{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: MASS\n",
      "Loading required package: mclust\n",
      "Package 'mclust' version 5.4.3\n",
      "Type 'citation(\"mclust\")' for citing this R package in publications.\n"
     ]
    }
   ],
   "source": [
    "library(prabclus)\n",
    "library(Matrix)\n",
    "library(Rtsne)\n",
    "library(ggplot2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bsub < count_reads_peaks.sh`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2034"
      ],
      "text/latex": [
       "2034"
      ],
      "text/markdown": [
       "2034"
      ],
      "text/plain": [
       "[1] 2034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = './count_reads_peaks_output/'\n",
    "files <- list.files(path,pattern = \"\\\\.txt$\")\n",
    "length(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assuming tab separated values with a header    \n",
    "datalist = lapply(files, function(x)fread(paste0(path,x))$V4) \n",
    "#assuming the same header/columns for all files\n",
    "datafr = do.call(\"cbind\", datalist) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>491437</li>\n",
       "\t<li>2034</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 491437\n",
       "\\item 2034\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 491437\n",
       "2. 2034\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 491437   2034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_regions = read.csv(\"../../input/GSE96769_PeakFile_20160207.bed\",\n",
    "                      sep = '\\t',header=FALSE,stringsAsFactors=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>491437</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 491437\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 491437\n",
       "2. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 491437     14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(df_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "peaknames = paste(df_regions$V1,df_regions$V2,df_regions$V3,sep = \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'chr1_10279_10779'</li>\n",
       "\t<li>'chr1_13252_13752'</li>\n",
       "\t<li>'chr1_16019_16519'</li>\n",
       "\t<li>'chr1_29026_29526'</li>\n",
       "\t<li>'chr1_96364_96864'</li>\n",
       "\t<li>'chr1_115440_115940'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'chr1\\_10279\\_10779'\n",
       "\\item 'chr1\\_13252\\_13752'\n",
       "\\item 'chr1\\_16019\\_16519'\n",
       "\\item 'chr1\\_29026\\_29526'\n",
       "\\item 'chr1\\_96364\\_96864'\n",
       "\\item 'chr1\\_115440\\_115940'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'chr1_10279_10779'\n",
       "2. 'chr1_13252_13752'\n",
       "3. 'chr1_16019_16519'\n",
       "4. 'chr1_29026_29526'\n",
       "5. 'chr1_96364_96864'\n",
       "6. 'chr1_115440_115940'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"chr1_10279_10779\"   \"chr1_13252_13752\"   \"chr1_16019_16519\"  \n",
       "[4] \"chr1_29026_29526\"   \"chr1_96364_96864\"   \"chr1_115440_115940\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(peaknames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'BM1077-CLP-Frozen-160106-13'</li>\n",
       "\t<li>'BM1077-CLP-Frozen-160106-14'</li>\n",
       "\t<li>'BM1077-CLP-Frozen-160106-2'</li>\n",
       "\t<li>'BM1077-CLP-Frozen-160106-21'</li>\n",
       "\t<li>'BM1077-CLP-Frozen-160106-27'</li>\n",
       "\t<li>'BM1077-CLP-Frozen-160106-3'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'BM1077-CLP-Frozen-160106-13'\n",
       "\\item 'BM1077-CLP-Frozen-160106-14'\n",
       "\\item 'BM1077-CLP-Frozen-160106-2'\n",
       "\\item 'BM1077-CLP-Frozen-160106-21'\n",
       "\\item 'BM1077-CLP-Frozen-160106-27'\n",
       "\\item 'BM1077-CLP-Frozen-160106-3'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'BM1077-CLP-Frozen-160106-13'\n",
       "2. 'BM1077-CLP-Frozen-160106-14'\n",
       "3. 'BM1077-CLP-Frozen-160106-2'\n",
       "4. 'BM1077-CLP-Frozen-160106-21'\n",
