Unit2-RNAseq/3.6_DESeq2_differential_expression_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 Gene expression analysis using DESeq2\n",
    "\n",
    "#### First use `git` to retrieve the gene counts files (total n=6). The path to files on the Github repo is:\n",
    "`Applied-Bioinformatics/Unit2-RNAseq/data/htseq_out/day5/`  \n",
    "  \n",
    "Then copy to a directory where you can find it (e.g. your homework folder).\n",
    "#### Locate counts files \n",
    "```R\n",
    "dir_counts <- \"/mnt/c/Users/Jerry/Applied-Bioinformatics-HW/data/htseq_out/day5\"\n",
    "counts_files <- list.files(dir_counts)\n",
    "counts_files\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'GSM2580321_counts.txt'</li>\n",
       "\t<li>'GSM2580322_counts.txt'</li>\n",
       "\t<li>'GSM2580325_counts.txt'</li>\n",
       "\t<li>'GSM2580326_counts.txt'</li>\n",
       "\t<li>'GSM2580329_counts.txt'</li>\n",
       "\t<li>'GSM2580330_counts.txt'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'GSM2580321\\_counts.txt'\n",
       "\\item 'GSM2580322\\_counts.txt'\n",
       "\\item 'GSM2580325\\_counts.txt'\n",
       "\\item 'GSM2580326\\_counts.txt'\n",
       "\\item 'GSM2580329\\_counts.txt'\n",
       "\\item 'GSM2580330\\_counts.txt'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'GSM2580321_counts.txt'\n",
       "2. 'GSM2580322_counts.txt'\n",
       "3. 'GSM2580325_counts.txt'\n",
       "4. 'GSM2580326_counts.txt'\n",
       "5. 'GSM2580329_counts.txt'\n",
       "6. 'GSM2580330_counts.txt'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"GSM2580321_counts.txt\" \"GSM2580322_counts.txt\" \"GSM2580325_counts.txt\"\n",
       "[4] \"GSM2580326_counts.txt\" \"GSM2580329_counts.txt\" \"GSM2580330_counts.txt\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dir_counts <- \"/mnt/c/Users/Jerry/Applied-Bioinformatics-HW/data/htseq_out/day5\"\n",
    "counts_files <- list.files(dir_counts)\n",
    "counts_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare a sample table in the format required by DESeq2\n",
    "Here we specify the group assignment manually. It is also possible to parse the sample data matrix from GEO, which can be found by following a link on the data series page: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97919\n",
    "```\n",
    "samplesInfo <- as.data.frame(matrix(ncol=2, nrow=length(counts_files)))  \n",
    "samplesInfo$samplename <- counts_files  \n",
    "samplesInfo$filename <- counts_files  \n",
    "samplesInfo$group <- c(\"mock\", \"ZIKV\", \"mock\", \"ZIKV\", \"mock\", \"ZIKV\") \n",
    "#Remove the blank columns\n",
    "samplesInfo <- samplesInfo[,-c(1:2)]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the sample table and make any corrections above if necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>samplename</th><th scope=col>filename</th><th scope=col>group</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GSM2580321_counts.txt</td><td>GSM2580321_counts.txt</td><td>mock                 </td></tr>\n",
       "\t<tr><td>GSM2580322_counts.txt</td><td>GSM2580322_counts.txt</td><td>ZIKV                 </td></tr>\n",
       "\t<tr><td>GSM2580325_counts.txt</td><td>GSM2580325_counts.txt</td><td>mock                 </td></tr>\n",
       "\t<tr><td>GSM2580326_counts.txt</td><td>GSM2580326_counts.txt</td><td>ZIKV                 </td></tr>\n",
       "\t<tr><td>GSM2580329_counts.txt</td><td>GSM2580329_counts.txt</td><td>mock                 </td></tr>\n",
       "\t<tr><td>GSM2580330_counts.txt</td><td>GSM2580330_counts.txt</td><td>ZIKV                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " samplename & filename & group\\\\\n",
       "\\hline\n",
       "\t GSM2580321\\_counts.txt & GSM2580321\\_counts.txt & mock                   \\\\\n",
       "\t GSM2580322\\_counts.txt & GSM2580322\\_counts.txt & ZIKV                   \\\\\n",
       "\t GSM2580325\\_counts.txt & GSM2580325\\_counts.txt & mock                   \\\\\n",
       "\t GSM2580326\\_counts.txt & GSM2580326\\_counts.txt & ZIKV                   \\\\\n",
       "\t GSM2580329\\_counts.txt & GSM2580329\\_counts.txt & mock                   \\\\\n",
       "\t GSM2580330\\_counts.txt & GSM2580330\\_counts.txt & ZIKV                   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "samplename | filename | group | \n",
       "|---|---|---|---|---|---|\n",
       "| GSM2580321_counts.txt | GSM2580321_counts.txt | mock                  | \n",
       "| GSM2580322_counts.txt | GSM2580322_counts.txt | ZIKV                  | \n",
