{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DESeq2: Create Data Objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective: Create a DESeqDataSet object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial note"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First step is to create a countData and colData object (see ? DESeqDataSet)\n",
    "\n",
    "countData: for matrix input: a matrix of non-negative integers\n",
    "\n",
    " colData: for matrix input: a ‘DataFrame’ or ‘data.frame’ with at least\n",
    "          a single column. Rows of colData correspond to columns of\n",
    "          countData\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.2.1 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.1.1     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.2\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 2.1.2     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 0.8.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 0.8.3     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.4.0\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "Loading required package: S4Vectors\n",
      "Loading required package: stats4\n",
      "Loading required package: BiocGenerics\n",
      "Loading required package: parallel\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\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",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\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, which.max, which.min\n",
      "\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    first, rename\n",
      "\n",
      "The following object is masked from ‘package:tidyr’:\n",
      "\n",
      "    expand\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    expand.grid\n",
      "\n",
      "Loading required package: IRanges\n",
      "\n",
      "Attaching package: ‘IRanges’\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    collapse, desc, slice\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    reduce\n",
      "\n",
      "Loading required package: GenomicRanges\n",
      "Loading required package: GenomeInfoDb\n",
      "Loading required package: SummarizedExperiment\n",
      "Loading required package: Biobase\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",
      "Loading required package: DelayedArray\n",
      "Loading required package: matrixStats\n",
      "\n",
      "Attaching package: ‘matrixStats’\n",
      "\n",
      "The following objects are masked from ‘package:Biobase’:\n",
      "\n",
      "    anyMissing, rowMedians\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    count\n",
      "\n",
      "Loading required package: BiocParallel\n",
      "\n",
      "Attaching package: ‘DelayedArray’\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    simplify\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    aperm, apply, rowsum\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(DESeq2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the 2019 pilot count objects from the image file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'/home/jovyan/work/scratch/analysis_output/img/pilotcnt2019.RData'"
      ],
      "text/latex": [
       "'/home/jovyan/work/scratch/analysis\\_output/img/pilotcnt2019.RData'"
      ],
      "text/markdown": [
       "'/home/jovyan/work/scratch/analysis_output/img/pilotcnt2019.RData'"
      ],
      "text/plain": [
       "[1] \"/home/jovyan/work/scratch/analysis_output/img/pilotcnt2019.RData\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>/home/jovyan/work/scratch/analysis_output/img/pilotcnt2019.RData:</strong> '0d1cdfc8bb97baf52ca4f8f33e2c0ae4'"
      ],
      "text/latex": [
       "\\textbf{/home/jovyan/work/scratch/analysis\\textbackslash{}\\_output/img/pilotcnt2019.RData:} '0d1cdfc8bb97baf52ca4f8f33e2c0ae4'"
      ],
      "text/markdown": [
       "**/home/jovyan/work/scratch/analysis_output/img/pilotcnt2019.RData:** '0d1cdfc8bb97baf52ca4f8f33e2c0ae4'"
      ],
      "text/plain": [
       "/home/jovyan/work/scratch/analysis_output/img/pilotcnt2019.RData \n",
       "                              \"0d1cdfc8bb97baf52ca4f8f33e2c0ae4\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'cnt2019'</li>\n",
       "\t<li>'mtdf2019'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'cnt2019'\n",
       "\\item 'mtdf2019'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'cnt2019'\n",
       "2. 'mtdf2019'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"cnt2019\"  \"mtdf2019\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "curdir <- \"/home/jovyan/work/scratch/analysis_output\"\n",
    "\n",
    "imgdir <- file.path(curdir, \"img\")\n",
    "\n",
    "imgfile <- file.path(imgdir, \"pilotcnt2019.RData\")\n",
    "\n",
    "imgfile\n",
    "\n",
    "attach(imgfile)\n",
    "\n",
    "tools::md5sum(imgfile)\n",
    "\n",
    "### List the objects that have been attached\n",
    "ls(2)\n",
    "\n",
    "cnt2019 <- cnt2019\n",
    "mtdf2019 <- mtdf2019\n",
    "\n",
    "detach(pos = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check dimensions of the two objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>24</li>\n",
       "\t<li>8500</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 24\n",
       "\\item 8500\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 24\n",
       "2. 8500\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]   24 8500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>24</li>\n",
       "\t<li>22</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 24\n",
       "\\item 22\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 24\n",
       "2. 22\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 24 22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(cnt2019)\n",
    "dim(mtdf2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 6 × 22</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Label</th><th scope=col>sample_year</th><th scope=col>group</th><th scope=col>enrich_rep</th><th scope=col>RNA_sample_num</th><th scope=col>genotype</th><th scope=col>condition</th><th scope=col>libprep_person</th><th scope=col>enrichment_method</th><th scope=col>enrichment_short</th><th scope=col>⋯</th><th scope=col>i5_primer</th><th scope=col>i7_primer</th><th scope=col>library_num</th><th scope=col>bio_replicate</th><th scope=col>Nanodrop_260_280</th><th scope=col>Nanodrop_260_230</th><th scope=col>Nanodrop_concentration_ng_ul</th><th scope=col>Bioanalyzer_concentration_ng_ul</th><th scope=col>RIN_normal_threshold</th><th scope=col>RIN_lowered_threshold</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&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;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;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1_2019_P_M1</td><td>2019</td><td>P</td><td>1</td><td>1</td><td>WT</td><td>pH4</td><td>C</td><td>mRNA</td><td>M</td><td>⋯</td><td>i501</td><td>i701</td><td>1</td><td>1</td><td>2.14</td><td>1.52</td><td>293</td><td>197</td><td>N/A</td><td> 9.8</td></tr>\n",
