02_quant_to_deseq2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f89c5e3-fd9e-474d-a036-62c2d9ae73c2",
   "metadata": {},
   "source": [
    "# Comparing Gene Expression in Young vs Aged Hematopoietic Stem Cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c2e697-53d0-4091-af24-146094285def",
   "metadata": {},
   "source": [
    "This is an analysis of bulk RNAseq data comparing expression in the hematopoietic stem cells of 10-week-old (young) vs 20-month-old (aged) mice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47cb8fec-f5e0-4df0-926e-10fd4aa52aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\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",
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      "    count\n",
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      "    combine, intersect, setdiff, union\n",
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      "The following objects are masked from 'package:stats':\n",
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      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Loading required package: ggrepel\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Suppress warnings\n",
    "options(warn = -1)\n",
    "\n",
    "# load packages\n",
    "library(tximport)\n",
    "library(ensembldb)\n",
    "library(AnnotationHub)\n",
    "library(DESeq2)\n",
    "\n",
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "library(pheatmap)\n",
    "library(RColorBrewer)\n",
    "\n",
    "library(biomaRt)\n",
    "\n",
    "library(EnhancedVolcano)\n",
    "\n",
    "# increase size of plots from default\n",
    "options(repr.plot.width = 14, \n",
    "        repr.plot.height = 14) # from 7, 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c2646d-e2c8-410f-a890-0e0b3a3785c4",
   "metadata": {},
   "source": [
    "## Create annotation dataframes for `tximport`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2997244-6b88-4df6-9061-4e532dae5e88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading from cache\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame with 149547 rows and 2 columns\n",
       "                    tx_id            gene_id\n",
       "              <character>        <character>\n",
       "1      ENSMUST00000082387 ENSMUSG00000064336\n",
       "2      ENSMUST00000179436 ENSMUSG00000095742\n",
       "3      ENSMUST00000082388 ENSMUSG00000064337\n",
       "4      ENSMUST00000082389 ENSMUSG00000064338\n",
       "5      ENSMUST00000082390 ENSMUSG00000064339\n",
       "...                   ...                ...\n",
       "149543 ENSMUST00000192752 ENSMUSG00000102236\n",
       "149544 ENSMUST00020183637 ENSMUSG00002076810\n",
       "149545 ENSMUST00020183310 ENSMUSG00002075165\n",
       "149546 ENSMUST00000192733 ENSMUSG00000104297\n",
       "149547 ENSMUST00000194529 ENSMUSG00000102307"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create an AnnotationHub object\n",
    "ah = AnnotationHub()\n",
    "\n",
    "# find relevant annotation records\n",
    "ahs2 <- query(ah, c(\"110\",\"Ensembl\", \"Mus musculus\"))\n",
    "\n",
    "# load latest annotation record for Mus musculus\n",
    "ensdb_110 <- ahs2[[\"AH113713\"]]\n",
    "\n",
    "# Extract transcript and gene information\n",
    "tx_data <- transcripts(ensdb_110, return.type = \"DataFrame\")\n",
    "\n",
    "# Create the tx2gene data.frame\n",
    "tx2gene <- tx_data[, c(\"tx_id\", \"gene_id\")]\n",
    "tx2gene"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c87e78b-e373-4207-ba98-259ffcf7c176",
   "metadata": {},
   "source": [
    "## Load `salmon` quantification files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e3780cf-a52f-40f3-9470-2b8e3ecc09cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>quant_files</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SRR13192285</th><td>results/03_salmon_quant//quant_SRR13192285/quant.sf</td></tr>\n",
       "\t<tr><th scope=row>SRR13192286</th><td>results/03_salmon_quant//quant_SRR13192286/quant.sf</td></tr>\n",
       "\t<tr><th scope=row>SRR13192301</th><td>results/03_salmon_quant//quant_SRR13192301/quant.sf</td></tr>\n",
       "\t<tr><th scope=row>SRR13192302</th><td>results/03_salmon_quant//quant_SRR13192302/quant.sf</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & quant\\_files\\\\\n",
       "  & <chr>\\\\\n",
       "\\hline\n",
       "\tSRR13192285 & results/03\\_salmon\\_quant//quant\\_SRR13192285/quant.sf\\\\\n",
       "\tSRR13192286 & results/03\\_salmon\\_quant//quant\\_SRR13192286/quant.sf\\\\\n",
