Celltype)] <- \"T_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"CD4+\",genesets_xcell$Celltype)] <- \"T_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Tgd\",genesets_xcell$Celltype)] <- \"T_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"CD8+\",genesets_xcell$Celltype)] <- \"T_cells\"\n",
    "\n",
    "genesets_xcell$Cellgroup[ grepl(\"Macrophages\",genesets_xcell$Celltype)] <- \"Macrophages\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Mast cells\",genesets_xcell$Celltype)] <- \"Myeloid_cells\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"Monocytes\",genesets_xcell$Celltype)] <- \"Monocytes\"\n",
    "genesets_xcell$Cellgroup[ grepl(\"NK cells\",genesets_xcell$Celltype)] <- \"NK_cells\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "      B_cells            DC  Granulocytes   Macrophages     Monocytes \n",
       "         1909           718           783          1415           810 \n",
       "Myeloid_cells      NK_cells       T_cells          <NA> \n",
       "          292           298          3881             0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(genesets_xcell$Cellgroup, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "genesets_xcell$GeneSignature <- NA\n",
    "\n",
    "genesets_xcell$GeneSignature[ grepl(\"HPCA_1\",genesets_xcell$Celltype)] <- \"HPCA_1\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"HPCA_2\",genesets_xcell$Celltype)] <- \"HPCA_2\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"HPCA_3\",genesets_xcell$Celltype)] <- \"HPCA_3\"\n",
    "\n",
    "genesets_xcell$GeneSignature[ grepl(\"IRIS_1\",genesets_xcell$Celltype)] <- \"IRIS_1\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"IRIS_2\",genesets_xcell$Celltype)] <- \"IRIS_2\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"IRIS_3\",genesets_xcell$Celltype)] <- \"IRIS_3\"\n",
    "\n",
    "genesets_xcell$GeneSignature[ grepl(\"FANTOM_1\",genesets_xcell$Celltype)] <- \"FANTOM_1\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"FANTOM_2\",genesets_xcell$Celltype)] <- \"FANTOM_2\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"FANTOM_3\",genesets_xcell$Celltype)] <- \"FANTOM_3\"\n",
    "\n",
    "genesets_xcell$GeneSignature[ grepl(\"NOVERSHTERN_1\",genesets_xcell$Celltype)] <- \"NOVERSHTERN_1\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"NOVERSHTERN_2\",genesets_xcell$Celltype)] <- \"NOVERSHTERN_2\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"NOVERSHTERN_3\",genesets_xcell$Celltype)] <- \"NOVERSHTERN_3\"\n",
    "\n",
    "genesets_xcell$GeneSignature[ grepl(\"BLUEPRINT_1\",genesets_xcell$Celltype)] <- \"BLUEPRINT_1\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"BLUEPRINT_2\",genesets_xcell$Celltype)] <- \"BLUEPRINT_2\" \n",
    "genesets_xcell$GeneSignature[ grepl(\"BLUEPRINT_3\",genesets_xcell$Celltype)] <- \"BLUEPRINT_3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a wide table with gene signatures\n",
    "genesets_xcell <- tidyr::spread(genesets_xcell, GeneSignature, Cellgroup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>10106</li><li>17</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 10106\n",
       "\\item 17\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 10106\n",
       "2. 17\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 10106    17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(genesets_xcell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile all celltypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a df to hold all celltypes across studies\n",
    "celltype_df <- as.data.frame(immune_genes_nontumour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(celltype_df) <- \"Gene\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype_df$Immunexut <- immune_immunexut_expressed$Cellgroup[match(celltype_df$Gene, immune_immunexut_expressed$Gene)]\n",
    "celltype_df$Blood_PA <- gene_cells_blood_sep_specific$Cellgroup[match(celltype_df$Gene, gene_cells_blood_sep_specific$Gene)]\n",
    "celltype_df$MCPcounter <- mcp_sig$Cellgroup[match(celltype_df$Gene, mcp_sig$Gene)]\n",
    "celltype_df$CIBERSORT <- lm22_celltypes$Cellgroup[match(celltype_df$Gene, lm22_celltypes$Gene)]\n",
    "celltype_df$Quantiseq <- quantiseq_celltypes$Cellgroup[match(celltype_df$Gene, quantiseq_celltypes$Gene)]\n",
    "celltype_df$EPIC <- genesets_epic$Cellgroup[match(celltype_df$Gene, genesets_epic$Gene)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcell_match <- genesets_xcell[match(celltype_df$Gene, genesets_xcell$Gene),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype_df <- cbind.data.frame(celltype_df, xcell_match[,3:17])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames(celltype_df) <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 22</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Gene</th><th scope=col>Immunexut</th><th scope=col>Blood_PA</th><th scope=col>MCPcounter</th><th scope=col>CIBERSORT</th><th scope=col>Quantiseq</th><th scope=col>EPIC</th><th scope=col>BLUEPRINT_1</th><th scope=col>BLUEPRINT_2</th><th scope=col>BLUEPRINT_3</th><th scope=col>⋯</th><th scope=col>FANTOM_3</th><th scope=col>HPCA_1</th><th scope=col>HPCA_2</th><th scope=col>HPCA_3</th><th scope=col>IRIS_1</th><th scope=col>IRIS_2</th><th scope=col>IRIS_3</th><th scope=col>NOVERSHTERN_1</th><th scope=col>NOVERSHTERN_2</th><th scope=col>NOVERSHTERN_3</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;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>⋯</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><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>AC004813.1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>AC006449.1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>AC006449.2</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>AC007325.1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>AC007326.1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>AC009950.1</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 22\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Gene & Immunexut & Blood\\_PA & MCPcounter & CIBERSORT & Quantiseq & EPIC & BLUEPRINT\\_1 & BLUEPRINT\\_2 & BLUEPRINT\\_3 & ⋯ & FANTOM\\_3 & HPCA\\_1 & HPCA\\_2 & HPCA\\_3 & IRIS\\_1 & IRIS\\_2 & IRIS\\_3 & NOVERSHTERN\\_1 & NOVERSHTERN\\_2 & NOVERSHTERN\\_3\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & ⋯ & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & AC004813.1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t2 & AC006449.1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t3 & AC006449.2 & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t4 & AC007325.1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t5 & AC007326.1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t6 & AC009950.1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 22\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Immunexut &lt;chr&gt; | Blood_PA &lt;chr&gt; | MCPcounter &lt;chr&gt; | CIBERSORT &lt;chr&gt; | Quantiseq &lt;chr&gt; | EPIC &lt;chr&gt; | BLUEPRINT_1 &lt;chr&gt; | BLUEPRINT_2 &lt;chr&gt; | BLUEPRINT_3 &lt;chr&gt; | ⋯ ⋯ | FANTOM_3 &lt;chr&gt; | HPCA_1 &lt;chr&gt; | HPCA_2 &lt;chr&gt; | HPCA_3 &lt;chr&gt; | IRIS_1 &lt;chr&gt; | IRIS_2 &lt;chr&gt; | IRIS_3 &lt;chr&gt; | NOVERSHTERN_1 &lt;chr&gt; | NOVERSHTERN_2 &lt;chr&gt; | NOVERSHTERN_3 &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | AC004813.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| 2 | AC006449.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| 3 | AC006449.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| 4 | AC007325.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| 5 | AC007326.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "| 6 | AC009950.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene       Immunexut Blood_PA MCPcounter CIBERSORT Quantiseq EPIC BLUEPRINT_1\n",
       "1 AC004813.1 NA        NA       NA         NA        NA        NA   NA         \n",
       "2 AC006449.1 NA        NA       NA         NA        NA        NA   NA         \n",
       "3 AC006449.2 NA        NA       NA         NA        NA        NA   NA         \n",
       "4 AC007325.1 NA        NA       NA         NA        NA        NA   NA         \n",
       "5 AC007326.1 NA        NA       NA         NA        NA        NA   NA         \n",
       "6 AC009950.1 NA        NA       NA         NA        NA        NA   NA         \n",
       "  BLUEPRINT_2 BLUEPRINT_3 ⋯ FANTOM_3 HPCA_1 HPCA_2 HPCA_3 IRIS_1 IRIS_2 IRIS_3\n",
       "1 NA          NA          ⋯ NA       NA     NA     NA     NA     NA     NA    \n",
       "2 NA          NA          ⋯ NA       NA     NA     NA     NA     NA     NA    \n",
       "3 NA          NA          ⋯ NA       NA     NA     NA     NA     NA     NA    \n",
       "4 NA          NA          ⋯ NA       NA     NA     NA     NA     NA     NA    \n",
       "5 NA          NA          ⋯ NA       NA     NA     NA     NA     NA     NA    \n",
       "6 NA          NA          ⋯ NA       NA     NA     NA     NA     NA     NA    \n",
       "  NOVERSHTERN_1 NOVERSHTERN_2 NOVERSHTERN_3\n",
       "1 NA            NA            NA           \n",
       "2 NA            NA            NA           \n",
       "3 NA            NA            NA           \n",
       "4 NA            NA            NA           \n",
       "5 NA            NA            NA           \n",
       "6 NA            NA            NA           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(celltype_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>791</li><li>22</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 791\n",
       "\\item 22\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 791\n",
       "2. 22\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 791  22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(celltype_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  14.00   19.00   20.00   19.68   21.00   21.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count NAs\n",
    "summary(rowSums(is.na(celltype_df[,2:22])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_cells_freq <- data.frame(Gene=character(),Celltype=character(),\n",
    "                               Freq=character(),stringsAsFactors=FALSE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC004813.1\n",
      "\n",
      "AC006449.1\n",
      "\n",
      "AC006449.2\n",
      "\n",
      "AC007325.1\n",
      "\n",
      "AC007326.1\n",
      "\n",
      "AC009950.1\n",
      "\n",
      "AC017081.1\n",
      "\n",
      "AC018755.18\n",
      "\n",
      "AC073333.1\n",
      "\n",
      "AC073657.1\n",
      "\n",
      "AC090154.1\n",
      "\n",
      "AC104534.3\n",
      "\n",
      "AC107081.1\n",
      "\n",
      "AC110602.1\n",
      "\n",
      "AC131263.1\n",
      "\n",
      "AC241585.2\n",
      "\n",
      "ACSL5\n",
      "\n",
      "ADAM28\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 ADAM28  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ADCY7\n",
      "\n",
      "ADGRE1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 ADGRE1 Granulocytes    1\n",
      "2 ADGRE1    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ADGRE2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 ADGRE2 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ADGRE3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 ADGRE3 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ADGRG5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 ADGRG5           DC    1\n",
      "2 ADGRG5 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AHSA2\n",
      "\n",
      "AL365202.1\n",
      "\n",
      "AL591806.1\n",
      "\n",
      "AL596220.1\n",
      "\n",
      "AMICA1\n",
      "\n",
      "APBB1IP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 APBB1IP Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "APOBEC3A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 APOBEC3A Granulocytes    1\n",
      "2 APOBEC3A    Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "APOBEC3D\n",
      "\n",
      "APOBEC3G\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 APOBEC3G NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "APOBEC3H\n",
      "\n",
      "AREG\n",
      "\n",
      "ARG1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 ARG1      B_cells    1\n",
      "2 ARG1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ARHGAP25\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 ARHGAP25 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ARHGAP27\n",
      "\n",
      "ARHGAP30\n",
      "\n",
      "ASGR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene  Celltype Freq\n",
      "1 ASGR2 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ASTE1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ASTE1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ATHL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ATHL1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ATP6V0D2\n",
      "\n",
      "BANK1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 BANK1  B_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BATF\n",
      "\n",
      "BIN2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 BIN2  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BLNK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 BLNK  B_cells    1\n",
      "2 BLNK       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BTLA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 BTLA  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C10orf54\n",
      "\n",
      "C11orf45\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 C11orf45       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C15orf57\n",
      "\n",
      "C17orf62\n",
      "\n",
      "C18orf8\n",
      "\n",
      "C19orf35\n",
      "\n",
      "C1orf186\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 C1orf186       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1   C2 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C5AR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 C5AR2 Granulocytes    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C5orf56\n",
      "\n",
      "CAMP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 CAMP Granulocytes    3\n",
      "2 CAMP     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CARD17\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 CARD17  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CARD9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene  Celltype Freq\n",
      "1 CARD9 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CASP5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene    Celltype Freq\n",
      "1 CASP5          DC    1\n",
      "2 CASP5 Macrophages    1\n",
      "3 CASP5   Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CASP8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CASP8  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CASS4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CASS4 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCDC125\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CCDC125 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCL22\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CCL22       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCL3L3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 CCL3L3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCL4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CCL4       DC    1\n",
      "2 CCL4 NK_cells    2\n",
      "3 CCL4  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCL5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CCL5  T_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 CCR2           DC    1\n",
      "2 CCR2 Granulocytes    1\n",
      "3 CCR2    Monocytes    1\n",
      "4 CCR2      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCR3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 CCR3 Granulocytes    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCR4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CCR4  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCR6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CCR6  B_cells    1\n",
      "2 CCR6  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCRL2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene    Celltype Freq\n",
      "1 CCRL2 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD101\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CD101 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD160\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CD160 NK_cells    3\n",
      "2 CD160  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD180\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene    Celltype Freq\n",
      "1 CD180     B_cells    2\n",
      "2 CD180 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD19\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD19  B_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD1C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD1C       DC    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD209\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene    Celltype Freq\n",
      "1 CD209          DC    1\n",
      "2 CD209 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD226\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CD226  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD244\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CD244 Granulocytes    2\n",
      "2 CD244    Monocytes    1\n",
      "3 CD244     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD247\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CD247 NK_cells    2\n",
      "2 CD247  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD27\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD27  B_cells    1\n",
      "2 CD27  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD28\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD28  T_cells    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD300A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 CD300A NK_cells    1\n",
      "2 CD300A  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD300C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene    Celltype Freq\n",
      "1 CD300C          DC    1\n",
      "2 CD300C Macrophages    1\n",
      "3 CD300C   Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD300LB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CD300LB Granulocytes    1\n",
      "2 CD300LB    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD33\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 CD33            DC    1\n",
      "2 CD33     Monocytes    2\n",
      "3 CD33 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD3D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD3D  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD3E\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD3E  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD3G\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD3G  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD48\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD48  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD52\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD52  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD69\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD69  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD79A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CD79A  B_cells    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD84\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene    Celltype Freq\n",
      "1 CD84 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD8B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD8B  T_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD96\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD96  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CEACAM4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CEACAM4 Granulocytes    2\n",
      "2 CEACAM4    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CEP295\n",
      "\n",
      "CFP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1  CFP Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CH507-42P11.8\n",
      "\n",
      "CH507-9B2.9\n",
      "\n",
      "CHURC1-FNTB\n",
      "\n",
      "CIDEB\n",
      "\n",
      "CIITA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CIITA  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CKLF-CMTM1\n",
      "\n",
      "CLEC12A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CLEC12A Granulocytes    1\n",
      "2 CLEC12A    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLEC17A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 CLEC17A  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLEC1B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 CLEC1B Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLEC4C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 CLEC4C       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLEC4D\n",
      "\n",
      "CLEC4E\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 CLEC4E Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLEC7A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 CLEC7A Granulocytes    3\n",
      "2 CLEC7A    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLECL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 CLECL1  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CLIC3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CLIC3       DC    2\n",
      "2 CLIC3 NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CNR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CNR2  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  CR1      B_cells    1\n",
      "2  CR1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CRTAM\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CRTAM NK_cells    1\n",
      "2 CRTAM  T_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CSF2RA\n",
      "\n",
      "CST7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CST7 NK_cells    1\n",
      "2 CST7  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CTD-2207O23.3\n",