       "5. 'BM1077-CLP-Frozen-160106-27'\n",
       "6. 'BM1077-CLP-Frozen-160106-3'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"BM1077-CLP-Frozen-160106-13\" \"BM1077-CLP-Frozen-160106-14\"\n",
       "[3] \"BM1077-CLP-Frozen-160106-2\"  \"BM1077-CLP-Frozen-160106-21\"\n",
       "[5] \"BM1077-CLP-Frozen-160106-27\" \"BM1077-CLP-Frozen-160106-3\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(sapply(strsplit(files,'\\\\.'),'[', 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(datafr) = sapply(strsplit(files,'\\\\.'),'[', 1)\n",
    "rownames(datafr) = peaknames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>BM1077-CLP-Frozen-160106-13</th><th scope=col>BM1077-CLP-Frozen-160106-14</th><th scope=col>BM1077-CLP-Frozen-160106-2</th><th scope=col>BM1077-CLP-Frozen-160106-21</th><th scope=col>BM1077-CLP-Frozen-160106-27</th><th scope=col>BM1077-CLP-Frozen-160106-3</th><th scope=col>BM1077-CLP-Frozen-160106-36</th><th scope=col>BM1077-CLP-Frozen-160106-42</th><th scope=col>BM1077-CLP-Frozen-160106-44</th><th scope=col>BM1077-CLP-Frozen-160106-50</th><th scope=col>⋯</th><th scope=col>singles-PB1022-mono-160128-81</th><th scope=col>singles-PB1022-mono-160128-82</th><th scope=col>singles-PB1022-mono-160128-83</th><th scope=col>singles-PB1022-mono-160128-84</th><th scope=col>singles-PB1022-mono-160128-85</th><th scope=col>singles-PB1022-mono-160128-86</th><th scope=col>singles-PB1022-mono-160128-89</th><th scope=col>singles-PB1022-mono-160128-90</th><th scope=col>singles-PB1022-mono-160128-95</th><th scope=col>singles-PB1022-mono-160128-96</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>chr1_10279_10779</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>⋯</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>chr1_13252_13752</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>⋯</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>chr1_16019_16519</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>⋯</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>chr1_29026_29526</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>2</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>2</td><td>0</td><td>2</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>chr1_96364_96864</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>⋯</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><td>0</td></tr>\n",
       "\t<tr><th scope=row>chr1_115440_115940</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>⋯</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",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & BM1077-CLP-Frozen-160106-13 & BM1077-CLP-Frozen-160106-14 & BM1077-CLP-Frozen-160106-2 & BM1077-CLP-Frozen-160106-21 & BM1077-CLP-Frozen-160106-27 & BM1077-CLP-Frozen-160106-3 & BM1077-CLP-Frozen-160106-36 & BM1077-CLP-Frozen-160106-42 & BM1077-CLP-Frozen-160106-44 & BM1077-CLP-Frozen-160106-50 & ⋯ & singles-PB1022-mono-160128-81 & singles-PB1022-mono-160128-82 & singles-PB1022-mono-160128-83 & singles-PB1022-mono-160128-84 & singles-PB1022-mono-160128-85 & singles-PB1022-mono-160128-86 & singles-PB1022-mono-160128-89 & singles-PB1022-mono-160128-90 & singles-PB1022-mono-160128-95 & singles-PB1022-mono-160128-96\\\\\n",
       "\\hline\n",
       "\tchr1\\_10279\\_10779 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tchr1\\_13252\\_13752 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tchr1\\_16019\\_16519 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\tchr1\\_29026\\_29526 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 0 & ⋯ & 0 & 0 & 2 & 0 & 2 & 3 & 0 & 0 & 0 & 0\\\\\n",
       "\tchr1\\_96364\\_96864 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 0 & 0\\\\\n",
