analysis/CHD/CHD_monoallelic_Heatmap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Gene vs Tumor Type Heatmap for CHD monoallelic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in rgl.init(initValue, onlyNULL):\n",
      "“RGL: unable to open X11 display”Warning message:\n",
      "“'rgl_init' failed, running with rgl.useNULL = TRUE”"
     ]
    }
   ],
   "source": [
    "source(\"~/bin/da.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_pheatmap <- function(x, filename, width=800, height=1000) {\n",
    "   stopifnot(!missing(x))\n",
    "   stopifnot(!missing(filename))\n",
    "   svg(filename, pointsize = 300 )\n",
    "   grid::grid.newpage()\n",
    "   grid::grid.draw(x$gtable)\n",
    "   dev.off()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = read.csv(\"../maf_res/gene_vs_tt_CHD_only_monoallelic.csv\", sep=\"\\t\")\n",
    "kmat = as.matrix( k[-1] )\n",
    "rownames(kmat) = k[,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ACT</th><th scope=col>CA</th><th scope=col>CNS</th><th scope=col>EWS</th><th scope=col>GCT</th><th scope=col>HGG</th><th scope=col>LGG</th><th scope=col>NBL</th><th scope=col>OS</th><th scope=col>OST</th><th scope=col>RHB</th><th scope=col>STS</th><th scope=col>WLM</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CHD7</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>EP300</th><td>1</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>FBN2</th><td>0</td><td>2</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>KMT2D</th><td>0</td><td>0</td><td> 1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>RAI1</th><td>0</td><td>0</td><td> 2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>NF1</th><td>0</td><td>0</td><td>15</td><td>0</td><td>0</td><td>2</td><td>1</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & ACT & CA & CNS & EWS & GCT & HGG & LGG & NBL & OS & OST & RHB & STS & WLM\\\\\n",
       "\\hline\n",
       "\tCHD7 & 0 & 0 &  0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\tEP300 & 1 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tFBN2 & 0 & 2 &  0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\tKMT2D & 0 & 0 &  1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tRAI1 & 0 & 0 &  2 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\tNF1 & 0 & 0 & 15 & 0 & 0 & 2 & 1 & 2 & 0 & 0 & 2 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | ACT | CA | CNS | EWS | GCT | HGG | LGG | NBL | OS | OST | RHB | STS | WLM |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CHD7 | 0 | 0 |  0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |\n",
       "| EP300 | 1 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| FBN2 | 0 | 2 |  0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |\n",
       "| KMT2D | 0 | 0 |  1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| RAI1 | 0 | 0 |  2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| NF1 | 0 | 0 | 15 | 0 | 0 | 2 | 1 | 2 | 0 | 0 | 2 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "      ACT CA CNS EWS GCT HGG LGG NBL OS OST RHB STS WLM\n",
       "CHD7  0   0   0  0   1   0   1   0   0  0   1   0   0  \n",
       "EP300 1   0   0  0   0   0   0   0   0  0   0   0   0  \n",
       "FBN2  0   2   0  0   0   0   0   1   0  0   1   0   0  \n",
       "KMT2D 0   0   1  0   0   0   0   0   0  0   0   0   0  \n",
       "RAI1  0   0   2  0   0   0   1   0   0  0   0   0   1  \n",
       "NF1   0   0  15  0   0   2   1   2   0  0   2   0   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmat %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>26</li>\n",
       "\t<li>13</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 26\n",
       "\\item 13\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 26\n",
       "2. 13\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 26 13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmat %>% dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACT  CA CNS EWS GCT HGG LGG NBL  OS OST RHB STS WLM \n",
      "  1   2  19   1   2   4   6  11   5   1   7   2   8 \n"
     ]
    }
   ],
   "source": [
    "print( colSums(kmat) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 26 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ACT</th><th scope=col>CA</th><th scope=col>CNS</th><th scope=col>EWS</th><th scope=col>GCT</th><th scope=col>HGG</th><th scope=col>LGG</th><th scope=col>NBL</th><th scope=col>OS</th><th scope=col>OST</th><th scope=col>RHB</th><th scope=col>STS</th><th scope=col>WLM</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CHD7</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>EP300</th><td>1</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>FBN2</th><td>0</td><td>2</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>KMT2D</th><td>0</td><td>0</td><td> 1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>RAI1</th><td>0</td><td>0</td><td> 2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>NF1</th><td>0</td><td>0</td><td>15</td><td>0</td><td>0</td><td>2</td><td>1</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>MYH7</th><td>0</td><td>0</td><td> 1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>TAB2</th><td>0</td><td>0</td><td> 0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>GATA4</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TGFBR2</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>TBX20</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>GDF1</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>PTPN11</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>SMARCA4</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>SOX9</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>NRXN1</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>MYH6</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>NSD1</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>CREBBP</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>NOTCH1</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>CITED2</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>COL3A1</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>FOXC2</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>LRP5</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>PKD2</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>ARID1A</th><td>0</td><td>0</td><td> 0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 26 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & ACT & CA & CNS & EWS & GCT & HGG & LGG & NBL & OS & OST & RHB & STS & WLM\\\\\n",
       "\\hline\n",