      "\n",
      "CTD-2370N5.3\n",
      "\n",
      "CTD-3088G3.8\n",
      "\n",
      "CTSW\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CTSW NK_cells    2\n",
      "2 CTSW  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXCR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CXCR1 Granulocytes    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXCR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CXCR2 Granulocytes    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXCR5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CXCR5  B_cells    3\n",
      "2 CXCR5  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXCR6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CXCR6  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CYP2S1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 CYP2S1       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CYSLTR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CYSLTR2 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CYTH4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CYTH4       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CYTIP\n",
      "\n",
      "DENND1C\n",
      "\n",
      "DNAJC25-GNG10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Gene     Celltype Freq\n",
      "1 DNAJC25-GNG10 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DOCK2\n",
      "\n",
      "DOPEY2\n",
      "\n",
      "DPEP2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 DPEP2 Granulocytes    2\n",
      "2 DPEP2      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EIF4E3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 EIF4E3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FAM105A\n",
      "\n",
      "FAM129A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 FAM129A Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FAM129C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 FAM129C       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FAM159A\n",
      "\n",
      "FAM177B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 FAM177B  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FAM46C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 FAM46C      B_cells    1\n",
      "2 FAM46C Granulocytes    1\n",
      "3 FAM46C      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FAM78A\n",
      "\n",
      "FCAR\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 FCAR Granulocytes    2\n",
      "2 FCAR    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCER1A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene      Celltype Freq\n",
      "1 FCER1A            DC    2\n",
      "2 FCER1A  Granulocytes    1\n",
      "3 FCER1A Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCGR1B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 FCGR1B Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCGR3B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 FCGR3B Granulocytes    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCMR\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 FCMR  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCN1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 FCN1 Granulocytes    1\n",
      "2 FCN1    Monocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCRL2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 FCRL2  B_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCRL3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 FCRL3  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCRL5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 FCRL5  B_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FFAR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 FFAR2 Granulocytes    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FGD3\n",
      "\n",
      "FKSG52\n",
      "\n",
      "FKSG56\n",
      "\n",
      "FKSG61\n",
      "\n",
      "FKSG68\n",
      "\n",
      "FKSG70\n",
      "\n",
      "FOLR3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene  Celltype Freq\n",
      "1 FOLR3 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FOXP3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 FOXP3  T_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FPR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 FPR2 Granulocytes    4\n",
      "2 FPR2  Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FUT4\n",
      "\n",
      "FUT7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 FUT7       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GAB3\n",
      "\n",
      "GAPT\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 GAPT           DC    1\n",
      "2 GAPT Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GFI1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 GFI1 Myeloid_cells    1\n",
      "2 GFI1       T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GHRL\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GHRL       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GNLY\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 GNLY Granulocytes    1\n",
      "2 GNLY     NK_cells    3\n",
      "3 GNLY      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GOLGA8N\n",
      "\n",
      "GPBAR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 GPBAR1 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPR132\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 GPR132       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPR141\n",
      "\n",
      "GPR174\n",
      "\n",
      "GPR183\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 GPR183 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPR65\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 GPR65 Granulocytes    1\n",
      "2 GPR65     NK_cells    1\n",
      "3 GPR65      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPR84\n",
      "\n",
      "GRAP2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 GRAP2  T_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GYLTL1B\n",
      "\n",
      "GZMA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GZMA NK_cells    2\n",
      "2 GZMA  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GZMB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 GZMB           DC    2\n",
      "2 GZMB Granulocytes    1\n",
      "3 GZMB     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GZMH\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 GZMH Granulocytes    1\n",
      "2 GZMH     NK_cells    1\n",
      "3 GZMH      T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GZMK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GZMK  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GZMM\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GZMM NK_cells    1\n",
      "2 GZMM  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HACD4\n",
      "\n",
      "HAL\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  HAL Granulocytes    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HCAR3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 HCAR3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HERC3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 HERC3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HK3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  HK3           DC    1\n",
      "2  HK3 Granulocytes    1\n",
      "3  HK3  Macrophages    1\n",
      "4  HK3    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HLA-DOB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 HLA-DOB  B_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HMHA1\n",
      "\n",
      "HPSE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 HPSE Monocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ICAM3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 ICAM3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGFLR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 IGFLR1       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHA2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IGHA2  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 IGHD  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHG2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IGHG2  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHM\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 IGHM  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV1-2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 IGHV1-2  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV1-3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 IGHV1-3  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV1-46\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGHV1-46  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV1OR15-1\n",
      "\n",
      "IGHV3-23\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGHV3-23  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV3-33\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGHV3-33  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV3-74\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGHV3-74  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGHV4-34\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGHV4-34  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGKV3-20\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGKV3-20  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGKV4-1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 IGKV4-1  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGLC1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IGLC1  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGLL5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IGLL5  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGLV1-51\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGLV1-51  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGLV2-14\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 IGLV2-14  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IGLV5-52\n",
      "\n",
      "IKZF3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IKZF3  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL12RB1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 IL12RB1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL18RAP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 IL18RAP NK_cells    3\n",
      "2 IL18RAP  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL1R2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 IL1R2 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL23A\n",
      "\n",
      "IL2RA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IL2RA       DC    1\n",
      "2 IL2RA  T_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL2RB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IL2RB NK_cells    2\n",
      "2 IL2RB  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL2RG\n",
      "\n",
      "IL7R\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 IL7R  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IPMK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 IPMK Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IRF8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene    Celltype Freq\n",
      "1 IRF8          DC    2\n",
      "2 IRF8 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "JAK2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 JAK2           DC    1\n",
      "2 JAK2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "JCHAIN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 JCHAIN  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KBTBD8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 KBTBD8  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KCNA3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 KCNA3  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KCNE3\n",
      "\n",
      "KIAA0226L\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 KIAA0226L  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIAA0430\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 KIAA0430  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIR2DL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 KIR2DL1 NK_cells    3\n",
      "2 KIR2DL1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIR2DL3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 KIR2DL3 NK_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIR2DL4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 KIR2DL4 NK_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIR3DL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 KIR3DL1 NK_cells    2\n",
      "2 KIR3DL1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIR3DL2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 KIR3DL2 NK_cells    2\n",
      "2 KIR3DL2  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KLHL14\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 KLHL14  B_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KLRF1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 KLRF1 NK_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KLRG1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 KLRG1  T_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KYNU\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 KYNU        DC    1\n",
      "2 KYNU Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LAIR2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 LAIR2 Granulocytes    1\n",
      "2 LAIR2     NK_cells    1\n",
      "3 LAIR2      T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LAT\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  LAT  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LBHD1\n",
      "\n",
      "LCP2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 LCP2 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LILRA3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene    Celltype Freq\n",
      "1 LILRA3 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LILRA4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 LILRA4       DC    2\n",
      "2 LILRA4  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LILRA5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 LILRA5        DC    1\n",
      "2 LILRA5 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LILRB3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 LILRB3 Granulocytes    2\n",
      "2 LILRB3    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LIPN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 LIPN Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LLNLR-260H6.1\n",
      "\n",
      "LRG1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 LRG1 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LRMP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 LRMP Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LRRC37B\n",
      "\n",
      "LSP1\n",
      "\n",
      "LTA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  LTA NK_cells    1\n",
      "2  LTA  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LY75\n",
      "\n",
      "MAP1LC3B2\n",
      "\n",
      "MARCH8\n",
      "\n",
      "MCEMP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 MCEMP1 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MEFV\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 MEFV      B_cells    1\n",
      "2 MEFV Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MNDA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 MNDA Granulocytes    3\n",
      "2 MNDA    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MPP7\n",
      "\n",
      "MRC1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 MRC1       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MRVI1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 MRVI1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MS4A1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 MS4A1  B_cells    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MS4A4E\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 MS4A4E Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MYO1F\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 MYO1F Granulocytes    1\n",
      "2 MYO1F      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MYO1G\n",
      "\n",
      "NALP7\n",
      "\n",
      "NAPSA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 NAPSA       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NBPF11\n",
      "\n",
      "NCR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 NCR1 Granulocytes    2\n",
      "2 NCR1     NK_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NCR3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 NCR3 NK_cells    1\n",
      "2 NCR3  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NDNL2\n",
      "\n",
      "NINJ2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 NINJ2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NKG7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 NKG7 Granulocytes    1\n",
      "2 NKG7     NK_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NLRC4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene  Celltype Freq\n",
      "1 NLRC4 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NLRP12\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 NLRP12 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NLRP3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 NLRP3 Granulocytes    1\n",
      "2 NLRP3    Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NLRP6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 NLRP6 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OSM\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  OSM           DC    1\n",
      "2  OSM Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "P2RX1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene      Celltype Freq\n",
      "1 P2RX1     Monocytes    2\n",
      "2 P2RX1 Myeloid_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "P2RY13\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 P2RY13 Granulocytes    3\n",
      "2 P2RY13    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "P2RY14\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 P2RY14           DC    1\n",
      "2 P2RY14 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PADI4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PADI4 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PARP15\n",
      "\n",
      "PATL2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PATL2  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PCED1B\n",
      "\n",
      "PDE6G\n",
      "\n",
      "PGLYRP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 PGLYRP1 Granulocytes    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PHOSPHO1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 PHOSPHO1 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PIK3CG\n",
      "\n",
      "PIK3R5\n",
      "\n",
      "PLA2G7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene    Celltype Freq\n",
      "1 PLA2G7 Macrophages    1\n",
      "2 PLA2G7   Monocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PLAC8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PLAC8       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "POM121L7\n",
      "\n",
      "POU2AF1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 POU2AF1  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRF1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 PRF1 NK_cells    2\n",
      "2 PRF1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRKCH\n",
      "\n",
      "PRKCQ\n",
      "\n",
      "PROC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 PROC       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PROK2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PROK2 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRR33\n",
      "\n",
      "PTAFR\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PTAFR Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PTCRA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PTCRA       DC    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PTGS2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PTGS2 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PTPN22\n",
      "\n",
      "PTPN7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PTPN7  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PYHIN1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 PYHIN1 NK_cells    1\n",
      "2 PYHIN1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RAB37\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 RAB37 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RAB3D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 RAB3D Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RAB7B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 RAB7B       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RASAL3\n",
      "\n",
      "RASGRP4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 RASGRP4 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "REM2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 REM2 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RETN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 RETN Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RFWD2\n",
      "\n",
      "RGL4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 RGL4 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RHOF\n",
      "\n",
      "RIPK3\n",
      "\n",
      "RLTPR\n",
      "\n",
      "RNASE2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 RNASE2 Granulocytes    3\n",
      "2 RNASE2    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RP11-294C11.4\n",
      "\n",
      "RP11-298I3.5\n",
      "\n",
      "RP11-330H6.5\n",
      "\n",
      "RP4-576H24.4\n",
      "\n",
      "S100A12\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 S100A12 Granulocytes    2\n",
      "2 S100A12    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "S100P\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 S100P Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "S100Z\n",
      "\n",
      "SAMSN1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 SAMSN1 Granulocytes    2\n",