       "\tchr1\\_115440\\_115940 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | BM1077-CLP-Frozen-160106-13 | BM1077-CLP-Frozen-160106-14 | BM1077-CLP-Frozen-160106-2 | BM1077-CLP-Frozen-160106-21 | BM1077-CLP-Frozen-160106-27 | BM1077-CLP-Frozen-160106-3 | BM1077-CLP-Frozen-160106-36 | BM1077-CLP-Frozen-160106-42 | BM1077-CLP-Frozen-160106-44 | BM1077-CLP-Frozen-160106-50 | ⋯ | singles-PB1022-mono-160128-81 | singles-PB1022-mono-160128-82 | singles-PB1022-mono-160128-83 | singles-PB1022-mono-160128-84 | singles-PB1022-mono-160128-85 | singles-PB1022-mono-160128-86 | singles-PB1022-mono-160128-89 | singles-PB1022-mono-160128-90 | singles-PB1022-mono-160128-95 | singles-PB1022-mono-160128-96 | \n",
       "|---|---|---|---|---|---|\n",
       "| chr1_10279_10779 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n",
       "| chr1_13252_13752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n",
       "| chr1_16019_16519 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n",
       "| chr1_29026_29526 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | ⋯ | 0 | 0 | 2 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | \n",
       "| chr1_96364_96864 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | \n",
       "| chr1_115440_115940 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                   BM1077-CLP-Frozen-160106-13 BM1077-CLP-Frozen-160106-14\n",
       "chr1_10279_10779   0                           0                          \n",
       "chr1_13252_13752   0                           0                          \n",
       "chr1_16019_16519   0                           0                          \n",
       "chr1_29026_29526   0                           0                          \n",
       "chr1_96364_96864   0                           0                          \n",
       "chr1_115440_115940 0                           0                          \n",
       "                   BM1077-CLP-Frozen-160106-2 BM1077-CLP-Frozen-160106-21\n",
       "chr1_10279_10779   0                          0                          \n",
       "chr1_13252_13752   0                          0                          \n",
       "chr1_16019_16519   0                          0                          \n",
       "chr1_29026_29526   0                          0                          \n",
       "chr1_96364_96864   0                          0                          \n",
       "chr1_115440_115940 0                          0                          \n",
       "                   BM1077-CLP-Frozen-160106-27 BM1077-CLP-Frozen-160106-3\n",
       "chr1_10279_10779   0                           0                         \n",
       "chr1_13252_13752   0                           0                         \n",
       "chr1_16019_16519   0                           0                         \n",
       "chr1_29026_29526   0                           0                         \n",
       "chr1_96364_96864   0                           0                         \n",
       "chr1_115440_115940 0                           0                         \n",
       "                   BM1077-CLP-Frozen-160106-36 BM1077-CLP-Frozen-160106-42\n",
       "chr1_10279_10779   0                           0                          \n",
       "chr1_13252_13752   0                           0                          \n",
       "chr1_16019_16519   0                           0                          \n",
       "chr1_29026_29526   0                           0                          \n",
       "chr1_96364_96864   0                           0                          \n",
       "chr1_115440_115940 0                           0                          \n",
       "                   BM1077-CLP-Frozen-160106-44 BM1077-CLP-Frozen-160106-50 ⋯\n",
       "chr1_10279_10779   0                           0                           ⋯\n",
       "chr1_13252_13752   0                           0                           ⋯\n",
       "chr1_16019_16519   0                           0                           ⋯\n",
       "chr1_29026_29526   2                           0                           ⋯\n",
       "chr1_96364_96864   0                           0                           ⋯\n",
       "chr1_115440_115940 0                           0                           ⋯\n",