       "\t<tr><td>2_2019_P_M1</td><td>2019</td><td>P</td><td>1</td><td>2</td><td>WT</td><td>pH4</td><td>C</td><td>mRNA</td><td>M</td><td>⋯</td><td>i502</td><td>i701</td><td>2</td><td>2</td><td>2.12</td><td>1.79</td><td>290</td><td>225</td><td>N/A</td><td> 9.9</td></tr>\n",
       "\t<tr><td>3_2019_P_M1</td><td>2019</td><td>P</td><td>1</td><td>3</td><td>WT</td><td>pH4</td><td>C</td><td>mRNA</td><td>M</td><td>⋯</td><td>i503</td><td>i701</td><td>3</td><td>3</td><td>2.11</td><td>2.49</td><td>302</td><td>241</td><td>N/A</td><td> 9.9</td></tr>\n",
       "\t<tr><td>4_2019_P_M1</td><td>2019</td><td>P</td><td>1</td><td>4</td><td>WT</td><td>pH4</td><td>P</td><td>mRNA</td><td>M</td><td>⋯</td><td>i504</td><td>i701</td><td>4</td><td>4</td><td>2.13</td><td>1.15</td><td>296</td><td>189</td><td>N/A</td><td> 9.7</td></tr>\n",
       "\t<tr><td>5_2019_P_M1</td><td>2019</td><td>P</td><td>1</td><td>5</td><td>WT</td><td>pH4</td><td>P</td><td>mRNA</td><td>M</td><td>⋯</td><td>i505</td><td>i701</td><td>5</td><td>5</td><td>2.09</td><td>2.42</td><td>337</td><td>268</td><td>10 </td><td>10.0</td></tr>\n",
       "\t<tr><td>6_2019_P_M1</td><td>2019</td><td>P</td><td>1</td><td>6</td><td>WT</td><td>pH4</td><td>P</td><td>mRNA</td><td>M</td><td>⋯</td><td>i506</td><td>i701</td><td>6</td><td>6</td><td>2.08</td><td>2.40</td><td>319</td><td>276</td><td>10 </td><td>10.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 22\n",
       "\\begin{tabular}{r|llllllllllllllllllllll}\n",
       " Label & sample\\_year & group & enrich\\_rep & RNA\\_sample\\_num & genotype & condition & libprep\\_person & enrichment\\_method & enrichment\\_short & i7\\_index & i5\\_index & i5\\_primer & i7\\_primer & library\\_num & bio\\_replicate & Nanodrop\\_260\\_280 & Nanodrop\\_260\\_230 & Nanodrop\\_concentration\\_ng\\_ul & Bioanalyzer\\_concentration\\_ng\\_ul & RIN\\_normal\\_threshold & RIN\\_lowered\\_threshold\\\\\n",
       " <chr> & <dbl> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1\\_2019\\_P\\_M1 & 2019 & P & 1 & 1 & WT & pH4 & C & mRNA & M & ATTACTCG & AGGCTATA & i501 & i701 & 1 & 1 & 2.14 & 1.52 & 293 & 197 & N/A &  9.8\\\\\n",
       "\t 2\\_2019\\_P\\_M1 & 2019 & P & 1 & 2 & WT & pH4 & C & mRNA & M & ATTACTCG & GCCTCTAT & i502 & i701 & 2 & 2 & 2.12 & 1.79 & 290 & 225 & N/A &  9.9\\\\\n",
       "\t 3\\_2019\\_P\\_M1 & 2019 & P & 1 & 3 & WT & pH4 & C & mRNA & M & ATTACTCG & AGGATAGG & i503 & i701 & 3 & 3 & 2.11 & 2.49 & 302 & 241 & N/A &  9.9\\\\\n",
       "\t 4\\_2019\\_P\\_M1 & 2019 & P & 1 & 4 & WT & pH4 & P & mRNA & M & ATTACTCG & TCAGAGCC & i504 & i701 & 4 & 4 & 2.13 & 1.15 & 296 & 189 & N/A &  9.7\\\\\n",
       "\t 5\\_2019\\_P\\_M1 & 2019 & P & 1 & 5 & WT & pH4 & P & mRNA & M & ATTACTCG & CTTCGCCT & i505 & i701 & 5 & 5 & 2.09 & 2.42 & 337 & 268 & 10  & 10.0\\\\\n",
       "\t 6\\_2019\\_P\\_M1 & 2019 & P & 1 & 6 & WT & pH4 & P & mRNA & M & ATTACTCG & TAAGATTA & i506 & i701 & 6 & 6 & 2.08 & 2.40 & 319 & 276 & 10  & 10.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 22\n",
       "\n",
       "| Label &lt;chr&gt; | sample_year &lt;dbl&gt; | group &lt;chr&gt; | enrich_rep &lt;dbl&gt; | RNA_sample_num &lt;dbl&gt; | genotype &lt;chr&gt; | condition &lt;chr&gt; | libprep_person &lt;chr&gt; | enrichment_method &lt;chr&gt; | enrichment_short &lt;chr&gt; | ⋯ ⋯ | i5_primer &lt;chr&gt; | i7_primer &lt;chr&gt; | library_num &lt;dbl&gt; | bio_replicate &lt;dbl&gt; | Nanodrop_260_280 &lt;dbl&gt; | Nanodrop_260_230 &lt;dbl&gt; | Nanodrop_concentration_ng_ul &lt;dbl&gt; | Bioanalyzer_concentration_ng_ul &lt;dbl&gt; | RIN_normal_threshold &lt;chr&gt; | RIN_lowered_threshold &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1_2019_P_M1 | 2019 | P | 1 | 1 | WT | pH4 | C | mRNA | M | ⋯ | i501 | i701 | 1 | 1 | 2.14 | 1.52 | 293 | 197 | N/A |  9.8 |\n",
       "| 2_2019_P_M1 | 2019 | P | 1 | 2 | WT | pH4 | C | mRNA | M | ⋯ | i502 | i701 | 2 | 2 | 2.12 | 1.79 | 290 | 225 | N/A |  9.9 |\n",
       "| 3_2019_P_M1 | 2019 | P | 1 | 3 | WT | pH4 | C | mRNA | M | ⋯ | i503 | i701 | 3 | 3 | 2.11 | 2.49 | 302 | 241 | N/A |  9.9 |\n",
       "| 4_2019_P_M1 | 2019 | P | 1 | 4 | WT | pH4 | P | mRNA | M | ⋯ | i504 | i701 | 4 | 4 | 2.13 | 1.15 | 296 | 189 | N/A |  9.7 |\n",
       "| 5_2019_P_M1 | 2019 | P | 1 | 5 | WT | pH4 | P | mRNA | M | ⋯ | i505 | i701 | 5 | 5 | 2.09 | 2.42 | 337 | 268 | 10  | 10.0 |\n",
       "| 6_2019_P_M1 | 2019 | P | 1 | 6 | WT | pH4 | P | mRNA | M | ⋯ | i506 | i701 | 6 | 6 | 2.08 | 2.40 | 319 | 276 | 10  | 10.0 |\n",
       "\n"
      ],
      "text/plain": [
       "  Label       sample_year group enrich_rep RNA_sample_num genotype condition\n",
       "1 1_2019_P_M1 2019        P     1          1              WT       pH4      \n",
       "2 2_2019_P_M1 2019        P     1          2              WT       pH4      \n",
       "3 3_2019_P_M1 2019        P     1          3              WT       pH4      \n",
       "4 4_2019_P_M1 2019        P     1          4              WT       pH4      \n",
       "5 5_2019_P_M1 2019        P     1          5              WT       pH4      \n",
       "6 6_2019_P_M1 2019        P     1          6              WT       pH4      \n",
       "  libprep_person enrichment_method enrichment_short ⋯ i5_primer i7_primer\n",
       "1 C              mRNA              M                ⋯ i501      i701     \n",
       "2 C              mRNA              M                ⋯ i502      i701     \n",
       "3 C              mRNA              M                ⋯ i503      i701     \n",
       "4 P              mRNA              M                ⋯ i504      i701     \n",
       "5 P              mRNA              M                ⋯ i505      i701     \n",
       "6 P              mRNA              M                ⋯ i506      i701     \n",
       "  library_num bio_replicate Nanodrop_260_280 Nanodrop_260_230\n",
       "1 1           1             2.14             1.52            \n",
       "2 2           2             2.12             1.79            \n",
       "3 3           3             2.11             2.49            \n",
       "4 4           4             2.13             1.15            \n",
       "5 5           5             2.09             2.42            \n",
       "6 6           6             2.08             2.40            \n",