      "2 SAMSN1      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SAP25\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 SAP25 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SCML4\n",
      "\n",
      "SEPT1\n",
      "\n",
      "SH2D1A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 SH2D1A  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SH2D1B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 SH2D1B NK_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SH2D2A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 SH2D2A NK_cells    1\n",
      "2 SH2D2A  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIGLEC14\n",
      "\n",
      "SIGLEC5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 SIGLEC5 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIGLEC6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene      Celltype Freq\n",
      "1 SIGLEC6            DC    1\n",
      "2 SIGLEC6 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIGLEC7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 SIGLEC7           DC    1\n",
      "2 SIGLEC7 Granulocytes    1\n",
      "3 SIGLEC7     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIRPB1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 SIRPB1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIRPG\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 SIRPG  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIT1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 SIT1  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SKAP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 SKAP1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLA2\n",
      "\n",
      "SLAMF1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene    Celltype Freq\n",
      "1 SLAMF1          DC    1\n",
      "2 SLAMF1 Macrophages    1\n",
      "3 SLAMF1     T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLAMF6\n",
      "\n",
      "SLC12A3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 SLC12A3  B_cells    1\n",
      "2 SLC12A3       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLC37A2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene  Celltype Freq\n",
      "1 SLC37A2 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLC46A2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene  Celltype Freq\n",
      "1 SLC46A2 Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLCO4C1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 SLCO4C1 Granulocytes    1\n",
      "2 SLCO4C1     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLFN12L\n",
      "\n",
      "SNX15\n",
      "\n",
      "SNX20\n",
      "\n",
      "SPIB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 SPIB  B_cells    3\n",
      "2 SPIB       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPINK1\n",
      "\n",
      "SPNS3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 SPNS3       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ST20\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 ST20 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ST20-MTHFS\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 ST20-MTHFS Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAG3L5P-PVRIG2P-PILRB\n",
      "\n",
      "STAP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 STAP1  B_cells    7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SULT1A3\n",
      "\n",
      "SUSD3\n",
      "\n",
      "SYTL3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 SYTL3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TAGAP\n",
      "\n",
      "TBC1D3L\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 TBC1D3L Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TCL1A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TCL1A  B_cells    5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TESPA1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 TESPA1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TIFAB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TIFAB       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TLDC2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 TLDC2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TLR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 TLR1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TLR10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TLR10  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TLR5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 TLR5 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TLR8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 TLR8 Granulocytes    1\n",
      "2 TLR8  Macrophages    1\n",
      "3 TLR8    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TM4SF19\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 TM4SF19  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TMEM106A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 TMEM106A Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TMEM150B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene  Celltype Freq\n",
      "1 TMEM150B Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TMIGD2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 TMIGD2  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNF\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1  TNF Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFAIP8\n",
      "\n",
      "TNFRSF10C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 TNFRSF10C Granulocytes    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFRSF13B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 TNFRSF13B  B_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFRSF17\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 TNFRSF17  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFSF12-TNFSF13\n",
      "\n",
      "TNFSF8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 TNFSF8  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRAC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 TRAC  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRAT1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TRAT1  T_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRBV20-1\n",
      "\n",
      "TRBV29-1\n",
      "\n",
      "TRBV7-2\n",
      "\n",
      "TRBV7-3\n",
      "\n",
      "TRBV9\n",
      "\n",
      "TRDC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 TRDC NK_cells    1\n",
      "2 TRDC  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TREM1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 TREM1 Granulocytes    3\n",
      "2 TREM1    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TREML2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 TREML2      B_cells    1\n",
      "2 TREML2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRGC1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TRGC1 NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRGC2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TRGC2  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRGV10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 TRGV10  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRGV2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TRGV2  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRGV3\n",
      "\n",
      "TRGV4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TRGV4  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRGV5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TRGV5  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRIM38\n",
      "\n",
      "TRIM73\n",
      "\n",
      "TTC24\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 TTC24       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TTC39A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 TTC39A       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TXK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  TXK NK_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UBASH3A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 UBASH3A  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VAV1\n",
      "\n",
      "VCPIP1\n",
      "\n",
      "VENTX\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene  Celltype Freq\n",
      "1 VENTX Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VNN1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 VNN1 Monocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VNN2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 VNN2 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VNN3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 VNN3 Granulocytes    4\n",
      "2 VNN3    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VPREB3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 VPREB3  B_cells    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VSTM1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 VSTM1 Granulocytes    1\n",
      "2 VSTM1    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WDFY4\n",
      "\n",
      "WHAMM\n",
      "\n",
      "XCL2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 XCL2 NK_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZBED2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ZBED2  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZBP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 ZBP1  B_cells    2\n",
      "2 ZBP1 NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZBTB25\n",
      "\n",
      "ZC3H12A\n",
      "\n",
      "ZCCHC6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 ZCCHC6 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZMYM6NB\n",
      "\n",
      "ZNF267\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 ZNF267 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZNF296\n",
      "\n",
      "ZNF600\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 ZNF600 NK_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZNF683\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 ZNF683  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZNF808\n",
      "\n",
      "ZNRF2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ZNRF2       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ITGAL\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ITGAL  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRAF3IP3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 TRAF3IP3  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BTK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  BTK      B_cells    1\n",
      "2  BTK Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BIRC3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 BIRC3  B_cells    1\n",
      "2 BIRC3       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRDM1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PRDM1  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DAPP1\n",
      "\n",
      "IPCEF1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 IPCEF1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SP140\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 SP140  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "JAK3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 JAK3  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD86\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene    Celltype Freq\n",
      "1 CD86          DC    2\n",
      "2 CD86 Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL1RL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene      Celltype Freq\n",
      "1 IL1RL1  Granulocytes    1\n",
      "2 IL1RL1 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PLEK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 PLEK Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  CD2  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CSTA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 CSTA Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TMEM156\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 TMEM156  B_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PTGER2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 PTGER2 NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFSF14\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 TNFSF14 Granulocytes    1\n",
      "2 TNFSF14  Macrophages    1\n",
      "3 TNFSF14     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "S1PR4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 S1PR4  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TEC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1  TEC  Granulocytes    1\n",
      "2  TEC Myeloid_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 IL10        DC    1\n",
      "2 IL10 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ITGB7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ITGB7  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DOK2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 DOK2 Granulocytes    1\n",
      "2 DOK2    Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FAM49B\n",
      "\n",
      "ENPP3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 ENPP3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GBP5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GBP5  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SVOPL\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 SVOPL Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NCF1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 NCF1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LAD1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 LAD1       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ICOSLG\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 ICOSLG  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "JAML\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 JAML Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLC25A45\n",
      "\n",
      "REL\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  REL  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HPGDS\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene      Celltype Freq\n",
      "1 HPGDS  Granulocytes    1\n",
      "2 HPGDS Myeloid_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HPGD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 HPGD  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TMC8\n",
      "\n",
      "MLKL\n",
      "\n",
      "RHOH\n",
      "\n",
      "IL16\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 IL16  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KLHL6\n",
      "\n",
      "CYSLTR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CYSLTR1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CATSPER1\n",
      "\n",
      "ANXA2R\n",
      "\n",
      "GIMAP7\n",
      "\n",
      "GPR157\n",
      "\n",
      "HSH2D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 HSH2D Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1  SPN Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C10orf128\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 C10orf128 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LTB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  LTB  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HLA-DQA2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HLA-DQA2       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MPO\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  MPO Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CEACAM21\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 CEACAM21  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFRSF9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 TNFRSF9  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ACAP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ACAP1  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD5L\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 CD5L Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "P2RY10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 P2RY10      B_cells    1\n",
      "2 P2RY10 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  GP6 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CEBPE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CEBPE Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ADA2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 ADA2 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR1I1\n",
      "\n",
      "BIK\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  BIK  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CTSG\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 CTSG Myeloid_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TUBB1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 TUBB1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GATA1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene      Celltype Freq\n",
      "1 GATA1  Granulocytes    1\n",
      "2 GATA1 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCL17\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CCL17       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRSS33\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 PRSS33 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL21R\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IL21R  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LYL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 LYL1  Granulocytes    1\n",
      "2 LYL1 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GIMAP2\n",
      "\n",
      "PF4V1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PF4V1 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL10RA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 IL10RA       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AICDA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 AICDA  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RHAG\n",
      "\n",
      "TSPO2\n",
      "\n",
      "PRSS16\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 PRSS16  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL12B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 IL12B           DC    2\n",
      "2 IL12B Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  IL4 Granulocytes    1\n",
      "2  IL4      T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PLA2G2D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 PLA2G2D  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MMP8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 MMP8 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MYB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1  MYB            DC    1\n",
      "2  MYB  Granulocytes    1\n",
      "3  MYB Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXorf21\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene  Celltype Freq\n",
      "1 CXorf21        DC    1\n",
      "2 CXorf21 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TNFRSF8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene  Celltype Freq\n",
      "1 TNFRSF8 Monocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EPX\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  EPX Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD80\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD80       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GCNT7\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 GCNT7 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BJ\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2BJ Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SIGLEC9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 SIGLEC9       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FUT5\n",
      "\n",
      "LILRB2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene    Celltype Freq\n",
      "1 LILRB2          DC    1\n",
      "2 LILRB2 Macrophages    1\n",
      "3 LILRB2   Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HRH4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 HRH4 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AOAH\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 AOAH Monocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZGRF1\n",
      "\n",
      "IL21\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 IL21  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PSTPIP1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene Celltype Freq\n",
      "1 PSTPIP1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ARL5C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 ARL5C       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MS4A3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene      Celltype Freq\n",
      "1 MS4A3  Granulocytes    2\n",
      "2 MS4A3 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MS4A2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene      Celltype Freq\n",
      "1 MS4A2  Granulocytes    1\n",
      "2 MS4A2 Myeloid_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GRAP\n",
      "\n",
      "LY96\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 LY96 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXCL13\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 CXCL13       DC    1\n",
      "2 CXCL13  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRG3\n",
      "\n",
      "ITGAD\n",
      "\n",
      "CDCP2\n",
      "\n",
      "HIST1H4H\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST1H4H Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD1A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD1A       DC    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD1B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD1B       DC    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD1E\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CD1E       DC    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CARMIL2\n",
      "\n",
      "OR3A3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 OR3A3       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCR5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CCR5       DC    1\n",
      "2 CCR5  T_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RNASEK-C17orf49\n",
      "\n",