       "                   singles-PB1022-mono-160128-81 singles-PB1022-mono-160128-82\n",
       "chr1_10279_10779   0                             0                            \n",
       "chr1_13252_13752   0                             0                            \n",
       "chr1_16019_16519   0                             0                            \n",
       "chr1_29026_29526   0                             0                            \n",
       "chr1_96364_96864   0                             0                            \n",
       "chr1_115440_115940 0                             0                            \n",
       "                   singles-PB1022-mono-160128-83 singles-PB1022-mono-160128-84\n",
       "chr1_10279_10779   0                             0                            \n",
       "chr1_13252_13752   0                             0                            \n",
       "chr1_16019_16519   0                             0                            \n",
       "chr1_29026_29526   2                             0                            \n",
       "chr1_96364_96864   0                             0                            \n",
       "chr1_115440_115940 0                             0                            \n",
       "                   singles-PB1022-mono-160128-85 singles-PB1022-mono-160128-86\n",
       "chr1_10279_10779   0                             0                            \n",
       "chr1_13252_13752   0                             0                            \n",
       "chr1_16019_16519   0                             0                            \n",
       "chr1_29026_29526   2                             3                            \n",
       "chr1_96364_96864   0                             0                            \n",
       "chr1_115440_115940 0                             0                            \n",
       "                   singles-PB1022-mono-160128-89 singles-PB1022-mono-160128-90\n",
       "chr1_10279_10779   0                             0                            \n",
       "chr1_13252_13752   0                             0                            \n",
       "chr1_16019_16519   0                             0                            \n",
       "chr1_29026_29526   0                             0                            \n",
       "chr1_96364_96864   0                             2                            \n",
       "chr1_115440_115940 0                             0                            \n",
       "                   singles-PB1022-mono-160128-95 singles-PB1022-mono-160128-96\n",
       "chr1_10279_10779   0                             0                            \n",
       "chr1_13252_13752   0                             0                            \n",
       "chr1_16019_16519   0                             0                            \n",
       "chr1_29026_29526   0                             0                            \n",
       "chr1_96364_96864   0                             0                            \n",
       "chr1_115440_115940 0                             0                            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>491437</li>\n",
       "\t<li>2034</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 491437\n",
       "\\item 2034\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 491437\n",
       "2. 2034\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 491437   2034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saveRDS(datafr, file = './datafr.rds')\n",
    "# datafr = readRDS('./datafr.rds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_peaks <- function (datafr,cutoff = 0.01){\n",
    "    binary_mat = as.matrix((datafr > 0) + 0)\n",
    "    binary_mat = Matrix(binary_mat, sparse = TRUE) \n",
    "    num_cells_ncounted = Matrix::rowSums(binary_mat)\n",
    "    ncounts = binary_mat[num_cells_ncounted >= dim(binary_mat)[2]*cutoff,]\n",
    "    ncounts = ncounts[rowSums(ncounts) > 0,]    \n",