       "| GSM2580325_counts.txt | GSM2580325_counts.txt | mock                  | \n",
       "| GSM2580326_counts.txt | GSM2580326_counts.txt | ZIKV                  | \n",
       "| GSM2580329_counts.txt | GSM2580329_counts.txt | mock                  | \n",
       "| GSM2580330_counts.txt | GSM2580330_counts.txt | ZIKV                  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  samplename            filename              group\n",
       "1 GSM2580321_counts.txt GSM2580321_counts.txt mock \n",
       "2 GSM2580322_counts.txt GSM2580322_counts.txt ZIKV \n",
       "3 GSM2580325_counts.txt GSM2580325_counts.txt mock \n",
       "4 GSM2580326_counts.txt GSM2580326_counts.txt ZIKV \n",
       "5 GSM2580329_counts.txt GSM2580329_counts.txt mock \n",
       "6 GSM2580330_counts.txt GSM2580330_counts.txt ZIKV "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'data.frame'"
      ],
      "text/latex": [
       "'data.frame'"
      ],
      "text/markdown": [
       "'data.frame'"
      ],
      "text/plain": [
       "[1] \"data.frame\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samplesInfo\n",
    "class(samplesInfo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load DESeq2 package\n",
    "```\n",
    "library(\"DESeq2\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data into R\n",
    "```\n",
    "dds1 <- DESeqDataSetFromHTSeqCount(sampleTable = samplesInfo, \n",
    "                                           directory = dir_counts, \n",
    "                                           design = ~ group)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "dds1 <- DESeqDataSetFromHTSeqCount(sampleTable = samplesInfo, \n",
    "                                           directory = dir_counts, \n",
    "                                           design = ~ group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the data imported correctly\n",
    "```R\n",
    "colData(dds1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 6 rows and 1 column\n",
       "                         group\n",
       "                      <factor>\n",
       "GSM2580321_counts.txt     mock\n",
       "GSM2580322_counts.txt     ZIKV\n",
       "GSM2580325_counts.txt     mock\n",
       "GSM2580326_counts.txt     ZIKV\n",
       "GSM2580329_counts.txt     mock\n",
       "GSM2580330_counts.txt     ZIKV"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colData(dds1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "estimating dispersions\n",
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n",
      "fitting model and testing\n"
     ]
    }
   ],
   "source": [
    "#### Run the main DESeq normalization and analysis function\n",
    "dds1_deseq <- DESeq(dds1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run additional DESeq2 normalization functions\n",
    "```\n",
    "rld <- rlog(dds1_deseq)\n",
    "vsd <- varianceStabilizingTransformation(dds1_deseq)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a dendrogram of sample transcriptomes\n",
    "```\n",
    "sampleDists <- dist(t(assay(vsd)))\n",
    "plot(hclust(sampleDists))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Principal component plot\n",
    "```\n",
    "plotPCA(rld, intgroup=\"group\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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ATFDgAAQBAUOwAAAEFQ7AAAAARBsQMA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\nq6++2rt374EDB6r98l3e9rHLysrq2LFjRkaG5+7q1aunTp06d+7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nPRodREeKosiy7HQ6jQ6iI1mWZVl2u91GB9FRbViOJpNJkiSxl6Oq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\nQCSEHQBAJIQdAEAk/gd8Typ5+uC1iwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPCA(rld, intgroup=\"group\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA plot with variance-stabilized counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "plotPCA(vsd, intgroup=\"group\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercises (class 3.5)\n",
    "1) Repeat the hierarchical clustering using rlog-normalized counts instead of vsd.  \n",
    "2) Examine the normalized counts using `head(assay(vsd))` and `head(assay(rld))`. Now find the corresponding raw counts and plot raw vs normalized counts for the first 6 genes of the first sample. HINT: It is possible to extract elements from `dds1`, including raw counts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Differential expression analysis\n",
