WHIM_24hr_R_DESeq2_full_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNA-Seq Analysis Training Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "system(\"sudo chmod -R a+rwx /usr/lib/R/library\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 1: Library Installation\n",
    "\n",
    "First install the R packages that will be used. <strong>This installation may take around 15 minutes.</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/home/jupyter/.R/library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Loading required package: ComplexHeatmap\n",
      "\n",
      "Loading required package: grid\n",
      "\n",
      "========================================\n",
      "ComplexHeatmap version 2.14.0\n",
      "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n",
      "Github page: https://github.com/jokergoo/ComplexHeatmap\n",
      "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n",
      "\n",
      "If you use it in published research, please cite either one:\n",
      "- Gu, Z. Complex Heatmap Visualization. iMeta 2022.\n",
      "- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n",
      "    genomic data. Bioinformatics 2016.\n",
      "\n",
      "\n",
      "The new InteractiveComplexHeatmap package can directly export static \n",
      "complex heatmaps into an interactive Shiny app with zero effort. Have a try!\n",
      "\n",
      "This message can be suppressed by:\n",
      "  suppressPackageStartupMessages(library(ComplexHeatmap))\n",
      "========================================\n",
      "\n",
      "\n",
      "Loading required package: DESeq2\n",
      "\n",
      "Loading required package: S4Vectors\n",
      "\n",
      "Loading required package: stats4\n",
      "\n",
      "Loading required package: BiocGenerics\n",
      "\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n",
      "    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n",
      "    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n",
      "    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n",
      "    Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n",
      "    table, tapply, union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    expand.grid, I, unname\n",
      "\n",
      "\n",
      "Loading required package: IRanges\n",
      "\n",
      "Loading required package: GenomicRanges\n",
      "\n",
      "Loading required package: GenomeInfoDb\n",
      "\n",
      "Loading required package: SummarizedExperiment\n",
      "\n",
      "Loading required package: MatrixGenerics\n",
      "\n",
      "Loading required package: matrixStats\n",
      "\n",
      "\n",
      "Attaching package: ‘MatrixGenerics’\n",
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      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,\n",
      "    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,\n",
      "    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,\n",
      "    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,\n",
      "    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,\n",
      "    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,\n",
      "    colWeightedMeans, colWeightedMedians, colWeightedSds,\n",
      "    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,\n",
      "    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,\n",
      "    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,\n",
      "    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,\n",
      "    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,\n",
      "    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,\n",
      "    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
      "    rowWeightedSds, rowWeightedVars\n",
      "\n",
      "\n",
      "Loading required package: Biobase\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘Biobase’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:MatrixGenerics’:\n",
      "\n",
      "    rowMedians\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    anyMissing, rowMedians\n",
      "\n",
      "\n",
      "Loading required package: EnhancedVolcano\n",
      "\n",
      "Loading required package: ggplot2\n",
      "\n",
      "Loading required package: ggrepel\n",
      "\n",
      "Loading required package: dplyr\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:Biobase’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:matrixStats’:\n",
      "\n",
      "    count\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:GenomicRanges’:\n",
      "\n",
      "    intersect, setdiff, union\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:GenomeInfoDb’:\n",
      "\n",
      "    intersect\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:IRanges’:\n",
      "\n",
      "    collapse, desc, intersect, setdiff, slice, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    first, intersect, rename, setdiff, setequal, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Loading required package: pheatmap\n",
      "\n",
      "\n",
      "Attaching package: ‘pheatmap’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:ComplexHeatmap’:\n",
      "\n",
      "    pheatmap\n",
      "\n",
      "\n",
      "Loading required package: ggfortify\n",
      "\n"
     ]
    }
   ],
   "source": [
    "###install the libraries first, if required\n",
    "install.packages(\"BiocManager\")\n",
    "\n",
    "if (!require(\"ComplexHeatmap\"))\n",
    "    BiocManager::install(\"ComplexHeatmap\")\n",
    "\n",
    "if (!require(\"DESeq2\"))\n",
    "    BiocManager::install(\"DESeq2\")\n",
    "\n",
    "if (!require(\"EnhancedVolcano\"))\n",
    "    BiocManager::install('EnhancedVolcano')\n",
    "\n",
    "if (!require(\"dplyr\"))\n",
    "    install.packages(\"dplyr\")\n",
    "\n",
    "if (!require(\"pheatmap\"))\n",
    "    install.packages(\"pheatmap\")\n",
    "\n",
    "if (!require(\"ggrepel\"))\n",
    "    install.packages(\"ggrepel\")\n",
    "\n",
    "if (!require(\"ggfortify\"))\n",
    "    install.packages(\"ggfortify\")\n",
    "\n",
    "\n",
    "###load the libraries\n",
    "library(DESeq2)\n",
    "library(dplyr)\n",
    "library(ComplexHeatmap)\n",
    "library(EnhancedVolcano)\n",
    "library(ggplot2)\n",
    "library(ggrepel)\n",
    "library(ggfortify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: S4Vectors\n",
      "\n",
      "Loading required package: stats4\n",
      "\n",
      "Loading required package: BiocGenerics\n",
      "\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n",
      "    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n",
      "    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n",
      "    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n",
      "    Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n",
      "    table, tapply, union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    expand.grid, I, unname\n",
      "\n",
      "\n",
      "Loading required package: IRanges\n",