    "    \n",
    "    options(repr.plot.width=4, repr.plot.height=4)\n",
    "    hist(log10(num_cells_ncounted),main=\"No. of Cells Each Site is Observed In\",breaks=50)\n",
    "    abline(v=log10(min(num_cells_ncounted[num_cells_ncounted >= dim(binary_mat)[2]*cutoff])),lwd=2,col=\"indianred\")\n",
    "#     hist(log10(new_counts),main=\"Number of Sites Each Cell Uses\",breaks=50)\n",
    "    datafr_filtered = datafr[rownames(ncounts),]\n",
    "    return(datafr_filtered)\n",
    "}\n",
    "\n",
    "\n",
    "getJaccardDist <- function(cdBinary){\n",
    "        \n",
    "    if(colnames(cdBinary[,2:3])[1] == 'start' && colnames(cdBinary[,2:3])[2] == 'end'){\n",
    "        SingleCell.Binary <- cdBinary[,4:(dim(cdBinary)[2])]\n",
    "    }\n",
    "    else\n",
    "        SingleCell.Binary <- cdBinary\n",
    "    \n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- jaccard(as.matrix(SingleCell.Binary))\n",
    "    \n",
    "    return(SingleCell.Binary.Jaccard)\n",
    "}\n",
    "\n",
    "\n",
    "plot_tSNE <- function(cdBinary, nDimToUSE, groups=NULL, cellName, perplexity_division, ret.val=FALSE , text.label=FALSE, title=\"\"){\n",
    "\n",
    "    if(missing(nDimToUSE)){\n",
    "        stop(\"ERROR: Number of PCA's \\\"nDimToUSE\\\" to use is missing\")\n",
    "    }\n",
    "    if(missing(ret.val)){\n",
    "        ret.val = FALSE\n",
    "    }\n",
    "    \n",
    "    if(text.label==TRUE & missing(cellName)){\n",
    "        stop(\"ERROR: Please give cellName\")\n",
    "    }\n",
    "    \n",
    "    if(missing(perplexity_division)){\n",
    "        stop(\"ERROR: Please enter the number with which you want to divide the dimension of your data for perplexity setting\")\n",
    "    }\n",
    "        \n",
    "    \n",
    "    #SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    #FinalPCAData <- t(SingleCell.Binary.Jaccard)\n",
    "    \n",
    "    SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "    fit <- cmdscale(as.dist(SingleCell.Binary.Jaccard),eig=TRUE, k=nDimToUSE)\n",
    "\n",
    "    #pcaPRComp <- prcomp(FinalPCAData)\n",
    "\n",
    "    \n",
    "    #rtsne_pca_out <- Rtsne(as.matrix(pcaPRComp$x[,1:nPCAToUSE]), perplexity = dim(pcaPRComp$x)[1]/perplexity_division, check_duplicates = FALSE)\n",
    "    rtsne_pca_out <- Rtsne(as.matrix(fit$points[,1:nDimToUSE]), perplexity = perplexity_division, \n",
    "                           check_duplicates = FALSE, pca=FALSE, theta=0.01, max_iter=3000)\n",
    "    \n",
    "    if(is.null(groups)){\n",
    "        if(ret.val==TRUE)\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], cellName=cellName)\n",
    "        else\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2])\n",
    "        p1 <- ggplot(df, aes_string(x=\"X\",y =\"Y\"))\n",
    "    }\n",
    "    else{\n",
    "        if(ret.val==TRUE)\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], Cell=colnames(SingleCell.Binary.Jaccard), Batch=groups, cellName=cellName)\n",
    "        else\n",
    "            df<-data.frame(X=rtsne_pca_out$Y[,1],Y=rtsne_pca_out$Y[,2], Cell=colnames(SingleCell.Binary.Jaccard), Batch=groups)\n",
    "        p1 <- ggplot(df, aes_string(x=\"X\",y =\"Y\", color=\"Batch\"))\n",
    "    }\n",
    "    \n",
    "\n",
    "    p1<-p1 + ggtitle(\"t-SNE plot\")\n",
    "    p1<-p1 + geom_point(size = 2) \n",
    "    p1<-p1 + xlab(paste(\"Dim-1\"))\n",
    "    p1<-p1 + ylab(\"Dim-2\")+\n",
    "        theme_light(base_size=24) +\n",
    "        theme(strip.background = element_blank(),\n",
    "              panel.border     = element_blank(),\n",
    "              plot.title = element_text(hjust = 0.5))\n",
    "#     if(length(levels(groups)) < 12)\n",
    "#         p1 <- p1 + scale_colour_brewer(palette=\"Set1\")\n",
    "    if(text.label==TRUE)\n",
    "        p1<-p1 + geom_text(data=df,aes(label=cellName),alpha=0.5,size=4, vjust=1,hjust=0.5,angle=45)\n",
    "    print(p1)\n",
