src/examples/RNASeqDE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "# RNA-seq workflow: gene-level exploratory analysis and differential expression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Here we reproduces in SoS analysis originally performed by [`rnaseqGene`](https://www.bioconductor.org/help/workflows/rnaseqGene/#introduction) Bioconductor workflow,  authored by:\n",
    "\n",
    "**Michael I. Love<sup>1</sup>, Simon Anders<sup>2,3</sup>, Vladislav Kim<sup>3</sup> and Wolfgang Huber<sup>3</sup>**\n",
    "\n",
    "<sup>1</sup>Departments of Biostatistics and Genetics, UNC-Chapel Hill, Chapel Hill, NC, US\n",
    "<sup>2</sup>Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland\n",
    "<sup>3</sup>European Molecular Biology Laboratory (EMBL), Heidelberg, Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "kernel": "SoS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table class=\"revision_table\">\n",
       "        <tr>\n",
       "        <th>Revision</th>\n",
       "        <th>Author</th>\n",
       "        <th>Date</th>\n",
       "        <th>Message</th>\n",
       "        <tr>\n",
       "        <tr><td><a target=\"_blank\" href=\"http://github.com/vatlab/sos-docs/blob/13d093ce4abdc1492f7179963f2918b110d9b513/RNASeqDE.ipynb\"><span class=\"revision_id\">13d093c<span></a></td>\n",
       "<td>Gao Wang</td>\n",
       "<td>2018-06-16</td>\n",
       "<td>Make STAR sparse mode a parameter</td></tr><tr><td><a target=\"_blank\" href=\"http://github.com/vatlab/sos-docs/blob/3da9e4b60369c6a9ce591f971ef9e70a5c881867/RNASeqDE.ipynb\"><span class=\"revision_id\">3da9e4b<span></a></td>\n",
       "<td>Gao Wang</td>\n",
       "<td>2018-06-16</td>\n",
       "<td>Fix & finalize DE example</td></tr><tr><td><a target=\"_blank\" href=\"http://github.com/vatlab/sos-docs/blob/3a5eb79e2fbfc3220ff7ac8977f6c0abb954f303/RNASeqDE.ipynb\"><span class=\"revision_id\">3a5eb79<span></a></td>\n",
       "<td>Gao Wang</td>\n",
       "<td>2018-06-16</td>\n",
       "<td>Update DE pipeline</td></tr><tr><td><a target=\"_blank\" href=\"http://github.com/vatlab/sos-docs/blob/22b1b5137034dea3ac027e13f749a9fdaa879f87/RNASeqDE.ipynb\"><span class=\"revision_id\">22b1b51<span></a></td>\n",
       "<td>Gao Wang</td>\n",
       "<td>2018-06-13</td>\n",
       "<td>Update workflow document</td></tr><tr><td><a target=\"_blank\" href=\"http://github.com/vatlab/sos-docs/blob/8027190f9f4537efb46c87655f8a97fe30590334/RNASeqDE.ipynb\"><span class=\"revision_id\">8027190<span></a></td>\n",
       "<td>Gao Wang</td>\n",
       "<td>2018-04-28</td>\n",
       "<td>Update DE align workflow</td></tr></table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%revisions -s -n 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "This notebook is converted from [a Jupyter implementation](https://github.com/Bioconductor-notebooks/Dynamic-Re-analysis-RNA-seq-differential-expression-workflow). See [this paper](http://dx.doi.org/10.1101/144816) for more details. The original Bioconductor workflow loads pre-processed BAM files packaged into `airway` library, and perform interactive analysis along the way. Here we highlight the advantage using SoS over a conventional Jupyter `R` notebook: \n",
    "\n",
    "* All command-line based procedures can be formally implemented in SoS and performed in an interactive environment.\n",
    "* This notebook configures the cell tags for `sos report` style of HTML output. When exported using that style, only important codes and results will be displayed. This improves workflow readability.\n",
    "* All figures are created in PDF format to the proper destination directory, and previewed as embedded image via SoS. With this feature users no longer have to write separated codes for interactive figure display and actually obtaining the high-resolution version of the plots in PDF format.\n",
    "\n",
    "It also demonstrates how SoS handles command tool and R library dependencies automatically. Since most command-line tools involved are available on [Docker Hub](https://hub.docker.com) SoS utilizes these images, this workflow requires [Docker](https://docs.docker.com/engine/installation/) be installed and configured (although alternatively one also install individual tools using SoS auxillary steps)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "kernel": "SoS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gaow/GIT/LargeFiles/RNAseqDE"
     ]
    }
   ],
   "source": [
    "%cd ~/GIT/LargeFiles/RNAseqDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "kernel": "SoS"
   },
   "outputs": [],
   "source": [
    "[global]\n",
    "parameter: cwd = path('~/GIT/LargeFiles/RNAseqDE')\n",
    "parameter: samples = ['SRR1039508', 'SRR1039509', 'SRR1039512', 'SRR1039513', 'SRR1039516', 'SRR1039517', 'SRR1039520', 'SRR1039521']\n",
    "parameter: ncpu = 4\n",
    "# Resource files\n",
    "resource_dir = f\"{cwd:a}/hg19\"\n",
    "ref_fa = \"hg19.fa\"\n",
    "ref_gtf = 'Homo_sapiens.GRCh38.91.gtf.gz'\n",
    "# when set to False (default) STAR will use ~30GB memory.\n",
    "# set it to `True` to reduce memory consumption to half.\n",
    "parameter: sparse_star = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Please note that here we have specified paths to pre-existing directories in the `[global]` section, with `parameter` keyword. This setup allows users to configure all paths in one places for a “default” run, and optionally can configure them from command-line or `%sosrun` magic. To elaborate, for example:\n",
    "\n",
    "```\n",
    "[global]\n",
    "parameter: cwd = path('~/GIT/LargeFiles/RNAseqDE')\n",
    "```\n",
    "\n",
    "can be override by command argument `--cwd /path/to/new/dir`. An alternative implementation is to use configuration file, eg, create a file called `config.yml` and write:\n",
    "\n",
    "```\n",
    "cwd: path('~/GIT/LargeFiles/RNAseqDE')\n",
    "```\n",
    "and execute with `%sosrun -c config.yml`. The downside is that a `config.yml` file will have to be maintained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Abstract\n",
    "\n",
    "Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages and other tools in [SoS](http://vatlab.github.io/sos) scripting environment. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Experimental data\n",
    "\n",
    "The data used in this workflow is stored in the [airway](http://bioconductor.org/packages/airway) package that summarizes an RNA-seq experiment wherein airway smooth muscle cells were treated with dexamethasone, a synthetic glucocorticoid steroid with anti-inflammatory effects (Himes et al. 2014). Glucocorticoids are used, for example, by people with asthma to reduce inflammation of the airways. In the experiment, four primary human airway smooth muscle cell lines were treated with 1 micromolar dexamethasone for 18 hours. For each of the four cell lines, we have a treated and an untreated sample. For more description of the experiment see the [PubMed entry 24926665](http://www.ncbi.nlm.nih.gov/pubmed/24926665) and for raw data see the [GEO entry GSE52778](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52778)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Preparing Count Matrices\n",
    "\n",
    "As input, the count-based statistical methods, such as [`DESeq2`](http://bioconductor.org/packages/DESeq2) (Love, Huber, and Anders 2014), [`edgeR`](http://bioconductor.org/packages/edgeR) (M. D. Robinson, McCarthy, and Smyth 2009), [`limma`](http://bioconductor.org/packages/limma) with the voom method (Law et al. 2014), [`DSS`](http://bioconductor.org/packages/DSS) (H. Wu, Wang, and Wu 2013), [`EBSeq`](http://bioconductor.org/packages/EBSeq) (Leng et al. 2013) and [`baySeq`](http://bioconductor.org/packages/baySeq) (Hardcastle and Kelly 2010), expect input data as obtained, e.g., from RNA-seq or another high-throughput sequencing experiment, in the form of a matrix of integer values. The value in the $i$-th row and the $j$-th column of the matrix tells how many reads (or fragments, for paired-end RNA-seq) have been assigned to gene $i$ in sample $j$. Analogously, for other types of assays, the rows of the matrix might correspond e.g., to binding regions (with ChIP-Seq), species of bacteria (with metagenomic datasets), or peptide sequences (with quantitative mass spectrometry).\n",
    "\n",
    "The values in the matrix should be counts of sequencing reads/fragments. This is important for `DESeq2`’s statistical model to hold, as only counts allow assessing the measurement precision correctly. It is important to never provide counts that were pre-normalized for sequencing depth/library size, as the statistical model is most powerful when applied to un-normalized counts, and is designed to account for library size differences internally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Recommended: transcript abundances and the tximport pipeline\n",
    "\n",
    "Before we demonstrate how to align and then count RNA-seq fragments, we mention that a newer and faster alternative pipeline is to use transcript abundance quantification methods such as [`Salmon`](https://combine-lab.github.io/salmon/) (Patro et al. 2016), [`Sailfish`](http://www.cs.cmu.edu/~ckingsf/software/sailfish/) (Patro, Mount, and Kingsford 2014), [`kallisto`](https://pachterlab.github.io/kallisto/) (Bray et al. 2016), or [`RSEM`](http://deweylab.github.io/RSEM/) (B. Li and Dewey 2011), to estimate abundances without aligning reads, followed by the [`tximport`](http://bioconductor.org/packages/tximport) package for assembling estimated count and offset matrices for use with Bioconductor differential gene expression packages.\n",
    "\n",
    "A tutorial on how to use the Salmon software for quantifying transcript abundance can be found [here](https://combine-lab.github.io/salmon/getting_started/). Following that tutorial, you can use the steps in the [`tximport` vignette](https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html), which will show how to build a DESeqDataSet. This is our current recommended pipeline for users, but below we still include steps on aligning reads to the genome and building count matrices from BAM files.\n",
    "\n",
    "The advantages of using the transcript abundance quantifiers in conjunction with tximport to produce gene-level count matrices and normalizing offsets, are: this approach corrects for any potential changes in gene length across samples (e.g. from differential isoform usage) (Trapnell et al. 2013); some of these methods are substantially faster and require less memory and disk usage compared to alignment-based methods; and it is possible to avoid discarding those fragments that can align to multiple genes with homologous sequence (Robert and Watson 2015). Note that transcript abundance quantifiers skip the generation of large files which store read alignments, instead producing smaller files which store estimated abundances, counts, and effective lengths per transcript. For more details, see the manuscript describing this approach (Soneson, Love, and Robinson 2015), and the [`tximport`](http://bioconductor.org/packages/tximport) package vignette for software details. The entry point back into this workflow after using a transcript quantifier and tximport would be the section on exploratory data analysis below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Aligning read to a reference genome\n",
    "\n",
    "The computational analysis of an RNA-seq experiment begins from the FASTQ files that contain the nucleotide sequence of each read and a quality score at each position. These reads must first be aligned to a reference genome or transcriptome, or the abundances and estimated counts per transcript can be estimated without alignment, as described above. In either case, it is important to know if the sequencing experiment was single-end or paired-end, as the alignment software will require the user to specify both FASTQ files for a paired-end experiment. The output of this alignment step is commonly stored in a file format called [SAM/BAM](http://samtools.github.io/hts-specs).\n",
    "\n",
    "A number of software programs exist to align reads to a reference genome, and we encourage you to check out some of the benchmarking papers that discuss the advantages and disadvantages of each software, which include accuracy, sensitivity in aligning reads over splice junctions, speed, memory required, usability, and many other features. Here, we used the [`STAR read aligner`](https://code.google.com/p/rna-star/) (Dobin et al. 2013) to align the reads for our current experiment to the Ensembl release 75 (Flicek et al. 2014) human reference genome. In the following code example, it is assumed that there is a file in the current directory called `files` with each line containing an identifier for each experiment, and we have all the FASTQ files in a subdirectory `fastq`. If you have downloaded the FASTQ files from the Sequence Read Archive, the identifiers would be SRA run IDs, e.g. `SRR1039520`. You should have two files for a paired-end experiment for each ID, `fastq/SRR1039520_1.fastq` and `fastq/SRR1039520_2.fastq`, which give the first and second read for the paired-end fragments. If you have performed a single-end experiment, you would only have one file per ID. We have also created a subdirectory, `aligned`, where `STAR` will output its alignment files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "#### Download and decrypt  SRA files\n",
    "\n",
    "This is an auxiliary step that gets `fastq` files for input to STAR aligner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# decrypt SRA files\n",