      "\n",
      "Loading required package: GenomicRanges\n",
      "\n",
      "Loading required package: GenomeInfoDb\n",
      "\n",
      "Loading required package: SummarizedExperiment\n",
      "\n",
      "Loading required package: MatrixGenerics\n",
      "\n",
      "Loading required package: matrixStats\n",
      "\n",
      "\n",
      "Attaching package: ‘MatrixGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,\n",
      "    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,\n",
      "    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,\n",
      "    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,\n",
      "    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,\n",
      "    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,\n",
      "    colWeightedMeans, colWeightedMedians, colWeightedSds,\n",
      "    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,\n",
      "    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,\n",
      "    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,\n",
      "    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,\n",
      "    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,\n",
      "    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,\n",
      "    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
      "    rowWeightedSds, rowWeightedVars\n",
      "\n",
      "\n",
      "Loading required package: Biobase\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘Biobase’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:MatrixGenerics’:\n",
      "\n",
      "    rowMedians\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    anyMissing, rowMedians\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(\"DESeq2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### STEP 2: Downloading Data\n",
    "\n",
    "First we need to download the readcounts we produced in the previous step.\n",
    "\n",
    "(Note this jupyter notebook file is set to use R, as opposed to the previous notebook which primarily used Python. \n",
    "\n",
    "In this setup, bash commands are called using the system() function, instead of with the '!' prefix.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Create folders we will store downloaded files in\n",
    "system(\"mkdir -p data/gene_counts/single\", intern=TRUE)\n",
    "system(\"mkdir -p data/gene_counts/merged\", intern=TRUE)\n",
    "system(\"mkdir -p data/reference\", intern=TRUE)\n",
    "\n",
    "# Download genecount files\n",
    "system(\"gsutil cp gs://hon_350/whim_results/*genes.results data/gene_counts/single\", intern=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining files into a single table.\n",
    "\n",
    "In order to run our pipeline, we need to do two things to our genecount files. First, we need to combine them into a single easy to use file, and second, we need to read that file into the R environment as a R Dataframe.\n",
    "\n",
    "We will combine our tables together such that rows will represent gene IDs, and each column will hold the TPM counts from each genecount file.\n",
    "\n",
    "The below code will both merge files into a single file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"data/gene_counts/single//SA60184.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60185.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60186.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60187.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60188.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60189.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60190.genes.results\"\n",
      "[1] \"data/gene_counts/single//SA60191.genes.results\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>GeneID</th><th scope=col>SA60184</th><th scope=col>SA60185</th><th scope=col>SA60186</th><th scope=col>SA60187</th><th scope=col>SA60188</th><th scope=col>SA60189</th><th scope=col>SA60190</th><th scope=col>SA60191</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ENSDARG00000000001</td><td> 21.00</td><td> 14.00</td><td> 12.00</td><td> 22.00</td><td>  9.00</td><td> 16.00</td><td>  5.00</td><td> 13.00</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ENSDARG00000000002</td><td> 52.00</td><td> 51.00</td><td> 47.00</td><td> 63.00</td><td> 41.00</td><td> 58.00</td><td> 60.00</td><td> 33.00</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ENSDARG00000000018</td><td> 87.00</td><td> 93.00</td><td> 80.00</td><td>139.00</td><td> 64.00</td><td> 84.00</td><td> 65.00</td><td> 65.00</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ENSDARG00000000019</td><td>289.63</td><td>285.92</td><td>303.06</td><td>378.21</td><td>199.40</td><td>337.04</td><td>203.38</td><td>151.89</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ENSDARG00000000068</td><td> 75.00</td><td> 49.57</td><td> 64.30</td><td> 86.02</td><td> 43.00</td><td> 71.00</td><td> 29.00</td><td> 36.00</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ENSDARG00000000069</td><td>221.00</td><td>182.07</td><td>195.38</td><td>245.00</td><td> 97.03</td><td>167.00</td><td> 86.00</td><td> 94.00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & GeneID & SA60184 & SA60185 & SA60186 & SA60187 & SA60188 & SA60189 & SA60190 & SA60191\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & ENSDARG00000000001 &  21.00 &  14.00 &  12.00 &  22.00 &   9.00 &  16.00 &   5.00 &  13.00\\\\\n",
       "\t2 & ENSDARG00000000002 &  52.00 &  51.00 &  47.00 &  63.00 &  41.00 &  58.00 &  60.00 &  33.00\\\\\n",
       "\t3 & ENSDARG00000000018 &  87.00 &  93.00 &  80.00 & 139.00 &  64.00 &  84.00 &  65.00 &  65.00\\\\\n",
       "\t4 & ENSDARG00000000019 & 289.63 & 285.92 & 303.06 & 378.21 & 199.40 & 337.04 & 203.38 & 151.89\\\\\n",
       "\t5 & ENSDARG00000000068 &  75.00 &  49.57 &  64.30 &  86.02 &  43.00 &  71.00 &  29.00 &  36.00\\\\\n",
       "\t6 & ENSDARG00000000069 & 221.00 & 182.07 & 195.38 & 245.00 &  97.03 & 167.00 &  86.00 &  94.00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | GeneID &lt;chr&gt; | SA60184 &lt;dbl&gt; | SA60185 &lt;dbl&gt; | SA60186 &lt;dbl&gt; | SA60187 &lt;dbl&gt; | SA60188 &lt;dbl&gt; | SA60189 &lt;dbl&gt; | SA60190 &lt;dbl&gt; | SA60191 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | ENSDARG00000000001 |  21.00 |  14.00 |  12.00 |  22.00 |   9.00 |  16.00 |   5.00 |  13.00 |\n",
       "| 2 | ENSDARG00000000002 |  52.00 |  51.00 |  47.00 |  63.00 |  41.00 |  58.00 |  60.00 |  33.00 |\n",
       "| 3 | ENSDARG00000000018 |  87.00 |  93.00 |  80.00 | 139.00 |  64.00 |  84.00 |  65.00 |  65.00 |\n",
       "| 4 | ENSDARG00000000019 | 289.63 | 285.92 | 303.06 | 378.21 | 199.40 | 337.04 | 203.38 | 151.89 |\n",
       "| 5 | ENSDARG00000000068 |  75.00 |  49.57 |  64.30 |  86.02 |  43.00 |  71.00 |  29.00 |  36.00 |\n",
       "| 6 | ENSDARG00000000069 | 221.00 | 182.07 | 195.38 | 245.00 |  97.03 | 167.00 |  86.00 |  94.00 |\n",
       "\n"
      ],
      "text/plain": [
       "  GeneID             SA60184 SA60185 SA60186 SA60187 SA60188 SA60189 SA60190\n",