    "    return(df)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain Feature Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time <- Sys.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata <- read.table('../../input/metadata.tsv',\n",
    "                         header = TRUE,\n",
    "                         stringsAsFactors=FALSE,quote=\"\",row.names=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sKECX369DE0NKQVrKysVq9e/ccff1y9elXN0RJ2i1ZWVoQQqVT6+vXr5ORkd3d3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\nRUADgFAENAAIRUADgFAENAAI9Qdbx5mfp0rnOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “No. of Cells Each Site is Observed In”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafr_filtered <- filter_peaks(datafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>135202</li>\n",
       "\t<li>2034</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 135202\n",
       "\\item 2034\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 135202\n",
       "2. 2034\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 135202   2034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(datafr_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cdBinary = as.matrix((datafr_filtered > 0) + 0)\n",
    "cdBinary = Matrix(cdBinary, sparse = TRUE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## use the similar number of dimensions as shown in tutorial https://github.com/ManchesterBioinference/Scasat/blob/master/ScAsAT_functions_Buenrostro_All_Bam_Together.ipynb\n",
    "k = 15\n",
    "SingleCell.Binary.Jaccard <- getJaccardDist(cdBinary)\n",
    "fit <- cmdscale(as.dist(SingleCell.Binary.Jaccard),eig=TRUE, k=k)\n",
    "fm_Scasat = t(fit$points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>15</li>\n",
       "\t<li>2034</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 15\n",
       "\\item 2034\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 15\n",
       "2. 2034\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]   15 2034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>BM1077-CLP-Frozen-160106-13</th><th scope=col>BM1077-CLP-Frozen-160106-14</th><th scope=col>BM1077-CLP-Frozen-160106-2</th><th scope=col>BM1077-CLP-Frozen-160106-21</th><th scope=col>BM1077-CLP-Frozen-160106-27</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-0.142323125</td><td>-0.154763611</td><td>-0.15515348 </td><td>-0.159275891</td><td>-0.146524262</td></tr>\n",
       "\t<tr><td> 0.004697283</td><td>-0.004506986</td><td>-0.00932735 </td><td> 0.020139275</td><td> 0.018788678</td></tr>\n",
       "\t<tr><td> 0.040761630</td><td> 0.034512472</td><td> 0.04068651 </td><td> 0.055054869</td><td> 0.046571009</td></tr>\n",
       "\t<tr><td>-0.006401996</td><td>-0.036451892</td><td>-0.03221646 </td><td>-0.019589568</td><td>-0.004549236</td></tr>\n",
       "\t<tr><td> 0.020621483</td><td> 0.014936280</td><td> 0.02327080 </td><td> 0.009497392</td><td> 0.019369840</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       " BM1077-CLP-Frozen-160106-13 & BM1077-CLP-Frozen-160106-14 & BM1077-CLP-Frozen-160106-2 & BM1077-CLP-Frozen-160106-21 & BM1077-CLP-Frozen-160106-27\\\\\n",
       "\\hline\n",
       "\t -0.142323125 & -0.154763611 & -0.15515348  & -0.159275891 & -0.146524262\\\\\n",
       "\t  0.004697283 & -0.004506986 & -0.00932735  &  0.020139275 &  0.018788678\\\\\n",
       "\t  0.040761630 &  0.034512472 &  0.04068651  &  0.055054869 &  0.046571009\\\\\n",
       "\t -0.006401996 & -0.036451892 & -0.03221646  & -0.019589568 & -0.004549236\\\\\n",
       "\t  0.020621483 &  0.014936280 &  0.02327080  &  0.009497392 &  0.019369840\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "BM1077-CLP-Frozen-160106-13 | BM1077-CLP-Frozen-160106-14 | BM1077-CLP-Frozen-160106-2 | BM1077-CLP-Frozen-160106-21 | BM1077-CLP-Frozen-160106-27 | \n",
       "|---|---|---|---|---|\n",
       "| -0.142323125 | -0.154763611 | -0.15515348  | -0.159275891 | -0.146524262 | \n",
       "|  0.004697283 | -0.004506986 | -0.00932735  |  0.020139275 |  0.018788678 | \n",