    "[sra (dump SRA to fastq): provides=['{data}_1.fastq', '{data}_2.fastq']]\n",
    "output: f'{data}_1.fastq', f'{data}_2.fastq'\n",
    "run: container = 'gaow/debian-ngs:1.0', workdir = cwd, expand=True\n",
    "    fastq-dump -I --split-files {os.path.basename(data)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "#### Prepare STAR reference genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Download `hg19.2bit` and `twoBitToFa` from UCSC\n",
    "[hg19_reference_1 (download)]\n",
    "ucsc_url = \"http://hgdownload.cse.ucsc.edu\"\n",
    "output: f\"{resource_dir}/hg19.2bit\", f\"{resource_dir}/twoBitToFa\"\n",
    "download: dest_dir = resource_dir, expand = True\n",
    "    {ucsc_url}/goldenPath/hg19/bigZips/hg19.2bit\n",
    "    {ucsc_url}/admin/exe/{\"macOSX.x86_64\" if sys.platform == 'darwin' else \"linux.x86_64\"}/twoBitToFa\n",
    "\n",
    "# Use `twoBitToFa` to extract `hg19.fa` from `hg19.2bit`\n",
    "[hg19_reference_2 (decompress hg19.fa)]\n",
    "output: f\"{resource_dir}/{ref_fa}\"\n",
    "bash: expand = True\n",
    "    chmod +x {_input[1]}\n",
    "    {_input[1]} {_input[0]} {_output}\n",
    "\n",
    "# Download `Homo_sapiens.GRCh38.91.gtf.gz` from Ensembl\n",
    "[hg19_reference_3 (gene annotations)]\n",
    "# https://useast.ensembl.org/info/data/ftp/index.html\n",
    "ensembl_ftp = 'ftp://ftp.ensembl.org/pub/release-91/gtf/homo_sapiens/'\n",
    "output: f\"{resource_dir}/{ref_gtf}\"\n",
    "download: dest_dir = resource_dir, expand = True\n",
    "    {ensembl_ftp}/{ref_gtf}\n",
    "    \n",
    "# Prepare STAR reference data\n",
    "[star: provides = f\"{resource_dir}/genomeParameters.txt\"]\n",
    "# Quite time & resource consuming (3hrs, 32GB memory if sparse_star is False)\n",
    "depends: sos_step('hg19_reference'), system_resource(mem='40G', disk='200G')\n",
    "output: f\"{resource_dir}/genomeParameters.txt\"\n",
    "run: workdir = '/tmp', container = 'gaow/debian-ngs:1.0', volumes = [f'{resource_dir}:{resource_dir}'], expand=True\n",
    "     STAR --runMode genomeGenerate \\\n",
    "        --genomeDir {resource_dir} \\\n",
    "        --genomeFastaFiles {resource_dir}/{ref_fa} \\\n",
    "        --sjdbGTFtagExonParentTranscript {resource_dir}/{ref_gtf} \\\n",
    "        --runThreadN {ncpu} {'--genomeSAsparseD 2' if sparse_star else ''}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "#### Alignment via STAR\n",
    "\n",
    "This step is memory intensive (32GB memory required per sample)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Align to SAM file\n",
    "[align_1 (STAR alignment)]\n",
    "depends: f\"{resource_dir}/genomeParameters.txt\"\n",
    "input: [(f'{cwd:a}/{s}_1.fastq', f'{cwd:a}/{s}_2.fastq') for s in samples], group_by = \"pairs\"\n",
    "output: [f\"{cwd:a}/{s}.Aligned.out.sam\" for s in samples], group_by = 1\n",
    "run: container = 'gaow/debian-ngs:1.0', volumes = [f'{resource_dir}:{resource_dir}'], expand=True, workdir = cwd\n",
    "    STAR --genomeDir {resource_dir} \\\n",
    "        --readFilesIn {_input[0]} {_input[1]} \\\n",
    "        --runThreadN {ncpu} --outFileNamePrefix {_output:nnn}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "#### Generate BAM files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Convert SAM to BAM file & zap the input\n",
    "[align_2 (SAM to BAM)]\n",
    "input: pattern = '{name}.Aligned.out.sam'\n",
    "output: expand_pattern('{_name}.bam')\n",
    "run: container = 'gaow/debian-ngs:1.0', expand=True, workdir = cwd\n",
    "    samtools view -bS {_input} -o {_output}\n",
    "_input.zap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The BAM files for a number of sequencing runs can then be used to generate count matrices, as described in the following section.\n",
    "\n",
    "Notice that the `align_2` step can be avoided if in `align_1` step options `--outStd BAM_SortedByCoordinate  --outSAMtype BAM SortedByCoordinate` are used for `STAR` aligner. We do not use those options because we would like to follow exactly how the Bioconductor tutorial instructs, for demonstration purpose. However we used `_input.zap()` at the end of the step to delete those SAM files without potentially triggering reruns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "#### Run the alignment pipeline\n",
    "\n",
    "The following executes the pipeline from inside this notebook using 2 CPU cores in parallel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "kernel": "SoS"
   },
   "outputs": [],
   "source": [
    "%sosrun align -j 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Or, to run it from command console:\n",
    "\n",
    "```\n",
    "sos run RNASeqDE.ipynb align -j 2\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Caution that large data-set will be downloaded at a result of this alignment workflow and the alignment process is computationally intensive. Also `align_1` STAR step uses ~ 30GB memory so `-j 2` is used on a machine with 64GB of memory. This workflow will not impact the rest of demonstrations to follow; readers can terminate the workflow at any time they deem fit.\n",
    "\n",
    "**If the goal is to test the workflow please only run it with one sample to avoid too much computation, eg:**\n",
    "\n",
    "```\n",
    "sos run RNASeqDE.ipynb align --samples SRR1039508\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Locating alignment files\n",
    "\n",
    "In the previous section we have shown the RNA-seq alignment via the `align` workflow, from downloading the SRA files and the tools (in Docker) needed, to generating BAM files. Here, for demonstration, we chose a subset of reads because the full alignment files are large (a few gigabytes each), and because it takes between 10-30 minutes to count the fragments for each sample. These files are provided as part of the `airway` library. We will use these files to demonstrate how a count matrix can be constructed from BAM files. Afterwards, we will load the full count matrix corresponding to all samples and all data, which is already provided in the same package, and will continue the analysis with that full matrix.\n",
    "\n",
    "We check via SoS if the required packages are available for the rest of the workflow to run, and install it otherwise:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_cell"
    ]
   },
   "outputs": [],
   "source": [
    "depends: R_library('airway'), R_library('Rsamtools'), R_library('GenomicFeatures'),\n",
    "    R_library('GenomicAlignments'), R_library('BiocParallel'), R_library('DESeq2'), \n",
    "    R_library(\"vsn\"), R_library('pheatmap'), R_library('ggplot2'), R_library(\"PoiClaClu\"), \n",
    "    R_library('ggbeeswarm'), R_library('genefilter'), R_library(\"AnnotationDbi\"), \n",
    "    R_library(\"org.Hs.eg.db\"), R_library(\"ReportingTools\"),\n",
    "    R_library(\"Gviz\"), R_library(\"sva\"), R_library('fission')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Now we are ready to explore the alignment data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"List of Files in the /extdata directory of the airway package:\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'GSE52778_series_matrix.txt'</li>\n",
       "\t<li>'Homo_sapiens.GRCh37.75_subset.gtf'</li>\n",
       "\t<li>'sample_table.csv'</li>\n",
       "\t<li>'SraRunInfo_SRP033351.csv'</li>\n",
       "\t<li>'SRR1039508_subset.bam'</li>\n",
       "\t<li>'SRR1039509_subset.bam'</li>\n",
       "\t<li>'SRR1039512_subset.bam'</li>\n",
       "\t<li>'SRR1039513_subset.bam'</li>\n",
       "\t<li>'SRR1039516_subset.bam'</li>\n",
       "\t<li>'SRR1039517_subset.bam'</li>\n",
       "\t<li>'SRR1039520_subset.bam'</li>\n",
       "\t<li>'SRR1039521_subset.bam'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'GSE52778\\_series\\_matrix.txt'\n",
       "\\item 'Homo\\_sapiens.GRCh37.75\\_subset.gtf'\n",
       "\\item 'sample\\_table.csv'\n",
       "\\item 'SraRunInfo\\_SRP033351.csv'\n",
       "\\item 'SRR1039508\\_subset.bam'\n",
       "\\item 'SRR1039509\\_subset.bam'\n",
       "\\item 'SRR1039512\\_subset.bam'\n",
       "\\item 'SRR1039513\\_subset.bam'\n",
       "\\item 'SRR1039516\\_subset.bam'\n",
       "\\item 'SRR1039517\\_subset.bam'\n",
       "\\item 'SRR1039520\\_subset.bam'\n",
       "\\item 'SRR1039521\\_subset.bam'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'GSE52778_series_matrix.txt'\n",
       "2. 'Homo_sapiens.GRCh37.75_subset.gtf'\n",
       "3. 'sample_table.csv'\n",
       "4. 'SraRunInfo_SRP033351.csv'\n",
       "5. 'SRR1039508_subset.bam'\n",
       "6. 'SRR1039509_subset.bam'\n",
       "7. 'SRR1039512_subset.bam'\n",
       "8. 'SRR1039513_subset.bam'\n",
       "9. 'SRR1039516_subset.bam'\n",
       "10. 'SRR1039517_subset.bam'\n",
       "11. 'SRR1039520_subset.bam'\n",
       "12. 'SRR1039521_subset.bam'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"GSE52778_series_matrix.txt\"        \"Homo_sapiens.GRCh37.75_subset.gtf\"\n",
       " [3] \"sample_table.csv\"                  \"SraRunInfo_SRP033351.csv\"         \n",
       " [5] \"SRR1039508_subset.bam\"             \"SRR1039509_subset.bam\"            \n",
       " [7] \"SRR1039512_subset.bam\"             \"SRR1039513_subset.bam\"            \n",
       " [9] \"SRR1039516_subset.bam\"             \"SRR1039517_subset.bam\"            \n",
       "[11] \"SRR1039520_subset.bam\"             \"SRR1039521_subset.bam\"            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressPackageStartupMessages(library(\"airway\"))\n",
    "indir <- system.file(\"extdata\", package=\"airway\", mustWork=TRUE)\n",
    "print(\"List of Files in the /extdata directory of the airway package:\")\n",
    "list.files(indir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Typically, we have a table with detailed information for each of our samples that links samples to the associated FASTQ and BAM files. For your own project, you might create such a comma-separated value (CSV) file using a text editor or spreadsheet software such as Excel.\n",
    "We load such a CSV file with read.csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>SampleName</th><th scope=col>cell</th><th scope=col>dex</th><th scope=col>albut</th><th scope=col>Run</th><th scope=col>avgLength</th><th scope=col>Experiment</th><th scope=col>Sample</th><th scope=col>BioSample</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SRR1039508</th><td>GSM1275862  </td><td>N61311      </td><td>untrt       </td><td>untrt       </td><td>SRR1039508  </td><td>126         </td><td>SRX384345   </td><td>SRS508568   </td><td>SAMN02422669</td></tr>\n",
       "\t<tr><th scope=row>SRR1039509</th><td>GSM1275863  </td><td>N61311      </td><td>trt         </td><td>untrt       </td><td>SRR1039509  </td><td>126         </td><td>SRX384346   </td><td>SRS508567   </td><td>SAMN02422675</td></tr>\n",
       "\t<tr><th scope=row>SRR1039512</th><td>GSM1275866  </td><td>N052611     </td><td>untrt       </td><td>untrt       </td><td>SRR1039512  </td><td>126         </td><td>SRX384349   </td><td>SRS508571   </td><td>SAMN02422678</td></tr>\n",
       "\t<tr><th scope=row>SRR1039513</th><td>GSM1275867  </td><td>N052611     </td><td>trt         </td><td>untrt       </td><td>SRR1039513  </td><td> 87         </td><td>SRX384350   </td><td>SRS508572   </td><td>SAMN02422670</td></tr>\n",
       "\t<tr><th scope=row>SRR1039516</th><td>GSM1275870  </td><td>N080611     </td><td>untrt       </td><td>untrt       </td><td>SRR1039516  </td><td>120         </td><td>SRX384353   </td><td>SRS508575   </td><td>SAMN02422682</td></tr>\n",
       "\t<tr><th scope=row>SRR1039517</th><td>GSM1275871  </td><td>N080611     </td><td>trt         </td><td>untrt       </td><td>SRR1039517  </td><td>126         </td><td>SRX384354   </td><td>SRS508576   </td><td>SAMN02422673</td></tr>\n",
       "\t<tr><th scope=row>SRR1039520</th><td>GSM1275874  </td><td>N061011     </td><td>untrt       </td><td>untrt       </td><td>SRR1039520  </td><td>101         </td><td>SRX384357   </td><td>SRS508579   </td><td>SAMN02422683</td></tr>\n",
       "\t<tr><th scope=row>SRR1039521</th><td>GSM1275875  </td><td>N061011     </td><td>trt         </td><td>untrt       </td><td>SRR1039521  </td><td> 98         </td><td>SRX384358   </td><td>SRS508580   </td><td>SAMN02422677</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       "  & SampleName & cell & dex & albut & Run & avgLength & Experiment & Sample & BioSample\\\\\n",
       "\\hline\n",
       "\tSRR1039508 & GSM1275862   & N61311       & untrt        & untrt        & SRR1039508   & 126          & SRX384345    & SRS508568    & SAMN02422669\\\\\n",
       "\tSRR1039509 & GSM1275863   & N61311       & trt          & untrt        & SRR1039509   & 126          & SRX384346    & SRS508567    & SAMN02422675\\\\\n",
       "\tSRR1039512 & GSM1275866   & N052611      & untrt        & untrt        & SRR1039512   & 126          & SRX384349    & SRS508571    & SAMN02422678\\\\\n",
       "\tSRR1039513 & GSM1275867   & N052611      & trt          & untrt        & SRR1039513   &  87          & SRX384350    & SRS508572    & SAMN02422670\\\\\n",
       "\tSRR1039516 & GSM1275870   & N080611      & untrt        & untrt        & SRR1039516   & 120          & SRX384353    & SRS508575    & SAMN02422682\\\\\n",
       "\tSRR1039517 & GSM1275871   & N080611      & trt          & untrt        & SRR1039517   & 126          & SRX384354    & SRS508576    & SAMN02422673\\\\\n",
       "\tSRR1039520 & GSM1275874   & N061011      & untrt        & untrt        & SRR1039520   & 101          & SRX384357    & SRS508579    & SAMN02422683\\\\\n",