       "1 ENSDARG00000000001  21.00   14.00   12.00   22.00    9.00   16.00    5.00 \n",
       "2 ENSDARG00000000002  52.00   51.00   47.00   63.00   41.00   58.00   60.00 \n",
       "3 ENSDARG00000000018  87.00   93.00   80.00  139.00   64.00   84.00   65.00 \n",
       "4 ENSDARG00000000019 289.63  285.92  303.06  378.21  199.40  337.04  203.38 \n",
       "5 ENSDARG00000000068  75.00   49.57   64.30   86.02   43.00   71.00   29.00 \n",
       "6 ENSDARG00000000069 221.00  182.07  195.38  245.00   97.03  167.00   86.00 \n",
       "  SA60191\n",
       "1  13.00 \n",
       "2  33.00 \n",
       "3  65.00 \n",
       "4 151.89 \n",
       "5  36.00 \n",
       "6  94.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the directory to fill up a list of all\n",
    "# gene count files to merge\n",
    "file_list <- list.files('data/gene_counts/single/', full.names = TRUE)\n",
    "\n",
    "for (file in file_list){\n",
    "  print(file)\n",
    "  # if the merged dataset doesn't exist, \n",
    "  # first, create a base dataframe to append\n",
    "  # further columns to.\n",
    "  if (!exists(\"dataset\")){\n",
    "    dataset <- read.table(file, header=TRUE, sep=\"\\t\")\n",
    "    dataset <- dataset[,c(1,5)]\n",
    "  }\n",
    " \n",
    "  # if the merged dataset does exist, \\\n",
    "  # append TPM columns to it\n",
    "  else {\n",
    "    temp_dataset <-read.table(file, header=TRUE, sep=\"\\t\")\n",
    "    # print(file)\n",
    "    dataset<-cbind(dataset, temp_dataset[,5])\n",
    "  }\n",
    " \n",
    "}\n",
    "\n",
    "# next we need to name the columns\n",
    "# of our combined dataset.\n",
    "\n",
    "# we can just use the filenames in the directory\n",
    "colnames <- c(\"GeneID\",list.files('data/gene_counts/single/'))\n",
    "# remove the filename extension\n",
    "colnames <- gsub(\".genes.results\", \"\", colnames)\n",
    "# set the columns\n",
    "names(dataset) <- colnames\n",
    "\n",
    "# write out the merged file for later use \n",
    "write.table(dataset,\"data/gene_counts/merged/merged_counts.txt\",sep=\"\\t\",row.names=FALSE)\n",
    "\n",
    "# example of what the file looks like\n",
    "head(dataset)\n",
    "\n",
    "# empty variable for now\n",
    "rm(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### STEP 2: Reading in Data\n",
    "\n",
    "To be manipulated in R, files need to be read-in as R dataframes. \n",
    "\n",
    "This read-in step often involves reformatting or adjusting the data.\n",
    "\n",
    "For instance, later on we will process the data using processes such as logarithms. So, to prevent errors, we will replace all readcount values of '0' in our data, with '1'. This will change the data only slightly, and will prevent these zeroes from causing an 'undefined' or 'N/A' math error in future steps.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SA60184</th><th scope=col>SA60185</th><th scope=col>SA60186</th><th scope=col>SA60187</th><th scope=col>SA60188</th><th scope=col>SA60189</th><th scope=col>SA60190</th><th scope=col>SA60191</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSDARG00000000001</th><td> 21</td><td> 14</td><td> 12</td><td> 22</td><td>  9</td><td> 16</td><td>  5</td><td> 13</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000002</th><td> 52</td><td> 51</td><td> 47</td><td> 63</td><td> 41</td><td> 58</td><td> 60</td><td> 33</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000018</th><td> 87</td><td> 93</td><td> 80</td><td>139</td><td> 64</td><td> 84</td><td> 65</td><td> 65</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000019</th><td>290</td><td>286</td><td>303</td><td>378</td><td>199</td><td>337</td><td>203</td><td>152</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000068</th><td> 75</td><td> 50</td><td> 64</td><td> 86</td><td> 43</td><td> 71</td><td> 29</td><td> 36</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000069</th><td>221</td><td>182</td><td>195</td><td>245</td><td> 97</td><td>167</td><td> 86</td><td> 94</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & SA60184 & SA60185 & SA60186 & SA60187 & SA60188 & SA60189 & SA60190 & SA60191\\\\\n",
       "\\hline\n",
       "\tENSDARG00000000001 &  21 &  14 &  12 &  22 &   9 &  16 &   5 &  13\\\\\n",
       "\tENSDARG00000000002 &  52 &  51 &  47 &  63 &  41 &  58 &  60 &  33\\\\\n",
       "\tENSDARG00000000018 &  87 &  93 &  80 & 139 &  64 &  84 &  65 &  65\\\\\n",
       "\tENSDARG00000000019 & 290 & 286 & 303 & 378 & 199 & 337 & 203 & 152\\\\\n",
       "\tENSDARG00000000068 &  75 &  50 &  64 &  86 &  43 &  71 &  29 &  36\\\\\n",
       "\tENSDARG00000000069 & 221 & 182 & 195 & 245 &  97 & 167 &  86 &  94\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | SA60184 | SA60185 | SA60186 | SA60187 | SA60188 | SA60189 | SA60190 | SA60191 |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| ENSDARG00000000001 |  21 |  14 |  12 |  22 |   9 |  16 |   5 |  13 |\n",
       "| ENSDARG00000000002 |  52 |  51 |  47 |  63 |  41 |  58 |  60 |  33 |\n",
       "| ENSDARG00000000018 |  87 |  93 |  80 | 139 |  64 |  84 |  65 |  65 |\n",
       "| ENSDARG00000000019 | 290 | 286 | 303 | 378 | 199 | 337 | 203 | 152 |\n",
       "| ENSDARG00000000068 |  75 |  50 |  64 |  86 |  43 |  71 |  29 |  36 |\n",
       "| ENSDARG00000000069 | 221 | 182 | 195 | 245 |  97 | 167 |  86 |  94 |\n",
       "\n"
      ],
      "text/plain": [
       "                   SA60184 SA60185 SA60186 SA60187 SA60188 SA60189 SA60190\n",
       "ENSDARG00000000001  21      14      12      22       9      16       5    \n",
       "ENSDARG00000000002  52      51      47      63      41      58      60    \n",
       "ENSDARG00000000018  87      93      80     139      64      84      65    \n",
       "ENSDARG00000000019 290     286     303     378     199     337     203    \n",
       "ENSDARG00000000068  75      50      64      86      43      71      29    \n",
       "ENSDARG00000000069 221     182     195     245      97     167      86    \n",
       "                   SA60191\n",
       "ENSDARG00000000001  13    \n",
       "ENSDARG00000000002  33    \n",
       "ENSDARG00000000018  65    \n",
       "ENSDARG00000000019 152    \n",
       "ENSDARG00000000068  36    \n",
       "ENSDARG00000000069  94    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read-in the merged gene count file\n",
    "# to a dataframe variable we named 'read_counts'\n",
    "read_counts <- read.table('data/gene_counts/merged/merged_counts.txt',head=TRUE)\n",
    "\n",
    "# replace all count entries of '0' in the raw gene count file with '1'.\n",
    "read_counts[read_counts==0] <- 1\n",
    "\n",
    "# assign rownames using the gene ID column\n",
    "# and remove the gene ID column\n",
    "# this format is important for DESEQ2\n",
    "rnaseqMatrix <- data.matrix(round(read_counts[,-1]))\n",
    "rownames(rnaseqMatrix) <- read_counts[,1]\n",
    "\n",
    "#example of how the read-in data frame looks\n",
    "\n",