       "  Nanodrop_concentration_ng_ul Bioanalyzer_concentration_ng_ul\n",
       "1 293                          197                            \n",
       "2 290                          225                            \n",
       "3 302                          241                            \n",
       "4 296                          189                            \n",
       "5 337                          268                            \n",
       "6 319                          276                            \n",
       "  RIN_normal_threshold RIN_lowered_threshold\n",
       "1 N/A                   9.8                 \n",
       "2 N/A                   9.9                 \n",
       "3 N/A                   9.9                 \n",
       "4 N/A                   9.7                 \n",
       "5 10                   10.0                 \n",
       "6 10                   10.0                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtdf2019 %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 24 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Label</th><th scope=col>CNAG_00001</th><th scope=col>CNAG_00002</th><th scope=col>CNAG_00003</th><th scope=col>CNAG_00004</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1_2019_P_M1 </td><td>0</td><td>158</td><td>201</td><td>904</td></tr>\n",
       "\t<tr><td>10_2019_P_M1</td><td>0</td><td>119</td><td>131</td><td>513</td></tr>\n",
       "\t<tr><td>11_2019_P_M1</td><td>0</td><td> 90</td><td>121</td><td>573</td></tr>\n",
       "\t<tr><td>12_2019_P_M1</td><td>0</td><td> 81</td><td>151</td><td>533</td></tr>\n",
       "\t<tr><td>13_2019_P_M1</td><td>0</td><td>188</td><td>215</td><td>474</td></tr>\n",
       "\t<tr><td>14_2019_P_M1</td><td>0</td><td>177</td><td>154</td><td>440</td></tr>\n",
       "\t<tr><td>15_2019_P_M1</td><td>0</td><td>216</td><td>197</td><td>425</td></tr>\n",
       "\t<tr><td>16_2019_P_M1</td><td>0</td><td>224</td><td>195</td><td>548</td></tr>\n",
       "\t<tr><td>17_2019_P_M1</td><td>0</td><td>234</td><td>211</td><td>517</td></tr>\n",
       "\t<tr><td>18_2019_P_M1</td><td>0</td><td>338</td><td>201</td><td>464</td></tr>\n",
       "\t<tr><td>19_2019_P_M1</td><td>0</td><td>191</td><td>118</td><td>458</td></tr>\n",
       "\t<tr><td>2_2019_P_M1 </td><td>0</td><td>204</td><td>156</td><td>902</td></tr>\n",
       "\t<tr><td>20_2019_P_M1</td><td>0</td><td>201</td><td>133</td><td>405</td></tr>\n",
       "\t<tr><td>21_2019_P_M1</td><td>0</td><td>192</td><td>109</td><td>339</td></tr>\n",
       "\t<tr><td>22_2019_P_M1</td><td>0</td><td>166</td><td>113</td><td>406</td></tr>\n",
       "\t<tr><td>23_2019_P_M1</td><td>0</td><td>202</td><td>154</td><td>442</td></tr>\n",
       "\t<tr><td>24_2019_P_M1</td><td>0</td><td>235</td><td>169</td><td>523</td></tr>\n",
       "\t<tr><td>3_2019_P_M1 </td><td>0</td><td>149</td><td>161</td><td>941</td></tr>\n",
       "\t<tr><td>4_2019_P_M1 </td><td>0</td><td>176</td><td>171</td><td>795</td></tr>\n",
       "\t<tr><td>5_2019_P_M1 </td><td>0</td><td>161</td><td>162</td><td>849</td></tr>\n",
       "\t<tr><td>6_2019_P_M1 </td><td>0</td><td>148</td><td>103</td><td>688</td></tr>\n",
       "\t<tr><td>7_2019_P_M1 </td><td>0</td><td>172</td><td>172</td><td>768</td></tr>\n",
       "\t<tr><td>8_2019_P_M1 </td><td>0</td><td>169</td><td>170</td><td>744</td></tr>\n",
       "\t<tr><td>9_2019_P_M1 </td><td>0</td><td>124</td><td>175</td><td>659</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 24 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " Label & CNAG\\_00001 & CNAG\\_00002 & CNAG\\_00003 & CNAG\\_00004\\\\\n",
       " <chr> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 1\\_2019\\_P\\_M1  & 0 & 158 & 201 & 904\\\\\n",
       "\t 10\\_2019\\_P\\_M1 & 0 & 119 & 131 & 513\\\\\n",
       "\t 11\\_2019\\_P\\_M1 & 0 &  90 & 121 & 573\\\\\n",
       "\t 12\\_2019\\_P\\_M1 & 0 &  81 & 151 & 533\\\\\n",
       "\t 13\\_2019\\_P\\_M1 & 0 & 188 & 215 & 474\\\\\n",
       "\t 14\\_2019\\_P\\_M1 & 0 & 177 & 154 & 440\\\\\n",
       "\t 15\\_2019\\_P\\_M1 & 0 & 216 & 197 & 425\\\\\n",
       "\t 16\\_2019\\_P\\_M1 & 0 & 224 & 195 & 548\\\\\n",
       "\t 17\\_2019\\_P\\_M1 & 0 & 234 & 211 & 517\\\\\n",
       "\t 18\\_2019\\_P\\_M1 & 0 & 338 & 201 & 464\\\\\n",
       "\t 19\\_2019\\_P\\_M1 & 0 & 191 & 118 & 458\\\\\n",
       "\t 2\\_2019\\_P\\_M1  & 0 & 204 & 156 & 902\\\\\n",
       "\t 20\\_2019\\_P\\_M1 & 0 & 201 & 133 & 405\\\\\n",
       "\t 21\\_2019\\_P\\_M1 & 0 & 192 & 109 & 339\\\\\n",
       "\t 22\\_2019\\_P\\_M1 & 0 & 166 & 113 & 406\\\\\n",
       "\t 23\\_2019\\_P\\_M1 & 0 & 202 & 154 & 442\\\\\n",
       "\t 24\\_2019\\_P\\_M1 & 0 & 235 & 169 & 523\\\\\n",
       "\t 3\\_2019\\_P\\_M1  & 0 & 149 & 161 & 941\\\\\n",
       "\t 4\\_2019\\_P\\_M1  & 0 & 176 & 171 & 795\\\\\n",
       "\t 5\\_2019\\_P\\_M1  & 0 & 161 & 162 & 849\\\\\n",
       "\t 6\\_2019\\_P\\_M1  & 0 & 148 & 103 & 688\\\\\n",
       "\t 7\\_2019\\_P\\_M1  & 0 & 172 & 172 & 768\\\\\n",
       "\t 8\\_2019\\_P\\_M1  & 0 & 169 & 170 & 744\\\\\n",
       "\t 9\\_2019\\_P\\_M1  & 0 & 124 & 175 & 659\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 24 × 5\n",
       "\n",
       "| Label &lt;chr&gt; | CNAG_00001 &lt;int&gt; | CNAG_00002 &lt;int&gt; | CNAG_00003 &lt;int&gt; | CNAG_00004 &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1_2019_P_M1  | 0 | 158 | 201 | 904 |\n",
       "| 10_2019_P_M1 | 0 | 119 | 131 | 513 |\n",
       "| 11_2019_P_M1 | 0 |  90 | 121 | 573 |\n",
       "| 12_2019_P_M1 | 0 |  81 | 151 | 533 |\n",
       "| 13_2019_P_M1 | 0 | 188 | 215 | 474 |\n",
       "| 14_2019_P_M1 | 0 | 177 | 154 | 440 |\n",
       "| 15_2019_P_M1 | 0 | 216 | 197 | 425 |\n",
       "| 16_2019_P_M1 | 0 | 224 | 195 | 548 |\n",
       "| 17_2019_P_M1 | 0 | 234 | 211 | 517 |\n",
       "| 18_2019_P_M1 | 0 | 338 | 201 | 464 |\n",
       "| 19_2019_P_M1 | 0 | 191 | 118 | 458 |\n",
       "| 2_2019_P_M1  | 0 | 204 | 156 | 902 |\n",
       "| 20_2019_P_M1 | 0 | 201 | 133 | 405 |\n",
       "| 21_2019_P_M1 | 0 | 192 | 109 | 339 |\n",
       "| 22_2019_P_M1 | 0 | 166 | 113 | 406 |\n",
       "| 23_2019_P_M1 | 0 | 202 | 154 | 442 |\n",
       "| 24_2019_P_M1 | 0 | 235 | 169 | 523 |\n",
       "| 3_2019_P_M1  | 0 | 149 | 161 | 941 |\n",
       "| 4_2019_P_M1  | 0 | 176 | 171 | 795 |\n",
       "| 5_2019_P_M1  | 0 | 161 | 162 | 849 |\n",
       "| 6_2019_P_M1  | 0 | 148 | 103 | 688 |\n",
       "| 7_2019_P_M1  | 0 | 172 | 172 | 768 |\n",
       "| 8_2019_P_M1  | 0 | 169 | 170 | 744 |\n",
       "| 9_2019_P_M1  | 0 | 124 | 175 | 659 |\n",
       "\n"
      ],
      "text/plain": [