       "\tSRR1039521 & GSM1275875   & N061011      & trt          & untrt        & SRR1039521   &  98          & SRX384358    & SRS508580    & SAMN02422677\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | SampleName | cell | dex | albut | Run | avgLength | Experiment | Sample | BioSample | \n",
       "|---|---|---|---|---|---|---|---|\n",
       "| SRR1039508 | GSM1275862   | N61311       | untrt        | untrt        | SRR1039508   | 126          | SRX384345    | SRS508568    | SAMN02422669 | \n",
       "| SRR1039509 | GSM1275863   | N61311       | trt          | untrt        | SRR1039509   | 126          | SRX384346    | SRS508567    | SAMN02422675 | \n",
       "| SRR1039512 | GSM1275866   | N052611      | untrt        | untrt        | SRR1039512   | 126          | SRX384349    | SRS508571    | SAMN02422678 | \n",
       "| SRR1039513 | GSM1275867   | N052611      | trt          | untrt        | SRR1039513   |  87          | SRX384350    | SRS508572    | SAMN02422670 | \n",
       "| SRR1039516 | GSM1275870   | N080611      | untrt        | untrt        | SRR1039516   | 120          | SRX384353    | SRS508575    | SAMN02422682 | \n",
       "| SRR1039517 | GSM1275871   | N080611      | trt          | untrt        | SRR1039517   | 126          | SRX384354    | SRS508576    | SAMN02422673 | \n",
       "| SRR1039520 | GSM1275874   | N061011      | untrt        | untrt        | SRR1039520   | 101          | SRX384357    | SRS508579    | SAMN02422683 | \n",
       "| SRR1039521 | GSM1275875   | N061011      | trt          | untrt        | SRR1039521   |  98          | SRX384358    | SRS508580    | SAMN02422677 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "           SampleName cell    dex   albut Run        avgLength Experiment\n",
       "SRR1039508 GSM1275862 N61311  untrt untrt SRR1039508 126       SRX384345 \n",
       "SRR1039509 GSM1275863 N61311  trt   untrt SRR1039509 126       SRX384346 \n",
       "SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512 126       SRX384349 \n",
       "SRR1039513 GSM1275867 N052611 trt   untrt SRR1039513  87       SRX384350 \n",
       "SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516 120       SRX384353 \n",
       "SRR1039517 GSM1275871 N080611 trt   untrt SRR1039517 126       SRX384354 \n",
       "SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520 101       SRX384357 \n",
       "SRR1039521 GSM1275875 N061011 trt   untrt SRR1039521  98       SRX384358 \n",
       "           Sample    BioSample   \n",
       "SRR1039508 SRS508568 SAMN02422669\n",
       "SRR1039509 SRS508567 SAMN02422675\n",
       "SRR1039512 SRS508571 SAMN02422678\n",
       "SRR1039513 SRS508572 SAMN02422670\n",
       "SRR1039516 SRS508575 SAMN02422682\n",
       "SRR1039517 SRS508576 SAMN02422673\n",
       "SRR1039520 SRS508579 SAMN02422683\n",
       "SRR1039521 SRS508580 SAMN02422677"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csvfile <- file.path(indir, \"sample_table.csv\")\n",
    "sampleTable <- read.csv(csvfile, row.names = 1)\n",
    "sampleTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Once the reads have been aligned, there are a number of tools that can be used to count the number of reads/fragments that can be assigned to genomic features for each sample. These often take as input SAM/BAM alignment files and a file specifying the genomic features, e.g. a GFF3 or GTF file specifying the gene models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>SRR1039508</li>\n",
       "\t<li>SRR1039509</li>\n",
       "\t<li>SRR1039512</li>\n",
       "\t<li>SRR1039513</li>\n",
       "\t<li>SRR1039516</li>\n",
       "\t<li>SRR1039517</li>\n",
       "\t<li>SRR1039520</li>\n",
       "\t<li>SRR1039521</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item SRR1039508\n",
       "\\item SRR1039509\n",
       "\\item SRR1039512\n",
       "\\item SRR1039513\n",
       "\\item SRR1039516\n",
       "\\item SRR1039517\n",
       "\\item SRR1039520\n",
       "\\item SRR1039521\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. SRR1039508\n",
       "2. SRR1039509\n",
       "3. SRR1039512\n",
       "4. SRR1039513\n",
       "5. SRR1039516\n",
       "6. SRR1039517\n",
       "7. SRR1039520\n",
       "8. SRR1039521\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520\n",
       "[8] SRR1039521\n",
       "8 Levels: SRR1039508 SRR1039509 SRR1039512 SRR1039513 ... SRR1039521"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking for the existance of files in the package directory.\n",
    "filenames <- file.path(indir, paste0(sampleTable$Run, \"_subset.bam\"))\n",
    "unique(sampleTable$Run)\n",
    "file.exists(filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "#### DESeq2 import functions\n",
    "\n",
    "The following tools can be used generate count matrices: summarizeOverlaps (Lawrence et al. 2013), featureCounts (Liao, Smyth, and Shi 2014), tximport (Soneson, Love, and Robinson 2015), htseq-count (Anders, Pyl, and Huber 2015).\n",
    "\n",
    "\n",
    "| **function** | **package** | **framework** | **output** | **DESeq2 input function** |\n",
    "|---|---|---|---|---|\n",
    "| summarizeOverlaps | GenomicAlignments | R/Bioconductor | SummarizedExperiment | DESeqDataSet |\n",
    "| featureCounts | Rsubread | R/Bioconductor | matrix | DESeqDataSetFromMatrix |\n",
    "| tximport | tximport | R/Bioconductor | list of matrices | DESeqDataSetFromTximport |\n",
    "| htseq-count | HTSeq | Python | files | DESeqDataSetFromHTSeq |\n",
    "\n",
    "We now proceed using summarizeOverlaps. Using the Run column in the sample table, we construct the full paths to the files we want to perform the counting operation on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "## Bam files list from the Rsamtools[3] package\n",
    "suppressPackageStartupMessages(library(\"Rsamtools\"))\n",
    "bamfiles <- BamFileList(filenames, yieldSize=2000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Note:** \n",
    "Make sure that the chromosome names of the genomic features in the annotation you use are consistent with the chromosome names of the reference used for read alignment. Otherwise, the scripts might fail to count any reads to features due to the mismatching names. For example, a common mistake is when the alignment files contain chromosome names in the style of 1 and the gene annotation in the style of chr1, or the other way around. See the seqlevelsStyle function in the GenomeInfoDb package for solutions. We can check the chromosome names (here called \"seqnames\") in the alignment files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seqinfo object with 84 sequences from an unspecified genome:\n",
       "  seqnames   seqlengths isCircular genome\n",
       "  1           249250621       <NA>   <NA>\n",
       "  10          135534747       <NA>   <NA>\n",
       "  11          135006516       <NA>   <NA>\n",
       "  12          133851895       <NA>   <NA>\n",
       "  13          115169878       <NA>   <NA>\n",
       "  ...               ...        ...    ...\n",
       "  GL000210.1      27682       <NA>   <NA>\n",
       "  GL000231.1      27386       <NA>   <NA>\n",
       "  GL000229.1      19913       <NA>   <NA>\n",
       "  GL000226.1      15008       <NA>   <NA>\n",
       "  GL000207.1       4262       <NA>   <NA>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## The sequence Info\n",
    "seqinfo(bamfiles[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Defining gene models\n",
    "\n",
    "Next, we need to read in the gene model that will be used for counting reads/fragments. We will read the gene model from an Ensembl GTF file (Flicek et al. 2014), using makeTxDbFromGFF from the `GenomicFeatures` package. GTF files can be downloaded from Ensembl’s FTP site or other gene model repositories. A TxDb object is a database that can be used to generate a variety of range-based objects, such as exons, transcripts, and genes. We want to make a list of exons grouped by gene for counting read/fragments.\n",
    "\n",
    "There are other options for constructing a TxDb. For the known genes track from the UCSC Genome Browser (Kent et al. 2002), one can use the pre-built Transcript DataBase: TxDb.Hsapiens.UCSC.hg19.knownGene. If the annotation file is accessible from AnnotationHub (as is the case for the Ensembl genes), a pre-scanned GTF file can be imported using makeTxDbFromGRanges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Import genomic features from the file as a GRanges object ...\n",
      "OK\n",
      "Prepare the 'metadata' data frame ...\n",
      "OK\n",
      "Make the TxDb object ...\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TxDb object:\n",
       "# Db type: TxDb\n",
       "# Supporting package: GenomicFeatures\n",
       "# Data source: /home/gaow/R/x86_64-pc-linux-gnu-library/3.3/airway/extdata/Homo_sapiens.GRCh37.75_subset.gtf\n",
       "# Organism: NA\n",
       "# Taxonomy ID: NA\n",
       "# miRBase build ID: NA\n",
       "# Genome: NA\n",
       "# transcript_nrow: 65\n",
       "# exon_nrow: 279\n",
       "# cds_nrow: 158\n",
       "# Db created by: GenomicFeatures package from Bioconductor\n",
       "# Creation time: 2017-09-22 16:08:35 -0500 (Fri, 22 Sep 2017)\n",
       "# GenomicFeatures version at creation time: 1.28.5\n",
       "# RSQLite version at creation time: 2.0\n",
       "# DBSCHEMAVERSION: 1.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressPackageStartupMessages(library(\"GenomicFeatures\"))\n",
    "## txdb format\n",
    "gtffile <- file.path(indir,\"Homo_sapiens.GRCh37.75_subset.gtf\")\n",
    "txdb <- makeTxDbFromGFF(gtffile, format = \"gtf\", circ_seqs = character())\n",
    "txdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We can produce a GRangesList of all the exons grouped by gene (Lawrence et al. 2013). Each element of the list is a GRanges object of the exons for a gene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GRangesList object of length 20:\n",
       "$ENSG00000009724 \n",
       "GRanges object with 18 ranges and 2 metadata columns:\n",
       "       seqnames               ranges strand |   exon_id       exon_name\n",
       "          <Rle>            <IRanges>  <Rle> | <integer>     <character>\n",
       "   [1]        1 [11086580, 11087705]      - |        98 ENSE00000818830\n",
       "   [2]        1 [11090233, 11090307]      - |        99 ENSE00000472123\n",
       "   [3]        1 [11090805, 11090939]      - |       100 ENSE00000743084\n",
       "   [4]        1 [11094885, 11094963]      - |       101 ENSE00000743085\n",
       "   [5]        1 [11097750, 11097868]      - |       102 ENSE00003482788\n",
       "   ...      ...                  ...    ... .       ...             ...\n",
       "  [14]        1 [11106948, 11107176]      - |       111 ENSE00003467404\n",
       "  [15]        1 [11106948, 11107176]      - |       112 ENSE00003489217\n",
       "  [16]        1 [11107260, 11107280]      - |       113 ENSE00001833377\n",
       "  [17]        1 [11107260, 11107284]      - |       114 ENSE00001472289\n",
       "  [18]        1 [11107260, 11107290]      - |       115 ENSE00001881401\n",
       "\n",
       "...\n",
       "<19 more elements>\n",
       "-------\n",
       "seqinfo: 1 sequence from an unspecified genome; no seqlengths"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebg <- exonsBy(txdb, by=\"gene\")\n",
    "ebg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Read counting steps\n",
    "\n",
    "After these preparations, the actual counting is easy. The function summarizeOverlaps from the `GenomicAlignments` package will do this. This produces a SummarizedExperiment object that contains a variety of information about the experiment, and will be described in more detail below.\n",
    "\n",
    "**Note:** If it is desired to perform counting using multiple cores, one can use the register and MulticoreParam or SnowParam functions from the `BiocParallel` package before the counting call below. Expect that the summarizeOverlaps call will take at least 30 minutes per file for a human RNA-seq file with 30 million aligned reads. By sending the files to separate cores, one can speed up the entire counting process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(\"GenomicAlignments\"))\n",
    "suppressPackageStartupMessages(library(\"BiocParallel\"))\n",
    "\n",
    "# register(MulitcoreParam())\n",
    "register(SerialParam())\n",
    "se <- summarizeOverlaps(features=ebg, reads=bamfiles,\n",
    "                        mode=\"Union\",\n",
    "                        singleEnd=FALSE,\n",
    "                        ignore.strand=TRUE,\n",
    "                        fragments=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We specify a number of arguments besides the `features` and the `reads`. The `mode` argument describes what kind of read overlaps will be counted. These modes are shown in Figure 1 of the Counting reads with summarizeOverlaps vignette for the `GenomicAlignments` package. Note that fragments will be counted only once to each gene, even if they overlap multiple exons of a gene which may themselves be overlapping. Setting `singleEnd` to `FALSE` indicates that the experiment produced paired-end reads, and we want to count a pair of reads (a fragment) only once toward the count for a gene. The `fragments` argument can be used when `singleEnd=FALSE` to specify if unpaired reads should be counted (yes if `fragments=TRUE`).\n",
    "\n",