    "head(rnaseqMatrix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 4: Specifying Experimental Design\n",
    "\n",
    "Next specify the experimental design. \n",
    "\n",
    "DESEQ2 uses an experimental design table for specifying grouping of samples.\n",
    "\n",
    "This is used, for instance to group samples together and output information about the statistical differences in gene expression between genes in specified groups.\n",
    "\n",
    "An experimental design table can be created however you want and then read into R as a dataframe, or created in R itself, as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"So our deseq2 experimental design table might look like this:\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>Treatment</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SA60184</th><td>SA60184</td><td>MUT</td></tr>\n",
       "\t<tr><th scope=row>SA60185</th><td>SA60185</td><td>MUT</td></tr>\n",
       "\t<tr><th scope=row>SA60186</th><td>SA60186</td><td>MUT</td></tr>\n",
       "\t<tr><th scope=row>SA60187</th><td>SA60187</td><td>MUT</td></tr>\n",
       "\t<tr><th scope=row>SA60188</th><td>SA60188</td><td>WT </td></tr>\n",
       "\t<tr><th scope=row>SA60189</th><td>SA60189</td><td>WT </td></tr>\n",
       "\t<tr><th scope=row>SA60190</th><td>SA60190</td><td>WT </td></tr>\n",
       "\t<tr><th scope=row>SA60191</th><td>SA60191</td><td>WT </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & ID & Treatment\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tSA60184 & SA60184 & MUT\\\\\n",
       "\tSA60185 & SA60185 & MUT\\\\\n",
       "\tSA60186 & SA60186 & MUT\\\\\n",
       "\tSA60187 & SA60187 & MUT\\\\\n",
       "\tSA60188 & SA60188 & WT \\\\\n",
       "\tSA60189 & SA60189 & WT \\\\\n",
       "\tSA60190 & SA60190 & WT \\\\\n",
       "\tSA60191 & SA60191 & WT \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 2\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | Treatment &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| SA60184 | SA60184 | MUT |\n",
       "| SA60185 | SA60185 | MUT |\n",
       "| SA60186 | SA60186 | MUT |\n",
       "| SA60187 | SA60187 | MUT |\n",
       "| SA60188 | SA60188 | WT  |\n",
       "| SA60189 | SA60189 | WT  |\n",
       "| SA60190 | SA60190 | WT  |\n",
       "| SA60191 | SA60191 | WT  |\n",
       "\n"
      ],
      "text/plain": [
       "        ID      Treatment\n",
       "SA60184 SA60184 MUT      \n",
       "SA60185 SA60185 MUT      \n",
       "SA60186 SA60186 MUT      \n",
       "SA60187 SA60187 MUT      \n",
       "SA60188 SA60188 WT       \n",
       "SA60189 SA60189 WT       \n",
       "SA60190 SA60190 WT       \n",
       "SA60191 SA60191 WT       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use R to create a DESEQ2 design table\n",
    "\n",
    "# first, the sample column will just be \n",
    "# sample IDs\n",
    "samples_ID <- data.frame(matrix(colnames(rnaseqMatrix),ncol=1))\n",
    "samples_ID <- c(\"SA60184\",\"SA60185\",\"SA60186\",\"SA60187\",\n",
    "                \"SA60188\",\"SA60189\",\"SA60190\",\"SA60191\")\n",
    "\n",
    "# second, \n",
    "# our experiment involves looks at IAV \n",
    "# and control zebrafish.\n",
    "\n",
    "# there are eight samples\n",
    "# 4 WHIM mutants at 24 hpi, and 4 sibling (wild-type) at 24 hpi.\n",
    "\n",
    "# SA60184 WHIM_GFP_24hpi_2\n",
    "# SA60185 WHIM_GFP_24hpi_3\n",
    "# SA60186 WHIM_GFP_24hpi_4\n",
    "# SA60187 WHIM_GFP_24hpi_5\n",
    "# SA60188 WHIM_sibling_24hpi_1\n",
    "# SA60189 WHIM_sibling_24hpi_3\n",
    "# SA60190 WHIM_sibling_24hpi_4\n",
    "# SA60191 WHIM_sibling_24hpi_5\n",
    "\n",
    "samples_treatment <- data.frame(matrix(c(\"MUT\",\"MUT\",\"MUT\",\"MUT\",\n",
    "                               \"WT\",\"WT\",\"WT\",\"WT\"),ncol=1))\n",
    "\n",
    "\n",
    "samples <- cbind(samples_ID, samples_treatment)\n",
    "names(samples) <- c(\"ID\",\"Treatment\")\n",
    "rownames(samples) <- samples[,1]\n",
    "\n",
    "print(\"So our deseq2 experimental design table might look like this:\")\n",
    "samples\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 5: Creating Deseq2 Object and Normalizing Data.\n",
    "\n",
    "Now use the treatment design matrix in combination with the readcount matrix to create a deseq2 object. \n",
    "\n",
    "Once created, this is also a good opportunity to filter out lowly expresseed genes, and to inspect the pre-normalized data using pairwise comparison plots.\n",
    "\n",
    "Finally, the deseq2 analysis can be run on the deseq2 object. Normalization is automatically preformed during the DESeq() step, and data is then transformed using deseq2s 'regularized log', more information about both can be find in the deseq2 documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "converting counts to integer mode\n",
      "\n",
      "Warning message in DESeqDataSet(se, design = design, ignoreRank):\n",
      "“some variables in design formula are characters, converting to factors”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "\n",
      "estimating dispersions\n",
      "\n",
      "gene-wise dispersion estimates\n",
      "\n",
      "mean-dispersion relationship\n",
      "\n",
      "final dispersion estimates\n",
      "\n",
      "fitting model and testing\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SA60184</th><th scope=col>SA60185</th><th scope=col>SA60186</th><th scope=col>SA60187</th><th scope=col>SA60188</th><th scope=col>SA60189</th><th scope=col>SA60190</th><th scope=col>SA60191</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSDARG00000000001</th><td>3.812936</td><td>3.715436</td><td>3.680984</td><td>3.775904</td><td>3.706801</td><td>3.732657</td><td>3.577631</td><td>3.825922</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000002</th><td>5.524703</td><td>5.582993</td><td>5.549833</td><td>5.544134</td><td>5.692871</td><td>5.616237</td><td>5.885615</td><td>5.600779</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000018</th><td>6.237423</td><td>6.372759</td><td>6.279429</td><td>6.482334</td><td>6.388728</td><td>6.239232</td><td>6.327569</td><td>6.444657</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000019</th><td>7.916819</td><td>8.011344</td><td>8.093250</td><td>8.021733</td><td>8.044364</td><td>8.098065</td><td>7.968792</td><td>7.853041</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000068</th><td>5.829228</td><td>5.636160</td><td>5.813442</td><td>5.817255</td><td>5.791341</td><td>5.816017</td><td>5.514058</td><td>5.720693</td></tr>\n",
       "\t<tr><th scope=row>ENSDARG00000000069</th><td>7.388125</td><td>7.311487</td><td>7.398952</td><td>7.340030</td><td>7.112928</td><td>7.167381</td><td>6.936887</td><td>7.138074</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & SA60184 & SA60185 & SA60186 & SA60187 & SA60188 & SA60189 & SA60190 & SA60191\\\\\n",
       "\\hline\n",
       "\tENSDARG00000000001 & 3.812936 & 3.715436 & 3.680984 & 3.775904 & 3.706801 & 3.732657 & 3.577631 & 3.825922\\\\\n",