    "Differential expression analysis compares two groups of samples to each other. In its simplest version, the analysis design uses a single factor to divide samples into groups and compares those to each other (single-factor design).\n",
    "\n",
    "### Single factor design differential analysis\n",
    "#### First check the sample table we are selecting from"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "colData(dds1_deseq)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specify sample groups that should be compared\n",
    "The general format of this command is:\n",
    "```\n",
    "results(dds_deseq, contrast = c(\"factorName\", \"factorLevel1\", \"factorLevel2\")\n",
    "```\n",
    "Note that `factorLevel2` will be used as the reference level (i.e. positive fold change means gene upregulated in factorLevel1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 37609 with nonzero total read count\n",
      "adjusted p-value < 0.1\n",
      "LFC > 0 (up)       : 461, 1.2%\n",
      "LFC < 0 (down)     : 476, 1.3%\n",
      "outliers [1]       : 9, 0.024%\n",
      "low counts [2]     : 20516, 55%\n",
      "(mean count < 24)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res <- results(dds1_deseq, contrast=c(\"group\", \"ZIKV\", \"mock\"))\n",
    "summary(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examine differential expression summary\n",
    "The summary above contains important information about the calculation.     \n",
    "  \n",
    "1) Applying a threshold of adjusted p<0.1, we see the number of genes that are differentially expressed.  \n",
    "2) Many genes fall into 'low counts', i.e. were filtered out due to low counts. This is caused by the independentFiltering function which can be swiched off (argument `independentFilering=FALSE`). One can also see the read count below which the genes were filtered out.  \n",
    "\n",
    "#### Examine differentially expressed genes\n",
    "First let us sort the gene by p value, then examine the top differentially expressed genes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "resSorted <- res[order(res$padj),]\n",
    "head(resSorted)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert gene symbols\n",
    "Interpreting Ensembl IDs is impractical. We can convert them to gene symbols, for example using the biomaRt package. You should have the package installed already (see RNAseq configuration). This package also functions as an API to retrieve various gene annotations so we can fetch them on the fly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "library(\"biomaRt\")\n",
    "bm <- useMart(biomart = \"ensembl\")\n",
    "bm <- useDataset(dataset = \"hsapiens_gene_ensembl\", mart = bm)\n",
    "ens2genesymbol <- getBM(mart = bm, attributes = c('ensembl_gene_id', 'external_gene_name'))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look up individual Ensembl IDs using the `%in%` operator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "which(ens2genesymbol$ensembl_gene_id %in% rownames(resSorted)[1])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the row number of the top gene by pvalue is 8416 in the `ens2genesymbol` lookup table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ensembl_gene_id</th><th scope=col>external_gene_name</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>8416</th><td>ENSG00000213928</td><td>IRF9           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & ensembl\\_gene\\_id & external\\_gene\\_name\\\\\n",
       "\\hline\n",
       "\t8416 & ENSG00000213928 & IRF9           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ensembl_gene_id | external_gene_name | \n",
       "|---|\n",
       "| 8416 | ENSG00000213928 | IRF9            | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     ensembl_gene_id external_gene_name\n",
       "8416 ENSG00000213928 IRF9              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ens2genesymbol[8416,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### But it is better to create a list of the positions of the genes of interest in the ens2genesymbol data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "ngenes <- 25\n",
    "genelist <- list()\n",
    "for (i in 1:ngenes){\n",
    "    genelist[[i]] <- which(ens2genesymbol$ensembl_gene_id %in% rownames(resSorted)[i])\n",
    "    names(genelist)[i] <- ens2genesymbol[genelist[[i]], \"external_gene_name\"]\n",
    "    }\n",
    "head(genelist)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot normalized counts\n",