    "In order to produce correct counts, it is important to know if the RNA-seq experiment was strand-specific or not. This experiment was not strand-specific so we set `ignore.strand` to `TRUE`. However, certain strand-specific protocols could have the reads align only to the opposite strand of the genes. The user must check if the experiment was strand-specific and if so, whether the reads should align to the forward or reverse strand of the genes. For various counting/quantifying tools, one specifies counting on the forward or reverse strand in different ways, although this task is currently easiest with htseq-count, featureCounts, or the transcript abundance quantifiers mentioned previously. It is always a good idea to check the column sums of the count matrix (see below) to make sure these totals match the expected of the number of reads or fragments aligning to genes. Additionally, one can visually check the read alignments using a genome visualization tool.\n",
    "\n",
    "### Summarized experiment\n",
    "\n",
    "The component parts of a SummarizedExperiment object. The assay (pink block) contains the matrix of counts, the rowRanges (blue block) contains information about the genomic ranges and the colData (green block) contains information about the samples. The highlighted line in each block represents the first row (note that the first row of colData lines up with the first column of the assay).\n",
    "\n",
    "The SummarizedExperiment container is diagrammed in the Figure below and discussed in the latest Bioconductor paper (Huber et al. 2015). In our case we have created a single matrix named “counts” that contains the fragment counts for each gene and sample, which is stored in assay. It is also possible to store multiple matrices, accessed with assays. The rowRanges for our object is the GRangesList we used for counting (one GRanges of exons for each row of the count matrix). The component parts of the SummarizedExperiment are accessed with an R function of the same name: assay (or assays), rowRanges and colData.\n",
    "\n",
    "This example code above actually only counted a small subset of fragments from the original experiment. Nevertheless, we can still investigate the resulting SummarizedExperiment by looking at the counts in the assay slot, the phenotypic data about the samples in colData slot (in this case an empty DataFrame), and the data about the genes in the rowRanges slot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/Summarized_Experiment.pdf\")\n",
    "par(mar=c(0,0,0,0))\n",
    "plot(1,1,xlim=c(0,100),ylim=c(0,100),bty=\"n\",\n",
    "     type=\"n\",xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\")\n",
    "polygon(c(45,90,90,45),c(5,5,70,70),col=\"pink\",border=NA)\n",
    "polygon(c(45,90,90,45),c(68,68,70,70),col=\"pink3\",border=NA)\n",
    "text(67.5,40,\"assay\")\n",
    "text(67.5,35,'e.g. \"counts\"')\n",
    "polygon(c(10,40,40,10),c(5,5,70,70),col=\"skyblue\",border=NA)\n",
    "polygon(c(10,40,40,10),c(68,68,70,70),col=\"skyblue3\",border=NA)\n",
    "text(25,40,\"rowRanges\")\n",
    "polygon(c(45,90,90,45),c(75,75,95,95),col=\"palegreen\",border=NA)\n",
    "polygon(c(45,47,47,45),c(75,75,95,95),col=\"palegreen3\",border=NA)\n",
    "text(67.5,85,\"colData\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/Summarized_Experiment.pdf (4.2 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/Summarized_Experiment.pdf (4.2 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/Summarized_Experiment.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Here are various other summary information. These information are by default hidden from display in the HTML file generated by `sos report` template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: RangedSummarizedExperiment \n",
       "dim: 20 8 \n",
       "metadata(0):\n",
       "assays(1): counts\n",
       "rownames(20): ENSG00000009724 ENSG00000116649 ... ENSG00000271794\n",
       "  ENSG00000271895\n",
       "rowData names(0):\n",
       "colnames(8): SRR1039508_subset.bam SRR1039509_subset.bam ...\n",
       "  SRR1039520_subset.bam SRR1039521_subset.bam\n",
       "colData names(0):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>20</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 20\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 20\n",
       "2. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 20  8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'counts'"
      ],
      "text/latex": [
       "'counts'"
      ],
      "text/markdown": [
       "'counts'"
      ],
      "text/plain": [
       "[1] \"counts\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assayNames(se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GRangesList object of length 20:\n",
       "$ENSG00000009724 \n",
       "GRanges object with 18 ranges and 2 metadata columns:\n",
       "       seqnames               ranges strand |   exon_id       exon_name\n",
       "          <Rle>            <IRanges>  <Rle> | <integer>     <character>\n",
       "   [1]        1 [11086580, 11087705]      - |        98 ENSE00000818830\n",
       "   [2]        1 [11090233, 11090307]      - |        99 ENSE00000472123\n",
       "   [3]        1 [11090805, 11090939]      - |       100 ENSE00000743084\n",
       "   [4]        1 [11094885, 11094963]      - |       101 ENSE00000743085\n",
       "   [5]        1 [11097750, 11097868]      - |       102 ENSE00003482788\n",
       "   ...      ...                  ...    ... .       ...             ...\n",
       "  [14]        1 [11106948, 11107176]      - |       111 ENSE00003467404\n",
       "  [15]        1 [11106948, 11107176]      - |       112 ENSE00003489217\n",
       "  [16]        1 [11107260, 11107280]      - |       113 ENSE00001833377\n",
       "  [17]        1 [11107260, 11107284]      - |       114 ENSE00001472289\n",
       "  [18]        1 [11107260, 11107290]      - |       115 ENSE00001881401\n",
       "\n",
       "...\n",
       "<19 more elements>\n",
       "-------\n",
       "seqinfo: 1 sequence from an unspecified genome; no seqlengths"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rowRanges(se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of 1\n",
      " $ genomeInfo:List of 15\n",
      "  ..$ Db type                                 : chr \"TxDb\"\n",
      "  ..$ Supporting package                      : chr \"GenomicFeatures\"\n",
      "  ..$ Data source                             : chr \"/home/gaow/R/x86_64-pc-linux-gnu-library/3.3/airway/extdata/Homo_sapiens.GRCh37.75_subset.gtf\"\n",
      "  ..$ Organism                                : chr NA\n",
      "  ..$ Taxonomy ID                             : chr NA\n",
      "  ..$ miRBase build ID                        : chr NA\n",
      "  ..$ Genome                                  : chr NA\n",
      "  ..$ transcript_nrow                         : chr \"65\"\n",
      "  ..$ exon_nrow                               : chr \"279\"\n",
      "  ..$ cds_nrow                                : chr \"158\"\n",
      "  ..$ Db created by                           : chr \"GenomicFeatures package from Bioconductor\"\n",
      "  ..$ Creation time                           : chr \"2017-09-22 16:08:35 -0500 (Fri, 22 Sep 2017)\"\n",
      "  ..$ GenomicFeatures version at creation time: chr \"1.28.5\"\n",
      "  ..$ RSQLite version at creation time        : chr \"2.0\"\n",
      "  ..$ DBSCHEMAVERSION                         : chr \"1.1\"\n"
     ]
    }
   ],
   "source": [
    "str(metadata(rowRanges(se)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 8 rows and 0 columns"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# column Data\n",
    "colData(se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>SRR1039508_subset.bam</th><th scope=col>SRR1039509_subset.bam</th><th scope=col>SRR1039512_subset.bam</th><th scope=col>SRR1039513_subset.bam</th><th scope=col>SRR1039516_subset.bam</th><th scope=col>SRR1039517_subset.bam</th><th scope=col>SRR1039520_subset.bam</th><th scope=col>SRR1039521_subset.bam</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSG00000009724</th><td>  38</td><td>  28</td><td>  66</td><td>  24</td><td>  42</td><td>  41</td><td>  47</td><td>  36</td></tr>\n",
       "\t<tr><th scope=row>ENSG00000116649</th><td>1004</td><td>1255</td><td>1122</td><td>1313</td><td>1100</td><td>1879</td><td> 745</td><td>1536</td></tr>\n",
       "\t<tr><th scope=row>ENSG00000120942</th><td> 218</td><td> 256</td><td> 233</td><td> 252</td><td> 269</td><td> 465</td><td> 207</td><td> 400</td></tr>\n",
       "\t<tr><th scope=row>ENSG00000120948</th><td>2751</td><td>2080</td><td>3353</td><td>1614</td><td>3519</td><td>3716</td><td>2220</td><td>1990</td></tr>\n",
       "\t<tr><th scope=row>ENSG00000171819</th><td>   4</td><td>  50</td><td>  19</td><td> 543</td><td>   1</td><td>  10</td><td>  14</td><td>1067</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & SRR1039508\\_subset.bam & SRR1039509\\_subset.bam & SRR1039512\\_subset.bam & SRR1039513\\_subset.bam & SRR1039516\\_subset.bam & SRR1039517\\_subset.bam & SRR1039520\\_subset.bam & SRR1039521\\_subset.bam\\\\\n",
       "\\hline\n",
       "\tENSG00000009724 &   38 &   28 &   66 &   24 &   42 &   41 &   47 &   36\\\\\n",
       "\tENSG00000116649 & 1004 & 1255 & 1122 & 1313 & 1100 & 1879 &  745 & 1536\\\\\n",
       "\tENSG00000120942 &  218 &  256 &  233 &  252 &  269 &  465 &  207 &  400\\\\\n",
       "\tENSG00000120948 & 2751 & 2080 & 3353 & 1614 & 3519 & 3716 & 2220 & 1990\\\\\n",
       "\tENSG00000171819 &    4 &   50 &   19 &  543 &    1 &   10 &   14 & 1067\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | SRR1039508_subset.bam | SRR1039509_subset.bam | SRR1039512_subset.bam | SRR1039513_subset.bam | SRR1039516_subset.bam | SRR1039517_subset.bam | SRR1039520_subset.bam | SRR1039521_subset.bam | \n",
       "|---|---|---|---|---|\n",
       "| ENSG00000009724 |   38 |   28 |   66 |   24 |   42 |   41 |   47 |   36 | \n",
       "| ENSG00000116649 | 1004 | 1255 | 1122 | 1313 | 1100 | 1879 |  745 | 1536 | \n",
       "| ENSG00000120942 |  218 |  256 |  233 |  252 |  269 |  465 |  207 |  400 | \n",
       "| ENSG00000120948 | 2751 | 2080 | 3353 | 1614 | 3519 | 3716 | 2220 | 1990 | \n",
       "| ENSG00000171819 |    4 |   50 |   19 |  543 |    1 |   10 |   14 | 1067 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                SRR1039508_subset.bam SRR1039509_subset.bam\n",
       "ENSG00000009724   38                    28                 \n",
       "ENSG00000116649 1004                  1255                 \n",
       "ENSG00000120942  218                   256                 \n",
       "ENSG00000120948 2751                  2080                 \n",
       "ENSG00000171819    4                    50                 \n",
       "                SRR1039512_subset.bam SRR1039513_subset.bam\n",
       "ENSG00000009724   66                    24                 \n",
       "ENSG00000116649 1122                  1313                 \n",
       "ENSG00000120942  233                   252                 \n",
       "ENSG00000120948 3353                  1614                 \n",
       "ENSG00000171819   19                   543                 \n",
       "                SRR1039516_subset.bam SRR1039517_subset.bam\n",
       "ENSG00000009724   42                    41                 \n",
       "ENSG00000116649 1100                  1879                 \n",
       "ENSG00000120942  269                   465                 \n",
       "ENSG00000120948 3519                  3716                 \n",
       "ENSG00000171819    1                    10                 \n",
       "                SRR1039520_subset.bam SRR1039521_subset.bam\n",
       "ENSG00000009724   47                    36                 \n",
       "ENSG00000116649  745                  1536                 \n",
       "ENSG00000120942  207                   400                 \n",
       "ENSG00000120948 2220                  1990                 \n",
       "ENSG00000171819   14                  1067                 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(assay(se), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
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       "\\begin{description*}\n",
       "\\item[SRR1039508\\textbackslash{}\\_subset.bam] 6478\n",
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       "\\item[SRR1039521\\textbackslash{}\\_subset.bam] 9168\n",
       "\\end{description*}\n"
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       "SRR1039508_subset.bam\n",
       ":   6478SRR1039509_subset.bam\n",
       ":   6501SRR1039512_subset.bam\n",
       ":   7699SRR1039513_subset.bam\n",
       ":   6801SRR1039516_subset.bam\n",
       ":   8009SRR1039517_subset.bam\n",
       ":   10849SRR1039520_subset.bam\n",
       ":   5254SRR1039521_subset.bam\n",
       ":   9168\n",
       "\n"
      ],
      "text/plain": [
       "SRR1039508_subset.bam SRR1039509_subset.bam SRR1039512_subset.bam \n",
       "                 6478                  6501                  7699 \n",
       "SRR1039513_subset.bam SRR1039516_subset.bam SRR1039517_subset.bam \n",
       "                 6801                  8009                 10849 \n",
       "SRR1039520_subset.bam SRR1039521_subset.bam \n",
       "                 5254                  9168 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colSums(assay(se))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 8 rows and 9 columns\n",
       "           SampleName     cell      dex    albut        Run avgLength\n",
       "             <factor> <factor> <factor> <factor>   <factor> <integer>\n",
       "SRR1039508 GSM1275862   N61311    untrt    untrt SRR1039508       126\n",