       "\tCHD7 & 0 & 0 &  0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\tEP300 & 1 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tFBN2 & 0 & 2 &  0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\tKMT2D & 0 & 0 &  1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tRAI1 & 0 & 0 &  2 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\tNF1 & 0 & 0 & 15 & 0 & 0 & 2 & 1 & 2 & 0 & 0 & 2 & 0 & 0\\\\\n",
       "\tMYH7 & 0 & 0 &  1 & 0 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 1\\\\\n",
       "\tTAB2 & 0 & 0 &  0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tGATA4 & 0 & 0 &  0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
       "\tTGFBR2 & 0 & 0 &  0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tTBX20 & 0 & 0 &  0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\tGDF1 & 0 & 0 &  0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tPTPN11 & 0 & 0 &  0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tSMARCA4 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\tSOX9 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tNRXN1 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tMYH6 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tNSD1 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tCREBBP & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1\\\\\n",
       "\tNOTCH1 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0\\\\\n",
       "\tCITED2 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 0\\\\\n",
       "\tCOL3A1 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\tFOXC2 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\tLRP5 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\tPKD2 & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\tARID1A & 0 & 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 26 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | ACT | CA | CNS | EWS | GCT | HGG | LGG | NBL | OS | OST | RHB | STS | WLM |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CHD7 | 0 | 0 |  0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |\n",
       "| EP300 | 1 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| FBN2 | 0 | 2 |  0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |\n",
       "| KMT2D | 0 | 0 |  1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| RAI1 | 0 | 0 |  2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| NF1 | 0 | 0 | 15 | 0 | 0 | 2 | 1 | 2 | 0 | 0 | 2 | 0 | 0 |\n",
       "| MYH7 | 0 | 0 |  1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 |\n",
       "| TAB2 | 0 | 0 |  0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| GATA4 | 0 | 0 |  0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |\n",
       "| TGFBR2 | 0 | 0 |  0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| TBX20 | 0 | 0 |  0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |\n",
       "| GDF1 | 0 | 0 |  0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| PTPN11 | 0 | 0 |  0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| SMARCA4 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |\n",
       "| SOX9 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| NRXN1 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |\n",
       "| MYH6 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| NSD1 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| CREBBP | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |\n",
       "| NOTCH1 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |\n",
       "| CITED2 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |\n",
       "| COL3A1 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| FOXC2 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| LRP5 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| PKD2 | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| ARID1A | 0 | 0 |  0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "        ACT CA CNS EWS GCT HGG LGG NBL OS OST RHB STS WLM\n",
       "CHD7    0   0   0  0   1   0   1   0   0  0   1   0   0  \n",
       "EP300   1   0   0  0   0   0   0   0   0  0   0   0   0  \n",
       "FBN2    0   2   0  0   0   0   0   1   0  0   1   0   0  \n",
       "KMT2D   0   0   1  0   0   0   0   0   0  0   0   0   0  \n",
       "RAI1    0   0   2  0   0   0   1   0   0  0   0   0   1  \n",
       "NF1     0   0  15  0   0   2   1   2   0  0   2   0   0  \n",
       "MYH7    0   0   1  0   0   0   0   0   3  0   0   0   1  \n",
       "TAB2    0   0   0  1   0   0   0   0   0  0   0   0   0  \n",
       "GATA4   0   0   0  0   1   0   0   0   1  0   0   0   0  \n",
       "TGFBR2  0   0   0  0   0   1   0   0   0  0   0   0   0  \n",
       "TBX20   0   0   0  0   0   1   1   0   0  0   1   0   0  \n",
       "GDF1    0   0   0  0   0   0   1   0   0  0   0   0   0  \n",
       "PTPN11  0   0   0  0   0   0   1   1   0  0   0   0   0  \n",
       "SMARCA4 0   0   0  0   0   0   0   1   0  0   1   0   0  \n",
       "SOX9    0   0   0  0   0   0   0   1   0  0   0   0   0  \n",
       "NRXN1   0   0   0  0   0   0   0   3   0  0   0   0   0  \n",
       "MYH6    0   0   0  0   0   0   0   1   0  0   0   0   0  \n",
       "NSD1    0   0   0  0   0   0   0   1   0  0   0   0   0  \n",
       "CREBBP  0   0   0  0   0   0   0   0   1  0   0   0   1  \n",
       "NOTCH1  0   0   0  0   0   0   0   0   0  1   1   0   0  \n",
       "CITED2  0   0   0  0   0   0   0   0   0  0   0   2   0  \n",
       "COL3A1  0   0   0  0   0   0   0   0   0  0   0   0   1  \n",
       "FOXC2   0   0   0  0   0   0   0   0   0  0   0   0   1  \n",
       "LRP5    0   0   0  0   0   0   0   0   0  0   0   0   1  \n",
       "PKD2    0   0   0  0   0   0   0   0   0  0   0   0   1  \n",
       "ARID1A  0   0   0  0   0   0   0   0   0  0   0   0   1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmat[1:26,colnames(kmat)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cXFxcXF//73vxctWuTh4dG1a1eFQmFl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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "ofp=\"../plots/CHD_monoallelic_gene_vs_tt_log_red_01.svg\"\n",
    "cols = colorRampPalette(c(\"#fff5f0\",\"#fee0d2\",\"#fcbba1\",\"#fc9272\",\"#fb6a4a\",\"#ef3b2c\",\"#cb181d\",\"#a50f15\",\"#67000d\"))(200)\n",
    "x= pheatmap(log10( kmat[1:26,colnames(kmat)]+1 ), cluster_cols = T, cluster_rows = F ,border_color = F, color =cols, angle_col=45, fontsize = 8.5, fontsize_row=8, fontsize_col = 12)\n",
    "save_pheatmap(x,ofp )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}