      "IL23R\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 IL23R  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FCRLB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 FCRLB Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NBPF20\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 NBPF20       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CIP2A\n",
      "\n",
      "FCRL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 FCRL1  B_cells    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SPTA1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 SPTA1  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CD200R1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CD200R1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PPBP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 PPBP Granulocytes    1\n",
      "2 PPBP  Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CPA3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 CPA3  Granulocytes    1\n",
      "2 CPA3 Myeloid_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IL3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1  IL3  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEFA4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 DEFA4 Granulocytes    2\n",
      "2 DEFA4     NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOS3\n",
      "\n",
      "SPIC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene  Celltype Freq\n",
      "1 SPIC Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RAG1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 RAG1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H1E\n",
      "\n",
      "OR5AU1\n",
      "\n",
      "RNASE3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 RNASE3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VPREB1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 VPREB1      B_cells    1\n",
      "2 VPREB1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1  GP9      B_cells    1\n",
      "2  GP9 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AHSP\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 AHSP  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SERPINA9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene  Celltype Freq\n",
      "1 SERPINA9 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GYPA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GYPA  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR10A2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR10A2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BFSP2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 BFSP2  B_cells    1\n",
      "2 BFSP2  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR7G2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR7G2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR1M1\n",
      "\n",
      "CEACAM3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CEACAM3 Granulocytes    6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2M4\n",
      "\n",
      "OR1L8\n",
      "\n",
      "OR2AT4\n",
      "\n",
      "GPR82\n",
      "\n",
      "OR52A5\n",
      "\n",
      "OR4X2\n",
      "\n",
      "AZU1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene      Celltype Freq\n",
      "1 AZU1       B_cells    1\n",
      "2 AZU1 Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR5A1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR5A1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR5B12\n",
      "\n",
      "OR5B2\n",
      "\n",
      "OR9G4\n",
      "\n",
      "OR10H5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR10H5 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR4D9\n",
      "\n",
      "AC097637.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene Celltype Freq\n",
      "1 AC097637.1       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "XCR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 XCR1       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CCR9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 CCR9  B_cells    1\n",
      "2 CCR9  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KIAA1551\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 KIAA1551 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RAG2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 RAG2  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR52W1\n",
      "\n",
      "LRRC25\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 LRRC25 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR5AN1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 OR5AN1       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR51V1\n",
      "\n",
      "UMODL1\n",
      "\n",
      "OR2B11\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 OR2B11 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TMEM78\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 TMEM78 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HNRNPCL1\n",
      "\n",
      "OR6C4\n",
      "\n",
      "OR6K6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR6K6 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2AC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2AC Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BC\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2BC Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2A4\n",
      "\n",
      "OR10K2\n",
      "\n",
      "OR56B1\n",
      "\n",
      "OR52N4\n",
      "\n",
      "OR5AK2\n",
      "\n",
      "OR8H1\n",
      "\n",
      "OR8H2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR8H2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR5AS1\n",
      "\n",
      "OR6S1\n",
      "\n",
      "OR4C6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 OR4C6  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR4P4\n",
      "\n",
      "OR52K2\n",
      "\n",
      "SHISAL2A\n",
      "\n",
      "IFNL1\n",
      "\n",
      "OR4F15\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR4F15 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ADGRG3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 ADGRG3 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GJD3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GJD3 NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2T34\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 OR2T34  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR56A4\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR56A4 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST2H3D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST2H3D Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST2H2AC\n",
      "\n",
      "HIST2H2AB\n",
      "\n",
      "HIST1H1B\n",
      "\n",
      "CCR10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CCR10  B_cells    1\n",
      "2 CCR10  T_cells    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "APOBR\n",
      "\n",
      "SPDYE16\n",
      "\n",
      "HIST1H2BL\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2BL Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP1BA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 GP1BA Granulocytes    1\n",
      "2 GP1BA  Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR7A17\n",
      "\n",
      "C16orf54\n",
      "\n",
      "LCE3A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 LCE3A       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EDARADD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 EDARADD Granulocytes    1\n",
      "2 EDARADD      T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CXCR3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 CXCR3       DC    1\n",
      "2 CXCR3  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR13C8\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 OR13C8  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LCE3B\n",
      "\n",
      "SOWAHD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 SOWAHD  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR51I2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR51I2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C17orf99\n",
      "\n",
      "OR2AG2\n",
      "\n",
      "PEAK3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PEAK3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR6N2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR6N2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PDCD1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PDCD1  T_cells    4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2G6\n",
      "\n",
      "RHCE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 RHCE Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CALHM6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene  Celltype Freq\n",
      "1 CALHM6 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ASTL\n",
      "\n",
      "NEMP2\n",
      "\n",
      "GIMAP5\n",
      "\n",
      "PRTN3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PRTN3      B_cells    1\n",
      "2 PRTN3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BORCS6\n",
      "\n",
      "HIST1H2AI\n",
      "\n",
      "HIST1H2AG\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2AG Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR11G2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR11G2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2AD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2AD Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST3H2BB\n",
      "\n",
      "TMEM26\n",
      "\n",
      "SIGLEC15\n",
      "\n",
      "OR8B8\n",
      "\n",
      "HIST1H4J\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST1H4J Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR6N1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR6N1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H3D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST1H3D Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR51D1\n",
      "\n",
      "OR2L5\n",
      "\n",
      "GYPE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 GYPE  B_cells    1\n",
      "2 GYPE  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ELANE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene      Celltype Freq\n",
      "1 ELANE Myeloid_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MPEG1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene  Celltype Freq\n",
      "1 MPEG1 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR52M1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR52M1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR10Z1\n",
      "\n",
      "OR6K3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR6K3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST2H3A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST2H3A Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GDF5OS\n",
      "\n",
      "CD300LD\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 CD300LD Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "CARD16\n",
      "\n",
      "MPIG6B\n",
      "\n",
      "DEFA1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 DEFA1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UBD\n",
      "\n",
      "TRIM64C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 TRIM64C Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPR33\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 GPR33       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2A5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 OR2A5 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2A25\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR2A25 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TAS2R40\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Gene     Celltype Freq\n",
      "1 TAS2R40 Granulocytes    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR4E2\n",
      "\n",
      "PSMB11\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 PSMB11  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TRIM49D1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 TRIM49D1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Z82206.1\n",
      "\n",
      "ZNF492\n",
      "\n",
      "DYTN\n",
      "\n",
      "TRIM49D2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 TRIM49D2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2BN Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "H2BFS\n",
      "\n",
      "SMIM27\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 SMIM27 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLFN14\n",
      "\n",
      "OR4A47\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 OR4A47  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IRGM\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 IRGM Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TLR9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 TLR9       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEFA3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 DEFA3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEFA1B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 DEFA1B  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TARM1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 TARM1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC004997.1\n",
      "\n",
      "AP000311.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene Celltype Freq\n",
      "1 AP000311.1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ATP5MGL\n",
      "\n",
      "AC069368.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene Celltype Freq\n",
      "1 AC069368.1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AP000295.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 AP000295.1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PTTG2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 PTTG2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AL669918.1\n",
      "\n",
      "ALG1L2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 ALG1L2       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UBTFL1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 UBTFL1       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR10G2\n",
      "\n",
      "RAB44\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 RAB44 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC011462.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene  Celltype Freq\n",
      "1 AC011462.1 Monocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC108488.2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene Celltype Freq\n",
      "1 AC108488.2       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR6J1\n",
      "\n",
      "DUXA\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 DUXA Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AL096870.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 AL096870.1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC136428.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene Celltype Freq\n",
      "1 AC136428.1  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC026464.3\n",
      "\n",
      "AC026464.4\n",
      "\n",
      "AL662899.2\n",
      "\n",
      "AC119396.1\n",
      "\n",
      "FAM72C\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 FAM72C  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RHEX\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 RHEX Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC134669.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 AC134669.1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC010616.1\n",
      "\n",
      "OPN1MW\n",
      "\n",
      "AC018755.2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 AC018755.2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC008878.3\n",
      "\n",
      "AC008763.3\n",
      "\n",
      "HIST2H4B\n",
      "\n",
      "HIST2H4A\n",
      "\n",
      "MILR1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 MILR1       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BM\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 HIST1H2BM  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BG\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 HIST1H2BG  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H3G\n",
      "\n",
      "HIST1H3B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HIST1H3B  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BE\n",
      "\n",
      "HIST1H4F\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HIST1H4F  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BO\n",
      "\n",
      "CCL23\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene     Celltype Freq\n",
      "1 CCL23           DC    1\n",
      "2 CCL23 Granulocytes    1\n",
      "3 CCL23  Macrophages    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MAFIP\n",
      "\n",
      "HIST1H2AH\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 HIST1H2AH  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H4L\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HIST1H4L  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2AK\n",
      "\n",
      "HIST1H3I\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HIST1H3I NK_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FP565260.1\n",
      "\n",
      "XKR5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene Celltype Freq\n",
      "1 XKR5       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BH\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2BH Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H4I\n",
      "\n",
      "OR4E1\n",
      "\n",
      "AC004556.1\n",
      "\n",
      "HIST1H2AJ\n",
      "\n",
      "HIST1H2BB\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 HIST1H2BB       DC    2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PADI6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gene Celltype Freq\n",
      "1 PADI6       DC    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2AL\n",
      "\n",
      "HIST1H4E\n",
      "\n",
      "HIST1H2AE\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene Celltype Freq\n",
      "1 HIST1H2AE  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H4D\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HIST1H4D  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2BF\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gene     Celltype Freq\n",
      "1 HIST1H2BF Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H3F\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene Celltype Freq\n",
      "1 HIST1H3F  B_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H3C\n",
      "\n",
      "HIST1H2AB\n",
      "\n",
      "HIST1H2BI\n",
      "\n",
      "HIST1H4A\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST1H4A Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H2AM\n",
      "\n",
      "HIST1H4B\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST1H4B Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HIST1H3H\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 HIST1H3H Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR10X1\n",
      "\n",
      "OR2T11\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR2T11 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OR2AG1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene     Celltype Freq\n",
      "1 OR2AG1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PRAMEF18\n",
      "\n",
      "FP565260.6\n",
      "\n",
      "OR1B1\n",
      "\n",
      "FP565260.7\n",
      "\n",
      "GIMAP1-GIMAP5\n",
      "\n",
      "AL512428.1\n",
      "\n",
      "AL031777.3\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 AL031777.3 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC068631.2\n",
      "\n",
      "AC001226.2\n",
      "\n",
      "BX088645.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 BX088645.1 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC106774.10\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Gene     Celltype Freq\n",
      "1 AC106774.10 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC092442.1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene Celltype Freq\n",
      "1 AC092442.1  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AC107959.5\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene     Celltype Freq\n",
      "1 AC107959.5 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AL160272.2\n",
      "\n",
      "Z83844.3\n",
      "\n",
      "Z84488.2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene     Celltype Freq\n",
      "1 Z84488.2 Granulocytes    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AL034430.1\n",
      "\n",
      "FYB\n",
      "\n",
      "NCF2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gene     Celltype Freq\n",
      "1 NCF2 Granulocytes    3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GIMAP6\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Gene Celltype Freq\n",
      "1 GIMAP6  T_cells    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RAB27A\n",
      "\n",
      "HLA-G\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for(i in 1:nrow(celltype_df)){\n",
    "    message(celltype_df$Gene[i])\n",
    "    tmp <- celltype_df[i,2:22]\n",
    "    tmp_tab <- as.data.frame(table(unlist(tmp)),stringsAsFactors = F)\n",
    "    if(nrow(tmp_tab) !=0){\n",
    "        tmp_tab$Gene <- celltype_df$Gene[i]\n",
    "        tmp_tab <- tmp_tab[,c(\"Gene\", \"Var1\", \"Freq\")]\n",
    "        colnames(tmp_tab) <- c(\"Gene\", \"Celltype\", \"Freq\")\n",
    "        print(tmp_tab)\n",
    "        \n",
    "        genes_cells_freq <- rbind(genes_cells_freq, tmp_tab)\n",
    "\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Gene</th><th scope=col>Celltype</th><th scope=col>Freq</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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ADAM28</td><td>B_cells     </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ADGRE1</td><td>Granulocytes</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ADGRE1</td><td>Monocytes   </td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ADGRE2</td><td>Monocytes   </td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ADGRE3</td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ADGRG5</td><td>DC          </td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Gene & Celltype & Freq\\\\\n",