       "SRR1039509 GSM1275863   N61311      trt    untrt SRR1039509       126\n",
       "SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126\n",
       "SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87\n",
       "SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120\n",
       "SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126\n",
       "SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101\n",
       "SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98\n",
       "           Experiment    Sample    BioSample\n",
       "             <factor>  <factor>     <factor>\n",
       "SRR1039508  SRX384345 SRS508568 SAMN02422669\n",
       "SRR1039509  SRX384346 SRS508567 SAMN02422675\n",
       "SRR1039512  SRX384349 SRS508571 SAMN02422678\n",
       "SRR1039513  SRX384350 SRS508572 SAMN02422670\n",
       "SRR1039516  SRX384353 SRS508575 SAMN02422682\n",
       "SRR1039517  SRX384354 SRS508576 SAMN02422673\n",
       "SRR1039520  SRX384357 SRS508579 SAMN02422683\n",
       "SRR1039521  SRX384358 SRS508580 SAMN02422677"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colData(se) <- DataFrame(sampleTable)\n",
    "colData(se)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Branching point\n",
    "\n",
    "At this point, we have counted the fragments which overlap the genes in the gene model we specified. This is a branching point where we could use a variety of Bioconductor packages for exploration and differential expression of the count data, including edgeR (M. D. Robinson, McCarthy, and Smyth 2009), limma with the voom method (Law et al. 2014), DSS (H. Wu, Wang, and Wu 2013), EBSeq (Leng et al. 2013) and baySeq (Hardcastle and Kelly 2010). Schurch et al. (2016) compared performance of different statistical methods for RNA-seq using a large number of biological replicates and can help users to decide which tools make sense to use, and how many biological replicates are necessary to obtain a certain sensitivity. We will continue using DESeq2 (Love, Huber, and Anders 2014). The SummarizedExperiment object is all we need to start our analysis. In the following section we will show how to use it to create the data object used by DESeq2.\n",
    "\n",
    "## The DESeqDataSet object, sample information and the design formula\n",
    "\n",
    "Bioconductor software packages often define and use a custom class for storing data that makes sure that all the needed data slots are consistently provided and fulfill the requirements. In addition, Bioconductor has general data classes (such as the SummarizedExperiment) that can be used to move data between packages. Additionally, the core Bioconductor classes provide useful functionality: for example, subsetting or reordering the rows or columns of a SummarizedExperiment automatically subsets or reorders the associated rowRanges and colData, which can help to prevent accidental sample swaps that would otherwise lead to spurious results. With SummarizedExperiment this is all taken care of behind the scenes.\n",
    "\n",
    "In DESeq2, the custom class is called DESeqDataSet. It is built on top of the SummarizedExperiment class, and it is easy to convert SummarizedExperiment objects into DESeqDataSet objects, which we show below. One of the two main differences is that the assay slot is instead accessed using the counts accessor function, and the DESeqDataSet class enforces that the values in this matrix are non-negative integers.\n",
    "\n",
    "A second difference is that the DESeqDataSet has an associated design formula. The experimental design is specified at the beginning of the analysis, as it will inform many of the DESeq2 functions how to treat the samples in the analysis (one exception is the size factor estimation, i.e., the adjustment for differing library sizes, which does not depend on the design formula). The design formula tells which columns in the sample information table (colData) specify the experimental design and how these factors should be used in the analysis.\n",
    "\n",
    "The simplest design formula for differential expression would be ~ condition, where condition is a column in colData(dds) that specifies which of two (or more groups) the samples belong to. For the airway experiment, we will specify ~ cell + dex meaning that we want to test for the effect of dexamethasone (dex) controlling for the effect of different cell line (cell). We can see each of the columns just using the $ directly on the SummarizedExperiment or DESeqDataSet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>N61311</li>\n",
       "\t<li>N61311</li>\n",
       "\t<li>N052611</li>\n",
       "\t<li>N052611</li>\n",
       "\t<li>N080611</li>\n",
       "\t<li>N080611</li>\n",
       "\t<li>N061011</li>\n",
       "\t<li>N061011</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item N61311\n",
       "\\item N61311\n",
       "\\item N052611\n",
       "\\item N052611\n",
       "\\item N080611\n",
       "\\item N080611\n",
       "\\item N061011\n",
       "\\item N061011\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. N61311\n",
       "2. N61311\n",
       "3. N052611\n",
       "4. N052611\n",
       "5. N080611\n",
       "6. N080611\n",
       "7. N061011\n",
       "8. N061011\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] N61311  N61311  N052611 N052611 N080611 N080611 N061011 N061011\n",
       "Levels: N052611 N061011 N080611 N61311"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "se$cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>untrt</li>\n",
       "\t<li>trt</li>\n",
       "\t<li>untrt</li>\n",
       "\t<li>trt</li>\n",
       "\t<li>untrt</li>\n",
       "\t<li>trt</li>\n",
       "\t<li>untrt</li>\n",
       "\t<li>trt</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item untrt\n",
       "\\item trt\n",
       "\\item untrt\n",
       "\\item trt\n",
       "\\item untrt\n",
       "\\item trt\n",
       "\\item untrt\n",
       "\\item trt\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. untrt\n",
       "2. trt\n",
       "3. untrt\n",
       "4. trt\n",
       "5. untrt\n",
       "6. trt\n",
       "7. untrt\n",
       "8. trt\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] untrt trt   untrt trt   untrt trt   untrt trt  \n",
       "Levels: untrt trt"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "se$dex <- relevel(se$dex, \"untrt\")\n",
    "se$dex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Starting from summarized expriment\n",
    "\n",
    "We now use R’s data command to load a prepared SummarizedExperiment that was generated from the publicly available sequencing data files associated with Himes et al. (2014), described above. The steps we used to produce this object were equivalent to those you worked through in the previous sections, except that we used all the reads and all the genes. For more details on the exact steps used to create this object, type vignette(\"airway\") into your R session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>SRR1039508</dt>\n",
       "\t\t<dd>20.6</dd>\n",
       "\t<dt>SRR1039509</dt>\n",
       "\t\t<dd>18.8</dd>\n",
       "\t<dt>SRR1039512</dt>\n",
       "\t\t<dd>25.3</dd>\n",
       "\t<dt>SRR1039513</dt>\n",
       "\t\t<dd>15.2</dd>\n",
       "\t<dt>SRR1039516</dt>\n",
       "\t\t<dd>24.4</dd>\n",
       "\t<dt>SRR1039517</dt>\n",
       "\t\t<dd>30.8</dd>\n",
       "\t<dt>SRR1039520</dt>\n",
       "\t\t<dd>19.1</dd>\n",
       "\t<dt>SRR1039521</dt>\n",
       "\t\t<dd>21.2</dd>\n",
       "</dl>\n"
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       "\\begin{description*}\n",
       "\\item[SRR1039508] 20.6\n",
       "\\item[SRR1039509] 18.8\n",
       "\\item[SRR1039512] 25.3\n",
       "\\item[SRR1039513] 15.2\n",
       "\\item[SRR1039516] 24.4\n",
       "\\item[SRR1039517] 30.8\n",
       "\\item[SRR1039520] 19.1\n",
       "\\item[SRR1039521] 21.2\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "SRR1039508\n",
       ":   20.6SRR1039509\n",
       ":   18.8SRR1039512\n",
       ":   25.3SRR1039513\n",
       ":   15.2SRR1039516\n",
       ":   24.4SRR1039517\n",
       ":   30.8SRR1039520\n",
       ":   19.1SRR1039521\n",
       ":   21.2\n",
       "\n"
      ],
      "text/plain": [
       "SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 \n",
       "      20.6       18.8       25.3       15.2       24.4       30.8       19.1 \n",
       "SRR1039521 \n",
       "      21.2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(\"airway\")\n",
    "se <- airway\n",
    "se$dex <- relevel(se$dex, \"untrt\")\n",
    "round( colSums(assay(se)) / 1e6, 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 8 rows and 9 columns\n",
       "           SampleName     cell      dex    albut        Run avgLength\n",
       "             <factor> <factor> <factor> <factor>   <factor> <integer>\n",
       "SRR1039508 GSM1275862   N61311    untrt    untrt SRR1039508       126\n",
       "SRR1039509 GSM1275863   N61311      trt    untrt SRR1039509       126\n",
       "SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126\n",
       "SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87\n",
       "SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120\n",
       "SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126\n",
       "SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101\n",
       "SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98\n",
       "           Experiment    Sample    BioSample\n",
       "             <factor>  <factor>     <factor>\n",
       "SRR1039508  SRX384345 SRS508568 SAMN02422669\n",
       "SRR1039509  SRX384346 SRS508567 SAMN02422675\n",
       "SRR1039512  SRX384349 SRS508571 SAMN02422678\n",
       "SRR1039513  SRX384350 SRS508572 SAMN02422670\n",
       "SRR1039516  SRX384353 SRS508575 SAMN02422682\n",
       "SRR1039517  SRX384354 SRS508576 SAMN02422673\n",
       "SRR1039520  SRX384357 SRS508579 SAMN02422683\n",
       "SRR1039521  SRX384358 SRS508580 SAMN02422677"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colData(se)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Here we see that this object already contains an informative colData slot – because we have already prepared it for you, as described in the airway vignette. However, when you work with your own data, you will have to add the pertinent sample / phenotypic information for the experiment at this stage. We highly recommend keeping this information in a comma-separated value (CSV) or tab-separated value (TSV) file, which can be exported from an Excel spreadsheet, and the assign this to the colData slot, making sure that the rows correspond to the columns of the SummarizedExperiment. We made sure of this correspondence earlier by specifying the BAM files using a column of the sample table.\n",
    "\n",
    "Once we have our fully annotated SummarizedExperiment object, we can construct a DESeqDataSet object from it that will then form the starting point of the analysis. We add an appropriate design for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "library(\"DESeq2\")\n",
    "dds <- DESeqDataSet(se, design = ~ cell + dex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Starting from count matrices\n",
    "\n",
    "In this section, we will show how to build an DESeqDataSet supposing we only have a count matrix and a table of sample information.\n",
    "\n",
    "**Note:** if you have prepared a SummarizedExperiment you should skip this section. While the previous section would be used to construct a DESeqDataSet from a SummarizedExperiment, here we first extract the individual object (count matrix and sample info) from the SummarizedExperiment in order to build it back up into a new object – only for demonstration purposes. In practice, the count matrix would either be read in from a file or perhaps generated by an R function like featureCounts from the Rsubread package (Liao, Smyth, and Shi 2014).\n",
    "\n",
    "The information in a SummarizedExperiment object can be accessed with accessor functions. For example, to see the actual data, i.e., here, the fragment counts, we use the assay function. (The head function restricts the output to the first few lines.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>SRR1039508</th><th scope=col>SRR1039509</th><th scope=col>SRR1039512</th><th scope=col>SRR1039513</th><th scope=col>SRR1039516</th><th scope=col>SRR1039517</th><th scope=col>SRR1039520</th><th scope=col>SRR1039521</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSG00000000003</th><td>679 </td><td>448 </td><td>873 </td><td>408 </td><td>1138</td><td>1047</td><td>770 </td><td>572 </td></tr>\n",
       "\t<tr><th scope=row>ENSG00000000005</th><td>  0 </td><td>  0 </td><td>  0 </td><td>  0 </td><td>   0</td><td>   0</td><td>  0 </td><td>  0 </td></tr>\n",
       "\t<tr><th scope=row>ENSG00000000419</th><td>467 </td><td>515 </td><td>621 </td><td>365 </td><td> 587</td><td> 799</td><td>417 </td><td>508 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & SRR1039508 & SRR1039509 & SRR1039512 & SRR1039513 & SRR1039516 & SRR1039517 & SRR1039520 & SRR1039521\\\\\n",
       "\\hline\n",
       "\tENSG00000000003 & 679  & 448  & 873  & 408  & 1138 & 1047 & 770  & 572 \\\\\n",
       "\tENSG00000000005 &   0  &   0  &   0  &   0  &    0 &    0 &   0  &   0 \\\\\n",
       "\tENSG00000000419 & 467  & 515  & 621  & 365  &  587 &  799 & 417  & 508 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | SRR1039508 | SRR1039509 | SRR1039512 | SRR1039513 | SRR1039516 | SRR1039517 | SRR1039520 | SRR1039521 | \n",
       "|---|---|---|\n",