       "\tENSDARG00000000002 & 5.524703 & 5.582993 & 5.549833 & 5.544134 & 5.692871 & 5.616237 & 5.885615 & 5.600779\\\\\n",
       "\tENSDARG00000000018 & 6.237423 & 6.372759 & 6.279429 & 6.482334 & 6.388728 & 6.239232 & 6.327569 & 6.444657\\\\\n",
       "\tENSDARG00000000019 & 7.916819 & 8.011344 & 8.093250 & 8.021733 & 8.044364 & 8.098065 & 7.968792 & 7.853041\\\\\n",
       "\tENSDARG00000000068 & 5.829228 & 5.636160 & 5.813442 & 5.817255 & 5.791341 & 5.816017 & 5.514058 & 5.720693\\\\\n",
       "\tENSDARG00000000069 & 7.388125 & 7.311487 & 7.398952 & 7.340030 & 7.112928 & 7.167381 & 6.936887 & 7.138074\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | SA60184 | SA60185 | SA60186 | SA60187 | SA60188 | SA60189 | SA60190 | SA60191 |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| ENSDARG00000000001 | 3.812936 | 3.715436 | 3.680984 | 3.775904 | 3.706801 | 3.732657 | 3.577631 | 3.825922 |\n",
       "| ENSDARG00000000002 | 5.524703 | 5.582993 | 5.549833 | 5.544134 | 5.692871 | 5.616237 | 5.885615 | 5.600779 |\n",
       "| ENSDARG00000000018 | 6.237423 | 6.372759 | 6.279429 | 6.482334 | 6.388728 | 6.239232 | 6.327569 | 6.444657 |\n",
       "| ENSDARG00000000019 | 7.916819 | 8.011344 | 8.093250 | 8.021733 | 8.044364 | 8.098065 | 7.968792 | 7.853041 |\n",
       "| ENSDARG00000000068 | 5.829228 | 5.636160 | 5.813442 | 5.817255 | 5.791341 | 5.816017 | 5.514058 | 5.720693 |\n",
       "| ENSDARG00000000069 | 7.388125 | 7.311487 | 7.398952 | 7.340030 | 7.112928 | 7.167381 | 6.936887 | 7.138074 |\n",
       "\n"
      ],
      "text/plain": [
       "                   SA60184  SA60185  SA60186  SA60187  SA60188  SA60189 \n",
       "ENSDARG00000000001 3.812936 3.715436 3.680984 3.775904 3.706801 3.732657\n",
       "ENSDARG00000000002 5.524703 5.582993 5.549833 5.544134 5.692871 5.616237\n",
       "ENSDARG00000000018 6.237423 6.372759 6.279429 6.482334 6.388728 6.239232\n",
       "ENSDARG00000000019 7.916819 8.011344 8.093250 8.021733 8.044364 8.098065\n",
       "ENSDARG00000000068 5.829228 5.636160 5.813442 5.817255 5.791341 5.816017\n",
       "ENSDARG00000000069 7.388125 7.311487 7.398952 7.340030 7.112928 7.167381\n",
       "                   SA60190  SA60191 \n",
       "ENSDARG00000000001 3.577631 3.825922\n",
       "ENSDARG00000000002 5.885615 5.600779\n",
       "ENSDARG00000000018 6.327569 6.444657\n",
       "ENSDARG00000000019 7.968792 7.853041\n",
       "ENSDARG00000000068 5.514058 5.720693\n",
       "ENSDARG00000000069 6.936887 7.138074"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create the DEseq2DataSet object\n",
    "deseq2Data <- DESeqDataSetFromMatrix(countData = rnaseqMatrix, colData = samples, design = ~ Treatment)\n",
    "\n",
    "# Perform pre-filtering of the data\n",
    "deseq2Data <- deseq2Data[rowSums(counts(deseq2Data)) > 10, ]\n",
    "\n",
    "# Inspect pre-normalized data \n",
    "png(\"pairwise_scatterplots.png\")\n",
    "pairs(log2((counts(deseq2Data)+1)))\n",
    "dev.off()\n",
    "\n",
    "png(\"boxplot.png\")\n",
    "boxplot(log2((counts(deseq2Data)+1)))\n",
    "dev.off()\n",
    "\n",
    "# Run pipeline for differential expression steps\n",
    "deseq2Data <- DESeq(deseq2Data)\n",
    "\n",
    "#perform rlog normalization on deseq2 object\n",
    "rld <- rlog(deseq2Data, blind=FALSE)\n",
    "\n",
    "#a handy variable so we can easily reference the rlog genecount matrix in the future, as deseq objects contain many tables of data.\n",
    "rlogcounts <- assay(rld)\n",
    "rownames(rlogcounts) <- rownames(deseq2Data)\n",
    "\n",
    "head(rlogcounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization of gene readcounts across samples will help to minimize sample variation noise and ensure more accurate comparisons.\n",
    "\n",
    "Normalization of data can is often verified by inspecting by various plots, for instance histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SRoICSFkKCBkBRCggZCUggJGghJISRo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\nCTCAkAADCAkwgJAAAwgJMOD/A8JooJd1hxPwAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Histogram of rlogcounts”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(rlogcounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "png(\"boxplot_norm.png\")\n",
    "boxplot(rlogcounts)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or pairwise comparison plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "png(\"pairwise_scatterplots_norm.png\")\n",
    "pairs(rlogcounts)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the plots of the pre-normalized and normalized data to see the effect of normalization.\n",
    "\n",
    "Notice how the samples in the box plot, and in-group samples in the pair plots display similar distributions.\n",
    "\n",
    "Looking at normalized plots can be an easy way to look out for any obvious inconsistencies or errors in the sample data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 7: PCA Plot\n",
    "\n",
    "We can now begin to produce plots to further analyze the differences between groups.\n",
    "\n",
    "PCA plots can be an effective way to visualize variation within and between sample sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_counts <- prcomp(t(rlogcounts))\n",
    "png(\"whim_pca.png\")\n",
    "autoplot(pca_counts, data = samples, colour=\"Treatment\",size=3)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plots can be output as above, or also saved as pdf or image files, as below. Try to navigate and click to the created file, after you run the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(\"data/gene_counts/PCA_Plot.pdf\")\n",
    "autoplot(pca_counts, data = samples, colour=\"Treatment\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 8: Contrast Comparisons\n",
    "\n",
    "Contrast comparisons between two treatment groups can be performed using the deseq2 tool to identify differentially up and down regulated genes.\n",
    "\n",
    "Using R, these results can be summarized, reordered, and/or trimmed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 23381 with nonzero total read count\n",
      "adjusted p-value < 0.1\n",
      "LFC > 0 (up)       : 202, 0.86%\n",
      "LFC < 0 (down)     : 184, 0.79%\n",
      "outliers [1]       : 1, 0.0043%\n",
      "low counts [2]     : 10878, 47%\n",
      "(mean count < 25)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n",
      "[1] \"Number of genes under a p value of 0.05\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "1777"
      ],
      "text/latex": [
       "1777"
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      "text/markdown": [
       "1777"
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      "text/plain": [
       "[1] 1777"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Number of genes under a p adjusted value of 0.05\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "219"
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       "219"
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      "text/markdown": [