    "DESeq2 provides a function to plot counts for a selected gene with samples spit up into groups of interest. These counts are normalized for library size but not by variance stabilizing or rlog transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "plotCounts(dds1_deseq, gene = rownames(resSorted)[1], \n",
    "           intgroup = \"group\", \n",
    "           main = ens2genesymbol[8416,2])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1**   \n",
    "Look up the top 10 differentially expressed genes by p value and print their gene names. Does this gene list suggest what biological pathway could be involved?  \n",
    "**Exercise 2**   \n",
    "Export the table of differentially expressed genes in a comma- or tab-delimited format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual summary of differential expression data\n",
    "Commonly used visualizations of DE data include the volcano plot (fold change vs -logP) and the heatmap. \n",
    "\n",
    "### Basic volcano plot\n",
    "NB: the default of the `log` function is a natural log (base = e). A base 10 log has to be specified explicitly using the argument `base = 10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yorjueNYa94sVgx4g5P3xPrT/cza5eYdWOkm3b6VxkendU1/omVP5T6uxtzo7Jt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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ngenes <- 1000\n",
    "volcanodf <- as.data.frame(matrix(nrow=ngenes, ncol=2))\n",
    "volcanodf$y <- -log(resSorted[1:ngenes, \"padj\"], base = 10)\n",
    "volcanodf$x <- resSorted[1:ngenes, \"log2FoldChange\"]\n",
    "volcanodf$gene <- rownames(resSorted)[1:ngenes]\n",
    "volcanodf <- volcanodf[,-c(1:2)]\n",
    "\n",
    "plot(volcanodf$x, volcanodf$y,\n",
    "    ylab = \"-log(Padj)\", \n",
    "    xlab = \"log2FC(ZIKV vs mock)\",\n",
    "    pch = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Volcano plot centered at fold change = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "fcmax <- max(na.omit(resSorted$log2FoldChange))\n",
    "\n",
    "plot(volcanodf$x, volcanodf$y,\n",
    "    ylab = \"-log(Padj)\", \n",
    "    xlab = \"log2FC(ZIKV vs mock)\",\n",
    "    pch = 20,\n",
    "    xlim = c(-(fcmax - 0.5), (fcmax + 0.5)))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Volcano plot with gene labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "fcmax <- max(na.omit(resSorted$log2FoldChange))\n",
    "\n",
    "plot(volcanodf$x, volcanodf$y,\n",
    "    ylab = \"-log(Padj)\", \n",
    "    xlab = \"log2FC(ZIKV vs mock)\",\n",
    "    pch = 20,\n",
    "    xlim = c(-(fcmax - 0.5), (fcmax + 0.5)))\n",
    "\n",
    "text(volcanodf[1:10, \"x\"], volcanodf[1:10, \"y\"], labels=volcanodf[1:10, \"gene\"])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmaps of DEGs\n",
    "A heatmap shows each gene's *relative* expression. In case of differential expression analysis, it is often best to choose the mean level across all samples as a reference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The minimal heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yurnp6ex48fx8XFGRgYcJ0RAAAMRykpKdOnP7UYi4+PD3tTqVSmpqaylxFjy8/P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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(\"pheatmap\")\n",
    "ngenes <- 25\n",
    "deg <- rownames(resSorted)[1:ngenes]\n",
    "mat <- assay(rld)[deg,]\n",
    "mat <- mat - rowMeans(mat)\n",
    "pheatmap(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot heatmap with sample group data and gene names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "df <- as.data.frame(colData(rld))\n",
    "\n",
    "ngenes <- 25\n",
    "deg <- rownames(resSorted)[1:ngenes]\n",
    "mat <- assay(rld)[deg,]\n",
    "mat <- mat - rowMeans(mat)\n",
    "rownames(mat) <- names(genelist)\n",
    "\n",
    "pheatmap(mat,\n",
    "        annotation_col = df,\n",
    "        cluster_cols = T)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises  \n",
    "**Exercise 3** Make a volcano plot on which all genes with an adjusted P < 10^-10 are coloured red, and add a vertical line highlighting a fold change cut-off of 2 (linear fold change = 2 or 1/2).  \n",
    "**Exercise 4** Change the volcano plot gene labels to gene symbols instead of Ensembl IDs (for top 5 or 10 genes by pvalue).  \n",
    "**Exercise 5** Make a heatmap of the top 25 genes that are *downregulated* in the mock infected vs ZIKV-infected group, (top 25 meaning by p value)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}