       "\tSRR13192301 & results/03\\_salmon\\_quant//quant\\_SRR13192301/quant.sf\\\\\n",
       "\tSRR13192302 & results/03\\_salmon\\_quant//quant\\_SRR13192302/quant.sf\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 1\n",
       "\n",
       "| <!--/--> | quant_files &lt;chr&gt; |\n",
       "|---|---|\n",
       "| SRR13192285 | results/03_salmon_quant//quant_SRR13192285/quant.sf |\n",
       "| SRR13192286 | results/03_salmon_quant//quant_SRR13192286/quant.sf |\n",
       "| SRR13192301 | results/03_salmon_quant//quant_SRR13192301/quant.sf |\n",
       "| SRR13192302 | results/03_salmon_quant//quant_SRR13192302/quant.sf |\n",
       "\n"
      ],
      "text/plain": [
       "            quant_files                                        \n",
       "SRR13192285 results/03_salmon_quant//quant_SRR13192285/quant.sf\n",
       "SRR13192286 results/03_salmon_quant//quant_SRR13192286/quant.sf\n",
       "SRR13192301 results/03_salmon_quant//quant_SRR13192301/quant.sf\n",
       "SRR13192302 results/03_salmon_quant//quant_SRR13192302/quant.sf"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prep salmon quant files for the 4 samples for tximport\n",
    "quants_dir <- \"results/03_salmon_quant/\"\n",
    "all_files <- list.files(quants_dir, pattern = \"quant.sf$\", recursive = TRUE, full.names = TRUE)\n",
    "quant_files <- all_files[grepl(\"SRR.*quant.sf\", all_files)] # Use grepl to select only quant.sf files\n",
    "quant_dirs <- list.files(quants_dir, pattern = \"quant_SRR.*\", full.names = TRUE)\n",
    "sample_names <- sub('.*SRR([0-9]+).*', 'SRR\\\\1', quant_dirs)  # get sample names from dir names\n",
    "names(quant_files) <- sample_names\n",
    "as.data.frame(quant_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85866bfc-2a32-4cd0-b998-b4d6f3cb6bb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "reading in files with read_tsv\n",
      "\n",
      "1 \n",
      "2 \n",
      "3 \n",
      "4 \n",
      "\n",
      "\n",
      "transcripts missing from tx2gene: 216\n",
      "\n",
      "summarizing abundance\n",
      "\n",
      "summarizing counts\n",
      "\n",
      "summarizing length\n",
      "\n",
      "summarizing inferential replicates\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of 5\n",
      " $ abundance          : num [1:55800, 1:4] 95.09 0 13.99 1.04 2.72 ...\n",
      "  ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. ..$ : chr [1:4] \"SRR13192285\" \"SRR13192286\" \"SRR13192301\" \"SRR13192302\"\n",
      " $ counts             : num [1:55800, 1:4] 3921 0 336 18.7 120 ...\n",
      "  ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. ..$ : chr [1:4] \"SRR13192285\" \"SRR13192286\" \"SRR13192301\" \"SRR13192302\"\n",
      " $ infReps            :List of 4\n",
      "  ..$ SRR13192285: num [1:55800, 1:20] 3901 0 316 30 111 ...\n",
      "  .. ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. .. ..$ : NULL\n",
      "  ..$ SRR13192286: num [1:55800, 1:20] 5340.4 0 348 30.4 125.1 ...\n",
      "  .. ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. .. ..$ : NULL\n",
      "  ..$ SRR13192301: num [1:55800, 1:20] 4567.23 0 398.79 8.95 93.45 ...\n",
      "  .. ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. .. ..$ : NULL\n",
      "  ..$ SRR13192302: num [1:55800, 1:20] 3913.6 0 541.9 12 98.3 ...\n",
      "  .. ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. .. ..$ : NULL\n",
      " $ length             : num [1:55800, 1:4] 2900 548 1689 1267 3101 ...\n",
      "  ..- attr(*, \"dimnames\")=List of 2\n",
      "  .. ..$ : chr [1:55800] \"ENSMUSG00000000001\" \"ENSMUSG00000000003\" \"ENSMUSG00000000028\" \"ENSMUSG00000000031\" ...\n",
      "  .. ..$ : chr [1:4] \"SRR13192285\" \"SRR13192286\" \"SRR13192301\" \"SRR13192302\"\n",
      " $ countsFromAbundance: chr \"no\"\n"
     ]
    }
   ],
   "source": [
    "# tximport quant_files\n",
    "txi <- tximport(quant_files, \n",
    "                type = \"salmon\", \n",
    "                tx2gene = tx2gene, \n",
    "                ignoreTxVersion = TRUE)\n",
    "str(txi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3c74e1-880e-4bb4-a11b-0b53daab53d7",
   "metadata": {},
   "source": [
    "## Create `DESeqDataSet`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa554a1a-bbe5-4819-bb30-8fd21eb3ed27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>condition</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SRR13192285</th><td>young</td></tr>\n",
       "\t<tr><th scope=row>SRR13192286</th><td>young</td></tr>\n",