       "   Label        CNAG_00001 CNAG_00002 CNAG_00003 CNAG_00004\n",
       "1  1_2019_P_M1  0          158        201        904       \n",
       "2  10_2019_P_M1 0          119        131        513       \n",
       "3  11_2019_P_M1 0           90        121        573       \n",
       "4  12_2019_P_M1 0           81        151        533       \n",
       "5  13_2019_P_M1 0          188        215        474       \n",
       "6  14_2019_P_M1 0          177        154        440       \n",
       "7  15_2019_P_M1 0          216        197        425       \n",
       "8  16_2019_P_M1 0          224        195        548       \n",
       "9  17_2019_P_M1 0          234        211        517       \n",
       "10 18_2019_P_M1 0          338        201        464       \n",
       "11 19_2019_P_M1 0          191        118        458       \n",
       "12 2_2019_P_M1  0          204        156        902       \n",
       "13 20_2019_P_M1 0          201        133        405       \n",
       "14 21_2019_P_M1 0          192        109        339       \n",
       "15 22_2019_P_M1 0          166        113        406       \n",
       "16 23_2019_P_M1 0          202        154        442       \n",
       "17 24_2019_P_M1 0          235        169        523       \n",
       "18 3_2019_P_M1  0          149        161        941       \n",
       "19 4_2019_P_M1  0          176        171        795       \n",
       "20 5_2019_P_M1  0          161        162        849       \n",
       "21 6_2019_P_M1  0          148        103        688       \n",
       "22 7_2019_P_M1  0          172        172        768       \n",
       "23 8_2019_P_M1  0          169        170        744       \n",
       "24 9_2019_P_M1  0          124        175        659       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt2019[,1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create columnData object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 6 rows and 3 columns\n",
       "                  Label    genotype   condition\n",
       "            <character> <character> <character>\n",
       "1_2019_P_M1 1_2019_P_M1          WT         pH4\n",
       "2_2019_P_M1 2_2019_P_M1          WT         pH4\n",
       "3_2019_P_M1 3_2019_P_M1          WT         pH4\n",
       "4_2019_P_M1 4_2019_P_M1          WT         pH4\n",
       "5_2019_P_M1 5_2019_P_M1          WT         pH4\n",
       "6_2019_P_M1 6_2019_P_M1          WT         pH4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# columnData --- metadata\n",
    "mtdf2019 %>%\n",
    "    DataFrame ->\n",
    "        columnData2019\n",
    "\n",
    "### Add the labels as rownames\n",
    "rownames(columnData2019) <- columnData2019[[\"Label\"]]\n",
    "\n",
    "columnData2019[, c(\"Label\", \"genotype\", \"condition\")] %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Label</th><th scope=col>CNAG_00001</th><th scope=col>CNAG_00002</th><th scope=col>CNAG_00003</th><th scope=col>CNAG_00004</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1_2019_P_M1 </td><td>0</td><td>158</td><td>201</td><td>904</td></tr>\n",
       "\t<tr><td>10_2019_P_M1</td><td>0</td><td>119</td><td>131</td><td>513</td></tr>\n",
       "\t<tr><td>11_2019_P_M1</td><td>0</td><td> 90</td><td>121</td><td>573</td></tr>\n",
       "\t<tr><td>12_2019_P_M1</td><td>0</td><td> 81</td><td>151</td><td>533</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " Label & CNAG\\_00001 & CNAG\\_00002 & CNAG\\_00003 & CNAG\\_00004\\\\\n",
       " <chr> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 1\\_2019\\_P\\_M1  & 0 & 158 & 201 & 904\\\\\n",
       "\t 10\\_2019\\_P\\_M1 & 0 & 119 & 131 & 513\\\\\n",
       "\t 11\\_2019\\_P\\_M1 & 0 &  90 & 121 & 573\\\\\n",
       "\t 12\\_2019\\_P\\_M1 & 0 &  81 & 151 & 533\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 5\n",
       "\n",
       "| Label &lt;chr&gt; | CNAG_00001 &lt;int&gt; | CNAG_00002 &lt;int&gt; | CNAG_00003 &lt;int&gt; | CNAG_00004 &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1_2019_P_M1  | 0 | 158 | 201 | 904 |\n",
       "| 10_2019_P_M1 | 0 | 119 | 131 | 513 |\n",
       "| 11_2019_P_M1 | 0 |  90 | 121 | 573 |\n",
       "| 12_2019_P_M1 | 0 |  81 | 151 | 533 |\n",
       "\n"
      ],
      "text/plain": [
       "  Label        CNAG_00001 CNAG_00002 CNAG_00003 CNAG_00004\n",
       "1 1_2019_P_M1  0          158        201        904       \n",
       "2 10_2019_P_M1 0          119        131        513       \n",
       "3 11_2019_P_M1 0           90        121        573       \n",
       "4 12_2019_P_M1 0           81        151        533       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Note that libraries are across rows and libraries across columns\n",
    "### DESeq2 requires that the matrix is transposed as that the gene names become row names\n",
    "cnt2019[1:4,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 5 × 6 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>1_2019_P_M1</th><th scope=col>10_2019_P_M1</th><th scope=col>11_2019_P_M1</th><th scope=col>12_2019_P_M1</th><th scope=col>13_2019_P_M1</th><th scope=col>14_2019_P_M1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CNAG_00001</th><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>CNAG_00002</th><td>158</td><td>119</td><td> 90</td><td> 81</td><td>188</td><td>177</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00003</th><td>201</td><td>131</td><td>121</td><td>151</td><td>215</td><td>154</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00004</th><td>904</td><td>513</td><td>573</td><td>533</td><td>474</td><td>440</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00005</th><td> 22</td><td> 24</td><td> 18</td><td> 20</td><td> 25</td><td> 13</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 5 × 6 of type int\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & 1\\_2019\\_P\\_M1 & 10\\_2019\\_P\\_M1 & 11\\_2019\\_P\\_M1 & 12\\_2019\\_P\\_M1 & 13\\_2019\\_P\\_M1 & 14\\_2019\\_P\\_M1\\\\\n",
       "\\hline\n",
       "\tCNAG\\_00001 &   0 &   0 &   0 &   0 &   0 &   0\\\\\n",
       "\tCNAG\\_00002 & 158 & 119 &  90 &  81 & 188 & 177\\\\\n",
       "\tCNAG\\_00003 & 201 & 131 & 121 & 151 & 215 & 154\\\\\n",
       "\tCNAG\\_00004 & 904 & 513 & 573 & 533 & 474 & 440\\\\\n",
       "\tCNAG\\_00005 &  22 &  24 &  18 &  20 &  25 &  13\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 5 × 6 of type int\n",
       "\n",
       "| <!--/--> | 1_2019_P_M1 | 10_2019_P_M1 | 11_2019_P_M1 | 12_2019_P_M1 | 13_2019_P_M1 | 14_2019_P_M1 |\n",
       "|---|---|---|---|---|---|---|\n",
       "| CNAG_00001 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