       "219"
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       "[1] 219"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Output the results of comparing WT and WHIM group\n",
    "res_IAV_CTRL <- results(deseq2Data, contrast=c(\"Treatment\",\"MUT\",\"WT\"))\n",
    "summary(res_IAV_CTRL)\n",
    "print(\"Number of genes under a p value of 0.05\")\n",
    "sum(res_IAV_CTRL$pvalue < 0.05, na.rm=TRUE)\n",
    "print(\"Number of genes under a p adjusted value of 0.05\")\n",
    "sum(res_IAV_CTRL$padj < 0.05, na.rm=TRUE)\n",
    "\n",
    "\n",
    "#deseq2 optionally output some padj values as 'NA' when genecounts meet certain criteria\n",
    "#that criteria can be found here: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#pvaluesNA\n",
    "#for practical uses, one could replace the NA values with '1', essentially NA values not significant.\n",
    "res_IAV_CTRL$padj[is.na(res_IAV_CTRL$padj)] <- 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 9: Annotating and Exporting Results\n",
    "\n",
    "These differentially expressed genes can be annotated and exported.\n",
    "\n",
    "Combining gene counts with gene annotation isn't always simple, and will vary heavily depending on the annotation file itself.\n",
    "\n",
    "For example, when merging genecounts by 'gene id' with feature tables from NCBI's assembly database, its very common to have duplicate rows, as a single 'gene id' may match to multiple features. \n",
    "\n",
    "Depending on how you want to use your annotated results, you may decide to further reformat your table in whatever suits your further analysis best, for instance by downloading and using a spreadsheet editor.\n",
    "\n",
    "Because the transcript table for m chelonae we have here is so simple, and contains essentially only one 'CDS' or '..RNA' feature for every gene identifier we are using, it is easy for us to reformat it here in the way we want just using R code. However, it is important to note that, again, this is very often not the case, and reformatting will vary depending on both the annotation file itself, and what that annotation file will be used for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Gene_stable_ID</th><th scope=col>baseMean</th><th scope=col>log2FoldChange</th><th scope=col>lfcSE</th><th scope=col>stat</th><th scope=col>pvalue</th><th scope=col>padj</th><th scope=col>Gene.name</th><th scope=col>Gene.description</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;I&lt;chr&gt;&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ENSDARG00000000001</td><td> 13.29456</td><td> 0.174587923</td><td>0.3941145</td><td> 0.442987767</td><td>0.657774583</td><td>1.00000000</td><td>slc35a5  </td><td>solute carrier family 35 member A5 [Source:NCBI gene;Acc:368418]                           </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ENSDARG00000000002</td><td> 50.00177</td><td>-0.351703192</td><td>0.2168393</td><td>-1.621953324</td><td>0.104813343</td><td>0.60930312</td><td>ccdc80   </td><td>coiled-coil domain containing 80 [Source:NCBI gene;Acc:368419]                             </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ENSDARG00000000018</td><td> 81.80772</td><td>-0.001347082</td><td>0.1829983</td><td>-0.007361171</td><td>0.994126688</td><td>0.99934193</td><td>nrf1     </td><td>nuclear respiratory factor 1 [Source:NCBI gene;Acc:64604]                                  </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ENSDARG00000000019</td><td>256.71348</td><td> 0.022143663</td><td>0.1297546</td><td> 0.170658009</td><td>0.864492682</td><td>0.98022458</td><td>ube2h    </td><td>ubiquitin-conjugating enzyme E2H (UBC8 homolog, yeast) [Source:ZFIN;Acc:ZDB-GENE-030616-67]</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ENSDARG00000000068</td><td> 53.69485</td><td> 0.132869173</td><td>0.2179671</td><td> 0.609583554</td><td>0.542137709</td><td>0.91434963</td><td>slc9a3r1a</td><td>SLC9A3 regulator 1a [Source:ZFIN;Acc:ZDB-GENE-031006-7]                                    </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ENSDARG00000000069</td><td>150.64439</td><td> 0.440058588</td><td>0.1460032</td><td> 3.014032914</td><td>0.002577998</td><td>0.09028049</td><td>dap      </td><td>death-associated protein [Source:ZFIN;Acc:ZDB-GENE-000511-3]                               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & Gene\\_stable\\_ID & baseMean & log2FoldChange & lfcSE & stat & pvalue & padj & Gene.name & Gene.description\\\\\n",
       "  & <I<chr>> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & ENSDARG00000000001 &  13.29456 &  0.174587923 & 0.3941145 &  0.442987767 & 0.657774583 & 1.00000000 & slc35a5   & solute carrier family 35 member A5 {[}Source:NCBI gene;Acc:368418{]}                           \\\\\n",
       "\t2 & ENSDARG00000000002 &  50.00177 & -0.351703192 & 0.2168393 & -1.621953324 & 0.104813343 & 0.60930312 & ccdc80    & coiled-coil domain containing 80 {[}Source:NCBI gene;Acc:368419{]}                             \\\\\n",
       "\t3 & ENSDARG00000000018 &  81.80772 & -0.001347082 & 0.1829983 & -0.007361171 & 0.994126688 & 0.99934193 & nrf1      & nuclear respiratory factor 1 {[}Source:NCBI gene;Acc:64604{]}                                  \\\\\n",
       "\t4 & ENSDARG00000000019 & 256.71348 &  0.022143663 & 0.1297546 &  0.170658009 & 0.864492682 & 0.98022458 & ube2h     & ubiquitin-conjugating enzyme E2H (UBC8 homolog, yeast) {[}Source:ZFIN;Acc:ZDB-GENE-030616-67{]}\\\\\n",
       "\t5 & ENSDARG00000000068 &  53.69485 &  0.132869173 & 0.2179671 &  0.609583554 & 0.542137709 & 0.91434963 & slc9a3r1a & SLC9A3 regulator 1a {[}Source:ZFIN;Acc:ZDB-GENE-031006-7{]}                                    \\\\\n",
       "\t6 & ENSDARG00000000069 & 150.64439 &  0.440058588 & 0.1460032 &  3.014032914 & 0.002577998 & 0.09028049 & dap       & death-associated protein {[}Source:ZFIN;Acc:ZDB-GENE-000511-3{]}                               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | Gene_stable_ID &lt;I&lt;chr&gt;&gt; | baseMean &lt;dbl&gt; | log2FoldChange &lt;dbl&gt; | lfcSE &lt;dbl&gt; | stat &lt;dbl&gt; | pvalue &lt;dbl&gt; | padj &lt;dbl&gt; | Gene.name &lt;chr&gt; | Gene.description &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | ENSDARG00000000001 |  13.29456 |  0.174587923 | 0.3941145 |  0.442987767 | 0.657774583 | 1.00000000 | slc35a5   | solute carrier family 35 member A5 [Source:NCBI gene;Acc:368418]                            |\n",
       "| 2 | ENSDARG00000000002 |  50.00177 | -0.351703192 | 0.2168393 | -1.621953324 | 0.104813343 | 0.60930312 | ccdc80    | coiled-coil domain containing 80 [Source:NCBI gene;Acc:368419]                              |\n",
       "| 3 | ENSDARG00000000018 |  81.80772 | -0.001347082 | 0.1829983 | -0.007361171 | 0.994126688 | 0.99934193 | nrf1      | nuclear respiratory factor 1 [Source:NCBI gene;Acc:64604]                                   |\n",