       "\t<tr><th scope=row>SRR13192301</th><td>aged </td></tr>\n",
       "\t<tr><th scope=row>SRR13192302</th><td>aged </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & condition\\\\\n",
       "  & <fct>\\\\\n",
       "\\hline\n",
       "\tSRR13192285 & young\\\\\n",
       "\tSRR13192286 & young\\\\\n",
       "\tSRR13192301 & aged \\\\\n",
       "\tSRR13192302 & aged \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 1\n",
       "\n",
       "| <!--/--> | condition &lt;fct&gt; |\n",
       "|---|---|\n",
       "| SRR13192285 | young |\n",
       "| SRR13192286 | young |\n",
       "| SRR13192301 | aged  |\n",
       "| SRR13192302 | aged  |\n",
       "\n"
      ],
      "text/plain": [
       "            condition\n",
       "SRR13192285 young    \n",
       "SRR13192286 young    \n",
       "SRR13192301 aged     \n",
       "SRR13192302 aged     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create sample table\n",
    "condition <- factor(c('young', 'young', 'aged', 'aged'))\n",
    "coldata <- data.frame(row.names = sample_names, condition)\n",
    "coldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6890d116-46dc-4b69-a9af-a21efc06670b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "using counts and average transcript lengths from tximport\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "class: DESeqDataSet \n",
       "dim: 55800 4 \n",
       "metadata(1): version\n",
       "assays(2): counts avgTxLength\n",
       "rownames(55800): ENSMUSG00000000001 ENSMUSG00000000003 ...\n",
       "  ENSMUSG00002076991 ENSMUSG00002076992\n",
       "rowData names(0):\n",
       "colnames(4): SRR13192285 SRR13192286 SRR13192301 SRR13192302\n",
       "colData names(1): condition"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create deseq object\n",
    "dds <- DESeqDataSetFromTximport(txi, coldata, ~ condition)\n",
    "dds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d16873d-95fd-49ed-ae06-05672ed8b230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: DESeqDataSet \n",
       "dim: 17174 4 \n",
       "metadata(1): version\n",
       "assays(2): counts avgTxLength\n",
       "rownames(17174): ENSMUSG00000000001 ENSMUSG00000000028 ...\n",
       "  ENSMUSG00002076814 ENSMUSG00002076847\n",
       "rowData names(0):\n",
       "colnames(4): SRR13192285 SRR13192286 SRR13192301 SRR13192302\n",
       "colData names(1): condition"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drop genes with fewer than 12 counts total across all samples\n",
    "keep <- rowSums(counts(dds)) > 12\n",
    "dds <- dds[keep, ]\n",
    "dds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd03b8cb-3afc-45ed-8151-302056e299d6",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca353441-66eb-4ff2-bf45-7cf9b249494c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "using 'avgTxLength' from assays(dds), correcting for library size\n",
      "\n"
     ]
    },
    {
     "data": {
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hoeZZQiaTafDgwUpaN3Dg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\npHJ3KMzw4cPffPNNEVm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eVyjK+sUZdzM0+f5tG8dKddv3yPVQpkYt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\nmhDip59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3MjIyVCqVu7t7zjFiEyZMWLt2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\nX758eeMziYmJ586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Pnp39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\nsACNevXM7vHjx7W1te7u7rGxsTgTFqAhH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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 840,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rlog transformation to stabilize variance\n",
    "rld <- rlog(dds)\n",
    "\n",
    "# sample distances\n",
    "sampleDists <- dist(t(assay(rld)))\n",
    "\n",
    "# heatmap\n",
    "sampleDistMatrix <- as.matrix( sampleDists )\n",
    "rownames(sampleDistMatrix) <- paste( rld$condition, colnames(rld), sep = \" - \" )\n",
    "colnames(sampleDistMatrix) <- rownames(sampleDistMatrix)#NULL\n",
    "colors <- colorRampPalette( rev(brewer.pal(9, \"Blues\")) )(255)\n",
    "pheatmap(sampleDistMatrix,\n",
    "         clustering_distance_rows = sampleDists,\n",
    "         clustering_distance_cols = sampleDists,\n",
    "         col = colors, \n",
    "        angle_col = 45, fontsize = 20, \n",