       "| CNAG_00002 | 158 | 119 |  90 |  81 | 188 | 177 |\n",
       "| CNAG_00003 | 201 | 131 | 121 | 151 | 215 | 154 |\n",
       "| CNAG_00004 | 904 | 513 | 573 | 533 | 474 | 440 |\n",
       "| CNAG_00005 |  22 |  24 |  18 |  20 |  25 |  13 |\n",
       "\n"
      ],
      "text/plain": [
       "           1_2019_P_M1 10_2019_P_M1 11_2019_P_M1 12_2019_P_M1 13_2019_P_M1\n",
       "CNAG_00001   0           0            0            0            0         \n",
       "CNAG_00002 158         119           90           81          188         \n",
       "CNAG_00003 201         131          121          151          215         \n",
       "CNAG_00004 904         513          573          533          474         \n",
       "CNAG_00005  22          24           18           20           25         \n",
       "           14_2019_P_M1\n",
       "CNAG_00001   0         \n",
       "CNAG_00002 177         \n",
       "CNAG_00003 154         \n",
       "CNAG_00004 440         \n",
       "CNAG_00005  13         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Transpose the count matrix (so that libraries are across the columns and genes across rows) \n",
    "### Note that as.matrix() converts the tibble to a matrix object\n",
    "cnt2019 %>%\n",
    "    gather(key = gene, value = value, 2:ncol(cnt2019)) %>% \n",
    "            spread_(key = names(cnt2019)[1],value = 'value') %>%\n",
    "                column_to_rownames(\"gene\") %>%\n",
    "                    as.matrix ->\n",
    "                        countData2019\n",
    "\n",
    "countData2019[1:5, 1:6]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reorder the columns of the count matrix according to the order of Label in columnData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### The two sets coincide\n",
    "setequal(columnData2019[[\"Label\"]], colnames(countData2019))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### but they are identical as they follow different orders\n",
    "identical(columnData2019[[\"Label\"]], colnames(countData2019))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reorder the columns of countData "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "countData2019 <- countData2019[,columnData2019[[\"Label\"]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 4 × 5 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>1_2019_P_M1</th><th scope=col>2_2019_P_M1</th><th scope=col>3_2019_P_M1</th><th scope=col>4_2019_P_M1</th><th scope=col>5_2019_P_M1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CNAG_00001</th><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00002</th><td>158</td><td>204</td><td>149</td><td>176</td><td>161</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00003</th><td>201</td><td>156</td><td>161</td><td>171</td><td>162</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00004</th><td>904</td><td>902</td><td>941</td><td>795</td><td>849</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 4 × 5 of type int\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & 1\\_2019\\_P\\_M1 & 2\\_2019\\_P\\_M1 & 3\\_2019\\_P\\_M1 & 4\\_2019\\_P\\_M1 & 5\\_2019\\_P\\_M1\\\\\n",
       "\\hline\n",
       "\tCNAG\\_00001 &   0 &   0 &   0 &   0 &   0\\\\\n",
       "\tCNAG\\_00002 & 158 & 204 & 149 & 176 & 161\\\\\n",
       "\tCNAG\\_00003 & 201 & 156 & 161 & 171 & 162\\\\\n",
       "\tCNAG\\_00004 & 904 & 902 & 941 & 795 & 849\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 4 × 5 of type int\n",
       "\n",
       "| <!--/--> | 1_2019_P_M1 | 2_2019_P_M1 | 3_2019_P_M1 | 4_2019_P_M1 | 5_2019_P_M1 |\n",
       "|---|---|---|---|---|---|\n",
       "| CNAG_00001 |   0 |   0 |   0 |   0 |   0 |\n",
       "| CNAG_00002 | 158 | 204 | 149 | 176 | 161 |\n",
       "| CNAG_00003 | 201 | 156 | 161 | 171 | 162 |\n",
       "| CNAG_00004 | 904 | 902 | 941 | 795 | 849 |\n",
       "\n"
      ],
      "text/plain": [
       "           1_2019_P_M1 2_2019_P_M1 3_2019_P_M1 4_2019_P_M1 5_2019_P_M1\n",
       "CNAG_00001   0           0           0           0           0        \n",
       "CNAG_00002 158         204         149         176         161        \n",
       "CNAG_00003 201         156         161         171         162        \n",
       "CNAG_00004 904         902         941         795         849        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countData2019[1:4,1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make sure that labels match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### The two variables coincide\n",
    "setequal(columnData2019[[\"Label\"]], colnames(countData2019))\n",
    "### but they are identical as they follow different orders\n",
    "identical(columnData2019[[\"Label\"]], colnames(countData2019))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make DESeq object on the basis of the counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The design option allows you to specify an additive or a multiplicitive model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additive model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in DESeqDataSet(se, design = design, ignoreRank):\n",
      "“some variables in design formula are characters, converting to factors”"
     ]
    }
   ],
   "source": [
    "dds_add <- DESeqDataSetFromMatrix(\n",
    "    countData2019,                      # Count matrix\n",
    "    columnData2019,                     # metadata\n",
    "    ~ condition + genotype)             # design formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: DESeqDataSet \n",
       "dim: 8499 24 \n",
       "metadata(1): version\n",
       "assays(1): counts\n",
       "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n",
       "rowData names(0):\n",
       "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n",
       "colData names(22): Label sample_year ... RIN_normal_threshold\n",
       "  RIN_lowered_threshold"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'design'</li>\n",
       "\t<li>'dispersionFunction'</li>\n",
       "\t<li>'rowRanges'</li>\n",
       "\t<li>'colData'</li>\n",
       "\t<li>'assays'</li>\n",
       "\t<li>'NAMES'</li>\n",
       "\t<li>'elementMetadata'</li>\n",
       "\t<li>'metadata'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'design'\n",
       "\\item 'dispersionFunction'\n",
       "\\item 'rowRanges'\n",
       "\\item 'colData'\n",
       "\\item 'assays'\n",
       "\\item 'NAMES'\n",
       "\\item 'elementMetadata'\n",
       "\\item 'metadata'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'design'\n",
       "2. 'dispersionFunction'\n",