       "|  0.040761630 |  0.034512472 |  0.04068651  |  0.055054869 |  0.046571009 | \n",
       "| -0.006401996 | -0.036451892 | -0.03221646  | -0.019589568 | -0.004549236 | \n",
       "|  0.020621483 |  0.014936280 |  0.02327080  |  0.009497392 |  0.019369840 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     BM1077-CLP-Frozen-160106-13 BM1077-CLP-Frozen-160106-14\n",
       "[1,] -0.142323125                -0.154763611               \n",
       "[2,]  0.004697283                -0.004506986               \n",
       "[3,]  0.040761630                 0.034512472               \n",
       "[4,] -0.006401996                -0.036451892               \n",
       "[5,]  0.020621483                 0.014936280               \n",
       "     BM1077-CLP-Frozen-160106-2 BM1077-CLP-Frozen-160106-21\n",
       "[1,] -0.15515348                -0.159275891               \n",
       "[2,] -0.00932735                 0.020139275               \n",
       "[3,]  0.04068651                 0.055054869               \n",
       "[4,] -0.03221646                -0.019589568               \n",
       "[5,]  0.02327080                 0.009497392               \n",
       "     BM1077-CLP-Frozen-160106-27\n",
       "[1,] -0.146524262               \n",
       "[2,]  0.018788678               \n",
       "[3,]  0.046571009               \n",
       "[4,] -0.004549236               \n",
       "[5,]  0.019369840               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(fm_Scasat)\n",
    "fm_Scasat[1:5,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_time <- Sys.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time difference of 46.10755 secs"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>BM1077-CLP-Frozen-160106-13</th><th scope=col>BM1077-CLP-Frozen-160106-14</th><th scope=col>BM1077-CLP-Frozen-160106-2</th><th scope=col>BM1077-CLP-Frozen-160106-21</th><th scope=col>BM1077-CLP-Frozen-160106-27</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Dim 1</th><td>-0.142323125</td><td>-0.154763611</td><td>-0.15515348 </td><td>-0.159275891</td><td>-0.146524262</td></tr>\n",
       "\t<tr><th scope=row>Dim 2</th><td> 0.004697283</td><td>-0.004506986</td><td>-0.00932735 </td><td> 0.020139275</td><td> 0.018788678</td></tr>\n",
       "\t<tr><th scope=row>Dim 3</th><td> 0.040761630</td><td> 0.034512472</td><td> 0.04068651 </td><td> 0.055054869</td><td> 0.046571009</td></tr>\n",
       "\t<tr><th scope=row>Dim 4</th><td>-0.006401996</td><td>-0.036451892</td><td>-0.03221646 </td><td>-0.019589568</td><td>-0.004549236</td></tr>\n",
       "\t<tr><th scope=row>Dim 5</th><td> 0.020621483</td><td> 0.014936280</td><td> 0.02327080 </td><td> 0.009497392</td><td> 0.019369840</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & BM1077-CLP-Frozen-160106-13 & BM1077-CLP-Frozen-160106-14 & BM1077-CLP-Frozen-160106-2 & BM1077-CLP-Frozen-160106-21 & BM1077-CLP-Frozen-160106-27\\\\\n",
       "\\hline\n",
       "\tDim 1 & -0.142323125 & -0.154763611 & -0.15515348  & -0.159275891 & -0.146524262\\\\\n",
       "\tDim 2 &  0.004697283 & -0.004506986 & -0.00932735  &  0.020139275 &  0.018788678\\\\\n",
       "\tDim 3 &  0.040761630 &  0.034512472 &  0.04068651  &  0.055054869 &  0.046571009\\\\\n",
       "\tDim 4 & -0.006401996 & -0.036451892 & -0.03221646  & -0.019589568 & -0.004549236\\\\\n",
       "\tDim 5 &  0.020621483 &  0.014936280 &  0.02327080  &  0.009497392 &  0.019369840\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | BM1077-CLP-Frozen-160106-13 | BM1077-CLP-Frozen-160106-14 | BM1077-CLP-Frozen-160106-2 | BM1077-CLP-Frozen-160106-21 | BM1077-CLP-Frozen-160106-27 | \n",
       "|---|---|---|---|---|\n",
       "| Dim 1 | -0.142323125 | -0.154763611 | -0.15515348  | -0.159275891 | -0.146524262 | \n",
       "| Dim 2 |  0.004697283 | -0.004506986 | -0.00932735  |  0.020139275 |  0.018788678 | \n",
       "| Dim 3 |  0.040761630 |  0.034512472 |  0.04068651  |  0.055054869 |  0.046571009 | \n",
       "| Dim 4 | -0.006401996 | -0.036451892 | -0.03221646  | -0.019589568 | -0.004549236 | \n",