       "  & <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & ADAM28 & B\\_cells      & 3\\\\\n",
       "\t2 & ADGRE1 & Granulocytes & 1\\\\\n",
       "\t3 & ADGRE1 & Monocytes    & 1\\\\\n",
       "\t4 & ADGRE2 & Monocytes    & 1\\\\\n",
       "\t5 & ADGRE3 & Granulocytes & 2\\\\\n",
       "\t6 & ADGRG5 & DC           & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; | Freq &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | ADAM28 | B_cells      | 3 |\n",
       "| 2 | ADGRE1 | Granulocytes | 1 |\n",
       "| 3 | ADGRE1 | Monocytes    | 1 |\n",
       "| 4 | ADGRE2 | Monocytes    | 1 |\n",
       "| 5 | ADGRE3 | Granulocytes | 2 |\n",
       "| 6 | ADGRG5 | DC           | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene   Celltype     Freq\n",
       "1 ADAM28 B_cells      3   \n",
       "2 ADGRE1 Granulocytes 1   \n",
       "3 ADGRE1 Monocytes    1   \n",
       "4 ADGRE2 Monocytes    1   \n",
       "5 ADGRE3 Granulocytes 2   \n",
       "6 ADGRG5 DC           1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(genes_cells_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_cells <- genes_cells_freq[ genes_cells_freq$Freq >= 2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 219 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Gene</th><th scope=col>Celltype</th><th scope=col>Freq</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;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ADAM28  </td><td>B_cells     </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ADGRE3  </td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>APOBEC3A</td><td>Monocytes   </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>ARG1    </td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>ASGR2   </td><td>Monocytes   </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>BANK1   </td><td>B_cells     </td><td>6</td></tr>\n",
       "\t<tr><th scope=row>22</th><td>BTLA    </td><td>B_cells     </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>C2      </td><td>Monocytes   </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>26</th><td>C5AR2   </td><td>Granulocytes</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>27</th><td>CAMP    </td><td>Granulocytes</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>30</th><td>CARD9   </td><td>Monocytes   </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>35</th><td>CASS4   </td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>36</th><td>CCDC125 </td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>37</th><td>CCL22   </td><td>DC          </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>40</th><td>CCL4    </td><td>NK_cells    </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>42</th><td>CCL5    </td><td>T_cells     </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>47</th><td>CCR3    </td><td>Granulocytes</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>48</th><td>CCR4    </td><td>T_cells     </td><td>4</td></tr>\n",
       "\t<tr><th scope=row>50</th><td>CCR6    </td><td>T_cells     </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>52</th><td>CD101   </td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>53</th><td>CD160   </td><td>NK_cells    </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>54</th><td>CD160   </td><td>T_cells     </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>55</th><td>CD180   </td><td>B_cells     </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>57</th><td>CD19    </td><td>B_cells     </td><td>6</td></tr>\n",
       "\t<tr><th scope=row>58</th><td>CD1C    </td><td>DC          </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>62</th><td>CD244   </td><td>Granulocytes</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>65</th><td>CD247   </td><td>NK_cells    </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>68</th><td>CD27    </td><td>T_cells     </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>69</th><td>CD28    </td><td>T_cells     </td><td>5</td></tr>\n",
       "\t<tr><th scope=row>78</th><td>CD33    </td><td>Monocytes   </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>480</th><td>IL4      </td><td>T_cells      </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>488</th><td>TNFRSF8  </td><td>Monocytes    </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>490</th><td>CD80     </td><td>DC           </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>497</th><td>HRH4     </td><td>Granulocytes </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>498</th><td>AOAH     </td><td>Monocytes    </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>499</th><td>IL21     </td><td>T_cells      </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>502</th><td>MS4A3    </td><td>Granulocytes </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>505</th><td>MS4A2    </td><td>Myeloid_cells</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>506</th><td>LY96     </td><td>Granulocytes </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>508</th><td>CXCL13   </td><td>T_cells      </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>510</th><td>CD1A     </td><td>DC           </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>511</th><td>CD1B     </td><td>DC           </td><td>4</td></tr>\n",
       "\t<tr><th scope=row>512</th><td>CD1E     </td><td>DC           </td><td>4</td></tr>\n",
       "\t<tr><th scope=row>515</th><td>CCR5     </td><td>T_cells      </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>516</th><td>IL23R    </td><td>T_cells      </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>519</th><td>FCRL1    </td><td>B_cells      </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>525</th><td>CPA3     </td><td>Myeloid_cells</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>527</th><td>DEFA4    </td><td>Granulocytes </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>543</th><td>CEACAM3  </td><td>Granulocytes </td><td>6</td></tr>\n",
       "\t<tr><th scope=row>549</th><td>XCR1     </td><td>DC           </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>552</th><td>KIAA1551 </td><td>Granulocytes </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>560</th><td>HIST1H2BC</td><td>Granulocytes </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>564</th><td>ADGRG3   </td><td>Granulocytes </td><td>3</td></tr>\n",
       "\t<tr><th scope=row>570</th><td>CCR10    </td><td>T_cells      </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>571</th><td>HIST1H2BL</td><td>Granulocytes </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>584</th><td>PDCD1    </td><td>T_cells      </td><td>4</td></tr>\n",
       "\t<tr><th scope=row>608</th><td>TAS2R40  </td><td>Granulocytes </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>616</th><td>TLR9     </td><td>DC           </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>649</th><td>HIST1H2BB</td><td>DC           </td><td>2</td></tr>\n",
       "\t<tr><th scope=row>666</th><td>NCF2     </td><td>Granulocytes </td><td>3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 219 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Gene & Celltype & Freq\\\\\n",
       "  & <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & ADAM28   & B\\_cells      & 3\\\\\n",
       "\t5 & ADGRE3   & Granulocytes & 2\\\\\n",
       "\t10 & APOBEC3A & Monocytes    & 2\\\\\n",
       "\t13 & ARG1     & Granulocytes & 2\\\\\n",
       "\t15 & ASGR2    & Monocytes    & 2\\\\\n",
       "\t18 & BANK1    & B\\_cells      & 6\\\\\n",
       "\t22 & BTLA     & B\\_cells      & 2\\\\\n",
       "\t25 & C2       & Monocytes    & 2\\\\\n",
       "\t26 & C5AR2    & Granulocytes & 4\\\\\n",
       "\t27 & CAMP     & Granulocytes & 3\\\\\n",
       "\t30 & CARD9    & Monocytes    & 2\\\\\n",
       "\t35 & CASS4    & Granulocytes & 2\\\\\n",
       "\t36 & CCDC125  & Granulocytes & 2\\\\\n",
       "\t37 & CCL22    & DC           & 2\\\\\n",
       "\t40 & CCL4     & NK\\_cells     & 2\\\\\n",
       "\t42 & CCL5     & T\\_cells      & 3\\\\\n",
       "\t47 & CCR3     & Granulocytes & 4\\\\\n",
       "\t48 & CCR4     & T\\_cells      & 4\\\\\n",
       "\t50 & CCR6     & T\\_cells      & 2\\\\\n",
       "\t52 & CD101    & Granulocytes & 2\\\\\n",
       "\t53 & CD160    & NK\\_cells     & 3\\\\\n",
       "\t54 & CD160    & T\\_cells      & 2\\\\\n",
       "\t55 & CD180    & B\\_cells      & 2\\\\\n",
       "\t57 & CD19     & B\\_cells      & 6\\\\\n",
       "\t58 & CD1C     & DC           & 3\\\\\n",
       "\t62 & CD244    & Granulocytes & 2\\\\\n",
       "\t65 & CD247    & NK\\_cells     & 2\\\\\n",
       "\t68 & CD27     & T\\_cells      & 2\\\\\n",
       "\t69 & CD28     & T\\_cells      & 5\\\\\n",
       "\t78 & CD33     & Monocytes    & 2\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t480 & IL4       & T\\_cells       & 2\\\\\n",
       "\t488 & TNFRSF8   & Monocytes     & 3\\\\\n",
       "\t490 & CD80      & DC            & 2\\\\\n",
       "\t497 & HRH4      & Granulocytes  & 2\\\\\n",
       "\t498 & AOAH      & Monocytes     & 2\\\\\n",
       "\t499 & IL21      & T\\_cells       & 2\\\\\n",
       "\t502 & MS4A3     & Granulocytes  & 2\\\\\n",
       "\t505 & MS4A2     & Myeloid\\_cells & 2\\\\\n",
       "\t506 & LY96      & Granulocytes  & 3\\\\\n",
       "\t508 & CXCL13    & T\\_cells       & 2\\\\\n",
       "\t510 & CD1A      & DC            & 3\\\\\n",
       "\t511 & CD1B      & DC            & 4\\\\\n",
       "\t512 & CD1E      & DC            & 4\\\\\n",
       "\t515 & CCR5      & T\\_cells       & 3\\\\\n",
       "\t516 & IL23R     & T\\_cells       & 2\\\\\n",
       "\t519 & FCRL1     & B\\_cells       & 3\\\\\n",
       "\t525 & CPA3      & Myeloid\\_cells & 2\\\\\n",
       "\t527 & DEFA4     & Granulocytes  & 2\\\\\n",
       "\t543 & CEACAM3   & Granulocytes  & 6\\\\\n",
       "\t549 & XCR1      & DC            & 2\\\\\n",
       "\t552 & KIAA1551  & Granulocytes  & 2\\\\\n",
       "\t560 & HIST1H2BC & Granulocytes  & 3\\\\\n",
       "\t564 & ADGRG3    & Granulocytes  & 3\\\\\n",
       "\t570 & CCR10     & T\\_cells       & 2\\\\\n",
       "\t571 & HIST1H2BL & Granulocytes  & 2\\\\\n",
       "\t584 & PDCD1     & T\\_cells       & 4\\\\\n",
       "\t608 & TAS2R40   & Granulocytes  & 2\\\\\n",
       "\t616 & TLR9      & DC            & 2\\\\\n",
       "\t649 & HIST1H2BB & DC            & 2\\\\\n",
       "\t666 & NCF2      & Granulocytes  & 3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 219 × 3\n",
       "\n",
       "| <!--/--> | Gene &lt;chr&gt; | Celltype &lt;chr&gt; | Freq &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | ADAM28   | B_cells      | 3 |\n",
       "| 5 | ADGRE3   | Granulocytes | 2 |\n",
       "| 10 | APOBEC3A | Monocytes    | 2 |\n",
       "| 13 | ARG1     | Granulocytes | 2 |\n",
       "| 15 | ASGR2    | Monocytes    | 2 |\n",
       "| 18 | BANK1    | B_cells      | 6 |\n",
       "| 22 | BTLA     | B_cells      | 2 |\n",
       "| 25 | C2       | Monocytes    | 2 |\n",
       "| 26 | C5AR2    | Granulocytes | 4 |\n",
       "| 27 | CAMP     | Granulocytes | 3 |\n",
       "| 30 | CARD9    | Monocytes    | 2 |\n",
       "| 35 | CASS4    | Granulocytes | 2 |\n",
       "| 36 | CCDC125  | Granulocytes | 2 |\n",
       "| 37 | CCL22    | DC           | 2 |\n",
       "| 40 | CCL4     | NK_cells     | 2 |\n",
       "| 42 | CCL5     | T_cells      | 3 |\n",
       "| 47 | CCR3     | Granulocytes | 4 |\n",
       "| 48 | CCR4     | T_cells      | 4 |\n",
       "| 50 | CCR6     | T_cells      | 2 |\n",
       "| 52 | CD101    | Granulocytes | 2 |\n",
       "| 53 | CD160    | NK_cells     | 3 |\n",
       "| 54 | CD160    | T_cells      | 2 |\n",
       "| 55 | CD180    | B_cells      | 2 |\n",
       "| 57 | CD19     | B_cells      | 6 |\n",
       "| 58 | CD1C     | DC           | 3 |\n",
       "| 62 | CD244    | Granulocytes | 2 |\n",
       "| 65 | CD247    | NK_cells     | 2 |\n",
       "| 68 | CD27     | T_cells      | 2 |\n",
       "| 69 | CD28     | T_cells      | 5 |\n",
       "| 78 | CD33     | Monocytes    | 2 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 480 | IL4       | T_cells       | 2 |\n",
       "| 488 | TNFRSF8   | Monocytes     | 3 |\n",
       "| 490 | CD80      | DC            | 2 |\n",
       "| 497 | HRH4      | Granulocytes  | 2 |\n",
       "| 498 | AOAH      | Monocytes     | 2 |\n",
       "| 499 | IL21      | T_cells       | 2 |\n",
       "| 502 | MS4A3     | Granulocytes  | 2 |\n",
       "| 505 | MS4A2     | Myeloid_cells | 2 |\n",
       "| 506 | LY96      | Granulocytes  | 3 |\n",
       "| 508 | CXCL13    | T_cells       | 2 |\n",
       "| 510 | CD1A      | DC            | 3 |\n",
       "| 511 | CD1B      | DC            | 4 |\n",
       "| 512 | CD1E      | DC            | 4 |\n",
       "| 515 | CCR5      | T_cells       | 3 |\n",
       "| 516 | IL23R     | T_cells       | 2 |\n",
       "| 519 | FCRL1     | B_cells       | 3 |\n",
       "| 525 | CPA3      | Myeloid_cells | 2 |\n",
       "| 527 | DEFA4     | Granulocytes  | 2 |\n",
       "| 543 | CEACAM3   | Granulocytes  | 6 |\n",
       "| 549 | XCR1      | DC            | 2 |\n",
       "| 552 | KIAA1551  | Granulocytes  | 2 |\n",
       "| 560 | HIST1H2BC | Granulocytes  | 3 |\n",
       "| 564 | ADGRG3    | Granulocytes  | 3 |\n",
       "| 570 | CCR10     | T_cells       | 2 |\n",
       "| 571 | HIST1H2BL | Granulocytes  | 2 |\n",
       "| 584 | PDCD1     | T_cells       | 4 |\n",
       "| 608 | TAS2R40   | Granulocytes  | 2 |\n",
       "| 616 | TLR9      | DC            | 2 |\n",
       "| 649 | HIST1H2BB | DC            | 2 |\n",
       "| 666 | NCF2      | Granulocytes  | 3 |\n",
       "\n"
      ],
      "text/plain": [
       "    Gene      Celltype      Freq\n",
       "1   ADAM28    B_cells       3   \n",
       "5   ADGRE3    Granulocytes  2   \n",
       "10  APOBEC3A  Monocytes     2   \n",
       "13  ARG1      Granulocytes  2   \n",
       "15  ASGR2     Monocytes     2   \n",
       "18  BANK1     B_cells       6   \n",
       "22  BTLA      B_cells       2   \n",
       "25  C2        Monocytes     2   \n",
       "26  C5AR2     Granulocytes  4   \n",
       "27  CAMP      Granulocytes  3   \n",
       "30  CARD9     Monocytes     2   \n",
       "35  CASS4     Granulocytes  2   \n",
       "36  CCDC125   Granulocytes  2   \n",
       "37  CCL22     DC            2   \n",
       "40  CCL4      NK_cells      2   \n",
       "42  CCL5      T_cells       3   \n",
       "47  CCR3      Granulocytes  4   \n",
       "48  CCR4      T_cells       4   \n",
       "50  CCR6      T_cells       2   \n",
       "52  CD101     Granulocytes  2   \n",
       "53  CD160     NK_cells      3   \n",
       "54  CD160     T_cells       2   \n",
       "55  CD180     B_cells       2   \n",
       "57  CD19      B_cells       6   \n",
       "58  CD1C      DC            3   \n",
       "62  CD244     Granulocytes  2   \n",
       "65  CD247     NK_cells      2   \n",
       "68  CD27      T_cells       2   \n",
       "69  CD28      T_cells       5   \n",
       "78  CD33      Monocytes     2   \n",
       "⋮   ⋮         ⋮             ⋮   \n",
       "480 IL4       T_cells       2   \n",
       "488 TNFRSF8   Monocytes     3   \n",
       "490 CD80      DC            2   \n",
       "497 HRH4      Granulocytes  2   \n",
       "498 AOAH      Monocytes     2   \n",
       "499 IL21      T_cells       2   \n",
       "502 MS4A3     Granulocytes  2   \n",
       "505 MS4A2     Myeloid_cells 2   \n",
       "506 LY96      Granulocytes  3   \n",
       "508 CXCL13    T_cells       2   \n",
       "510 CD1A      DC            3   \n",
       "511 CD1B      DC            4   \n",
       "512 CD1E      DC            4   \n",
       "515 CCR5      T_cells       3   \n",
       "516 IL23R     T_cells       2   \n",
       "519 FCRL1     B_cells       3   \n",
       "525 CPA3      Myeloid_cells 2   \n",
       "527 DEFA4     Granulocytes  2   \n",
       "543 CEACAM3   Granulocytes  6   \n",
       "549 XCR1      DC            2   \n",
       "552 KIAA1551  Granulocytes  2   \n",
       "560 HIST1H2BC Granulocytes  3   \n",
       "564 ADGRG3    Granulocytes  3   \n",
       "570 CCR10     T_cells       2   \n",
       "571 HIST1H2BL Granulocytes  2   \n",
       "584 PDCD1     T_cells       4   \n",
       "608 TAS2R40   Granulocytes  2   \n",
       "616 TLR9      DC            2   \n",
       "649 HIST1H2BB DC            2   \n",
       "666 NCF2      Granulocytes  3   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "consensus_cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(consensus_cells)){    \n",
    "    gen <- consensus_cells$Gene[i]\n",
    "    celltype_df$Cellgroup[celltype_df$Gene == gen] <- consensus_cells$Celltype[i]  \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "      B_cells            DC  Granulocytes     Monocytes Myeloid_cells \n",
       "           32            28            71            18             6 \n",
       "     NK_cells       T_cells          <NA> \n",
       "           20            41           575 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(celltype_df$Cellgroup, useNA = \"always\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find gene description and ensembl for suppl tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ensembl site unresponsive, trying useast mirror\n",
      "\n"
     ]
    }
   ],
   "source": [
    "hg38 <- useMart(biomart=\"ENSEMBL_MART_ENSEMBL\", host=\"www.ensembl.org\", \n",
    "                path=\"/biomart/martservice\", dataset=\"hsapiens_gene_ensembl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgncs <- celltype_df$Gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgnc_type <- getBM(filters=\"hgnc_symbol\",\n",
    "                   attributes=c(\"hgnc_symbol\",\"ensembl_gene_id\", \"description\", \n",
    "                                \"go_id\",\"name_1006\", \"definition_1006\"),\n",
    "                   values= hgncs, mart = hg38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(celltype_df, file = paste0(datapath, \"TME_clusters/Genes_Cells.csv\"), row.names = FALSE)\n",
    "write.csv(hgnc_type, file = paste0(datapath, \"TME_clusters/hgnc_ensembl_GO_immunegenes.csv\"), row.names = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determine clusters with CC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype_specific_genes_nontumour <- celltype_df[!is.na(celltype_df$Cellgroup),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove myeloid cells due to low number of genes\n",
    "celltype_specific_genes_nontumour <- celltype_specific_genes_nontumour[ celltype_specific_genes_nontumour$Cellgroup != \"Myeloid_cells\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "     B_cells           DC Granulocytes    Monocytes     NK_cells      T_cells \n",
       "          32           28           71           18           20           41 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(celltype_specific_genes_nontumour$Cellgroup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>210</li><li>23</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 210\n",
       "\\item 23\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 210\n",
       "2. 23\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 210  23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(celltype_specific_genes_nontumour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype_genes_list <- list()\n",
    "\n",
    "for( cel in unique(celltype_specific_genes_nontumour$Cellgroup)){\n",
    "    tmp <- celltype_specific_genes_nontumour[celltype_specific_genes_nontumour$Cellgroup == cel,]\n",
    "    cellgenes <- tmp$Gene\n",
    "    celltype_genes_list[length(celltype_genes_list) + 1] <- list(cellgenes)\n",
    "}\n",
    "\n",
    "names(celltype_genes_list) <- unique(celltype_specific_genes_nontumour$Cellgroup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$B_cells</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'ADAM28'</li><li>'BANK1'</li><li>'BTLA'</li><li>'CD180'</li><li>'CD19'</li><li>'CD79A'</li><li>'CLEC17A'</li><li>'CNR2'</li><li>'CXCR5'</li><li>'FAM177B'</li><li>'FCRL2'</li><li>'FCRL3'</li><li>'FCRL5'</li><li>'HLA-DOB'</li><li>'IGHD'</li><li>'IGHM'</li><li>'IGLL5'</li><li>'KLHL14'</li><li>'MS4A1'</li><li>'POU2AF1'</li><li>'SPIB'</li><li>'STAP1'</li><li>'TCL1A'</li><li>'TLR10'</li><li>'TNFRSF13B'</li><li>'TNFRSF17'</li><li>'VPREB3'</li><li>'ZBP1'</li><li>'SP140'</li><li>'TMEM156'</li><li>'AICDA'</li><li>'FCRL1'</li></ol>\n",
       "</dd>\n",
       "\t<dt>$Granulocytes</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>'ADGRE3'</li><li>'ARG1'</li><li>'C5AR2'</li><li>'CAMP'</li><li>'CASS4'</li><li>'CCDC125'</li><li>'CCR3'</li><li>'CD101'</li><li>'CD244'</li><li>'CEACAM4'</li><li>'CLEC4E'</li><li>'CLEC7A'</li><li>'CR1'</li><li>'CXCR1'</li><li>'CXCR2'</li><li>'CYSLTR2'</li><li>'DPEP2'</li><li>'FCAR'</li><li>'FCGR3B'</li><li>'FFAR2'</li><li>'FPR2'</li><li>'GPR183'</li><li>'HAL'</li><li>'IL1R2'</li><li>'LILRB3'</li><li>'LRG1'</li><li>'LRMP'</li><li>'MEFV'</li><li>'MNDA'</li><li>'MRVI1'</li><li>'NLRP12'</li><li>'NLRP6'</li><li>'OSM'</li><li>'P2RY13'</li><li>'P2RY14'</li><li>'PADI4'</li><li>'PGLYRP1'</li><li>'PHOSPHO1'</li><li>'PROK2'</li><li>'PTGS2'</li><li>'RAB3D'</li><li>'RASGRP4'</li><li>'REM2'</li><li>'RNASE2'</li><li>'S100A12'</li><li>'S100P'</li><li>'SAMSN1'</li><li>'SIGLEC5'</li><li>'SIRPB1'</li><li>'ST20'</li><li>'TLR1'</li><li>'TNFRSF10C'</li><li>'TREM1'</li><li>'VNN2'</li><li>'VNN3'</li><li>'ZCCHC6'</li><li>'P2RY10'</li><li>'CEBPE'</li><li>'TUBB1'</li><li>'PF4V1'</li><li>'HRH4'</li><li>'MS4A3'</li><li>'LY96'</li><li>'DEFA4'</li><li>'CEACAM3'</li><li>'KIAA1551'</li><li>'HIST1H2BC'</li><li>'ADGRG3'</li><li>'HIST1H2BL'</li><li>'TAS2R40'</li><li>'NCF2'</li></ol>\n",
       "</dd>\n",
       "\t<dt>$Monocytes</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'APOBEC3A'</li><li>'ASGR2'</li><li>'C2'</li><li>'CARD9'</li><li>'CD33'</li><li>'CFP'</li><li>'FCN1'</li><li>'FOLR3'</li><li>'GPBAR1'</li><li>'HPSE'</li><li>'NLRP3'</li><li>'PLA2G7'</li><li>'SLC37A2'</li><li>'SLC46A2'</li><li>'VNN1'</li><li>'CSTA'</li><li>'TNFRSF8'</li><li>'AOAH'</li></ol>\n",