    "        display_numbers = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1cab7db-9161-4a85-9d51-8fe6f0a7c933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UXUETZPrGJI1BGAXhk8t8S+8MILt9566/rbDz300AMPPDA7vv7661es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\n97vfzZkzJ/7xmWeeadCgwR4MrIqeeOKJBx54YHevateu3bhx48o9tGnTphd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r2JYrKysreUNiXl5eJYP5yU9+UsVhf0NjyMzMTH/arpQm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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 840,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA\n",
    "object <- rld\n",
    "ntop <- 500 # number of variable genes to use for PCA\n",
    "intgroup <- 'condition'\n",
    "pcsToUse = 1:2\n",
    "\n",
    "rv <- rowVars(assay(object))\n",
    "select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]\n",
    "pca <- prcomp(t(assay(object)[select, ]))\n",
    "percentVar <- pca$sdev^2/sum(pca$sdev^2)\n",
    "\n",
    "intgroup.df <- as.data.frame(colData(object)[, intgroup, drop = FALSE])\n",
    "group <- colData(object)[[intgroup]]\n",
    "\n",
    "pcs <- paste0(\"PC\", pcsToUse)\n",
    "d <- data.frame(V1 = pca$x[, pcsToUse[1]], V2 = pca$x[, pcsToUse[2]], \n",
    "                group = group, intgroup.df, name = colnames(object))\n",
    "colnames(d)[1:2] <- pcs\n",
    "\n",
    "ggplot(data = d, aes(x = PC1, y = PC2, color = group)) + \n",
    "    geom_point(size = 16) + \n",
    "    xlab(paste0(pcs[1], \": \", round(percentVar[pcsToUse[1]] * 100), \"% variance\")) + \n",
    "    ylab(paste0(pcs[2], \": \", round(percentVar[pcsToUse[2]] * 100), \"% variance\")) + \n",
    "    coord_fixed() + \n",
    "    theme_gray(base_size = 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05457b03-567f-4def-a836-39cae4408829",
   "metadata": {},
   "source": [
    "## Differential expression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9480902-2d68-4583-aa6c-ed6326e7bd69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "\n",
      "using 'avgTxLength' from assays(dds), correcting for library size\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/plain": [
       "class: DESeqDataSet \n",
       "dim: 17174 4 \n",
       "metadata(1): version\n",
       "assays(6): counts avgTxLength ... H cooks\n",
       "rownames(17174): ENSMUSG00000000001 ENSMUSG00000000028 ...\n",
       "  ENSMUSG00002076814 ENSMUSG00002076847\n",
       "rowData names(22): baseMean baseVar ... deviance maxCooks\n",
       "colnames(4): SRR13192285 SRR13192286 SRR13192301 SRR13192302\n",
       "colData names(1): condition"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds <- DESeq(dds)\n",
    "dds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b0b2028-b890-4a43-a704-443d9b5dae4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 17174 with nonzero total read count\n",
      "adjusted p-value < 0.1\n",
      "LFC > 0 (up)       : 352, 2%\n",
      "LFC < 0 (down)     : 573, 3.3%\n",
      "outliers [1]       : 0, 0%\n",
      "low counts [2]     : 1665, 9.7%\n",
      "(mean count < 6)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): condition young vs aged \n",
       "Wald test p-value: condition young vs aged \n",
       "DataFrame with 17174 rows and 6 columns\n",
       "                     baseMean log2FoldChange     lfcSE      stat    pvalue\n",
       "                    <numeric>      <numeric> <numeric> <numeric> <numeric>\n",
       "ENSMUSG00000000001 4448.00617       0.116540  0.180609  0.645261 0.5187580\n",
       "ENSMUSG00000000028  401.92741      -0.305754  0.243951 -1.253341 0.2100818\n",
       "ENSMUSG00000000031    8.85933       2.886619  1.554810  1.856573 0.0633719\n",
       "ENSMUSG00000000037  103.69991       0.784844  0.425637  1.843929 0.0651935\n",
       "ENSMUSG00000000056  503.75208       0.350392  0.299206  1.171071 0.2415703\n",
       "...                       ...            ...       ...       ...       ...\n",
       "ENSMUSG00002076664   79.79029      -0.108099  0.471625 -0.229206 0.8187087\n",
       "ENSMUSG00002076697   51.80738       1.208351  0.597565  2.022124 0.0431636\n",
       "ENSMUSG00002076809   10.25885      -1.451036  1.590038 -0.912580 0.3614636\n",
       "ENSMUSG00002076814    3.51613       0.660392  2.391861  0.276100 0.7824715\n",
       "ENSMUSG00002076847    4.90768       5.757565  2.467498  2.333362 0.0196292\n",
       "                        padj\n",
       "                   <numeric>\n",
       "ENSMUSG00000000001  0.933220\n",
       "ENSMUSG00000000028  0.734150\n",
       "ENSMUSG00000000031  0.431446\n",
       "ENSMUSG00000000037  0.436449\n",
       "ENSMUSG00000000056  0.767099\n",
       "...                      ...\n",