       "3. 'rowRanges'\n",
       "4. 'colData'\n",
       "5. 'assays'\n",
       "6. 'NAMES'\n",
       "7. 'elementMetadata'\n",
       "8. 'metadata'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"design\"             \"dispersionFunction\" \"rowRanges\"         \n",
       "[4] \"colData\"            \"assays\"             \"NAMES\"             \n",
       "[7] \"elementMetadata\"    \"metadata\"          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slotNames(dds_add)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "~condition + genotype"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds_add@design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check column data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 24 rows and 22 columns\n",
       "                    Label sample_year       group enrich_rep RNA_sample_num\n",
       "              <character>   <numeric> <character>  <numeric>      <numeric>\n",
       "1_2019_P_M1   1_2019_P_M1        2019           P          1              1\n",
       "2_2019_P_M1   2_2019_P_M1        2019           P          1              2\n",
       "3_2019_P_M1   3_2019_P_M1        2019           P          1              3\n",
       "4_2019_P_M1   4_2019_P_M1        2019           P          1              4\n",
       "5_2019_P_M1   5_2019_P_M1        2019           P          1              5\n",
       "...                   ...         ...         ...        ...            ...\n",
       "20_2019_P_M1 20_2019_P_M1        2019           P          1             20\n",
       "21_2019_P_M1 21_2019_P_M1        2019           P          1             21\n",
       "22_2019_P_M1 22_2019_P_M1        2019           P          1             22\n",
       "23_2019_P_M1 23_2019_P_M1        2019           P          1             23\n",
       "24_2019_P_M1 24_2019_P_M1        2019           P          1             24\n",
       "             genotype condition libprep_person enrichment_method\n",
       "             <factor>  <factor>    <character>       <character>\n",
       "1_2019_P_M1        WT       pH4              C              mRNA\n",
       "2_2019_P_M1        WT       pH4              C              mRNA\n",
       "3_2019_P_M1        WT       pH4              C              mRNA\n",
       "4_2019_P_M1        WT       pH4              P              mRNA\n",
       "5_2019_P_M1        WT       pH4              P              mRNA\n",
       "...               ...       ...            ...               ...\n",
       "20_2019_P_M1    sre1d       pH8              C              mRNA\n",
       "21_2019_P_M1    sre1d       pH8              C              mRNA\n",
       "22_2019_P_M1    sre1d       pH8              P              mRNA\n",
       "23_2019_P_M1    sre1d       pH8              P              mRNA\n",
       "24_2019_P_M1    sre1d       pH8              P              mRNA\n",
       "             enrichment_short    i7_index    i5_index   i5_primer   i7_primer\n",
       "                  <character> <character> <character> <character> <character>\n",
       "1_2019_P_M1                 M    ATTACTCG    AGGCTATA        i501        i701\n",
       "2_2019_P_M1                 M    ATTACTCG    GCCTCTAT        i502        i701\n",
       "3_2019_P_M1                 M    ATTACTCG    AGGATAGG        i503        i701\n",
       "4_2019_P_M1                 M    ATTACTCG    TCAGAGCC        i504        i701\n",
       "5_2019_P_M1                 M    ATTACTCG    CTTCGCCT        i505        i701\n",
       "...                       ...         ...         ...         ...         ...\n",
       "20_2019_P_M1                M    CGCTCATT    TCAGAGCC        i504        i703\n",
       "21_2019_P_M1                M    CGCTCATT    CTTCGCCT        i505        i703\n",
       "22_2019_P_M1                M    CGCTCATT    TAAGATTA        i506        i703\n",
       "23_2019_P_M1                M    CGCTCATT    ACGTCCTG        i507        i703\n",
       "24_2019_P_M1                M    CGCTCATT    GTCAGTAC        i508        i703\n",
       "             library_num bio_replicate Nanodrop_260_280 Nanodrop_260_230\n",
       "               <numeric>     <numeric>        <numeric>        <numeric>\n",
       "1_2019_P_M1            1             1             2.14             1.52\n",
       "2_2019_P_M1            2             2             2.12             1.79\n",
       "3_2019_P_M1            3             3             2.11             2.49\n",
       "4_2019_P_M1            4             4             2.13             1.15\n",
       "5_2019_P_M1            5             5             2.09             2.42\n",
       "...                  ...           ...              ...              ...\n",
       "20_2019_P_M1          20             2             2.14             2.39\n",
       "21_2019_P_M1          21             3             2.01             2.22\n",
       "22_2019_P_M1          22             4             2.22             2.33\n",
       "23_2019_P_M1          23             5             2.13             2.56\n",
       "24_2019_P_M1          24             6             2.11             2.16\n",
       "             Nanodrop_concentration_ng_ul Bioanalyzer_concentration_ng_ul\n",
       "                                <numeric>                       <numeric>\n",
       "1_2019_P_M1                           293                             197\n",
       "2_2019_P_M1                           290                             225\n",
       "3_2019_P_M1                           302                             241\n",
       "4_2019_P_M1                           296                             189\n",
       "5_2019_P_M1                           337                             268\n",
       "...                                   ...                             ...\n",
       "20_2019_P_M1                          172                             131\n",
       "21_2019_P_M1                          397                             295\n",
       "22_2019_P_M1                          567                             499\n",
       "23_2019_P_M1                          254                             206\n",
       "24_2019_P_M1                          215                             336\n",
       "             RIN_normal_threshold RIN_lowered_threshold\n",
       "                      <character>             <numeric>\n",
       "1_2019_P_M1                   N/A                   9.8\n",
       "2_2019_P_M1                   N/A                   9.9\n",
       "3_2019_P_M1                   N/A                   9.9\n",