       "</dd>\n",
       "\t<dt>$DC</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>'CCL22'</li><li>'CD1C'</li><li>'CLEC4C'</li><li>'CLIC3'</li><li>'FCER1A'</li><li>'FUT7'</li><li>'GZMB'</li><li>'IGFLR1'</li><li>'IRF8'</li><li>'LILRA4'</li><li>'MRC1'</li><li>'PLAC8'</li><li>'PROC'</li><li>'PTCRA'</li><li>'SLC12A3'</li><li>'TTC24'</li><li>'TTC39A'</li><li>'ZNRF2'</li><li>'CD86'</li><li>'CCL17'</li><li>'IL12B'</li><li>'CD80'</li><li>'CD1A'</li><li>'CD1B'</li><li>'CD1E'</li><li>'XCR1'</li><li>'TLR9'</li><li>'HIST1H2BB'</li></ol>\n",
       "</dd>\n",
       "\t<dt>$NK_cells</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'CCL4'</li><li>'CD247'</li><li>'CTSW'</li><li>'GNLY'</li><li>'GZMA'</li><li>'IL18RAP'</li><li>'IL2RB'</li><li>'KIR2DL1'</li><li>'KIR2DL3'</li><li>'KIR2DL4'</li><li>'KIR3DL1'</li><li>'KIR3DL2'</li><li>'KLRF1'</li><li>'NCR1'</li><li>'NKG7'</li><li>'PRF1'</li><li>'SH2D1B'</li><li>'TXK'</li><li>'XCL2'</li><li>'ZNF600'</li></ol>\n",
       "</dd>\n",
       "\t<dt>$T_cells</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>'CCL5'</li><li>'CCR4'</li><li>'CCR6'</li><li>'CD160'</li><li>'CD27'</li><li>'CD28'</li><li>'CD3D'</li><li>'CD3E'</li><li>'CD3G'</li><li>'CD8B'</li><li>'CD96'</li><li>'CRTAM'</li><li>'CST7'</li><li>'CXCR6'</li><li>'FOXP3'</li><li>'GRAP2'</li><li>'GZMH'</li><li>'GZMK'</li><li>'GZMM'</li><li>'IL2RA'</li><li>'IL7R'</li><li>'KLRG1'</li><li>'LAIR2'</li><li>'NCR3'</li><li>'PATL2'</li><li>'SH2D1A'</li><li>'SIRPG'</li><li>'SIT1'</li><li>'TRAT1'</li><li>'UBASH3A'</li><li>'ZNF683'</li><li>'CD2'</li><li>'HPGD'</li><li>'ACAP1'</li><li>'IL4'</li><li>'IL21'</li><li>'CXCL13'</li><li>'CCR5'</li><li>'IL23R'</li><li>'CCR10'</li><li>'PDCD1'</li></ol>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$B\\_cells] \\begin{enumerate*}\n",
       "\\item 'ADAM28'\n",
       "\\item 'BANK1'\n",
       "\\item 'BTLA'\n",
       "\\item 'CD180'\n",
       "\\item 'CD19'\n",
       "\\item 'CD79A'\n",
       "\\item 'CLEC17A'\n",
       "\\item 'CNR2'\n",
       "\\item 'CXCR5'\n",
       "\\item 'FAM177B'\n",
       "\\item 'FCRL2'\n",
       "\\item 'FCRL3'\n",
       "\\item 'FCRL5'\n",
       "\\item 'HLA-DOB'\n",
       "\\item 'IGHD'\n",
       "\\item 'IGHM'\n",
       "\\item 'IGLL5'\n",
       "\\item 'KLHL14'\n",
       "\\item 'MS4A1'\n",
       "\\item 'POU2AF1'\n",
       "\\item 'SPIB'\n",
       "\\item 'STAP1'\n",
       "\\item 'TCL1A'\n",
       "\\item 'TLR10'\n",
       "\\item 'TNFRSF13B'\n",
       "\\item 'TNFRSF17'\n",
       "\\item 'VPREB3'\n",
       "\\item 'ZBP1'\n",
       "\\item 'SP140'\n",
       "\\item 'TMEM156'\n",
       "\\item 'AICDA'\n",
       "\\item 'FCRL1'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$Granulocytes] \\begin{enumerate*}\n",
       "\\item 'ADGRE3'\n",
       "\\item 'ARG1'\n",
       "\\item 'C5AR2'\n",
       "\\item 'CAMP'\n",
       "\\item 'CASS4'\n",
       "\\item 'CCDC125'\n",
       "\\item 'CCR3'\n",
       "\\item 'CD101'\n",
       "\\item 'CD244'\n",
       "\\item 'CEACAM4'\n",
       "\\item 'CLEC4E'\n",
       "\\item 'CLEC7A'\n",
       "\\item 'CR1'\n",
       "\\item 'CXCR1'\n",
       "\\item 'CXCR2'\n",
       "\\item 'CYSLTR2'\n",
       "\\item 'DPEP2'\n",
       "\\item 'FCAR'\n",
       "\\item 'FCGR3B'\n",
       "\\item 'FFAR2'\n",
       "\\item 'FPR2'\n",
       "\\item 'GPR183'\n",
       "\\item 'HAL'\n",
       "\\item 'IL1R2'\n",
       "\\item 'LILRB3'\n",
       "\\item 'LRG1'\n",
       "\\item 'LRMP'\n",
       "\\item 'MEFV'\n",
       "\\item 'MNDA'\n",
       "\\item 'MRVI1'\n",
       "\\item 'NLRP12'\n",
       "\\item 'NLRP6'\n",
       "\\item 'OSM'\n",
       "\\item 'P2RY13'\n",
       "\\item 'P2RY14'\n",
       "\\item 'PADI4'\n",
       "\\item 'PGLYRP1'\n",
       "\\item 'PHOSPHO1'\n",
       "\\item 'PROK2'\n",
       "\\item 'PTGS2'\n",
       "\\item 'RAB3D'\n",
       "\\item 'RASGRP4'\n",
       "\\item 'REM2'\n",
       "\\item 'RNASE2'\n",
       "\\item 'S100A12'\n",
       "\\item 'S100P'\n",
       "\\item 'SAMSN1'\n",
       "\\item 'SIGLEC5'\n",
       "\\item 'SIRPB1'\n",
       "\\item 'ST20'\n",
       "\\item 'TLR1'\n",
       "\\item 'TNFRSF10C'\n",
       "\\item 'TREM1'\n",
       "\\item 'VNN2'\n",
       "\\item 'VNN3'\n",
       "\\item 'ZCCHC6'\n",
       "\\item 'P2RY10'\n",
       "\\item 'CEBPE'\n",
       "\\item 'TUBB1'\n",
       "\\item 'PF4V1'\n",
       "\\item 'HRH4'\n",
       "\\item 'MS4A3'\n",
       "\\item 'LY96'\n",
       "\\item 'DEFA4'\n",
       "\\item 'CEACAM3'\n",
       "\\item 'KIAA1551'\n",
       "\\item 'HIST1H2BC'\n",
       "\\item 'ADGRG3'\n",
       "\\item 'HIST1H2BL'\n",
       "\\item 'TAS2R40'\n",
       "\\item 'NCF2'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$Monocytes] \\begin{enumerate*}\n",
       "\\item 'APOBEC3A'\n",
       "\\item 'ASGR2'\n",
       "\\item 'C2'\n",
       "\\item 'CARD9'\n",
       "\\item 'CD33'\n",
       "\\item 'CFP'\n",
       "\\item 'FCN1'\n",
       "\\item 'FOLR3'\n",
       "\\item 'GPBAR1'\n",
       "\\item 'HPSE'\n",
       "\\item 'NLRP3'\n",
       "\\item 'PLA2G7'\n",
       "\\item 'SLC37A2'\n",
       "\\item 'SLC46A2'\n",
       "\\item 'VNN1'\n",
       "\\item 'CSTA'\n",
       "\\item 'TNFRSF8'\n",
       "\\item 'AOAH'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$DC] \\begin{enumerate*}\n",
       "\\item 'CCL22'\n",
       "\\item 'CD1C'\n",
       "\\item 'CLEC4C'\n",
       "\\item 'CLIC3'\n",
       "\\item 'FCER1A'\n",
       "\\item 'FUT7'\n",
       "\\item 'GZMB'\n",
       "\\item 'IGFLR1'\n",
       "\\item 'IRF8'\n",
       "\\item 'LILRA4'\n",
       "\\item 'MRC1'\n",
       "\\item 'PLAC8'\n",
       "\\item 'PROC'\n",
       "\\item 'PTCRA'\n",
       "\\item 'SLC12A3'\n",
       "\\item 'TTC24'\n",
       "\\item 'TTC39A'\n",
       "\\item 'ZNRF2'\n",
       "\\item 'CD86'\n",
       "\\item 'CCL17'\n",
       "\\item 'IL12B'\n",
       "\\item 'CD80'\n",
       "\\item 'CD1A'\n",
       "\\item 'CD1B'\n",
       "\\item 'CD1E'\n",
       "\\item 'XCR1'\n",
       "\\item 'TLR9'\n",
       "\\item 'HIST1H2BB'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$NK\\_cells] \\begin{enumerate*}\n",
       "\\item 'CCL4'\n",
       "\\item 'CD247'\n",
       "\\item 'CTSW'\n",
       "\\item 'GNLY'\n",
       "\\item 'GZMA'\n",
       "\\item 'IL18RAP'\n",
       "\\item 'IL2RB'\n",
       "\\item 'KIR2DL1'\n",
       "\\item 'KIR2DL3'\n",
       "\\item 'KIR2DL4'\n",
       "\\item 'KIR3DL1'\n",
       "\\item 'KIR3DL2'\n",
       "\\item 'KLRF1'\n",
       "\\item 'NCR1'\n",
       "\\item 'NKG7'\n",
       "\\item 'PRF1'\n",
       "\\item 'SH2D1B'\n",
       "\\item 'TXK'\n",
       "\\item 'XCL2'\n",
       "\\item 'ZNF600'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$T\\_cells] \\begin{enumerate*}\n",
       "\\item 'CCL5'\n",
       "\\item 'CCR4'\n",
       "\\item 'CCR6'\n",
       "\\item 'CD160'\n",
       "\\item 'CD27'\n",
       "\\item 'CD28'\n",
       "\\item 'CD3D'\n",
       "\\item 'CD3E'\n",
       "\\item 'CD3G'\n",
       "\\item 'CD8B'\n",
       "\\item 'CD96'\n",
       "\\item 'CRTAM'\n",
       "\\item 'CST7'\n",
       "\\item 'CXCR6'\n",
       "\\item 'FOXP3'\n",
       "\\item 'GRAP2'\n",
       "\\item 'GZMH'\n",
       "\\item 'GZMK'\n",
       "\\item 'GZMM'\n",
       "\\item 'IL2RA'\n",
       "\\item 'IL7R'\n",
       "\\item 'KLRG1'\n",
       "\\item 'LAIR2'\n",
       "\\item 'NCR3'\n",
       "\\item 'PATL2'\n",
       "\\item 'SH2D1A'\n",
       "\\item 'SIRPG'\n",
       "\\item 'SIT1'\n",
       "\\item 'TRAT1'\n",
       "\\item 'UBASH3A'\n",
       "\\item 'ZNF683'\n",
       "\\item 'CD2'\n",
       "\\item 'HPGD'\n",
       "\\item 'ACAP1'\n",
       "\\item 'IL4'\n",
       "\\item 'IL21'\n",
       "\\item 'CXCL13'\n",
       "\\item 'CCR5'\n",
       "\\item 'IL23R'\n",
       "\\item 'CCR10'\n",
       "\\item 'PDCD1'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$B_cells\n",
       ":   1. 'ADAM28'\n",
       "2. 'BANK1'\n",
       "3. 'BTLA'\n",
       "4. 'CD180'\n",
       "5. 'CD19'\n",
       "6. 'CD79A'\n",
       "7. 'CLEC17A'\n",
       "8. 'CNR2'\n",
       "9. 'CXCR5'\n",
       "10. 'FAM177B'\n",
       "11. 'FCRL2'\n",
       "12. 'FCRL3'\n",
       "13. 'FCRL5'\n",
       "14. 'HLA-DOB'\n",
       "15. 'IGHD'\n",
       "16. 'IGHM'\n",
       "17. 'IGLL5'\n",
       "18. 'KLHL14'\n",
       "19. 'MS4A1'\n",
       "20. 'POU2AF1'\n",
       "21. 'SPIB'\n",
       "22. 'STAP1'\n",
       "23. 'TCL1A'\n",
       "24. 'TLR10'\n",
       "25. 'TNFRSF13B'\n",
       "26. 'TNFRSF17'\n",
       "27. 'VPREB3'\n",
       "28. 'ZBP1'\n",
       "29. 'SP140'\n",
       "30. 'TMEM156'\n",
       "31. 'AICDA'\n",
       "32. 'FCRL1'\n",
       "\n",
       "\n",
       "\n",
       "$Granulocytes\n",
       ":   1. 'ADGRE3'\n",
       "2. 'ARG1'\n",
       "3. 'C5AR2'\n",
       "4. 'CAMP'\n",
       "5. 'CASS4'\n",
       "6. 'CCDC125'\n",
       "7. 'CCR3'\n",
       "8. 'CD101'\n",
       "9. 'CD244'\n",
       "10. 'CEACAM4'\n",
       "11. 'CLEC4E'\n",
       "12. 'CLEC7A'\n",
       "13. 'CR1'\n",
       "14. 'CXCR1'\n",
       "15. 'CXCR2'\n",
       "16. 'CYSLTR2'\n",
       "17. 'DPEP2'\n",
       "18. 'FCAR'\n",
       "19. 'FCGR3B'\n",
       "20. 'FFAR2'\n",
       "21. 'FPR2'\n",
       "22. 'GPR183'\n",
       "23. 'HAL'\n",
       "24. 'IL1R2'\n",
       "25. 'LILRB3'\n",
       "26. 'LRG1'\n",
       "27. 'LRMP'\n",
       "28. 'MEFV'\n",
       "29. 'MNDA'\n",
       "30. 'MRVI1'\n",
       "31. 'NLRP12'\n",
       "32. 'NLRP6'\n",
       "33. 'OSM'\n",
       "34. 'P2RY13'\n",
       "35. 'P2RY14'\n",
       "36. 'PADI4'\n",
       "37. 'PGLYRP1'\n",
       "38. 'PHOSPHO1'\n",
       "39. 'PROK2'\n",
       "40. 'PTGS2'\n",
       "41. 'RAB3D'\n",
       "42. 'RASGRP4'\n",
       "43. 'REM2'\n",
       "44. 'RNASE2'\n",
       "45. 'S100A12'\n",
       "46. 'S100P'\n",
       "47. 'SAMSN1'\n",
       "48. 'SIGLEC5'\n",
       "49. 'SIRPB1'\n",
       "50. 'ST20'\n",
       "51. 'TLR1'\n",
       "52. 'TNFRSF10C'\n",
       "53. 'TREM1'\n",
       "54. 'VNN2'\n",
       "55. 'VNN3'\n",
       "56. 'ZCCHC6'\n",
       "57. 'P2RY10'\n",
       "58. 'CEBPE'\n",
       "59. 'TUBB1'\n",
       "60. 'PF4V1'\n",
       "61. 'HRH4'\n",
       "62. 'MS4A3'\n",
       "63. 'LY96'\n",
       "64. 'DEFA4'\n",
       "65. 'CEACAM3'\n",
       "66. 'KIAA1551'\n",
       "67. 'HIST1H2BC'\n",
       "68. 'ADGRG3'\n",
       "69. 'HIST1H2BL'\n",
       "70. 'TAS2R40'\n",
       "71. 'NCF2'\n",
       "\n",
       "\n",
       "\n",
       "$Monocytes\n",
       ":   1. 'APOBEC3A'\n",
       "2. 'ASGR2'\n",
       "3. 'C2'\n",
       "4. 'CARD9'\n",
       "5. 'CD33'\n",
       "6. 'CFP'\n",
       "7. 'FCN1'\n",
       "8. 'FOLR3'\n",
       "9. 'GPBAR1'\n",
       "10. 'HPSE'\n",
       "11. 'NLRP3'\n",
       "12. 'PLA2G7'\n",
       "13. 'SLC37A2'\n",
       "14. 'SLC46A2'\n",
       "15. 'VNN1'\n",
       "16. 'CSTA'\n",
       "17. 'TNFRSF8'\n",
       "18. 'AOAH'\n",
       "\n",
       "\n",
       "\n",
       "$DC\n",
       ":   1. 'CCL22'\n",
       "2. 'CD1C'\n",
       "3. 'CLEC4C'\n",
       "4. 'CLIC3'\n",
       "5. 'FCER1A'\n",
       "6. 'FUT7'\n",
       "7. 'GZMB'\n",
       "8. 'IGFLR1'\n",
       "9. 'IRF8'\n",
       "10. 'LILRA4'\n",
       "11. 'MRC1'\n",
       "12. 'PLAC8'\n",
       "13. 'PROC'\n",
       "14. 'PTCRA'\n",
       "15. 'SLC12A3'\n",
       "16. 'TTC24'\n",
       "17. 'TTC39A'\n",
       "18. 'ZNRF2'\n",
       "19. 'CD86'\n",
       "20. 'CCL17'\n",
       "21. 'IL12B'\n",
       "22. 'CD80'\n",
       "23. 'CD1A'\n",
       "24. 'CD1B'\n",
       "25. 'CD1E'\n",
       "26. 'XCR1'\n",
       "27. 'TLR9'\n",
       "28. 'HIST1H2BB'\n",
       "\n",
       "\n",
       "\n",
       "$NK_cells\n",
       ":   1. 'CCL4'\n",
       "2. 'CD247'\n",
       "3. 'CTSW'\n",
       "4. 'GNLY'\n",
       "5. 'GZMA'\n",
       "6. 'IL18RAP'\n",
       "7. 'IL2RB'\n",
       "8. 'KIR2DL1'\n",
       "9. 'KIR2DL3'\n",
       "10. 'KIR2DL4'\n",
       "11. 'KIR3DL1'\n",
       "12. 'KIR3DL2'\n",
       "13. 'KLRF1'\n",
       "14. 'NCR1'\n",
       "15. 'NKG7'\n",
       "16. 'PRF1'\n",
       "17. 'SH2D1B'\n",
       "18. 'TXK'\n",
       "19. 'XCL2'\n",
       "20. 'ZNF600'\n",
       "\n",
       "\n",
       "\n",
       "$T_cells\n",
       ":   1. 'CCL5'\n",
       "2. 'CCR4'\n",
       "3. 'CCR6'\n",
       "4. 'CD160'\n",
       "5. 'CD27'\n",
       "6. 'CD28'\n",
       "7. 'CD3D'\n",
       "8. 'CD3E'\n",
       "9. 'CD3G'\n",
       "10. 'CD8B'\n",
       "11. 'CD96'\n",
       "12. 'CRTAM'\n",
       "13. 'CST7'\n",
       "14. 'CXCR6'\n",
       "15. 'FOXP3'\n",
       "16. 'GRAP2'\n",
       "17. 'GZMH'\n",
       "18. 'GZMK'\n",
       "19. 'GZMM'\n",
       "20. 'IL2RA'\n",
       "21. 'IL7R'\n",
       "22. 'KLRG1'\n",
       "23. 'LAIR2'\n",
       "24. 'NCR3'\n",
       "25. 'PATL2'\n",
       "26. 'SH2D1A'\n",
       "27. 'SIRPG'\n",
       "28. 'SIT1'\n",
       "29. 'TRAT1'\n",
       "30. 'UBASH3A'\n",
       "31. 'ZNF683'\n",
       "32. 'CD2'\n",
       "33. 'HPGD'\n",
       "34. 'ACAP1'\n",
       "35. 'IL4'\n",
       "36. 'IL21'\n",
       "37. 'CXCL13'\n",
       "38. 'CCR5'\n",
       "39. 'IL23R'\n",
       "40. 'CCR10'\n",
       "41. 'PDCD1'\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$B_cells\n",
       " [1] \"ADAM28\"    \"BANK1\"     \"BTLA\"      \"CD180\"     \"CD19\"      \"CD79A\"    \n",
       " [7] \"CLEC17A\"   \"CNR2\"      \"CXCR5\"     \"FAM177B\"   \"FCRL2\"     \"FCRL3\"    \n",
       "[13] \"FCRL5\"     \"HLA-DOB\"   \"IGHD\"      \"IGHM\"      \"IGLL5\"     \"KLHL14\"   \n",
       "[19] \"MS4A1\"     \"POU2AF1\"   \"SPIB\"      \"STAP1\"     \"TCL1A\"     \"TLR10\"    \n",
       "[25] \"TNFRSF13B\" \"TNFRSF17\"  \"VPREB3\"    \"ZBP1\"      \"SP140\"     \"TMEM156\"  \n",
       "[31] \"AICDA\"     \"FCRL1\"    \n",
       "\n",
       "$Granulocytes\n",
       " [1] \"ADGRE3\"    \"ARG1\"      \"C5AR2\"     \"CAMP\"      \"CASS4\"     \"CCDC125\"  \n",
       " [7] \"CCR3\"      \"CD101\"     \"CD244\"     \"CEACAM4\"   \"CLEC4E\"    \"CLEC7A\"   \n",
       "[13] \"CR1\"       \"CXCR1\"     \"CXCR2\"     \"CYSLTR2\"   \"DPEP2\"     \"FCAR\"     \n",
       "[19] \"FCGR3B\"    \"FFAR2\"     \"FPR2\"      \"GPR183\"    \"HAL\"       \"IL1R2\"    \n",
       "[25] \"LILRB3\"    \"LRG1\"      \"LRMP\"      \"MEFV\"      \"MNDA\"      \"MRVI1\"    \n",
       "[31] \"NLRP12\"    \"NLRP6\"     \"OSM\"       \"P2RY13\"    \"P2RY14\"    \"PADI4\"    \n",
       "[37] \"PGLYRP1\"   \"PHOSPHO1\"  \"PROK2\"     \"PTGS2\"     \"RAB3D\"     \"RASGRP4\"  \n",
       "[43] \"REM2\"      \"RNASE2\"    \"S100A12\"   \"S100P\"     \"SAMSN1\"    \"SIGLEC5\"  \n",
       "[49] \"SIRPB1\"    \"ST20\"      \"TLR1\"      \"TNFRSF10C\" \"TREM1\"     \"VNN2\"     \n",
       "[55] \"VNN3\"      \"ZCCHC6\"    \"P2RY10\"    \"CEBPE\"     \"TUBB1\"     \"PF4V1\"    \n",
       "[61] \"HRH4\"      \"MS4A3\"     \"LY96\"      \"DEFA4\"     \"CEACAM3\"   \"KIAA1551\" \n",
       "[67] \"HIST1H2BC\" \"ADGRG3\"    \"HIST1H2BL\" \"TAS2R40\"   \"NCF2\"     \n",
       "\n",
       "$Monocytes\n",
       " [1] \"APOBEC3A\" \"ASGR2\"    \"C2\"       \"CARD9\"    \"CD33\"     \"CFP\"     \n",
       " [7] \"FCN1\"     \"FOLR3\"    \"GPBAR1\"   \"HPSE\"     \"NLRP3\"    \"PLA2G7\"  \n",
       "[13] \"SLC37A2\"  \"SLC46A2\"  \"VNN1\"     \"CSTA\"     \"TNFRSF8\"  \"AOAH\"    \n",
       "\n",
       "$DC\n",
       " [1] \"CCL22\"     \"CD1C\"      \"CLEC4C\"    \"CLIC3\"     \"FCER1A\"    \"FUT7\"     \n",
       " [7] \"GZMB\"      \"IGFLR1\"    \"IRF8\"      \"LILRA4\"    \"MRC1\"      \"PLAC8\"    \n",
       "[13] \"PROC\"      \"PTCRA\"     \"SLC12A3\"   \"TTC24\"     \"TTC39A\"    \"ZNRF2\"    \n",
       "[19] \"CD86\"      \"CCL17\"     \"IL12B\"     \"CD80\"      \"CD1A\"      \"CD1B\"     \n",
       "[25] \"CD1E\"      \"XCR1\"      \"TLR9\"      \"HIST1H2BB\"\n",
       "\n",
       "$NK_cells\n",
       " [1] \"CCL4\"    \"CD247\"   \"CTSW\"    \"GNLY\"    \"GZMA\"    \"IL18RAP\" \"IL2RB\"  \n",
       " [8] \"KIR2DL1\" \"KIR2DL3\" \"KIR2DL4\" \"KIR3DL1\" \"KIR3DL2\" \"KLRF1\"   \"NCR1\"   \n",
       "[15] \"NKG7\"    \"PRF1\"    \"SH2D1B\"  \"TXK\"     \"XCL2\"    \"ZNF600\" \n",
       "\n",
       "$T_cells\n",
       " [1] \"CCL5\"    \"CCR4\"    \"CCR6\"    \"CD160\"   \"CD27\"    \"CD28\"    \"CD3D\"   \n",
       " [8] \"CD3E\"    \"CD3G\"    \"CD8B\"    \"CD96\"    \"CRTAM\"   \"CST7\"    \"CXCR6\"  \n",
       "[15] \"FOXP3\"   \"GRAP2\"   \"GZMH\"    \"GZMK\"    \"GZMM\"    \"IL2RA\"   \"IL7R\"   \n",
       "[22] \"KLRG1\"   \"LAIR2\"   \"NCR3\"    \"PATL2\"   \"SH2D1A\"  \"SIRPG\"   \"SIT1\"   \n",
       "[29] \"TRAT1\"   \"UBASH3A\" \"ZNF683\"  \"CD2\"     \"HPGD\"    \"ACAP1\"   \"IL4\"    \n",
       "[36] \"IL21\"    \"CXCL13\"  \"CCR5\"    \"IL23R\"   \"CCR10\"   \"PDCD1\"  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "celltype_genes_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_genesets <- celltype_genes_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in .filterFeatures(expr, method):\n",
      "“160 genes with constant expression values throuhgout the samples.”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating ssGSEA scores for 6 gene sets.\n",
      "  |======================================================================| 100%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "geneset_cc <- gsva(tpms, final_genesets, method = \"ssgsea\", ssgsea.norm = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  -8042   -4939   -4167   -4054   -3314    3629 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(as.vector(geneset_cc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "#median center mad scale\n",
    "geneset_cc_norm <- (geneset_cc - median(as.vector(geneset_cc)))/mad(as.vector(geneset_cc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 925 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>TARGET-30-PAIFXV-01A</th><th scope=col>TARGET-30-PAIPGU-01A</th><th scope=col>TARGET-30-PAISNS-01A</th><th scope=col>TARGET-30-PAITCI-01A</th><th scope=col>TARGET-30-PAIVHE-01A</th><th scope=col>TARGET-30-PAIXIF-01A</th><th scope=col>TARGET-30-PAKYZS-01A</th><th scope=col>TARGET-30-PAKZRH-01A</th><th scope=col>TARGET-30-PALBFW-01A</th><th scope=col>TARGET-30-PALCBW-01A</th><th scope=col>⋯</th><th scope=col>ICGC_PA89</th><th scope=col>ICGC_PA9</th><th scope=col>ICGC_PA91</th><th scope=col>ICGC_PA92</th><th scope=col>ICGC_PA93</th><th scope=col>ICGC_PA94</th><th scope=col>ICGC_PA95</th><th scope=col>ICGC_PA96</th><th scope=col>ICGC_PA97</th><th scope=col>ICGC_PA99</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>B_cells</th><td>-1.0261811</td><td>-0.2663472</td><td>-0.5500331</td><td>-1.6734032</td><td>0.3790016</td><td>-0.68443148</td><td>-2.3938567</td><td>-2.3391479</td><td>-0.69584948</td><td>-0.1989627</td><td>⋯</td><td>-0.4294500</td><td>-0.1895001</td><td>-0.08708725</td><td>-0.24467422</td><td> 0.18449614</td><td>-0.3363069</td><td>-0.009581817</td><td>-0.5669550</td><td>-0.008541654</td><td>-0.2312175</td></tr>\n",
       "\t<tr><th scope=row>Granulocytes</th><td> 1.0154032</td><td> 1.5352628</td><td> 0.3735776</td><td> 1.1657142</td><td>1.0875300</td><td> 1.62840168</td><td> 0.8566935</td><td> 0.5867553</td><td> 0.59711497</td><td> 1.4388706</td><td>⋯</td><td> 0.5986012</td><td> 0.9764662</td><td> 1.09418248</td><td> 1.20580307</td><td> 1.59507546</td><td> 0.6462704</td><td> 1.014127410</td><td> 1.1984187</td><td> 1.623139948</td><td> 1.1852914</td></tr>\n",
       "\t<tr><th scope=row>Monocytes</th><td> 1.1560147</td><td> 3.2377299</td><td>-0.1424045</td><td> 1.3159159</td><td>1.0783997</td><td> 2.24252540</td><td> 0.5307257</td><td> 1.1801533</td><td> 0.44303005</td><td> 1.3987383</td><td>⋯</td><td> 0.7337680</td><td> 1.6864077</td><td> 1.59960434</td><td> 1.02899639</td><td> 2.71492144</td><td> 1.0711519</td><td> 2.012054482</td><td> 1.1595255</td><td> 1.927787219</td><td> 1.9380339</td></tr>\n",
       "\t<tr><th scope=row>DC</th><td> 0.6116269</td><td> 1.7166637</td><td> 0.3242817</td><td> 0.9269765</td><td>0.8864241</td><td> 1.20057021</td><td> 0.2268468</td><td>-0.1522459</td><td> 0.49141649</td><td> 1.3751148</td><td>⋯</td><td> 0.2025195</td><td> 0.4377004</td><td> 0.82783742</td><td> 0.31070266</td><td> 1.28033748</td><td> 0.4651458</td><td> 0.843106181</td><td> 0.5533022</td><td> 0.503512791</td><td> 0.9905531</td></tr>\n",
       "\t<tr><th scope=row>NK_cells</th><td> 0.5080152</td><td> 1.2370455</td><td>-0.2447717</td><td>-0.4259744</td><td>0.3518645</td><td>-0.08196236</td><td>-1.5027704</td><td>-1.5369380</td><td> 0.36873168</td><td>-0.0508021</td><td>⋯</td><td>-0.7132550</td><td>-0.3555373</td><td> 0.11713649</td><td> 0.03383472</td><td>-0.06413519</td><td>-0.9230160</td><td>-0.259694051</td><td>-0.3449225</td><td> 0.145732336</td><td>-0.0730824</td></tr>\n",
       "\t<tr><th scope=row>T_cells</th><td> 0.1329171</td><td> 1.2636560</td><td> 0.5094226</td><td>-0.0656706</td><td>0.7437377</td><td> 0.05919117</td><td>-1.6221636</td><td>-1.8604589</td><td> 0.07688679</td><td> 0.1175262</td><td>⋯</td><td>-0.6417722</td><td>-0.2823479</td><td>-0.21028556</td><td>-0.16341890</td><td> 0.09400842</td><td>-0.6638549</td><td> 0.068445412</td><td>-0.5339814</td><td>-0.247713121</td><td>-0.1558411</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 925 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & TARGET-30-PAIFXV-01A & TARGET-30-PAIPGU-01A & TARGET-30-PAISNS-01A & TARGET-30-PAITCI-01A & TARGET-30-PAIVHE-01A & TARGET-30-PAIXIF-01A & TARGET-30-PAKYZS-01A & TARGET-30-PAKZRH-01A & TARGET-30-PALBFW-01A & TARGET-30-PALCBW-01A & ⋯ & ICGC\\_PA89 & ICGC\\_PA9 & ICGC\\_PA91 & ICGC\\_PA92 & ICGC\\_PA93 & ICGC\\_PA94 & ICGC\\_PA95 & ICGC\\_PA96 & ICGC\\_PA97 & ICGC\\_PA99\\\\\n",
       "\\hline\n",
       "\tB\\_cells & -1.0261811 & -0.2663472 & -0.5500331 & -1.6734032 & 0.3790016 & -0.68443148 & -2.3938567 & -2.3391479 & -0.69584948 & -0.1989627 & ⋯ & -0.4294500 & -0.1895001 & -0.08708725 & -0.24467422 &  0.18449614 & -0.3363069 & -0.009581817 & -0.5669550 & -0.008541654 & -0.2312175\\\\\n",