       "| 4 | ENSDARG00000000019 | 256.71348 |  0.022143663 | 0.1297546 |  0.170658009 | 0.864492682 | 0.98022458 | ube2h     | ubiquitin-conjugating enzyme E2H (UBC8 homolog, yeast) [Source:ZFIN;Acc:ZDB-GENE-030616-67] |\n",
       "| 5 | ENSDARG00000000068 |  53.69485 |  0.132869173 | 0.2179671 |  0.609583554 | 0.542137709 | 0.91434963 | slc9a3r1a | SLC9A3 regulator 1a [Source:ZFIN;Acc:ZDB-GENE-031006-7]                                     |\n",
       "| 6 | ENSDARG00000000069 | 150.64439 |  0.440058588 | 0.1460032 |  3.014032914 | 0.002577998 | 0.09028049 | dap       | death-associated protein [Source:ZFIN;Acc:ZDB-GENE-000511-3]                                |\n",
       "\n"
      ],
      "text/plain": [
       "  Gene_stable_ID     baseMean  log2FoldChange lfcSE     stat        \n",
       "1 ENSDARG00000000001  13.29456  0.174587923   0.3941145  0.442987767\n",
       "2 ENSDARG00000000002  50.00177 -0.351703192   0.2168393 -1.621953324\n",
       "3 ENSDARG00000000018  81.80772 -0.001347082   0.1829983 -0.007361171\n",
       "4 ENSDARG00000000019 256.71348  0.022143663   0.1297546  0.170658009\n",
       "5 ENSDARG00000000068  53.69485  0.132869173   0.2179671  0.609583554\n",
       "6 ENSDARG00000000069 150.64439  0.440058588   0.1460032  3.014032914\n",
       "  pvalue      padj       Gene.name\n",
       "1 0.657774583 1.00000000 slc35a5  \n",
       "2 0.104813343 0.60930312 ccdc80   \n",
       "3 0.994126688 0.99934193 nrf1     \n",
       "4 0.864492682 0.98022458 ube2h    \n",
       "5 0.542137709 0.91434963 slc9a3r1a\n",
       "6 0.002577998 0.09028049 dap      \n",
       "  Gene.description                                                                           \n",
       "1 solute carrier family 35 member A5 [Source:NCBI gene;Acc:368418]                           \n",
       "2 coiled-coil domain containing 80 [Source:NCBI gene;Acc:368419]                             \n",
       "3 nuclear respiratory factor 1 [Source:NCBI gene;Acc:64604]                                  \n",
       "4 ubiquitin-conjugating enzyme E2H (UBC8 homolog, yeast) [Source:ZFIN;Acc:ZDB-GENE-030616-67]\n",
       "5 SLC9A3 regulator 1a [Source:ZFIN;Acc:ZDB-GENE-031006-7]                                    \n",
       "6 death-associated protein [Source:ZFIN;Acc:ZDB-GENE-000511-3]                               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "system(\"gsutil cp gs://hon_350/reference/biomart_GRCz11_annotation.txt data/reference/\", intern=TRUE)\n",
    "\n",
    "#read-in the annotation file (see extended tutorial for more info on how to get annotation files) and store it into a variable.\n",
    "annotation <- read.table(\"data/reference/biomart_GRCz11_annotation.txt\", header=TRUE,sep='\\t', quote=\"\\\"\",comment.char = \"\")\n",
    "\n",
    "#put the previous deseq results into an easier to manipulate data.frame format\n",
    "results <- data.frame(res_IAV_CTRL)\n",
    "\n",
    "#merge the annotation with the deseq2 contrast comparison table\n",
    "results.annot <- merge(results,annotation,by.x=0,by.y=1,all.x=TRUE)\n",
    "\n",
    "\n",
    "#labeling the columns properly after the merge\n",
    "colnames(results.annot)[1] <- 'Gene_stable_ID'\n",
    "\n",
    "#write out the file\n",
    "write.table(results.annot,\"data/gene_counts/24hrs_CTRL_IAV_DESeq2_annot.txt\",row.names=FALSE,sep=\"\\t\")\n",
    "\n",
    "head(results.annot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#upload annotated file to google cloud\n",
    "system(\"gsutil cp data/gene_counts/24hrs_CTRL_IAV_DESeq2_annot.txt gs://hon_350/24hours_results/\", intern=TRUE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STEP 10: More Plots and Clustering Data\n",
    "\n",
    "Finally, although several other different kind of analysis can be done, it is common to plot the results of differential gene expression analysis in forms such as volcano, MA, and heatmaps. These plots help one to further examine the output list of differentially expressed genes in different ways, and potentially identify or further investigate candidate gene(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PF4PJlMZmxsjFcJBILPP/8cvxD2jt3g4ODly5ednZ09PT3fe+89Op0uEol6e3vf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\n0MKOEEIIIQSJ/wcOIKTX5wN2QQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#volcano and ma plots can be used to visualize significant differences in gene expression\n",
    "results_volc_df <- results.annot\n",
    "png(\"whim_volcano_plot.png\")\n",
    "EnhancedVolcano(results_volc_df,\n",
    "    lab = results_volc_df[,1],\n",
    "    x = 'log2FoldChange',\n",
    "    y = 'padj')\n",
    "dev.off()\n",
    "\n",
    "#ma plot\n",
    "png(\"whim_MA_plot.png\")\n",
    "plotMA(res_IAV_CTRL)\n",
    "dev.off()\n",
    "\n",
    "\n",
    "#heatmaps can used to look at clustering and expression of various gene lists.\n",
    "filtered_res <- results.annot %>% filter(padj < 0.05)\n",
    "filtered_res_counts <- subset(rlogcounts, rownames(rlogcounts) %in% filtered_res[,1])\n",
    "filtered_res_counts = t(scale(t(filtered_res_counts)))\n",
    "#showing 40 of the genes under padj 0.05 (in no order)\n",
    "Heatmap(filtered_res_counts[1:40,], name = \"Z-Score\", row_names_gp = gpar(fontsize = 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: org.Dr.eg.db\n",
      "\n",
      "Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :\n",
      "“there is no package called ‘org.Dr.eg.db’”\n",
      "'getOption(\"repos\")' replaces Bioconductor standard repositories, see\n",
      "'help(\"repositories\", package = \"BiocManager\")' for details.\n",
      "Replacement repositories:\n",
      "    CRAN: https://cloud.r-project.org\n",
      "\n",
      "Bioconductor version 3.16 (BiocManager 1.30.22), R 4.2.3 (2023-03-15)\n",
      "\n",
      "Installing package(s) 'org.Dr.eg.db'\n",
      "\n",
      "Installation paths not writeable, unable to update packages\n",
      "  path: /usr/local/lib/R/site-library\n",
      "  packages:\n",
      "    brew, brio, bslib, cli, covr, cpp11, crosstalk, curl, data.table, DBI,\n",
      "    DBItest, dbplyr, desc, dplyr, DT, evaluate, fansi, future, gert, ggplot2,\n",
      "    glue, haven, htmltools, htmlwidgets, httpuv, httr2, hunspell, jsonlite,\n",
      "    knitr, later, lattice, leaflet.providers, lifecycle, lintr, lubridate,\n",
      "    Matrix, mgcv, mockery, nlme, pingr, pkgbuild, plotly, processx, progress,\n",
      "    ragg, Rcpp, RCurl, reticulate, rgl, rlang, rprojroot, sass, scales, shiny,\n",
      "    sodium, sp, stringi, stringr, systemfonts, tensorflow, testthat,\n",
      "    textshaping, tinytex, utf8, vctrs, vroom, waldo, withr, wk, xfun, xml2,\n",
      "    yaml\n",
      "\n",
      "Old packages: 'BH'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if (!require(\"org.Dr.eg.db\"))\n",
    "    BiocManager::install('org.Dr.eg.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(\"org.Dr.eg.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'select()' returned 1:many mapping between keys and columns\n",
      "\n",