       "4_2019_P_M1                   N/A                   9.7\n",
       "5_2019_P_M1                    10                    10\n",
       "...                           ...                   ...\n",
       "20_2019_P_M1                  N/A                    10\n",
       "21_2019_P_M1                  N/A                    10\n",
       "22_2019_P_M1                   10                    10\n",
       "23_2019_P_M1                  N/A                    10\n",
       "24_2019_P_M1                   10                    10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds_add@colData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get count matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 10 × 10 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>1_2019_P_M1</th><th scope=col>2_2019_P_M1</th><th scope=col>3_2019_P_M1</th><th scope=col>4_2019_P_M1</th><th scope=col>5_2019_P_M1</th><th scope=col>6_2019_P_M1</th><th scope=col>7_2019_P_M1</th><th scope=col>8_2019_P_M1</th><th scope=col>9_2019_P_M1</th><th scope=col>10_2019_P_M1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CNAG_00001</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></tr>\n",
       "\t<tr><th scope=row>CNAG_00002</th><td> 158</td><td> 204</td><td> 149</td><td> 176</td><td> 161</td><td> 148</td><td> 172</td><td> 169</td><td> 124</td><td> 119</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00003</th><td> 201</td><td> 156</td><td> 161</td><td> 171</td><td> 162</td><td> 103</td><td> 172</td><td> 170</td><td> 175</td><td> 131</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00004</th><td> 904</td><td> 902</td><td> 941</td><td> 795</td><td> 849</td><td> 688</td><td> 768</td><td> 744</td><td> 659</td><td> 513</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00005</th><td>  22</td><td>  33</td><td>  12</td><td>  15</td><td>  26</td><td>  13</td><td>  29</td><td>  33</td><td>  21</td><td>  24</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00006</th><td>5964</td><td>4854</td><td>4362</td><td>4489</td><td>4368</td><td>4171</td><td>4859</td><td>4267</td><td>4239</td><td>3712</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00007</th><td>3119</td><td>3496</td><td>2628</td><td>2437</td><td>2498</td><td>2594</td><td>2505</td><td>2383</td><td>2086</td><td>2021</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00008</th><td>1481</td><td>1744</td><td>1602</td><td>1391</td><td>1433</td><td>1183</td><td>1313</td><td>1389</td><td> 981</td><td> 934</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00009</th><td> 494</td><td> 750</td><td> 541</td><td> 436</td><td> 502</td><td> 522</td><td> 490</td><td> 470</td><td> 404</td><td> 433</td></tr>\n",
       "\t<tr><th scope=row>CNAG_00010</th><td>1527</td><td>1613</td><td>1564</td><td>1319</td><td>1286</td><td>1020</td><td> 949</td><td>1227</td><td> 757</td><td> 682</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 10 of type int\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & 1\\_2019\\_P\\_M1 & 2\\_2019\\_P\\_M1 & 3\\_2019\\_P\\_M1 & 4\\_2019\\_P\\_M1 & 5\\_2019\\_P\\_M1 & 6\\_2019\\_P\\_M1 & 7\\_2019\\_P\\_M1 & 8\\_2019\\_P\\_M1 & 9\\_2019\\_P\\_M1 & 10\\_2019\\_P\\_M1\\\\\n",
       "\\hline\n",
       "\tCNAG\\_00001 &    0 &    0 &    0 &    0 &    0 &    0 &    0 &    0 &    0 &    0\\\\\n",
       "\tCNAG\\_00002 &  158 &  204 &  149 &  176 &  161 &  148 &  172 &  169 &  124 &  119\\\\\n",
       "\tCNAG\\_00003 &  201 &  156 &  161 &  171 &  162 &  103 &  172 &  170 &  175 &  131\\\\\n",
       "\tCNAG\\_00004 &  904 &  902 &  941 &  795 &  849 &  688 &  768 &  744 &  659 &  513\\\\\n",
       "\tCNAG\\_00005 &   22 &   33 &   12 &   15 &   26 &   13 &   29 &   33 &   21 &   24\\\\\n",
       "\tCNAG\\_00006 & 5964 & 4854 & 4362 & 4489 & 4368 & 4171 & 4859 & 4267 & 4239 & 3712\\\\\n",
       "\tCNAG\\_00007 & 3119 & 3496 & 2628 & 2437 & 2498 & 2594 & 2505 & 2383 & 2086 & 2021\\\\\n",
       "\tCNAG\\_00008 & 1481 & 1744 & 1602 & 1391 & 1433 & 1183 & 1313 & 1389 &  981 &  934\\\\\n",
       "\tCNAG\\_00009 &  494 &  750 &  541 &  436 &  502 &  522 &  490 &  470 &  404 &  433\\\\\n",
       "\tCNAG\\_00010 & 1527 & 1613 & 1564 & 1319 & 1286 & 1020 &  949 & 1227 &  757 &  682\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 10 of type int\n",
       "\n",
       "| <!--/--> | 1_2019_P_M1 | 2_2019_P_M1 | 3_2019_P_M1 | 4_2019_P_M1 | 5_2019_P_M1 | 6_2019_P_M1 | 7_2019_P_M1 | 8_2019_P_M1 | 9_2019_P_M1 | 10_2019_P_M1 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CNAG_00001 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |\n",
       "| CNAG_00002 |  158 |  204 |  149 |  176 |  161 |  148 |  172 |  169 |  124 |  119 |\n",
       "| CNAG_00003 |  201 |  156 |  161 |  171 |  162 |  103 |  172 |  170 |  175 |  131 |\n",
       "| CNAG_00004 |  904 |  902 |  941 |  795 |  849 |  688 |  768 |  744 |  659 |  513 |\n",
       "| CNAG_00005 |   22 |   33 |   12 |   15 |   26 |   13 |   29 |   33 |   21 |   24 |\n",
       "| CNAG_00006 | 5964 | 4854 | 4362 | 4489 | 4368 | 4171 | 4859 | 4267 | 4239 | 3712 |\n",
       "| CNAG_00007 | 3119 | 3496 | 2628 | 2437 | 2498 | 2594 | 2505 | 2383 | 2086 | 2021 |\n",
       "| CNAG_00008 | 1481 | 1744 | 1602 | 1391 | 1433 | 1183 | 1313 | 1389 |  981 |  934 |\n",
       "| CNAG_00009 |  494 |  750 |  541 |  436 |  502 |  522 |  490 |  470 |  404 |  433 |\n",
       "| CNAG_00010 | 1527 | 1613 | 1564 | 1319 | 1286 | 1020 |  949 | 1227 |  757 |  682 |\n",
       "\n"
      ],
      "text/plain": [
       "           1_2019_P_M1 2_2019_P_M1 3_2019_P_M1 4_2019_P_M1 5_2019_P_M1\n",
       "CNAG_00001    0           0           0           0           0       \n",
       "CNAG_00002  158         204         149         176         161       \n",
       "CNAG_00003  201         156         161         171         162       \n",
       "CNAG_00004  904         902         941         795         849       \n",
       "CNAG_00005   22          33          12          15          26       \n",
       "CNAG_00006 5964        4854        4362        4489        4368       \n",
       "CNAG_00007 3119        3496        2628        2437        2498       \n",
       "CNAG_00008 1481        1744        1602        1391        1433       \n",
       "CNAG_00009  494         750         541         436         502       \n",
       "CNAG_00010 1527        1613        1564        1319        1286       \n",
       "           6_2019_P_M1 7_2019_P_M1 8_2019_P_M1 9_2019_P_M1 10_2019_P_M1\n",
       "CNAG_00001    0           0           0           0           0        \n",
       "CNAG_00002  148         172         169         124         119        \n",