       "ENSMUSG00002076664  0.989294\n",
       "ENSMUSG00002076697  0.358746\n",
       "ENSMUSG00002076809  0.866045\n",
       "ENSMUSG00002076814        NA\n",
       "ENSMUSG00002076847        NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- results(dds)\n",
    "summary(res)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "caec0ca2-bbc0-4512-9a93-782fb8d3c657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 17174 with nonzero total read count\n",
      "adjusted p-value < 0.01\n",
      "LFC > 0.10 (up)    : 83, 0.48%\n",
      "LFC < -0.10 (down) : 242, 1.4%\n",
      "outliers [1]       : 0, 0%\n",
      "low counts [2]     : 2331, 14%\n",
      "(mean count < 9)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): condition young vs aged \n",
       "Wald test p-value: condition young vs aged \n",
       "DataFrame with 17174 rows and 6 columns\n",
       "                     baseMean log2FoldChange     lfcSE       stat    pvalue\n",
       "                    <numeric>      <numeric> <numeric>  <numeric> <numeric>\n",
       "ENSMUSG00000000001 4448.00617       0.116540  0.180609  0.0915794 0.9270322\n",
       "ENSMUSG00000000028  401.92741      -0.305754  0.243951 -0.8434226 0.3989922\n",
       "ENSMUSG00000000031    8.85933       2.886619  1.554810  1.7922569 0.0730918\n",
       "ENSMUSG00000000037  103.69991       0.784844  0.425637  1.6089868 0.1076192\n",
       "ENSMUSG00000000056  503.75208       0.350392  0.299206  0.8368534 0.4026749\n",
       "...                       ...            ...       ...        ...       ...\n",
       "ENSMUSG00002076664   79.79029      -0.108099  0.471625 -0.0171732 0.9862985\n",
       "ENSMUSG00002076697   51.80738       1.208351  0.597565  1.8547779 0.0636279\n",
       "ENSMUSG00002076809   10.25885      -1.451036  1.590038 -0.8496883 0.3954984\n",
       "ENSMUSG00002076814    3.51613       0.660392  2.391861  0.2342912 0.8147589\n",
       "ENSMUSG00002076847    4.90768       5.757565  2.467498  2.2928348 0.0218575\n",
       "                        padj\n",
       "                   <numeric>\n",
       "ENSMUSG00000000001  1.000000\n",
       "ENSMUSG00000000028  1.000000\n",
       "ENSMUSG00000000031  0.685438\n",
       "ENSMUSG00000000037  0.833968\n",
       "ENSMUSG00000000056  1.000000\n",
       "...                      ...\n",
       "ENSMUSG00002076664  1.000000\n",
       "ENSMUSG00002076697  0.629143\n",
       "ENSMUSG00002076809  1.000000\n",
       "ENSMUSG00002076814        NA\n",
       "ENSMUSG00002076847        NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using more stringent thresholds: BH FDR adjusted p-value 0.01 vs 0.1, LFC 0.1 vs 0\n",
    "res <- results(dds, alpha = 0.01, lfcThreshold=0.1)\n",
    "summary(res)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "883cdb04-7b07-4ab7-a5ff-8f760da224e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): condition young vs aged \n",
       "Wald test p-value: condition young vs aged \n",
       "DataFrame with 17174 rows and 7 columns\n",
       "                     baseMean log2FoldChange     lfcSE       stat    pvalue\n",
       "                    <numeric>      <numeric> <numeric>  <numeric> <numeric>\n",
       "ENSMUSG00000000001 4448.00617       0.116540  0.180609  0.0915794 0.9270322\n",
       "ENSMUSG00000000028  401.92741      -0.305754  0.243951 -0.8434226 0.3989922\n",
       "ENSMUSG00000000031    8.85933       2.886619  1.554810  1.7922569 0.0730918\n",
       "ENSMUSG00000000037  103.69991       0.784844  0.425637  1.6089868 0.1076192\n",
       "ENSMUSG00000000056  503.75208       0.350392  0.299206  0.8368534 0.4026749\n",
       "...                       ...            ...       ...        ...       ...\n",
       "ENSMUSG00002076664   79.79029      -0.108099  0.471625 -0.0171732 0.9862985\n",
       "ENSMUSG00002076697   51.80738       1.208351  0.597565  1.8547779 0.0636279\n",
       "ENSMUSG00002076809   10.25885      -1.451036  1.590038 -0.8496883 0.3954984\n",
       "ENSMUSG00002076814    3.51613       0.660392  2.391861  0.2342912 0.8147589\n",
       "ENSMUSG00002076847    4.90768       5.757565  2.467498  2.2928348 0.0218575\n",
       "                        padj      symbol\n",
       "                   <numeric> <character>\n",
       "ENSMUSG00000000001  1.000000       Gnai3\n",
       "ENSMUSG00000000028  1.000000       Cdc45\n",
       "ENSMUSG00000000031  0.685438         H19\n",
       "ENSMUSG00000000037  0.833968       Scml2\n",
       "ENSMUSG00000000056  1.000000        Narf\n",