       "| ENSG00000000003 | 679  | 448  | 873  | 408  | 1138 | 1047 | 770  | 572  | \n",
       "| ENSG00000000005 |   0  |   0  |   0  |   0  |    0 |    0 |   0  |   0  | \n",
       "| ENSG00000000419 | 467  | 515  | 621  | 365  |  587 |  799 | 417  | 508  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516\n",
       "ENSG00000000003 679        448        873        408        1138      \n",
       "ENSG00000000005   0          0          0          0           0      \n",
       "ENSG00000000419 467        515        621        365         587      \n",
       "                SRR1039517 SRR1039520 SRR1039521\n",
       "ENSG00000000003 1047       770        572       \n",
       "ENSG00000000005    0         0          0       \n",
       "ENSG00000000419  799       417        508       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countdata <- assay(se)\n",
    "head(countdata, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "In this count matrix, each row represents an Ensembl gene, each column a sequenced RNA library, and the values give the raw numbers of fragments that were uniquely assigned to the respective gene in each library. We also have information on each of the samples (the columns of the count matrix). If you’ve counted reads with some other software, it is very important to check that the columns of the count matrix correspond to the rows of the sample information table.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "coldata <- colData(se)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We now have all the ingredients to prepare our data object in a form that is suitable for analysis, namely:\n",
    "    * countdata: a table with the fragment counts\n",
    "    * coldata: a table with information about the samples\n",
    "\n",
    "We then construct the DESeqDataSet object from the matrix of counts and the sample information table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "ddsMat <- DESeqDataSetFromMatrix(countData = countdata,\n",
    "                                  colData = coldata,\n",
    "                                  design = ~ cell + dex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We will continue with the object generated from the SummarizedExperiment section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Exploratory analysis and visualization\n",
    "\n",
    "There are two separate paths in this workflow; the one we will see first involves transformations of the counts in order to visually explore sample relationships. In the second part, we will go back to the original raw counts for statistical testing. This is critical because the statistical testing methods rely on original count data (not scaled or transformed) for calculating the precision of measurements.\n",
    "\n",
    "### Pre Filtering the dataset\n",
    "\n",
    "The count matrix with the DESeqDataSet contains many rows with only zeros, and additionally many rows with only a few fragments total. In order to reduce the size of the object, and to increase the speed of our functions, we can remove the rows that have no or nearly no information about the amount of gene expression. Here we apply the most minimal filtering rule: removing rows of the DESeqDataSet that have no counts, or only a single count across all samples. Additional weighting/filtering to improve power is applied at a later step in the workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "64102"
      ],
      "text/latex": [
       "64102"
      ],
      "text/markdown": [
       "64102"
      ],
      "text/plain": [
       "[1] 64102"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "29391"
      ],
      "text/latex": [
       "29391"
      ],
      "text/markdown": [
       "29391"
      ],
      "text/plain": [
       "[1] 29391"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(dds)\n",
    "dds <- dds[ rowSums(counts(dds)) > 1, ]\n",
    "nrow(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### The rlog and variance stabilizing transformations\n",
    "\n",
    "Many common statistical methods for exploratory analysis of multidimensional data, for example clustering and principal components analysis (PCA), work best for data that generally has the same range of variance at different ranges of the mean values. When the expected amount of variance is approximately the same across different mean values, the data is said to be homoskedastic. For RNA-seq counts, however, the expected variance grows with the mean. For example, if one performs PCA directly on a matrix of counts or normalized counts (e.g. correcting for differences in sequencing depth), the resulting plot typically depends mostly on the genes with highest counts because they show the largest absolute differences between samples. A simple and often used strategy to avoid this is to take the logarithm of the normalized count values plus a pseudocount of 1; however, depending on the choice of pseudocount, now the genes with the very lowest counts will contribute a great deal of noise to the resulting plot, because taking the logarithm of small counts actually inflates their variance. We can quickly show this property of counts with some simulated data (here, Poisson counts with a range of lambda from 0.1 to 100). We plot the standard deviation of each row (genes) against the mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/mean_sd.pdf\")\n",
    "lambda <- 10^seq(from = -1, to = 2, length = 1000)\n",
    "cts <- matrix(rpois(1000*100, lambda), ncol = 100)\n",
    "vsn::meanSdPlot(cts, ranks = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/mean_sd.pdf (10.4 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/mean_sd.pdf (10.4 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/mean_sd.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Logarithm-transformed counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/mean_sd_log.pdf\")\n",
    "lambda <- 10^seq(from = -1, to = 2, length = 1000)\n",
    "cts <- matrix(rpois(1000*100, lambda), ncol = 100)\n",
    "log.cts.one <- log2(cts + 1)\n",
    "vsn::meanSdPlot(log.cts.one, ranks = FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/mean_sd_log.pdf (13.8 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/mean_sd_log.pdf (13.8 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/mean_sd_log.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The logarithm with a small pseudocount amplifies differences when the values are close to 0. The low count genes with low signal-to-noise ratio will overly contribute to sample-sample distances and PCA plots.\n",
    "\n",
    "As a solution, DESeq2 offers two transformations for count data that stabilize the variance across the mean: the regularized-logarithm transformation or rlog (Love, Huber, and Anders 2014), and the variance stabilizing transformation (VST) for negative binomial data with a dispersion-mean trend (Anders and Huber 2010), implemented in the vst function.\n",
    "\n",
    "For genes with high counts, the rlog and VST will give similar result to the ordinary log2 transformation of normalized counts. For genes with lower counts, however, the values are shrunken towards the genes’ averages across all samples. The rlog-transformed or VST data then becomes approximately homoskedastic, and can be used directly for computing distances between samples, making PCA plots, or as input to downstream methods which perform best with homoskedastic data.\n",
    "\n",
    "\n",
    "**Which transformation to choose?** The rlog tends to work well on small datasets (n < 30), sometimes outperforming the VST when there is a large range of sequencing depth across samples (an order of magnitude difference). The VST is much faster to compute and is less sensitive to high count outliers than the rlog. We therefore recommend the VST for large datasets (hundreds of samples). You can perform both transformations and compare the meanSdPlot or PCA plots generated, as described below.\n",
    "\n",
    "Note that the two transformations offered by DESeq2 are provided for applications other than differential testing. For differential testing we recommend the DESeq function applied to raw counts, as described later in this workflow, which also takes into account the dependence of the variance of counts on the mean value during the dispersion estimation step.\n",
    "\n",
    "The function rlog returns an object based on the SummarizedExperiment class that contains the rlog-transformed values in its assay slot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>SRR1039508</th><th scope=col>SRR1039509</th><th scope=col>SRR1039512</th><th scope=col>SRR1039513</th><th scope=col>SRR1039516</th><th scope=col>SRR1039517</th><th scope=col>SRR1039520</th><th scope=col>SRR1039521</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSG00000000003</th><td>9.385683</td><td>9.052608</td><td>9.516875</td><td>9.285338</td><td>9.839085</td><td>9.530311</td><td>9.663255</td><td>9.277699</td></tr>\n",
       "\t<tr><th scope=row>ENSG00000000419</th><td>8.869616</td><td>9.138271</td><td>9.036116</td><td>9.075295</td><td>8.972126</td><td>9.131824</td><td>8.861534</td><td>9.060905</td></tr>\n",
       "\t<tr><th scope=row>ENSG00000000457</th><td>7.961369</td><td>7.881385</td><td>7.824079</td><td>7.921490</td><td>7.751699</td><td>7.886441</td><td>7.957121</td><td>7.912123</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & SRR1039508 & SRR1039509 & SRR1039512 & SRR1039513 & SRR1039516 & SRR1039517 & SRR1039520 & SRR1039521\\\\\n",
       "\\hline\n",
       "\tENSG00000000003 & 9.385683 & 9.052608 & 9.516875 & 9.285338 & 9.839085 & 9.530311 & 9.663255 & 9.277699\\\\\n",
       "\tENSG00000000419 & 8.869616 & 9.138271 & 9.036116 & 9.075295 & 8.972126 & 9.131824 & 8.861534 & 9.060905\\\\\n",
       "\tENSG00000000457 & 7.961369 & 7.881385 & 7.824079 & 7.921490 & 7.751699 & 7.886441 & 7.957121 & 7.912123\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | SRR1039508 | SRR1039509 | SRR1039512 | SRR1039513 | SRR1039516 | SRR1039517 | SRR1039520 | SRR1039521 | \n",
       "|---|---|---|\n",
       "| ENSG00000000003 | 9.385683 | 9.052608 | 9.516875 | 9.285338 | 9.839085 | 9.530311 | 9.663255 | 9.277699 | \n",
       "| ENSG00000000419 | 8.869616 | 9.138271 | 9.036116 | 9.075295 | 8.972126 | 9.131824 | 8.861534 | 9.060905 | \n",
       "| ENSG00000000457 | 7.961369 | 7.881385 | 7.824079 | 7.921490 | 7.751699 | 7.886441 | 7.957121 | 7.912123 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516\n",
       "ENSG00000000003 9.385683   9.052608   9.516875   9.285338   9.839085  \n",
       "ENSG00000000419 8.869616   9.138271   9.036116   9.075295   8.972126  \n",
       "ENSG00000000457 7.961369   7.881385   7.824079   7.921490   7.751699  \n",
       "                SRR1039517 SRR1039520 SRR1039521\n",
       "ENSG00000000003 9.530311   9.663255   9.277699  \n",
       "ENSG00000000419 9.131824   8.861534   9.060905  \n",
       "ENSG00000000457 7.886441   7.957121   7.912123  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rld <- rlog(dds, blind = FALSE)\n",
    "head(assay(rld), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The function *vst* returns a similar object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>SRR1039508</th><th scope=col>SRR1039509</th><th scope=col>SRR1039512</th><th scope=col>SRR1039513</th><th scope=col>SRR1039516</th><th scope=col>SRR1039517</th><th scope=col>SRR1039520</th><th scope=col>SRR1039521</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ENSG00000000003</th><td>9.742340 </td><td>9.430742 </td><td>9.867872 </td><td>9.646127 </td><td>10.183344</td><td>9.880660 </td><td>10.010591</td><td>9.640065 </td></tr>\n",
       "\t<tr><th scope=row>ENSG00000000419</th><td>9.334009 </td><td>9.582000 </td><td>9.486456 </td><td>9.523397 </td><td> 9.427605</td><td>9.575154 </td><td> 9.326341</td><td>9.509553 </td></tr>\n",
       "\t<tr><th scope=row>ENSG00000000457</th><td>8.765748 </td><td>8.698941 </td><td>8.651978 </td><td>8.732909 </td><td> 8.593308</td><td>8.703164 </td><td> 8.762420</td><td>8.724586 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & SRR1039508 & SRR1039509 & SRR1039512 & SRR1039513 & SRR1039516 & SRR1039517 & SRR1039520 & SRR1039521\\\\\n",
       "\\hline\n",
       "\tENSG00000000003 & 9.742340  & 9.430742  & 9.867872  & 9.646127  & 10.183344 & 9.880660  & 10.010591 & 9.640065 \\\\\n",
       "\tENSG00000000419 & 9.334009  & 9.582000  & 9.486456  & 9.523397  &  9.427605 & 9.575154  &  9.326341 & 9.509553 \\\\\n",
       "\tENSG00000000457 & 8.765748  & 8.698941  & 8.651978  & 8.732909  &  8.593308 & 8.703164  &  8.762420 & 8.724586 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | SRR1039508 | SRR1039509 | SRR1039512 | SRR1039513 | SRR1039516 | SRR1039517 | SRR1039520 | SRR1039521 | \n",
       "|---|---|---|\n",
       "| ENSG00000000003 | 9.742340  | 9.430742  | 9.867872  | 9.646127  | 10.183344 | 9.880660  | 10.010591 | 9.640065  | \n",
       "| ENSG00000000419 | 9.334009  | 9.582000  | 9.486456  | 9.523397  |  9.427605 | 9.575154  |  9.326341 | 9.509553  | \n",
       "| ENSG00000000457 | 8.765748  | 8.698941  | 8.651978  | 8.732909  |  8.593308 | 8.703164  |  8.762420 | 8.724586  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516\n",
       "ENSG00000000003 9.742340   9.430742   9.867872   9.646127   10.183344 \n",
       "ENSG00000000419 9.334009   9.582000   9.486456   9.523397    9.427605 \n",
       "ENSG00000000457 8.765748   8.698941   8.651978   8.732909    8.593308 \n",
       "                SRR1039517 SRR1039520 SRR1039521\n",
       "ENSG00000000003 9.880660   10.010591  9.640065  \n",