       "CNAG_00003  103         172         170         175         131        \n",
       "CNAG_00004  688         768         744         659         513        \n",
       "CNAG_00005   13          29          33          21          24        \n",
       "CNAG_00006 4171        4859        4267        4239        3712        \n",
       "CNAG_00007 2594        2505        2383        2086        2021        \n",
       "CNAG_00008 1183        1313        1389         981         934        \n",
       "CNAG_00009  522         490         470         404         433        \n",
       "CNAG_00010 1020         949        1227         757         682        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts(dds_add)[1:10,1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change design: multiplicative model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in DESeqDataSet(se, design = design, ignoreRank):\n",
      "“some variables in design formula are characters, converting to factors”"
     ]
    }
   ],
   "source": [
    "dds_mult <- DESeqDataSetFromMatrix(\n",
    "    countData2019,                       # Count matrix\n",
    "    columnData2019,                      # metadata\n",
    "    ~ condition + genotype + condition:genotype) # design formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following demonstration, we will use the additive model. The multiplicitive model will be illustrated in the appendix below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dds2019 <- dds_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData'"
      ],
      "text/latex": [
       "'/home/jovyan/work/scratch/analysis\\_output/img/pilotdds2019.RData'"
      ],
      "text/markdown": [
       "'/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData'"
      ],
      "text/plain": [
       "[1] \"/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "curdir <- \"/home/jovyan/work/scratch/analysis_output\"\n",
    "imgdir <- file.path(curdir, \"img\")\n",
    "\n",
    "imgfile <- file.path(imgdir, \"pilotdds2019.RData\")\n",
    "\n",
    "imgfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData:</strong> '6632de5c8a2eed06d8c40c958e6f5d03'"
      ],
      "text/latex": [
       "\\textbf{/home/jovyan/work/scratch/analysis\\textbackslash{}\\_output/img/pilotdds2019.RData:} '6632de5c8a2eed06d8c40c958e6f5d03'"
      ],
      "text/markdown": [
       "**/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData:** '6632de5c8a2eed06d8c40c958e6f5d03'"
      ],
      "text/plain": [
       "/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData \n",
       "                              \"6632de5c8a2eed06d8c40c958e6f5d03\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save(dds2019, file = imgfile)\n",
    "tools::md5sum(imgfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.6.0 (2019-04-26)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Debian GNU/Linux 9 (stretch)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS:   /usr/lib/openblas-base/libblas.so.3\n",
       "LAPACK: /usr/lib/libopenblasp-r0.2.19.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] parallel  stats4    stats     graphics  grDevices utils     datasets \n",
       "[8] methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] DESeq2_1.24.0               SummarizedExperiment_1.14.0\n",
       " [3] DelayedArray_0.10.0         BiocParallel_1.18.0        \n",
       " [5] matrixStats_0.54.0          Biobase_2.44.0             \n",
       " [7] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        \n",
       " [9] IRanges_2.18.1              S4Vectors_0.22.0           \n",
       "[11] BiocGenerics_0.30.0         forcats_0.4.0              \n",
       "[13] stringr_1.4.0               dplyr_0.8.1                \n",
       "[15] purrr_0.3.2                 readr_1.3.1                \n",
       "[17] tidyr_0.8.3                 tibble_2.1.2               \n",
       "[19] ggplot2_3.1.1               tidyverse_1.2.1            \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] nlme_3.1-139           bitops_1.0-6           bit64_0.9-7           \n",
       " [4] lubridate_1.7.4        RColorBrewer_1.1-2     httr_1.4.0            \n",
       " [7] repr_1.0.1             tools_3.6.0            backports_1.1.4       \n",
       "[10] R6_2.4.0               rpart_4.1-15           DBI_1.0.0             \n",
       "[13] Hmisc_4.2-0            lazyeval_0.2.2         colorspace_1.4-1      \n",
       "[16] nnet_7.3-12            withr_2.1.2            tidyselect_0.2.5      \n",
       "[19] gridExtra_2.3          bit_1.1-14             compiler_3.6.0        \n",
       "[22] cli_1.1.0              rvest_0.3.4            htmlTable_1.13.1      \n",
       "[25] xml2_1.2.0             scales_1.0.0           checkmate_1.9.3       \n",
       "[28] genefilter_1.66.0      pbdZMQ_0.3-3           digest_0.6.19         \n",
       "[31] foreign_0.8-71         XVector_0.24.0         base64enc_0.1-3       \n",
       "[34] pkgconfig_2.0.2        htmltools_0.3.6        htmlwidgets_1.3       \n",
       "[37] rlang_0.3.4            readxl_1.3.1           RSQLite_2.1.1         \n",
       "[40] rstudioapi_0.10        generics_0.0.2         jsonlite_1.6          \n",
       "[43] acepack_1.4.1          RCurl_1.95-4.12        magrittr_1.5          \n",
       "[46] GenomeInfoDbData_1.2.1 Formula_1.2-3          Matrix_1.2-17         \n",
       "[49] Rcpp_1.0.1             IRkernel_1.0.1         munsell_0.5.0         \n",
       "[52] stringi_1.4.3          zlibbioc_1.30.0        plyr_1.8.4            \n",
       "[55] blob_1.1.1             grid_3.6.0             crayon_1.3.4          \n",
       "[58] lattice_0.20-38        IRdisplay_0.7.0        haven_2.1.0           \n",
       "[61] splines_3.6.0          annotate_1.62.0        hms_0.4.2             \n",
       "[64] locfit_1.5-9.1         zeallot_0.1.0          knitr_1.23            \n",
       "[67] pillar_1.4.1           uuid_0.1-2             geneplotter_1.62.0    \n",
       "[70] XML_3.98-1.19          glue_1.3.1             evaluate_0.14         \n",
       "[73] latticeExtra_0.6-28    data.table_1.12.2      modelr_0.1.4          \n",
       "[76] vctrs_0.1.0            cellranger_1.1.0       gtable_0.3.0          \n",
       "[79] assertthat_0.2.1       xfun_0.7               xtable_1.8-4          \n",
       "[82] broom_0.5.2            survival_2.44-1.1      memoise_1.1.0         \n",
       "[85] AnnotationDbi_1.46.0   cluster_2.0.8         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
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