       "\tGranulocytes &  1.0154032 &  1.5352628 &  0.3735776 &  1.1657142 & 1.0875300 &  1.62840168 &  0.8566935 &  0.5867553 &  0.59711497 &  1.4388706 & ⋯ &  0.5986012 &  0.9764662 &  1.09418248 &  1.20580307 &  1.59507546 &  0.6462704 &  1.014127410 &  1.1984187 &  1.623139948 &  1.1852914\\\\\n",
       "\tMonocytes &  1.1560147 &  3.2377299 & -0.1424045 &  1.3159159 & 1.0783997 &  2.24252540 &  0.5307257 &  1.1801533 &  0.44303005 &  1.3987383 & ⋯ &  0.7337680 &  1.6864077 &  1.59960434 &  1.02899639 &  2.71492144 &  1.0711519 &  2.012054482 &  1.1595255 &  1.927787219 &  1.9380339\\\\\n",
       "\tDC &  0.6116269 &  1.7166637 &  0.3242817 &  0.9269765 & 0.8864241 &  1.20057021 &  0.2268468 & -0.1522459 &  0.49141649 &  1.3751148 & ⋯ &  0.2025195 &  0.4377004 &  0.82783742 &  0.31070266 &  1.28033748 &  0.4651458 &  0.843106181 &  0.5533022 &  0.503512791 &  0.9905531\\\\\n",
       "\tNK\\_cells &  0.5080152 &  1.2370455 & -0.2447717 & -0.4259744 & 0.3518645 & -0.08196236 & -1.5027704 & -1.5369380 &  0.36873168 & -0.0508021 & ⋯ & -0.7132550 & -0.3555373 &  0.11713649 &  0.03383472 & -0.06413519 & -0.9230160 & -0.259694051 & -0.3449225 &  0.145732336 & -0.0730824\\\\\n",
       "\tT\\_cells &  0.1329171 &  1.2636560 &  0.5094226 & -0.0656706 & 0.7437377 &  0.05919117 & -1.6221636 & -1.8604589 &  0.07688679 &  0.1175262 & ⋯ & -0.6417722 & -0.2823479 & -0.21028556 & -0.16341890 &  0.09400842 & -0.6638549 &  0.068445412 & -0.5339814 & -0.247713121 & -0.1558411\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 925 of type dbl\n",
       "\n",
       "| <!--/--> | TARGET-30-PAIFXV-01A | TARGET-30-PAIPGU-01A | TARGET-30-PAISNS-01A | TARGET-30-PAITCI-01A | TARGET-30-PAIVHE-01A | TARGET-30-PAIXIF-01A | TARGET-30-PAKYZS-01A | TARGET-30-PAKZRH-01A | TARGET-30-PALBFW-01A | TARGET-30-PALCBW-01A | ⋯ | ICGC_PA89 | ICGC_PA9 | ICGC_PA91 | ICGC_PA92 | ICGC_PA93 | ICGC_PA94 | ICGC_PA95 | ICGC_PA96 | ICGC_PA97 | ICGC_PA99 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| B_cells | -1.0261811 | -0.2663472 | -0.5500331 | -1.6734032 | 0.3790016 | -0.68443148 | -2.3938567 | -2.3391479 | -0.69584948 | -0.1989627 | ⋯ | -0.4294500 | -0.1895001 | -0.08708725 | -0.24467422 |  0.18449614 | -0.3363069 | -0.009581817 | -0.5669550 | -0.008541654 | -0.2312175 |\n",
       "| Granulocytes |  1.0154032 |  1.5352628 |  0.3735776 |  1.1657142 | 1.0875300 |  1.62840168 |  0.8566935 |  0.5867553 |  0.59711497 |  1.4388706 | ⋯ |  0.5986012 |  0.9764662 |  1.09418248 |  1.20580307 |  1.59507546 |  0.6462704 |  1.014127410 |  1.1984187 |  1.623139948 |  1.1852914 |\n",
       "| Monocytes |  1.1560147 |  3.2377299 | -0.1424045 |  1.3159159 | 1.0783997 |  2.24252540 |  0.5307257 |  1.1801533 |  0.44303005 |  1.3987383 | ⋯ |  0.7337680 |  1.6864077 |  1.59960434 |  1.02899639 |  2.71492144 |  1.0711519 |  2.012054482 |  1.1595255 |  1.927787219 |  1.9380339 |\n",
       "| DC |  0.6116269 |  1.7166637 |  0.3242817 |  0.9269765 | 0.8864241 |  1.20057021 |  0.2268468 | -0.1522459 |  0.49141649 |  1.3751148 | ⋯ |  0.2025195 |  0.4377004 |  0.82783742 |  0.31070266 |  1.28033748 |  0.4651458 |  0.843106181 |  0.5533022 |  0.503512791 |  0.9905531 |\n",
       "| NK_cells |  0.5080152 |  1.2370455 | -0.2447717 | -0.4259744 | 0.3518645 | -0.08196236 | -1.5027704 | -1.5369380 |  0.36873168 | -0.0508021 | ⋯ | -0.7132550 | -0.3555373 |  0.11713649 |  0.03383472 | -0.06413519 | -0.9230160 | -0.259694051 | -0.3449225 |  0.145732336 | -0.0730824 |\n",
       "| T_cells |  0.1329171 |  1.2636560 |  0.5094226 | -0.0656706 | 0.7437377 |  0.05919117 | -1.6221636 | -1.8604589 |  0.07688679 |  0.1175262 | ⋯ | -0.6417722 | -0.2823479 | -0.21028556 | -0.16341890 |  0.09400842 | -0.6638549 |  0.068445412 | -0.5339814 | -0.247713121 | -0.1558411 |\n",
       "\n"
      ],
      "text/plain": [
       "             TARGET-30-PAIFXV-01A TARGET-30-PAIPGU-01A TARGET-30-PAISNS-01A\n",
       "B_cells      -1.0261811           -0.2663472           -0.5500331          \n",
       "Granulocytes  1.0154032            1.5352628            0.3735776          \n",
       "Monocytes     1.1560147            3.2377299           -0.1424045          \n",
       "DC            0.6116269            1.7166637            0.3242817          \n",
       "NK_cells      0.5080152            1.2370455           -0.2447717          \n",
       "T_cells       0.1329171            1.2636560            0.5094226          \n",
       "             TARGET-30-PAITCI-01A TARGET-30-PAIVHE-01A TARGET-30-PAIXIF-01A\n",
       "B_cells      -1.6734032           0.3790016            -0.68443148         \n",
       "Granulocytes  1.1657142           1.0875300             1.62840168         \n",
       "Monocytes     1.3159159           1.0783997             2.24252540         \n",
       "DC            0.9269765           0.8864241             1.20057021         \n",
       "NK_cells     -0.4259744           0.3518645            -0.08196236         \n",
       "T_cells      -0.0656706           0.7437377             0.05919117         \n",
       "             TARGET-30-PAKYZS-01A TARGET-30-PAKZRH-01A TARGET-30-PALBFW-01A\n",
       "B_cells      -2.3938567           -2.3391479           -0.69584948         \n",
       "Granulocytes  0.8566935            0.5867553            0.59711497         \n",
       "Monocytes     0.5307257            1.1801533            0.44303005         \n",
       "DC            0.2268468           -0.1522459            0.49141649         \n",
       "NK_cells     -1.5027704           -1.5369380            0.36873168         \n",
       "T_cells      -1.6221636           -1.8604589            0.07688679         \n",
       "             TARGET-30-PALCBW-01A ⋯ ICGC_PA89  ICGC_PA9   ICGC_PA91  \n",
       "B_cells      -0.1989627           ⋯ -0.4294500 -0.1895001 -0.08708725\n",
       "Granulocytes  1.4388706           ⋯  0.5986012  0.9764662  1.09418248\n",
       "Monocytes     1.3987383           ⋯  0.7337680  1.6864077  1.59960434\n",
       "DC            1.3751148           ⋯  0.2025195  0.4377004  0.82783742\n",
       "NK_cells     -0.0508021           ⋯ -0.7132550 -0.3555373  0.11713649\n",
       "T_cells       0.1175262           ⋯ -0.6417722 -0.2823479 -0.21028556\n",
       "             ICGC_PA92   ICGC_PA93   ICGC_PA94  ICGC_PA95    ICGC_PA96 \n",
       "B_cells      -0.24467422  0.18449614 -0.3363069 -0.009581817 -0.5669550\n",
       "Granulocytes  1.20580307  1.59507546  0.6462704  1.014127410  1.1984187\n",
       "Monocytes     1.02899639  2.71492144  1.0711519  2.012054482  1.1595255\n",
       "DC            0.31070266  1.28033748  0.4651458  0.843106181  0.5533022\n",
       "NK_cells      0.03383472 -0.06413519 -0.9230160 -0.259694051 -0.3449225\n",
       "T_cells      -0.16341890  0.09400842 -0.6638549  0.068445412 -0.5339814\n",
       "             ICGC_PA97    ICGC_PA99 \n",
       "B_cells      -0.008541654 -0.2312175\n",
       "Granulocytes  1.623139948  1.1852914\n",
       "Monocytes     1.927787219  1.9380339\n",
       "DC            0.503512791  0.9905531\n",
       "NK_cells      0.145732336 -0.0730824\n",
       "T_cells      -0.247713121 -0.1558411"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(geneset_cc_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(geneset_cc_norm, file = paste0(datapath, \"TME_clusters/geneset_cc_norm.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "end fraction\n",
      "\n",
      "clustered\n",
      "\n",
      "clustered\n",
      "\n",
      "clustered\n",
      "\n",
      "clustered\n",
      "\n",
      "clustered\n",
      "\n",
      "clustered\n",
      "\n",
      "clustered\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results <- ConsensusClusterPlus(geneset_cc_norm, maxK = 8, reps = 200, pItem=0.8, pFeature=1,\n",
    "                                clusterAlg=\"km\", distance=\"euclidean\",\n",
    "                                seed=888, writeTable= FALSE, title = \"clusters_TME\", plot=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(results, file = paste0(datapath, \"TME_clusters/results_consensusclustering.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = paste0(datapath, \"TME_clusters/results_consensusclustering.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "tm <- results[[4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  1   2   3   4 \n",
       "279  90 393 163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(tm$consensusClass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "vars$immune_cluster <- tm$consensusClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC <- vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recode clusters from hottest to coldets\n",
    "metadata_IC$immune_cluster[metadata_IC$immune_cluster == \"1\"] <- \"Myeloid-driven\"\n",
    "metadata_IC$immune_cluster[metadata_IC$immune_cluster == \"2\"] <- \"Pediatric inflamed\"\n",
    "metadata_IC$immune_cluster[metadata_IC$immune_cluster == \"3\"] <- \"Pediatric cold\"\n",
    "metadata_IC$immune_cluster[metadata_IC$immune_cluster == \"4\"] <- \"Immune excluded\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "   Immune excluded     Myeloid-driven     Pediatric cold Pediatric inflamed \n",
       "               163                279                393                 90 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC$immune_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC$immune_cluster <- factor(metadata_IC$immune_cluster,\n",
    "                                       levels = c(\n",
    "                                       \"Pediatric inflamed\", \"Myeloid-driven\", \"Pediatric cold\", \"Immune excluded\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "save(metadata_IC, file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.csv(metadata_IC, file = paste0(datapath, \"TME_clusters/IPD_estimate_metadata_IC.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "load(file = file.path(datapath,\"TME_clusters/IPD_estimate_metadata_IC.RData\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "load( file = paste0(datapath, \"TME_clusters/geneset_cc_norm.RData\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_cohort <- metadata_IC[order(metadata_IC$immune_cluster, metadata_IC$cohort),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycluster <- as.character(cluster_cohort$immune_cluster)\n",
    "names(mycluster) <- rownames(cluster_cohort)\n",
    "cluster_hm <- class_hm.fx(mycluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycohort <- cluster_cohort$cohort\n",
    "names(mycohort) <- rownames(cluster_cohort)\n",
    "\n",
    "mycohorts <- t(as.matrix(mycohort))\n",
    "rownames(mycohorts) <- \"Cohort\"\n",
    "cohorts_hm <- cohorts_hm.fx(mycohorts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geneset scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells_mat <- geneset_cc_norm[,rownames(cluster_cohort)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 925 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>7316-1137</th><th scope=col>7316-2582</th><th scope=col>7316-41</th><th scope=col>H049-0CWK</th><th scope=col>H049-7SD3</th><th scope=col>H049-GWNW</th><th scope=col>H049-UV6K</th><th scope=col>7316-1082</th><th scope=col>7316-1748</th><th scope=col>7316-1773</th><th scope=col>⋯</th><th scope=col>7316-3574</th><th scope=col>7316-359</th><th scope=col>7316-3768</th><th scope=col>7316-3936</th><th scope=col>7316-470</th><th scope=col>7316-642</th><th scope=col>7316-77</th><th scope=col>7316-915</th><th scope=col>7316-923</th><th scope=col>7316-97</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>B_cells</th><td>0.8781518</td><td>4.183430</td><td>2.128592</td><td>1.254576</td><td>2.784994</td><td>0.2413679</td><td>1.459019</td><td>3.116237</td><td>3.214110</td><td>2.698993</td><td>⋯</td><td>-1.09895602</td><td>-1.07094246</td><td>-1.21548809</td><td>-1.56916951</td><td>-1.2680743</td><td>-1.16561529</td><td>-0.810489200</td><td>-1.27638532</td><td>-0.9093326</td><td>-1.15567527</td></tr>\n",
       "\t<tr><th scope=row>Granulocytes</th><td>2.0394454</td><td>2.495313</td><td>2.006593</td><td>1.073148</td><td>2.246158</td><td>1.4440585</td><td>1.365864</td><td>2.731103</td><td>5.856786</td><td>1.822971</td><td>⋯</td><td> 0.28375481</td><td>-0.02097290</td><td> 0.20883215</td><td>-0.08909531</td><td>-0.1634655</td><td>-0.08885868</td><td>-0.005934645</td><td> 0.03372058</td><td> 0.4337059</td><td>-0.09953267</td></tr>\n",
       "\t<tr><th scope=row>Monocytes</th><td>3.8665973</td><td>2.982730</td><td>3.083446</td><td>2.980268</td><td>3.741642</td><td>2.4657429</td><td>3.062799</td><td>4.359749</td><td>4.777348</td><td>2.261230</td><td>⋯</td><td>-0.09622229</td><td>-0.09867117</td><td>-0.04502704</td><td>-0.10885257</td><td>-0.2966868</td><td>-0.20445942</td><td>-0.224052695</td><td>-0.48860927</td><td> 0.1255357</td><td> 0.14612705</td></tr>\n",
       "\t<tr><th scope=row>DC</th><td>2.7869567</td><td>2.373179</td><td>2.079514</td><td>1.244206</td><td>2.347652</td><td>0.9229822</td><td>1.362942</td><td>3.122798</td><td>1.453885</td><td>1.402094</td><td>⋯</td><td>-0.80353328</td><td>-0.73645172</td><td>-0.55878953</td><td>-0.75934335</td><td>-0.6231024</td><td>-0.05101250</td><td>-0.291110245</td><td>-0.79069402</td><td>-0.6415289</td><td>-0.09922761</td></tr>\n",
       "\t<tr><th scope=row>NK_cells</th><td>1.8766351</td><td>4.679796</td><td>1.468996</td><td>2.335302</td><td>4.323897</td><td>1.5873751</td><td>2.588224</td><td>3.202744</td><td>3.827398</td><td>1.153484</td><td>⋯</td><td>-1.33198326</td><td>-1.72572276</td><td>-1.21715795</td><td>-1.22539506</td><td>-1.3090375</td><td>-1.70387668</td><td>-1.480009973</td><td>-1.48146992</td><td>-1.6488658</td><td>-1.46443628</td></tr>\n",
       "\t<tr><th scope=row>T_cells</th><td>1.7662977</td><td>6.579879</td><td>2.426231</td><td>3.475631</td><td>4.085210</td><td>1.5241934</td><td>3.487713</td><td>3.659146</td><td>2.778628</td><td>1.159787</td><td>⋯</td><td>-1.28676190</td><td>-1.40745523</td><td>-1.50898720</td><td>-1.32311741</td><td>-1.0170671</td><td>-1.27250326</td><td>-1.433442397</td><td>-1.33093217</td><td>-1.3552094</td><td>-1.12422857</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 925 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & 7316-1137 & 7316-2582 & 7316-41 & H049-0CWK & H049-7SD3 & H049-GWNW & H049-UV6K & 7316-1082 & 7316-1748 & 7316-1773 & ⋯ & 7316-3574 & 7316-359 & 7316-3768 & 7316-3936 & 7316-470 & 7316-642 & 7316-77 & 7316-915 & 7316-923 & 7316-97\\\\\n",
       "\\hline\n",
       "\tB\\_cells & 0.8781518 & 4.183430 & 2.128592 & 1.254576 & 2.784994 & 0.2413679 & 1.459019 & 3.116237 & 3.214110 & 2.698993 & ⋯ & -1.09895602 & -1.07094246 & -1.21548809 & -1.56916951 & -1.2680743 & -1.16561529 & -0.810489200 & -1.27638532 & -0.9093326 & -1.15567527\\\\\n",
       "\tGranulocytes & 2.0394454 & 2.495313 & 2.006593 & 1.073148 & 2.246158 & 1.4440585 & 1.365864 & 2.731103 & 5.856786 & 1.822971 & ⋯ &  0.28375481 & -0.02097290 &  0.20883215 & -0.08909531 & -0.1634655 & -0.08885868 & -0.005934645 &  0.03372058 &  0.4337059 & -0.09953267\\\\\n",
       "\tMonocytes & 3.8665973 & 2.982730 & 3.083446 & 2.980268 & 3.741642 & 2.4657429 & 3.062799 & 4.359749 & 4.777348 & 2.261230 & ⋯ & -0.09622229 & -0.09867117 & -0.04502704 & -0.10885257 & -0.2966868 & -0.20445942 & -0.224052695 & -0.48860927 &  0.1255357 &  0.14612705\\\\\n",
       "\tDC & 2.7869567 & 2.373179 & 2.079514 & 1.244206 & 2.347652 & 0.9229822 & 1.362942 & 3.122798 & 1.453885 & 1.402094 & ⋯ & -0.80353328 & -0.73645172 & -0.55878953 & -0.75934335 & -0.6231024 & -0.05101250 & -0.291110245 & -0.79069402 & -0.6415289 & -0.09922761\\\\\n",
       "\tNK\\_cells & 1.8766351 & 4.679796 & 1.468996 & 2.335302 & 4.323897 & 1.5873751 & 2.588224 & 3.202744 & 3.827398 & 1.153484 & ⋯ & -1.33198326 & -1.72572276 & -1.21715795 & -1.22539506 & -1.3090375 & -1.70387668 & -1.480009973 & -1.48146992 & -1.6488658 & -1.46443628\\\\\n",
       "\tT\\_cells & 1.7662977 & 6.579879 & 2.426231 & 3.475631 & 4.085210 & 1.5241934 & 3.487713 & 3.659146 & 2.778628 & 1.159787 & ⋯ & -1.28676190 & -1.40745523 & -1.50898720 & -1.32311741 & -1.0170671 & -1.27250326 & -1.433442397 & -1.33093217 & -1.3552094 & -1.12422857\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 925 of type dbl\n",
       "\n",
       "| <!--/--> | 7316-1137 | 7316-2582 | 7316-41 | H049-0CWK | H049-7SD3 | H049-GWNW | H049-UV6K | 7316-1082 | 7316-1748 | 7316-1773 | ⋯ | 7316-3574 | 7316-359 | 7316-3768 | 7316-3936 | 7316-470 | 7316-642 | 7316-77 | 7316-915 | 7316-923 | 7316-97 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| B_cells | 0.8781518 | 4.183430 | 2.128592 | 1.254576 | 2.784994 | 0.2413679 | 1.459019 | 3.116237 | 3.214110 | 2.698993 | ⋯ | -1.09895602 | -1.07094246 | -1.21548809 | -1.56916951 | -1.2680743 | -1.16561529 | -0.810489200 | -1.27638532 | -0.9093326 | -1.15567527 |\n",
       "| Granulocytes | 2.0394454 | 2.495313 | 2.006593 | 1.073148 | 2.246158 | 1.4440585 | 1.365864 | 2.731103 | 5.856786 | 1.822971 | ⋯ |  0.28375481 | -0.02097290 |  0.20883215 | -0.08909531 | -0.1634655 | -0.08885868 | -0.005934645 |  0.03372058 |  0.4337059 | -0.09953267 |\n",
       "| Monocytes | 3.8665973 | 2.982730 | 3.083446 | 2.980268 | 3.741642 | 2.4657429 | 3.062799 | 4.359749 | 4.777348 | 2.261230 | ⋯ | -0.09622229 | -0.09867117 | -0.04502704 | -0.10885257 | -0.2966868 | -0.20445942 | -0.224052695 | -0.48860927 |  0.1255357 |  0.14612705 |\n",
       "| DC | 2.7869567 | 2.373179 | 2.079514 | 1.244206 | 2.347652 | 0.9229822 | 1.362942 | 3.122798 | 1.453885 | 1.402094 | ⋯ | -0.80353328 | -0.73645172 | -0.55878953 | -0.75934335 | -0.6231024 | -0.05101250 | -0.291110245 | -0.79069402 | -0.6415289 | -0.09922761 |\n",
       "| NK_cells | 1.8766351 | 4.679796 | 1.468996 | 2.335302 | 4.323897 | 1.5873751 | 2.588224 | 3.202744 | 3.827398 | 1.153484 | ⋯ | -1.33198326 | -1.72572276 | -1.21715795 | -1.22539506 | -1.3090375 | -1.70387668 | -1.480009973 | -1.48146992 | -1.6488658 | -1.46443628 |\n",
       "| T_cells | 1.7662977 | 6.579879 | 2.426231 | 3.475631 | 4.085210 | 1.5241934 | 3.487713 | 3.659146 | 2.778628 | 1.159787 | ⋯ | -1.28676190 | -1.40745523 | -1.50898720 | -1.32311741 | -1.0170671 | -1.27250326 | -1.433442397 | -1.33093217 | -1.3552094 | -1.12422857 |\n",
       "\n"
      ],
      "text/plain": [
       "             7316-1137 7316-2582 7316-41  H049-0CWK H049-7SD3 H049-GWNW\n",
       "B_cells      0.8781518 4.183430  2.128592 1.254576  2.784994  0.2413679\n",
       "Granulocytes 2.0394454 2.495313  2.006593 1.073148  2.246158  1.4440585\n",
       "Monocytes    3.8665973 2.982730  3.083446 2.980268  3.741642  2.4657429\n",
       "DC           2.7869567 2.373179  2.079514 1.244206  2.347652  0.9229822\n",
       "NK_cells     1.8766351 4.679796  1.468996 2.335302  4.323897  1.5873751\n",
       "T_cells      1.7662977 6.579879  2.426231 3.475631  4.085210  1.5241934\n",
       "             H049-UV6K 7316-1082 7316-1748 7316-1773 ⋯ 7316-3574   7316-359   \n",
       "B_cells      1.459019  3.116237  3.214110  2.698993  ⋯ -1.09895602 -1.07094246\n",
       "Granulocytes 1.365864  2.731103  5.856786  1.822971  ⋯  0.28375481 -0.02097290\n",
       "Monocytes    3.062799  4.359749  4.777348  2.261230  ⋯ -0.09622229 -0.09867117\n",
       "DC           1.362942  3.122798  1.453885  1.402094  ⋯ -0.80353328 -0.73645172\n",
       "NK_cells     2.588224  3.202744  3.827398  1.153484  ⋯ -1.33198326 -1.72572276\n",
       "T_cells      3.487713  3.659146  2.778628  1.159787  ⋯ -1.28676190 -1.40745523\n",
       "             7316-3768   7316-3936   7316-470   7316-642    7316-77     \n",
       "B_cells      -1.21548809 -1.56916951 -1.2680743 -1.16561529 -0.810489200\n",
       "Granulocytes  0.20883215 -0.08909531 -0.1634655 -0.08885868 -0.005934645\n",
       "Monocytes    -0.04502704 -0.10885257 -0.2966868 -0.20445942 -0.224052695\n",
       "DC           -0.55878953 -0.75934335 -0.6231024 -0.05101250 -0.291110245\n",
       "NK_cells     -1.21715795 -1.22539506 -1.3090375 -1.70387668 -1.480009973\n",
       "T_cells      -1.50898720 -1.32311741 -1.0170671 -1.27250326 -1.433442397\n",
       "             7316-915    7316-923   7316-97    \n",
       "B_cells      -1.27638532 -0.9093326 -1.15567527\n",
       "Granulocytes  0.03372058  0.4337059 -0.09953267\n",
       "Monocytes    -0.48860927  0.1255357  0.14612705\n",
       "DC           -0.79069402 -0.6415289 -0.09922761\n",
       "NK_cells     -1.48146992 -1.6488658 -1.46443628\n",
       "T_cells      -1.33093217 -1.3552094 -1.12422857"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(cells_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell type name clean up\n",
    "rownames(cells_mat) <- c(\"B cells\", \"Granulocytes\", \"Monocytes\", \"Dendritic cells\", \"NK cells\", \"T cells\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "-3.27068 -0.65187  0.00000  0.09492  0.71999  6.57988 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(as.vector(cells_mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells_hm <- cells_hm.fx(cells_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                90                279                393                163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(cluster_cohort$immune_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation_order <- c(\"Pediatric inflammed\", \"Myeloid-driven\", \"Pediatric cold\", \"Immune excluded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_ha = HeatmapAnnotation(clusters = anno_mark(at = c(50, 235, 566, 844), labels_rot = 0,\n",
    "                                                    labels = annotation_order, side = \"top\",\n",
    "                                                    labels_gp = gpar(fontsize = 20), \n",
    "                                                    link_height = unit(0.5, \"cm\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nmwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_ha %v% cluster_hm %v% cells_hm %v% cohorts_hm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"IPD_immune_clusters_heatmap.pdf\"),\n",