       "| Dim 5 |  0.020621483 |  0.014936280 |  0.02327080  |  0.009497392 |  0.019369840 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "      BM1077-CLP-Frozen-160106-13 BM1077-CLP-Frozen-160106-14\n",
       "Dim 1 -0.142323125                -0.154763611               \n",
       "Dim 2  0.004697283                -0.004506986               \n",
       "Dim 3  0.040761630                 0.034512472               \n",
       "Dim 4 -0.006401996                -0.036451892               \n",
       "Dim 5  0.020621483                 0.014936280               \n",
       "      BM1077-CLP-Frozen-160106-2 BM1077-CLP-Frozen-160106-21\n",
       "Dim 1 -0.15515348                -0.159275891               \n",
       "Dim 2 -0.00932735                 0.020139275               \n",
       "Dim 3  0.04068651                 0.055054869               \n",
       "Dim 4 -0.03221646                -0.019589568               \n",
       "Dim 5  0.02327080                 0.009497392               \n",
       "      BM1077-CLP-Frozen-160106-27\n",
       "Dim 1 -0.146524262               \n",
       "Dim 2  0.018788678               \n",
       "Dim 3  0.046571009               \n",
       "Dim 4 -0.004549236               \n",
       "Dim 5  0.019369840               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rownames(fm_Scasat) = paste('Dim',1:dim(fm_Scasat)[1])\n",
    "fm_Scasat[1:5,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all(colnames(fm_Scasat) == rownames(metadata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveRDS(fm_Scasat, file = '../../output/feature_matrices/FM_Scasat_buenrostro2018bulkpeaks.rds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downstream Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SYr379/v2FJrVZrfcMuXbpUP5nVsGHD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\nBI4bTNYGgqAsdpyQPyIiYqxW23wlZ0ll1T7Hx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Ky5r/4q1c26m8Sa+ZjU6yuLCg8kPnk0JHig5TWprLpm+cUA7kJtdkJYrcFBAccLL9Tf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\ndpbK2zTbWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "set.seed(2019)\n",
    "tsNE_out_2PCs <- plot_tSNE(cdBinary, k, groups=metadata$label, perplexity=30, \n",
    "                              cellName=rownames(metadata),ret.val=TRUE, text.label=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.1 (2018-07-02)\n",
       "Platform: x86_64-conda_cos6-linux-gnu (64-bit)\n",
       "Running under: CentOS Linux 7 (Core)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /data/pinello/SHARED_SOFTWARE/anaconda3/envs/ATACseq_scasat/lib/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] ggplot2_3.0.0  Rtsne_0.15     Matrix_1.2-14  prabclus_2.2-7 mclust_5.4.3  \n",
       "[6] MASS_7.3-50   \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] Rcpp_1.0.1       compiler_3.5.1   pillar_1.3.1     plyr_1.8.4      \n",
       " [5] bindr_0.1.1      base64enc_0.1-3  tools_3.5.1      digest_0.6.18   \n",
       " [9] uuid_0.1-2       jsonlite_1.5     evaluate_0.11    tibble_2.1.1    \n",
       "[13] gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2  rlang_0.3.3     \n",
       "[17] IRdisplay_0.5.0  IRkernel_0.8.12  bindrcpp_0.2.2   withr_2.1.2     \n",
       "[21] repr_0.15.0      stringr_1.3.1    dplyr_0.7.6      tidyselect_0.2.5\n",
       "[25] grid_3.5.1       glue_1.3.1       R6_2.4.0         pbdZMQ_0.3-3    \n",
       "[29] purrr_0.3.2      magrittr_1.5     scales_0.5.0     htmltools_0.3.6 \n",
       "[33] assertthat_0.2.1 colorspace_1.3-2 labeling_0.3     stringi_1.2.4   \n",
       "[37] lazyeval_0.2.1   munsell_0.5.0    crayon_1.3.4    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "save.image(file = 'Scasat_buenrostro2018bulkpeaks.RData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R [conda env:ATACseq_scasat]",
   "language": "R",
   "name": "conda-env-ATACseq_scasat-r"
  },
  "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
}