       "...                      ...         ...\n",
       "ENSMUSG00002076664  1.000000     Gm55891\n",
       "ENSMUSG00002076697  0.629143     Gm54987\n",
       "ENSMUSG00002076809  1.000000     Gm54754\n",
       "ENSMUSG00002076814        NA     Gm54907\n",
       "ENSMUSG00002076847        NA     Gm56425"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add BIOMART gene symbols to res based on Ensembl IDs in rownames\n",
    "ensembl <- useMart(\"ensembl\", dataset = 'mmusculus_gene_ensembl')\n",
    "res$symbol <- getBM(attributes = c('ensembl_gene_id','external_gene_name'),\n",
    "                          filters = \"ensembl_gene_id\",\n",
    "                          values = rownames(res),\n",
    "                          mart = ensembl)$external_gene_name\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "40aa7901-ad02-43b6-aa04-7d4507572cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): condition young vs aged \n",
       "Wald test p-value: condition young vs aged \n",
       "DataFrame with 6 rows and 7 columns\n",
       "                    baseMean log2FoldChange     lfcSE      stat      pvalue\n",
       "                   <numeric>      <numeric> <numeric> <numeric>   <numeric>\n",
       "ENSMUSG00000069008   91.8324      -23.36077   4.78533  -4.86085 1.16883e-06\n",
       "ENSMUSG00000078377   96.7285      -10.01129   1.53151  -6.47157 9.69873e-11\n",
       "ENSMUSG00002076515   86.8913       -9.85767   1.51695  -6.43244 1.25573e-10\n",
       "ENSMUSG00000056399   73.3835       -9.61666   1.55967  -6.10170 1.04949e-09\n",
       "ENSMUSG00000096780   56.0776       -9.22806   1.56153  -5.84558 5.04803e-09\n",
       "ENSMUSG00000020907   24.9830       -8.06077   1.67425  -4.75483 1.98617e-06\n",
       "                          padj      symbol\n",
       "                     <numeric> <character>\n",
       "ENSMUSG00000069008 1.02053e-04      Gm5537\n",
       "ENSMUSG00000078377 1.73444e-08      Gm4294\n",
       "ENSMUSG00002076515 2.19280e-08     Gm55628\n",
       "ENSMUSG00000056399 1.56376e-07      Prss34\n",
       "ENSMUSG00000096780 6.81162e-07 Tmem181b-ps\n",
       "ENSMUSG00000020907 1.66558e-04       Rcvrn"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what genes have the strongest down-regulation in young?\n",
    "resSig <- subset(res, padj < 0.01)\n",
    "head(resSig[ order(resSig$log2FoldChange), ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8b379e6-43df-41bb-b03f-8a85196d6a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): condition young vs aged \n",
       "Wald test p-value: condition young vs aged \n",
       "DataFrame with 6 rows and 7 columns\n",
       "                    baseMean log2FoldChange     lfcSE      stat      pvalue\n",
       "                   <numeric>      <numeric> <numeric> <numeric>   <numeric>\n",
       "ENSMUSG00000095545   85.1770       22.60390   4.78539   4.70263 2.56837e-06\n",
       "ENSMUSG00000118588  190.4591       11.04104   1.47942   7.39549 1.40885e-13\n",
       "ENSMUSG00000040715   24.3184        8.06887   1.70652   4.66967 3.01685e-06\n",
       "ENSMUSG00000096718   19.8433        7.78027   1.73242   4.43326 9.28207e-06\n",
       "ENSMUSG00000081309   15.3672        7.40883   1.80461   4.05009 5.11982e-05\n",
       "ENSMUSG00000036040   15.5610        6.03517   1.52324   3.89642 9.76251e-05\n",
       "                          padj      symbol\n",
       "                     <numeric> <character>\n",
       "ENSMUSG00000095545 2.03863e-04      Zfp969\n",
       "ENSMUSG00000118588 3.80210e-11     Gm56487\n",
       "ENSMUSG00000040715 2.36067e-04      Rsc1a1\n",
       "ENSMUSG00000096718 6.78688e-04      Zfp781\n",
       "ENSMUSG00000081309 3.03189e-03     Gm12397\n",
       "ENSMUSG00000036040 5.15676e-03    Adamtsl2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what genes have the strongest UP-regulation in young?\n",
    "head(resSig[ order(resSig$log2FoldChange, decreasing = TRUE), ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7727eb42-e25e-413d-bfd1-3e75e1119d1d",
   "metadata": {},
   "source": [
    "## plotting differential expression results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2da5c43-9b94-43ad-9d3f-c9aacf4e57c4",
   "metadata": {},
   "source": [
    "### Volcano plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3b614763-2441-4aea-bfa9-5c2fa2a20fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