       "ENSG00000000419 9.575154    9.326341  9.509553  \n",
       "ENSG00000000457 8.703164    8.762420  8.724586  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vsd <- vst(dds, blind = FALSE)\n",
    "head(assay(vsd), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "In the above function calls, we specified blind = FALSE, which means that differences between cell lines and treatment (the variables in the design) will not contribute to the expected variance-mean trend of the experiment. The experimental design is not used directly in the transformation, only in estimating the global amount of variability in the counts. For a fully unsupervised transformation, one can set blind = TRUE (which is the default).\n",
    "\n",
    "To show the effect of the transformation, in the figure below we plot the first sample against the second, first simply using the log2 function (after adding 1, to avoid taking the log of zero), and then using the rlog- and VST-transformed values. For the log2 approach, we need to first estimate size factors to account for sequencing depth, and then specify normalized=TRUE. Sequencing depth correction is done automatically for the rlog and the vst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {},
     "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"
    }
   ],
   "source": [
    "pdf(\"figures/transformation.pdf\")\n",
    "suppressPackageStartupMessages(library(\"dplyr\"))\n",
    "suppressPackageStartupMessages(library(\"ggplot2\"))\n",
    "\n",
    "dds <- DESeq2::estimateSizeFactors(dds)\n",
    "\n",
    "df <- bind_rows(\n",
    "  as_data_frame(log2(counts(dds, normalized=TRUE)[, 1:2]+1)) %>%\n",
    "         mutate(transformation = \"log2(x + 1)\"),\n",
    "  as_data_frame(assay(rld)[, 1:2]) %>% mutate(transformation = \"rlog\"),\n",
    "  as_data_frame(assay(vsd)[, 1:2]) %>% mutate(transformation = \"vst\"))\n",
    "  \n",
    "colnames(df)[1:2] <- c(\"x\", \"y\")  \n",
    "\n",
    "ggplot(df, aes(x = x, y = y)) + geom_hex(bins = 80) +\n",
    "  coord_fixed() + facet_grid( . ~ transformation)  \n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/transformation.pdf (47.7 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/transformation.pdf (47.7 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/transformation.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Scatterplot of transformed counts from two samples.** \n",
    "Shown are scatterplots using the log2 transform of normalized counts (left), using the rlog (middle), and using the VST (right). While the rlog is on roughly the same scale as the log2 counts, the VST has a upward shift for the smaller values. It is the differences between samples (deviation from y=x in these scatterplots) which will contribute to the distance calculations and the PCA plot.\n",
    "\n",
    "We can see how genes with low counts (bottom left-hand corner) seem to be excessively variable on the ordinary logarithmic scale, while the rlog transform and VST compress differences for the low count genes for which the data provide little information about differential expression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Sample Distance\n",
    "\n",
    "A useful first step in an RNA-seq analysis is often to assess overall similarity between samples: Which samples are similar to each other, which are different? Does this fit to the expectation from the experiment’s design?\n",
    "\n",
    "We use the R function dist to calculate the Euclidean distance between samples. To ensure we have a roughly equal contribution from all genes, we use it on the rlog-transformed data. We need to transpose the matrix of values using t, because the dist function expects the different samples to be rows of its argument, and different dimensions (here, genes) to be columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517\n",
       "SRR1039509   45.69859                                                       \n",
       "SRR1039512   39.25239   54.90828                                            \n",
       "SRR1039513   62.63201   44.52740   48.72579                                 \n",
       "SRR1039516   44.50557   59.06364   43.57856   63.74275                      \n",
       "SRR1039517   64.49410   51.44882   59.22962   49.87992   47.48200           \n",
       "SRR1039520   39.57693   57.46259   36.74434   58.49014   46.40786   63.59942\n",
       "SRR1039521   63.36124   45.05732   57.87616   36.49484   65.54600   52.31695\n",
       "           SRR1039520\n",
       "SRR1039509           \n",
       "SRR1039512           \n",
       "SRR1039513           \n",
       "SRR1039516           \n",
       "SRR1039517           \n",
       "SRR1039520           \n",
       "SRR1039521   50.13430"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampleDists <- dist(t(assay(rld)))\n",
    "sampleDists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We visualize the distances in a heatmap in a figure below, using the function pheatmap from the pheatmap package. In order to plot the sample distance matrix with the rows/columns arranged by the distances in our distance matrix, we manually provide sampleDists to the clustering_distance argument of the pheatmap function. Otherwise the pheatmap function would assume that the matrix contains the data values themselves, and would calculate distances between the rows/columns of the distance matrix, which is not desired. We also manually specify a blue color palette using the colorRampPalette function from the RColorBrewer package.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "kernel": "R"
   },
   "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": [
    "library(\"pheatmap\")\n",
    "library(\"RColorBrewer\")\n",
    "pdf(\"figures/dist.pdf\")\n",
    "## ----distheatmap, fig.width = 6.1, fig.height = 4.5----------------------\n",
    "sampleDistMatrix <- as.matrix( sampleDists )\n",
    "rownames(sampleDistMatrix) <- paste( rld$dex, rld$cell, sep = \" - \" )\n",
    "colnames(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",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/dist.pdf (7.3 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/dist.pdf (7.3 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/dist.pdf --pages 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Heatmap of sample-to-sample distances using the rlog-transformed values.**\n",
    "\n",
    "Note that we have changed the row names of the distance matrix to contain treatment type and patient number instead of sample ID, so that we have all this information in view when looking at the heatmap.\n",
    "\n",
    "\n",
    "Another option for calculating sample distances is to use the Poisson Distance (Witten 2011), implemented in the PoiClaClu package. This measure of dissimilarity between counts also takes the inherent variance structure of counts into consideration when calculating the distances between samples. The PoissonDistance function takes the original count matrix (not normalized) with samples as rows instead of columns, so we need to transpose the counts in dds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "kernel": "R"
   },
   "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": [
    "library(\"PoiClaClu\")\n",
    "poisd <- PoissonDistance(t(counts(dds)))\n",
    "pdf(\"figures/dist_pois.pdf\")\n",
    "samplePoisDistMatrix <- as.matrix( poisd$dd )\n",
    "rownames(samplePoisDistMatrix) <- paste( rld$dex, rld$cell, sep=\" - \" )\n",
    "colnames(samplePoisDistMatrix) <- NULL\n",
    "pheatmap(samplePoisDistMatrix,\n",
    "         clustering_distance_rows = poisd$dd,\n",
    "         clustering_distance_cols = poisd$dd,\n",
    "         col = colors)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/dist_pois.pdf (7.3 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/dist_pois.pdf (7.3 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/dist_pois.pdf --pages 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Heatmap of sample-to-sample distances using the Poisson Distance.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### PCA plot\n",
    "\n",
    "Another way to visualize sample-to-sample distances is a principal components analysis (PCA). In this ordination method, the data points (here, the samples) are projected onto the 2D plane such that they spread out in the two directions that explain most of the differences (figure below). The x-axis is the direction that separates the data points the most. The values of the samples in this direction are written PC1. The y-axis is a direction (it must be orthogonal to the first direction) that separates the data the second most. The values of the samples in this direction are written PC2. The percent of the total variance that is contained in the direction is printed in the axis label. Note that these percentages do not add to 100%, because there are more dimensions that contain the remaining variance (although each of these remaining dimensions will explain less than the two that we see).\n",
    "\n",
    "Here we make PCA plot using 2 approaches: `DESeq2::plotPCA` function, and a plain `ggplot2` implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {},
     "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"
    }
   ],
   "source": [
    "pcaData <- DESeq2::plotPCA(rld, intgroup = c( \"dex\", \"cell\"), returnData = TRUE)\n",
    "percentVar <- round(100 * attr(pcaData, \"percentVar\"))\n",
    "pdf(\"figures/pca.pdf\")\n",
    "DESeq2::plotPCA(rld, intgroup = c(\"dex\", \"cell\"))\n",
    "## ----ggplotpca, fig.width=6, fig.height=4.5------------------------------\n",
    "ggplot(pcaData, aes(x = PC1, y = PC2, color = dex, shape = cell)) +\n",
    "  geom_point(size =3) +\n",
    "  xlab(paste0(\"PC1: \", percentVar[1], \"% variance\")) +\n",
    "  ylab(paste0(\"PC2: \", percentVar[2], \"% variance\")) +\n",
    "  coord_fixed()\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/pca.pdf (6.4 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/pca.pdf (6.4 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/pca.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**PCA plot using the rlog-transformed values.** Each unique combination of treatment and cell line is given its own color.\n",
    "\n",
    "Here, we have used the function plotPCA that comes with DESeq2. The two terms specified by intgroup are the interesting groups for labeling the samples; they tell the function to use them to choose colors. We can also build the PCA plot from scratch using the ggplot2 package (Wickham 2009). This is done by asking the plotPCA function to return the data used for plotting rather than building the plot. See the ggplot2 documentation for more details on using ggplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>PC1</th><th scope=col>PC2</th><th scope=col>group</th><th scope=col>dex</th><th scope=col>cell</th><th scope=col>name</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SRR1039508</th><td>-17.81773      </td><td> -4.020836     </td><td>untrt : N61311 </td><td>untrt          </td><td>N61311         </td><td>SRR1039508     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039509</th><td>  8.38790      </td><td> -1.490805     </td><td>trt : N61311   </td><td>trt            </td><td>N61311         </td><td>SRR1039509     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039512</th><td>-10.22735      </td><td> -5.004069     </td><td>untrt : N052611</td><td>untrt          </td><td>N052611        </td><td>SRR1039512     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039513</th><td> 17.53277      </td><td> -3.909890     </td><td>trt : N052611  </td><td>trt            </td><td>N052611        </td><td>SRR1039513     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039516</th><td>-14.67169      </td><td> 15.873239     </td><td>untrt : N080611</td><td>untrt          </td><td>N080611        </td><td>SRR1039516     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039517</th><td> 10.98782      </td><td> 20.598625     </td><td>trt : N080611  </td><td>trt            </td><td>N080611        </td><td>SRR1039517     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039520</th><td>-12.06035      </td><td>-11.985876     </td><td>untrt : N061011</td><td>untrt          </td><td>N061011        </td><td>SRR1039520     </td></tr>\n",
       "\t<tr><th scope=row>SRR1039521</th><td> 17.86863      </td><td>-10.060389     </td><td>trt : N061011  </td><td>trt            </td><td>N061011        </td><td>SRR1039521     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & PC1 & PC2 & group & dex & cell & name\\\\\n",
       "\\hline\n",
       "\tSRR1039508 & -17.81773       &  -4.020836      & untrt : N61311  & untrt           & N61311          & SRR1039508     \\\\\n",
       "\tSRR1039509 &   8.38790       &  -1.490805      & trt : N61311    & trt             & N61311          & SRR1039509     \\\\\n",
       "\tSRR1039512 & -10.22735       &  -5.004069      & untrt : N052611 & untrt           & N052611         & SRR1039512     \\\\\n",
       "\tSRR1039513 &  17.53277       &  -3.909890      & trt : N052611   & trt             & N052611         & SRR1039513     \\\\\n",
       "\tSRR1039516 & -14.67169       &  15.873239      & untrt : N080611 & untrt           & N080611         & SRR1039516     \\\\\n",
       "\tSRR1039517 &  10.98782       &  20.598625      & trt : N080611   & trt             & N080611         & SRR1039517     \\\\\n",
       "\tSRR1039520 & -12.06035       & -11.985876      & untrt : N061011 & untrt           & N061011         & SRR1039520     \\\\\n",