      "converting counts to integer mode\n",
      "\n",
      "Warning message in DESeqDataSet(se, design = design, ignoreRank):\n",
      "“152 duplicate rownames were renamed by adding numbers”\n",
      "Warning message in DESeqDataSet(se, design = design, ignoreRank):\n",
      "“some variables in design formula are characters, converting to factors”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "\n",
      "estimating dispersions\n",
      "\n",
      "gene-wise dispersion estimates\n",
      "\n",
      "mean-dispersion relationship\n",
      "\n",
      "final dispersion estimates\n",
      "\n",
      "fitting model and testing\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>SA60184</th><th scope=col>SA60185</th><th scope=col>SA60186</th><th scope=col>SA60187</th><th scope=col>SA60188</th><th scope=col>SA60189</th><th scope=col>SA60190</th><th scope=col>SA60191</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>slc35a5</th><td>3.808296</td><td>3.717493</td><td>3.682843</td><td>3.774506</td><td>3.709030</td><td>3.732852</td><td>3.589887</td><td>3.822502</td></tr>\n",
       "\t<tr><th scope=row>ccdc80</th><td>5.526974</td><td>5.585502</td><td>5.548471</td><td>5.547580</td><td>5.689539</td><td>5.615608</td><td>5.878827</td><td>5.605908</td></tr>\n",
       "\t<tr><th scope=row>nrf1</th><td>6.238880</td><td>6.373039</td><td>6.276238</td><td>6.479446</td><td>6.386796</td><td>6.241528</td><td>6.331643</td><td>6.446399</td></tr>\n",
       "\t<tr><th scope=row>ube2h</th><td>7.915784</td><td>8.012469</td><td>8.084428</td><td>8.022141</td><td>8.042829</td><td>8.094875</td><td>7.973889</td><td>7.862991</td></tr>\n",
       "\t<tr><th scope=row>slc9a3r1a</th><td>5.824744</td><td>5.641558</td><td>5.806319</td><td>5.815684</td><td>5.789222</td><td>5.812736</td><td>5.527716</td><td>5.726204</td></tr>\n",
       "\t<tr><th scope=row>dap</th><td>7.382482</td><td>7.311009</td><td>7.388698</td><td>7.338640</td><td>7.115849</td><td>7.167604</td><td>6.949528</td><td>7.146771</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & SA60184 & SA60185 & SA60186 & SA60187 & SA60188 & SA60189 & SA60190 & SA60191\\\\\n",
       "\\hline\n",
       "\tslc35a5 & 3.808296 & 3.717493 & 3.682843 & 3.774506 & 3.709030 & 3.732852 & 3.589887 & 3.822502\\\\\n",
       "\tccdc80 & 5.526974 & 5.585502 & 5.548471 & 5.547580 & 5.689539 & 5.615608 & 5.878827 & 5.605908\\\\\n",
       "\tnrf1 & 6.238880 & 6.373039 & 6.276238 & 6.479446 & 6.386796 & 6.241528 & 6.331643 & 6.446399\\\\\n",
       "\tube2h & 7.915784 & 8.012469 & 8.084428 & 8.022141 & 8.042829 & 8.094875 & 7.973889 & 7.862991\\\\\n",
       "\tslc9a3r1a & 5.824744 & 5.641558 & 5.806319 & 5.815684 & 5.789222 & 5.812736 & 5.527716 & 5.726204\\\\\n",
       "\tdap & 7.382482 & 7.311009 & 7.388698 & 7.338640 & 7.115849 & 7.167604 & 6.949528 & 7.146771\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | SA60184 | SA60185 | SA60186 | SA60187 | SA60188 | SA60189 | SA60190 | SA60191 |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| slc35a5 | 3.808296 | 3.717493 | 3.682843 | 3.774506 | 3.709030 | 3.732852 | 3.589887 | 3.822502 |\n",
       "| ccdc80 | 5.526974 | 5.585502 | 5.548471 | 5.547580 | 5.689539 | 5.615608 | 5.878827 | 5.605908 |\n",
       "| nrf1 | 6.238880 | 6.373039 | 6.276238 | 6.479446 | 6.386796 | 6.241528 | 6.331643 | 6.446399 |\n",
       "| ube2h | 7.915784 | 8.012469 | 8.084428 | 8.022141 | 8.042829 | 8.094875 | 7.973889 | 7.862991 |\n",
       "| slc9a3r1a | 5.824744 | 5.641558 | 5.806319 | 5.815684 | 5.789222 | 5.812736 | 5.527716 | 5.726204 |\n",
       "| dap | 7.382482 | 7.311009 | 7.388698 | 7.338640 | 7.115849 | 7.167604 | 6.949528 | 7.146771 |\n",
       "\n"
      ],
      "text/plain": [
       "          SA60184  SA60185  SA60186  SA60187  SA60188  SA60189  SA60190 \n",
       "slc35a5   3.808296 3.717493 3.682843 3.774506 3.709030 3.732852 3.589887\n",
       "ccdc80    5.526974 5.585502 5.548471 5.547580 5.689539 5.615608 5.878827\n",
       "nrf1      6.238880 6.373039 6.276238 6.479446 6.386796 6.241528 6.331643\n",
       "ube2h     7.915784 8.012469 8.084428 8.022141 8.042829 8.094875 7.973889\n",
       "slc9a3r1a 5.824744 5.641558 5.806319 5.815684 5.789222 5.812736 5.527716\n",
       "dap       7.382482 7.311009 7.388698 7.338640 7.115849 7.167604 6.949528\n",
       "          SA60191 \n",
       "slc35a5   3.822502\n",
       "ccdc80    5.605908\n",
       "nrf1      6.446399\n",
       "ube2h     7.862991\n",
       "slc9a3r1a 5.726204\n",
       "dap       7.146771"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ens <- rownames(rnaseqMatrix)\n",
    "\n",
    "symbols <- mapIds(org.Dr.eg.db, keys = ens,\n",
    "                  column = c('SYMBOL'), keytype = 'ENSEMBL')\n",
    "symbols <- symbols[!is.na(symbols)]\n",
    "symbols <- symbols[match(rownames(rnaseqMatrix), names(symbols))]\n",
    "rownames(rnaseqMatrix) <- symbols\n",
    "keep <- !is.na(rownames(rnaseqMatrix))\n",
    "rnaseqMatrix <- rnaseqMatrix[keep,]\n",
    "\n",
    "# Create the DEseq2DataSet object\n",
    "deseq2Data <- DESeqDataSetFromMatrix(countData=rnaseqMatrix, colData=samples, design= ~ Treatment)\n",
    "\n",
    "\n",
    "# Perform pre-filtering of the data\n",
    "deseq2Data <- deseq2Data[rowSums(counts(deseq2Data)) > 10, ]\n",
    "\n",
    "# Inspect pre-normalized data \n",
    "png(\"pairwise_scatterplots.png\")\n",
    "pairs(log2((counts(deseq2Data)+1)))\n",
    "dev.off()\n",
    "\n",
    "png(\"boxplot.png\")\n",
    "boxplot(log2((counts(deseq2Data)+1)))\n",
    "dev.off()\n",
    "\n",
    "# Run pipeline for differential expression steps\n",
    "deseq2Data <- DESeq(deseq2Data)\n",
    "\n",
    "#perform rlog normalization on deseq2 object\n",
    "rld <- rlog(deseq2Data, blind=FALSE)\n",
    "\n",
    "#a handy variable so we can easily reference the rlog genecount matrix in the future, as deseq objects contain many tables of data.\n",
    "rlogcounts <- assay(rld)\n",
    "rownames(rlogcounts) <- rownames(deseq2Data)\n",
    "\n",
    "head(rlogcounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare MUT to WT\n",
    "res_MUT_WT <- results(deseq2Data, contrast = c(\"Treatment\", \"MUT\", \"WT\"))\n",
    "resOrdered_MUT_WT <- res_MUT_WT[order(res_MUT_WT$pvalue),]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“ggrepel: 17 unlabeled data points (too many overlaps). Consider increasing max.overlaps”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "png(\"WHIM_Volcano_plot_symbols.png\")\n",
    "EnhancedVolcano(res_MUT_WT,\n",
    "                lab = rownames(res_MUT_WT),\n",
    "                x = 'log2FoldChange',\n",
    "                y = 'padj',\n",
    "                pCutoff = 0.05,\n",
    "                drawConnectors = TRUE,\n",
    "                widthConnectors = 0.5,\n",
    "                FCcutoff = 1.0,\n",
    "                pointSize = 2.0,\n",
    "                labSize = 3.0)\n",
    "dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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