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jo8XKSsrhPX+fCQAAIABJREFUoBQKQsj/du6ssVr/mZpKysqYTp0uVoPF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\nKrvNbp7h49m40+SUFKnaV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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 840,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "EnhancedVolcano(as.data.frame(res), x = 'log2FoldChange', y = 'padj', lab = as.data.frame(res)$'symbol', \n",
    "               pCutoff = 1e-06, FCcutoff = 3, \n",
    "               xlim = c(-12, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3341b107-a13b-415e-ab83-97750863356b",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33790540-bae7-49d3-ac87-0483151f614b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ehmetq1s6drutcuaV1K37EABiQrIzM46GJGRgYJO7wPWBILvCESdlrYW+WWL0PC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\nv//+e+fOnVNSUrZs2RIQENC9e3cXF5e4uLiwsLCjR49KMyL79+/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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 840,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of genes with strongest signal\n",
    "resSig <- subset(res, padj < 1e-6 & abs(log2FoldChange) > 3)\n",
    "# Create a named vector for mapping Ensembl IDs to gene symbols\n",
    "ensembl_to_symbol <- setNames(res$symbol, rownames(res))\n",
    "# select only resSig genes instead of heatmap of all ~17,000 genes\n",
    "mat  <- assay(rld)[rownames(resSig), ] \n",
    "# heatmap of deviation from mean expression instead of actual expression\n",
    "mat  <- mat - rowMeans(mat) \n",
    "# annotate heatmap columns with sample name\n",
    "anno <- as.data.frame(colData(rld))\n",
    "pheatmap(mat, \n",
    "         annotation_col = anno, \n",
    "        labels_row = ensembl_to_symbol[rownames(mat)], \n",
    "        fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ad7496d-aa9c-4da1-b4dd-943e0fd35b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): condition young vs aged \n",
       " \n",
       "DataFrame with 34 rows and 4 columns\n",
       "                    baseMean log2FoldChange         padj      symbol\n",
       "                   <numeric>      <numeric>    <numeric> <character>\n",
       "ENSMUSG00000026580  4174.971       -3.45237 1.07575e-107        Selp\n",
       "ENSMUSG00000032372   956.505       -3.86791  8.69141e-65      Plscr2\n",
       "ENSMUSG00000022037  2046.566       -3.06303  5.65562e-63         Clu\n",
       "ENSMUSG00000073418   530.118       -4.85742  9.03681e-45         C4b\n",
       "ENSMUSG00000030717   764.574       -3.00453  1.91975e-41       Nupr1\n",
       "...                      ...            ...          ...         ...\n",
       "ENSMUSG00000046159  116.1336       -4.01382  1.34493e-07       Chrm3\n",
       "ENSMUSG00000049350   60.4063       -4.84364  1.56376e-07        Zg16\n",
       "ENSMUSG00000056399   73.3835       -9.61666  1.56376e-07      Prss34\n",
       "ENSMUSG00000038156   83.4629       -3.22480  6.54188e-07       Spon1\n",
       "ENSMUSG00000096780   56.0776       -9.22806  6.81162e-07 Tmem181b-ps"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resSig[ order(resSig$padj, decreasing = FALSE), c('baseMean', 'log2FoldChange', 'padj', 'symbol')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3037d916-9af9-4a42-9299-55828502c4c3",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Both the volcano plot and the heatmap show P-selectin (Selp) and Clusterin (Clu) which are hallmark genes of aging in HSCs having >8X higher expression in aged HSCs compared to young HSCs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a79e42-8c38-4f26-a624-71d034ceddd8",
   "metadata": {},
   "source": [
    "---\n",
    "## NEXT:\n",
    "\n",
    "- gene set enrichment analysis (GSEA) to identify enriched pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92a32f0b-2c43-4e61-8166-a9366b054cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 4.3.2 (2023-10-31)\n",
       "Platform: x86_64-conda-linux-gnu (64-bit)\n",
       "Running under: CentOS Linux 7 (Core)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /home/fmbuga/.conda/envs/seurat5/lib/libopenblasp-r0.3.24.so;  LAPACK version 3.11.0\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "time zone: US/Pacific\n",
       "tzcode source: system (glibc)\n",
       "\n",
       "attached base packages:\n",
       "[1] stats4    stats     graphics  grDevices utils     datasets  methods  \n",
       "[8] base     \n",
       "\n",
       "other attached packages:\n",
       " [1] org.Mm.eg.db_3.18.0         RColorBrewer_1.1-3         \n",
       " [3] pheatmap_1.0.12             dplyr_1.1.4                \n",