    "   width = 18, height = 10)\n",
    "\n",
    "draw(cluster_ha%v% cluster_hm %v% cells_hm %v% cohorts_hm, gap = unit(0.5, \"cm\"))\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgd_cohort = Legend(labels = names(cohort_col)[2:13], title = \"Cohort\",\n",
    "             legend_gp = gpar(fill = cohort_col[2:13]))\n",
    "\n",
    "pdf(file = paste0(plotpath,\"cohort_legend.pdf\"),\n",
    "      width = 8, \n",
    "      height = 8,\n",
    "      useDingbats = FALSE)\n",
    "  draw(lgd_cohort)\n",
    "  dev.off() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_fun = colorRamp2(c(-3, 0, 3), c(\"blue\", \"white\", \"red\"))\n",
    "celltype_lgd = Legend(col_fun = col_fun, \n",
    "                      at = c(-3,0,3),\n",
    "                      labels = c(\"<3\", \"0\", \">3\"),\n",
    "                      title = \"Cell-type\\nscore\")\n",
    "\n",
    "\n",
    "  pdf(file = paste0(plotpath,\"celltype_legend.pdf\"),\n",
    "      width = 8, \n",
    "      height = 8,\n",
    "      useDingbats = FALSE)\n",
    "  draw(celltype_lgd)\n",
    "  dev.off()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge plots and stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygenesets <- as.data.frame(t(geneset_cc_norm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygenesets$sample_id <- rownames(mygenesets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_IC_genesets <- merge(metadata_IC, mygenesets, by = \"sample_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcells_rp <- plot_ridge.fx(metadata_IC_genesets, \"T_cells\") \n",
    "\n",
    "bcells_rp <- plot_ridge.fx(metadata_IC_genesets, \"B_cells\") \n",
    "\n",
    "nkcells_rp <- plot_ridge.fx(metadata_IC_genesets, \"NK_cells\") \n",
    "\n",
    "dc_rp <- plot_ridge.fx(metadata_IC_genesets, \"DC\") \n",
    "\n",
    "granul_rp <- plot_ridge.fx(metadata_IC_genesets, \"Granulocytes\") \n",
    "\n",
    "mono_rp <- plot_ridge.fx(metadata_IC_genesets, \"Monocytes\") \n",
    "\n",
    "myeloid_rp <- plot_ridge.fx(metadata_IC_genesets, \"Myeloid_cells\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Picking joint bandwidth of 0.197\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mono_rp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_genesets$T_cells and metadata_IC_genesets$immune_cluster \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  <2e-16             -              -             \n",
       "Pediatric cold  <2e-16             <2e-16         -             \n",
       "Immune excluded <2e-16             <2e-16         <2e-16        \n",
       "\n",
       "P value adjustment method: bonferroni "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_genesets$T_cells, metadata_IC_genesets$immune_cluster, p.adjust.method = \"bonf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_genesets$B_cells and metadata_IC_genesets$immune_cluster \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  <2e-16             -              -             \n",
       "Pediatric cold  <2e-16             <2e-16         -             \n",
       "Immune excluded <2e-16             <2e-16         <2e-16        \n",
       "\n",
       "P value adjustment method: bonferroni "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_genesets$B_cells, metadata_IC_genesets$immune_cluster, p.adjust.method = \"bonf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_genesets$NK_cells and metadata_IC_genesets$immune_cluster \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  <2e-16             -              -             \n",
       "Pediatric cold  <2e-16             <2e-16         -             \n",
       "Immune excluded <2e-16             <2e-16         <2e-16        \n",
       "\n",
       "P value adjustment method: bonferroni "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_genesets$NK_cells, metadata_IC_genesets$immune_cluster, p.adjust.method = \"bonf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_genesets$Monocytes and metadata_IC_genesets$immune_cluster \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  <2e-16             -              -             \n",
       "Pediatric cold  <2e-16             <2e-16         -             \n",
       "Immune excluded <2e-16             <2e-16         <2e-16        \n",
       "\n",
       "P value adjustment method: bonferroni "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_genesets$Monocytes, metadata_IC_genesets$immune_cluster, p.adjust.method = \"bonf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_genesets$DC and metadata_IC_genesets$immune_cluster \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  <2e-16             -              -             \n",
       "Pediatric cold  <2e-16             <2e-16         -             \n",
       "Immune excluded <2e-16             <2e-16         <2e-16        \n",
       "\n",
       "P value adjustment method: bonferroni "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_genesets$DC, metadata_IC_genesets$immune_cluster, p.adjust.method = \"bonf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  metadata_IC_genesets$Granulocytes and metadata_IC_genesets$immune_cluster \n",
       "\n",
       "                Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "Myeloid-driven  <2e-16             -              -             \n",
       "Pediatric cold  <2e-16             <2e-16         -             \n",
       "Immune excluded <2e-16             <2e-16         <2e-16        \n",
       "\n",
       "P value adjustment method: bonferroni "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise.t.test(metadata_IC_genesets$Granulocytes, metadata_IC_genesets$immune_cluster, p.adjust.method = \"bonf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>925</li><li>30</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 925\n",
       "\\item 30\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 925\n",
       "2. 30\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 925  30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(metadata_IC_genesets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  ATRT     CP    CPH    EPN   ETMR     MB    MNG    NBL    NFB pedHGG pedLGG \n",
       "    30     16     27     65      9    208     13    151     11     83    298 \n",
       "  SCHW \n",
       "    14 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC_genesets$cohort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                90                279                393                163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC_genesets$immune_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap Cancer type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab <- as.data.frame(table(metadata_IC_genesets$cohort), stringsAsFactors = F)\n",
    "tab <- tab[order(tab$Freq, decreasing = F),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>12</li><li>2</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 12\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 12\n",
       "2. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 12  2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_IC_mat <- matrix(nrow = 12, ncol = 4,\n",
    "                       dimnames = list(tab$Var1, \n",
    "                                       c(\"Pediatric inflamed\", \"Myeloid-driven\", \n",
    "                                         \"Pediatric cold\", \"Immune excluded\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(cancer_IC_mat)){\n",
    "    \n",
    "    mycancer <- metadata_IC_genesets[ metadata_IC_genesets$cohort == rownames(cancer_IC_mat)[i],]    \n",
    "    freq_tab <- as.data.frame(table(mycancer$immune_cluster), stringsAsFactors = F)\n",
    "    freq_tab$perc <- freq_tab$Freq/sum(freq_tab$Freq)    \n",
    "    \n",
    "    cancer_IC_mat[i, freq_tab$Var1] <- freq_tab$perc *100\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_IC_mat[is.na(cancer_IC_mat)] <- 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                90                279                393                163 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(metadata_IC_genesets$immune_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.176216216216216"
      ],
      "text/latex": [
       "0.176216216216216"
      ],
      "text/markdown": [
       "0.176216216216216"
      ],
      "text/plain": [
       "[1] 0.1762162"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "163/925"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "-0.8978  0.1648  0.5295  0.7105  1.1248  6.5350 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(metadata_IC_genesets$Granulocytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$`Pediatric inflamed`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.3834  1.2774  1.6465  1.6971  1.9827  3.8213 \n",
       "\n",
       "$`Myeloid-driven`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "-0.3084  0.4878  0.7555  0.7477  0.9806  2.2363 \n",
       "\n",
       "$`Pediatric cold`\n",
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "-0.94038 -0.17819  0.02132  0.02999  0.24595  1.00020 \n",
       "\n",
       "$`Immune excluded`\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "-2.2741 -0.6921 -0.4610 -0.5408 -0.2647  0.2334 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(metadata_IC_genesets$DC, metadata_IC_genesets$immune_cluster, summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_fun= colorRamp2(c(0, 100), c(\"white\", \"red\"))\n",
    "    \n",
    "cancer_hm = Heatmap(cancer_IC_mat,\n",
    "                    #titles and names   \n",
    "                    name = \"% cancer\",   \n",
    "                    show_row_names = TRUE,\n",
    "                    show_column_names = TRUE,     \n",
    "                    #clusters and orders  \n",
    "                    cluster_columns = FALSE,\n",
    "                    cluster_rows = FALSE,\n",
    "                    show_column_dend = TRUE,\n",
    "                    #aesthestics\n",
    "                    row_names_side = \"left\",\n",
    "                    col = col_fun,\n",
    "                    column_names_rot = 45,\n",
    "                    column_names_gp = gpar(fontsize = 20),\n",
    "                    row_names_gp = gpar(fontsize = 20),\n",
    "                    height = unit(nrow(cancer_IC_mat), \"cm\"),\n",
    "                    width = unit(ncol(cancer_IC_mat), \"cm\"),\n",
    "                    column_title_gp = gpar(fontsize = 20),\n",
    "                    column_title = NULL,\n",
    "                    row_title = NULL,\n",
    "                    show_heatmap_legend = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "ha = rowAnnotation(`cohort size` = anno_barplot(tab$Freq, bar_width = 1,\n",
    "                                 gp = gpar(col = \"white\", fill = \"#4d4d4d\"), \n",
    "                                 border = FALSE,\n",
    "                                 axis_param = list(at = c(0, 100,200,300), labels_rot = 45),\n",
    "                                 width = unit(4, \"cm\")), \n",
    "    show_annotation_name = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer_hm + ha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"Immune_clusters_cancers.pdf\"),\n",
    "   width = 10, height = 10)\n",
    "\n",
    "cancer_hm + ha\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap CRI cluster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$`IFN-gamma dominant`\n",
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                11                 13                  2                  0 \n",
       "\n",
       "$`Immunologically quiet`\n",
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                 0                 24                 62                 24 \n",
       "\n",
       "$Inflammatory\n",
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                38                 59                 40                 11 \n",
       "\n",
       "$`Lymphocyte depleted`\n",
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                27                164                270                 93 \n",
       "\n",
       "$`TGFbeta dominant`\n",
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                 5                  1                  0                  0 \n",
       "\n",
       "$`Wound Healing`\n",
       "\n",
       "Pediatric inflamed     Myeloid-driven     Pediatric cold    Immune excluded \n",
       "                 4                 14                 16                 30 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(metadata_IC_genesets$immune_cluster,\n",
    "      metadata_IC_genesets$CRI_cluster,\n",
    "      table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab <- as.data.frame(table(metadata_IC_genesets$CRI_cluster), stringsAsFactors = F)\n",
    "tab <- tab[order(tab$Freq, decreasing = F),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.717837837837838"
      ],
      "text/latex": [
       "0.717837837837838"
      ],
      "text/markdown": [
       "0.717837837837838"
      ],
      "text/plain": [
       "[1] 0.7178378"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(554 +110)/925"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Var1</th><th scope=col>Freq</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>5</th><td>TGFbeta dominant     </td><td>  6</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>IFN-gamma dominant   </td><td> 26</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Wound Healing        </td><td> 64</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Immunologically quiet</td><td>110</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Inflammatory         </td><td>148</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Lymphocyte depleted  </td><td>554</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Var1 & Freq\\\\\n",
       "  & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t5 & TGFbeta dominant      &   6\\\\\n",
       "\t1 & IFN-gamma dominant    &  26\\\\\n",
       "\t6 & Wound Healing         &  64\\\\\n",
       "\t2 & Immunologically quiet & 110\\\\\n",
       "\t3 & Inflammatory          & 148\\\\\n",
       "\t4 & Lymphocyte depleted   & 554\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Var1 &lt;chr&gt; | Freq &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| 5 | TGFbeta dominant      |   6 |\n",
       "| 1 | IFN-gamma dominant    |  26 |\n",
       "| 6 | Wound Healing         |  64 |\n",
       "| 2 | Immunologically quiet | 110 |\n",
       "| 3 | Inflammatory          | 148 |\n",
       "| 4 | Lymphocyte depleted   | 554 |\n",
       "\n"
      ],
      "text/plain": [
       "  Var1                  Freq\n",
       "5 TGFbeta dominant        6 \n",
       "1 IFN-gamma dominant     26 \n",
       "6 Wound Healing          64 \n",
       "2 Immunologically quiet 110 \n",
       "3 Inflammatory          148 \n",
       "4 Lymphocyte depleted   554 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "cri_IC_mat <- matrix(nrow = 6, ncol = 4,\n",
    "                     dimnames = list(tab$Var1,c(\"Pediatric inflamed\", \"Myeloid-driven\", \n",
    "                                                \"Pediatric cold\", \"Immune excluded\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(cri_IC_mat)){\n",
    "    \n",
    "    mycancer <- metadata_IC_genesets[ metadata_IC_genesets$CRI_cluster == rownames(cri_IC_mat)[i],]    \n",
    "    freq_tab <- as.data.frame(table(mycancer$immune_cluster), stringsAsFactors = F)\n",
    "    freq_tab$perc <- freq_tab$Freq/sum(freq_tab$Freq)    \n",
    "    \n",
    "    cri_IC_mat[i, freq_tab$Var1] <- freq_tab$perc *100\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Pediatric inflamed</th><th scope=col>Myeloid-driven</th><th scope=col>Pediatric cold</th><th scope=col>Immune excluded</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TGFbeta dominant</th><td>83.333333</td><td>16.66667</td><td> 0.000000</td><td> 0.000000</td></tr>\n",
       "\t<tr><th scope=row>IFN-gamma dominant</th><td>42.307692</td><td>50.00000</td><td> 7.692308</td><td> 0.000000</td></tr>\n",
       "\t<tr><th scope=row>Wound Healing</th><td> 6.250000</td><td>21.87500</td><td>25.000000</td><td>46.875000</td></tr>\n",
       "\t<tr><th scope=row>Immunologically quiet</th><td> 0.000000</td><td>21.81818</td><td>56.363636</td><td>21.818182</td></tr>\n",
       "\t<tr><th scope=row>Inflammatory</th><td>25.675676</td><td>39.86486</td><td>27.027027</td><td> 7.432432</td></tr>\n",
       "\t<tr><th scope=row>Lymphocyte depleted</th><td> 4.873646</td><td>29.60289</td><td>48.736462</td><td>16.787004</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Pediatric inflamed & Myeloid-driven & Pediatric cold & Immune excluded\\\\\n",
       "\\hline\n",
       "\tTGFbeta dominant & 83.333333 & 16.66667 &  0.000000 &  0.000000\\\\\n",
       "\tIFN-gamma dominant & 42.307692 & 50.00000 &  7.692308 &  0.000000\\\\\n",
       "\tWound Healing &  6.250000 & 21.87500 & 25.000000 & 46.875000\\\\\n",
       "\tImmunologically quiet &  0.000000 & 21.81818 & 56.363636 & 21.818182\\\\\n",
       "\tInflammatory & 25.675676 & 39.86486 & 27.027027 &  7.432432\\\\\n",
       "\tLymphocyte depleted &  4.873646 & 29.60289 & 48.736462 & 16.787004\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | Pediatric inflamed | Myeloid-driven | Pediatric cold | Immune excluded |\n",
       "|---|---|---|---|---|\n",
       "| TGFbeta dominant | 83.333333 | 16.66667 |  0.000000 |  0.000000 |\n",
       "| IFN-gamma dominant | 42.307692 | 50.00000 |  7.692308 |  0.000000 |\n",
       "| Wound Healing |  6.250000 | 21.87500 | 25.000000 | 46.875000 |\n",
       "| Immunologically quiet |  0.000000 | 21.81818 | 56.363636 | 21.818182 |\n",
       "| Inflammatory | 25.675676 | 39.86486 | 27.027027 |  7.432432 |\n",
       "| Lymphocyte depleted |  4.873646 | 29.60289 | 48.736462 | 16.787004 |\n",
       "\n"
      ],
      "text/plain": [
       "                      Pediatric inflamed Myeloid-driven Pediatric cold\n",
       "TGFbeta dominant      83.333333          16.66667        0.000000     \n",
       "IFN-gamma dominant    42.307692          50.00000        7.692308     \n",
       "Wound Healing          6.250000          21.87500       25.000000     \n",
       "Immunologically quiet  0.000000          21.81818       56.363636     \n",
       "Inflammatory          25.675676          39.86486       27.027027     \n",
       "Lymphocyte depleted    4.873646          29.60289       48.736462     \n",
       "                      Immune excluded\n",
       "TGFbeta dominant       0.000000      \n",
       "IFN-gamma dominant     0.000000      \n",
       "Wound Healing         46.875000      \n",
       "Immunologically quiet 21.818182      \n",
       "Inflammatory           7.432432      \n",
       "Lymphocyte depleted   16.787004      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cri_IC_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "cri_IC_mat[is.na(cri_IC_mat)] <- 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_fun= colorRamp2(c(0, 100), c(\"white\", \"red\"))\n",
    "    \n",
    "cri_hm = Heatmap(cri_IC_mat,\n",
    "                    #titles and names   \n",
    "                    name = \"% CRI-iAtlas cluster\",   \n",
    "                    show_row_names = TRUE,\n",
    "                    show_column_names = TRUE,     \n",
    "                    #clusters and orders  \n",
    "                    cluster_columns = FALSE,\n",
    "                    cluster_rows = FALSE,\n",
    "                    show_column_dend = TRUE,\n",
    "                    #aesthestics\n",
    "                    row_names_side = \"left\",\n",
    "                    col = col_fun,\n",
    "                    column_names_rot = 45,                 \n",
    "                    column_names_gp = gpar(fontsize = 20),\n",
    "                    row_names_gp = gpar(fontsize = 20),\n",
    "                    height = unit(nrow(cri_IC_mat), \"cm\"),\n",
    "                    width = unit(ncol(cri_IC_mat), \"cm\"),\n",
    "                    column_title_gp = gpar(fontsize = 20),\n",
    "                    column_title = NULL,\n",
    "                    row_title = NULL,\n",
    "                show_heatmap_legend = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "ha = rowAnnotation(\n",
    "    `cohort size` = anno_barplot(tab$Freq, bar_width = 1, \n",
    "                                 gp = gpar(col = \"white\", fill = \"#4d4d4d\"), \n",
    "                                 border = FALSE,\n",
    "                                 axis_param = list(at = c(0, 100,250,500), labels_rot = 45),\n",
    "                                 width = unit(4, \"cm\")), \n",
    "    show_annotation_name = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cri_hm + ha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(paste0(plotpath, \"Immune_clusters_cri.pdf\"),\n",
    "   width = 10, height = 10)\n",
    "\n",
    "cri_hm + ha\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_fun = colorRamp2(c(0, 100), c(\"white\", \"red\"))\n",
    "cancer_lgd = Legend(col_fun = col_fun, \n",
    "                      title = \"% cancer\")\n",
    "\n",
    "  pdf(file = paste0(plotpath,\"cancer_legend.pdf\"),\n",
    "      width = 8, \n",
    "      height = 8,\n",
    "      useDingbats = FALSE)\n",
    "  draw(cancer_lgd)\n",
    "  dev.off() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_fun = colorRamp2(c(0, 100), c(\"white\", \"red\"))\n",
    "cri_lgd = Legend(col_fun = col_fun, \n",
    "                      title = \"% CRI-iAtlas\\ncluster\")\n",
    "\n",
    "  pdf(file = paste0(plotpath,\"CRI_legend.pdf\"),\n",
    "      width = 8, \n",
    "      height = 8,\n",
    "      useDingbats = FALSE)\n",
    "  draw(cri_lgd)\n",
    "  dev.off() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 4.0",
   "language": "R",
   "name": "ir40"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "280px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}