       " [5] ggplot2_3.4.4               DESeq2_1.42.0              \n",
       " [7] SummarizedExperiment_1.32.0 MatrixGenerics_1.14.0      \n",
       " [9] matrixStats_1.1.0           AnnotationHub_3.10.0       \n",
       "[11] BiocFileCache_2.10.1        dbplyr_2.4.0               \n",
       "[13] ensembldb_2.26.0            AnnotationFilter_1.26.0    \n",
       "[15] GenomicFeatures_1.54.1      AnnotationDbi_1.64.1       \n",
       "[17] Biobase_2.62.0              GenomicRanges_1.54.1       \n",
       "[19] GenomeInfoDb_1.38.1         IRanges_2.36.0             \n",
       "[21] S4Vectors_0.40.1            BiocGenerics_0.48.1        \n",
       "[23] tximport_1.30.0            \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] DBI_1.1.3                     bitops_1.0-7                 \n",
       " [3] biomaRt_2.58.0                rlang_1.1.2                  \n",
       " [5] magrittr_2.0.3                compiler_4.3.2               \n",
       " [7] RSQLite_2.3.3                 png_0.1-8                    \n",
       " [9] vctrs_0.6.4                   stringr_1.5.1                \n",
       "[11] ProtGenerics_1.34.0           pkgconfig_2.0.3              \n",
       "[13] crayon_1.5.2                  fastmap_1.1.1                \n",
       "[15] XVector_0.42.0                ellipsis_0.3.2               \n",
       "[17] labeling_0.4.3                utf8_1.2.4                   \n",
       "[19] Rsamtools_2.18.0              promises_1.2.1               \n",
       "[21] tzdb_0.4.0                    purrr_1.0.2                  \n",
       "[23] bit_4.0.5                     zlibbioc_1.48.0              \n",
       "[25] cachem_1.0.8                  jsonlite_1.8.7               \n",
       "[27] progress_1.2.2                blob_1.2.4                   \n",
       "[29] later_1.3.1                   DelayedArray_0.28.0          \n",
       "[31] uuid_1.1-1                    BiocParallel_1.36.0          \n",
       "[33] interactiveDisplayBase_1.40.0 parallel_4.3.2               \n",
       "[35] prettyunits_1.2.0             R6_2.5.1                     \n",
       "[37] stringi_1.8.1                 rtracklayer_1.62.0           \n",
       "[39] Rcpp_1.0.11                   IRkernel_1.3.2               \n",
       "[41] base64enc_0.1-3               readr_2.1.4                  \n",
       "[43] httpuv_1.6.12                 Matrix_1.6-3                 \n",
       "[45] tidyselect_1.2.0              abind_1.4-5                  \n",
       "[47] yaml_2.3.7                    codetools_0.2-19             \n",
       "[49] curl_5.1.0                    lattice_0.22-5               \n",
       "[51] tibble_3.2.1                  withr_2.5.2                  \n",
       "[53] shiny_1.8.0                   KEGGREST_1.42.0              \n",
       "[55] evaluate_0.23                 xml2_1.3.5                   \n",
       "[57] Biostrings_2.70.1             pillar_1.9.0                 \n",
       "[59] BiocManager_1.30.22           filelock_1.0.2               \n",
       "[61] generics_0.1.3                vroom_1.6.4                  \n",
       "[63] RCurl_1.98-1.13               IRdisplay_1.1                \n",
       "[65] BiocVersion_3.18.1            hms_1.1.3                    \n",
       "[67] munsell_0.5.0                 scales_1.2.1                 \n",
       "[69] xtable_1.8-4                  glue_1.6.2                   \n",
       "[71] lazyeval_0.2.2                tools_4.3.2                  \n",
       "[73] BiocIO_1.12.0                 locfit_1.5-9.8               \n",
       "[75] GenomicAlignments_1.38.0      pbdZMQ_0.3-10                \n",
       "[77] XML_3.99-0.15                 grid_4.3.2                   \n",
       "[79] colorspace_2.1-0              GenomeInfoDbData_1.2.11      \n",
       "[81] repr_1.1.6                    restfulr_0.0.15              \n",
       "[83] cli_3.6.1                     rappdirs_0.3.3               \n",
       "[85] fansi_1.0.5                   S4Arrays_1.2.0               \n",
       "[87] gtable_0.3.4                  digest_0.6.33                \n",
       "[89] SparseArray_1.2.2             farver_2.1.1                 \n",
       "[91] rjson_0.2.21                  memoise_2.0.1                \n",
       "[93] htmltools_0.5.7               lifecycle_1.0.4              \n",
       "[95] httr_1.4.7                    mime_0.12                    \n",
       "[97] bit64_4.0.5                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
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