       "\tSRR1039521 &  17.86863       & -10.060389      & trt : N061011   & trt             & N061011         & SRR1039521     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | PC1 | PC2 | group | dex | cell | name | \n",
       "|---|---|---|---|---|---|---|---|\n",
       "| SRR1039508 | -17.81773       |  -4.020836      | untrt : N61311  | untrt           | N61311          | SRR1039508      | \n",
       "| SRR1039509 |   8.38790       |  -1.490805      | trt : N61311    | trt             | N61311          | SRR1039509      | \n",
       "| SRR1039512 | -10.22735       |  -5.004069      | untrt : N052611 | untrt           | N052611         | SRR1039512      | \n",
       "| SRR1039513 |  17.53277       |  -3.909890      | trt : N052611   | trt             | N052611         | SRR1039513      | \n",
       "| SRR1039516 | -14.67169       |  15.873239      | untrt : N080611 | untrt           | N080611         | SRR1039516      | \n",
       "| SRR1039517 |  10.98782       |  20.598625      | trt : N080611   | trt             | N080611         | SRR1039517      | \n",
       "| SRR1039520 | -12.06035       | -11.985876      | untrt : N061011 | untrt           | N061011         | SRR1039520      | \n",
       "| SRR1039521 |  17.86863       | -10.060389      | trt : N061011   | trt             | N061011         | SRR1039521      | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "           PC1       PC2        group           dex   cell    name      \n",
       "SRR1039508 -17.81773  -4.020836 untrt : N61311  untrt N61311  SRR1039508\n",
       "SRR1039509   8.38790  -1.490805 trt : N61311    trt   N61311  SRR1039509\n",
       "SRR1039512 -10.22735  -5.004069 untrt : N052611 untrt N052611 SRR1039512\n",
       "SRR1039513  17.53277  -3.909890 trt : N052611   trt   N052611 SRR1039513\n",
       "SRR1039516 -14.67169  15.873239 untrt : N080611 untrt N080611 SRR1039516\n",
       "SRR1039517  10.98782  20.598625 trt : N080611   trt   N080611 SRR1039517\n",
       "SRR1039520 -12.06035 -11.985876 untrt : N061011 untrt N061011 SRR1039520\n",
       "SRR1039521  17.86863 -10.060389 trt : N061011   trt   N061011 SRR1039521"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pcaData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### MDS plot\n",
    "\n",
    "Another plot, very similar to the PCA plot, can be made using the multidimensional scaling (MDS) function in base R. This is useful when we don’t have a matrix of data, but only a matrix of distances. Here we compute the MDS for the distances calculated from the rlog transformed counts and plot these in a figure below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {},
     "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"
    }
   ],
   "source": [
    "pdf(\"figures/mds_rlog.pdf\")\n",
    "mds <- as.data.frame(colData(rld))  %>%\n",
    "         cbind(cmdscale(sampleDistMatrix))\n",
    "ggplot(mds, aes(x = `1`, y = `2`, color = dex, shape = cell)) +\n",
    "  geom_point(size = 3) + coord_fixed()\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/mds_rlog.pdf (5.2 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/mds_rlog.pdf (5.2 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/mds_rlog.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**MDS plot using rlog-transformed values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {},
     "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"
    }
   ],
   "source": [
    "pdf(\"figures/mds_pois.pdf\")\n",
    "mdsPois <- as.data.frame(colData(dds)) %>%\n",
    "   cbind(cmdscale(samplePoisDistMatrix))\n",
    "ggplot(mdsPois, aes(x = `1`, y = `2`, color = dex, shape = cell)) +\n",
    "  geom_point(size = 3) + coord_fixed()\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "kernel": "SoS",
    "scrolled": true,
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/mds_pois.pdf (5.1 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/mds_pois.pdf (5.1 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/mds_pois.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**MDS plot using the *Poisson Distance* **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Differentital expression analysis\n",
    "\n",
    "### Running the differential expression pipeline\n",
    "\n",
    "As we have already specified an experimental design when we created the DESeqDataSet, we can run the differential expression pipeline on the raw counts with a single call to the function DESeq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "using pre-existing 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": [
    "dds <- DESeq(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "This function will print out a message for the various steps it performs. These are described in more detail in the manual page for DESeq, which can be accessed by typing ?DESeq. Briefly these are: the estimation of size factors (controlling for differences in the sequencing depth of the samples), the estimation of dispersion values for each gene, and fitting a generalized linear model.\n",
    "\n",
    "A DESeqDataSet is returned that contains all the fitted parameters within it, and the following section describes how to extract out results tables of interest from this object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Building the results table\n",
    "\n",
    "Calling results without any arguments will extract the estimated log2 fold changes and p values for the last variable in the design formula. If there are more than 2 levels for this variable, results will extract the results table for a comparison of the last level over the first level. The comparison is printed at the top of the output: dex trt vs untrt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): dex trt vs untrt \n",
       "Wald test p-value: dex trt vs untrt \n",
       "DataFrame with 29391 rows and 6 columns\n",
       "                   baseMean log2FoldChange     lfcSE        stat       pvalue\n",
       "                  <numeric>      <numeric> <numeric>   <numeric>    <numeric>\n",
       "ENSG00000000003 708.6021697    -0.38125388 0.1006560  -3.7876928 0.0001520527\n",
       "ENSG00000000419 520.2979006     0.20681271 0.1122218   1.8428925 0.0653447023\n",
       "ENSG00000000457 237.1630368     0.03792050 0.1434532   0.2643405 0.7915175170\n",
       "ENSG00000000460  57.9326331    -0.08816322 0.2871677  -0.3070095 0.7588361137\n",
       "ENSG00000000938   0.3180984    -1.37823397 3.4998753  -0.3937952 0.6937322727\n",
       "...                     ...            ...       ...         ...          ...\n",
       "ENSG00000273485   1.2864477     -0.1271363 1.6005571 -0.07943253    0.9366886\n",
       "ENSG00000273486  15.4525365     -0.1509944 0.4865490 -0.31033758    0.7563043\n",
       "ENSG00000273487   8.1632350      1.0464169 0.6990336  1.49694792    0.1344068\n",
       "ENSG00000273488   8.5844790      0.1078190 0.6381646  0.16895172    0.8658346\n",
       "ENSG00000273489   0.2758994      1.4837258 3.5139452  0.42223933    0.6728503\n",
       "                       padj\n",
       "                  <numeric>\n",
       "ENSG00000000003 0.001281516\n",
       "ENSG00000000419 0.196230403\n",
       "ENSG00000000457 0.911219224\n",
       "ENSG00000000460 0.894672849\n",
       "ENSG00000000938          NA\n",
       "...                     ...\n",
       "ENSG00000273485          NA\n",
       "ENSG00000273486   0.8936787\n",
       "ENSG00000273487   0.3291460\n",
       "ENSG00000273488   0.9451750\n",
       "ENSG00000273489          NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- results(dds)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 6 rows and 2 columns\n",
       "                       type                               description\n",
       "                <character>                               <character>\n",
       "baseMean       intermediate mean of normalized counts for all samples\n",
       "log2FoldChange      results  log2 fold change (MLE): dex trt vs untrt\n",
       "lfcSE               results          standard error: dex trt vs untrt\n",
       "stat                results          Wald statistic: dex trt vs untrt\n",
       "pvalue              results       Wald test p-value: dex trt vs untrt\n",
       "padj                results                      BH adjusted p-values"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcols(res, use.names = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The first column, baseMean, is a just the average of the normalized count values, divided by the size factors, taken over all samples in the DESeqDataSet. The remaining four columns refer to a specific contrast, namely the comparison of the trt level over the untrt level for the factor variable dex. We will find out below how to obtain other contrasts.\n",
    "\n",
    "The column log2FoldChange is the effect size estimate. It tells us how much the gene’s expression seems to have changed due to treatment with dexamethasone in comparison to untreated samples. This value is reported on a logarithmic scale to base 2: for example, a log2 fold change of 1.5 means that the gene’s expression is increased by a multiplicative factor of 21.5 ≈ 2.82.\n",
    "\n",
    "Of course, this estimate has an uncertainty associated with it, which is available in the column lfcSE, the standard error estimate for the log2 fold change estimate. We can also express the uncertainty of a particular effect size estimate as the result of a statistical test. The purpose of a test for differential expression is to test whether the data provides sufficient evidence to conclude that this value is really different from zero. DESeq2 [8] performs for each gene a hypothesis test to see whether evidence is sufficient to decide against the null hypothesis that there is zero effect of the treatment on the gene and that the observed difference between treatment and control was merely caused by experimental variability (i.e., the type of variability that you can expect between different samples in the same treatment group). As usual in statistics, the result of this test is reported as a p value, and it is found in the column pvalue. Remember that a p value indicates the probability that a fold change as strong as the observed one, or even stronger, would be seen under the situation described by the null hypothesis.\n",
    "\n",
    "\n",
    "We can also summarize the results with the following line of code, which reports some additional information, that will be covered in later sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 29391 with nonzero total read count\n",
      "adjusted p-value < 0.1\n",
      "LFC > 0 (up)     : 2607, 8.9% \n",
      "LFC < 0 (down)   : 2218, 7.5% \n",
      "outliers [1]     : 0, 0% \n",
      "low counts [2]   : 11397, 39% \n",
      "(mean count < 5)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Note that there are many genes with differential expression due to dexamethasone treatment at the FDR level of 10%. This makes sense, as the smooth muscle cells of the airway are known to react to glucocorticoid steroids. However, there are two ways to be more strict about which set of genes are considered significant:\n",
    "\n",
    "* lower the false discovery rate threshold (the threshold on padj in the results table)\n",
    "* raise the log2 fold change threshold from 0 using the lfcThreshold argument of results\n",
    "\n",
    "If we lower the false discovery rate threshold, we should also inform the results() function about it, so that the function can use this threshold for the optimal independent filtering that it performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "12831  4024 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.05 <- results(dds, alpha = 0.05)\n",
    "table(res.05$padj < 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "If we want to raise the log2 fold change threshold, so that we test for genes that show more substantial changes due to treatment, we simply supply a value on the log2 scale. For example, by specifying lfcThreshold = 1, we test for genes that show significant effects of treatment on gene counts more than doubling or less than halving, because $2^1 = 2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "FALSE  TRUE \n",
       "20034   240 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resLFC1 <- results(dds, lfcThreshold=1)\n",
    "table(resLFC1$padj < 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Sometimes a subset of the p values in res will be NA (“not available”). This is DESeq’s way of reporting that all counts for this gene were zero, and hence no test was applied. In addition, p values can be assigned NA if the gene was excluded from analysis because it contained an extreme count outlier. For more information, see the outlier detection section of the DESeq2 vignette.\n",
    "\n",
    "If you use the results from an R analysis package in published research, you can find the proper citation for the software by typing citation(\"pkgName\"), where you would substitute the name of the package for pkgName. Citing methods papers helps to support and reward the individuals who put time into open source software for genomic data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Other comaprisions\n",
    "\n",
    "In general, the results for a comparison of any two levels of a variable can be extracted using the contrast argument to results. The user should specify three values: the name of the variable, the name of the level for the numerator, and the name of the level for the denominator. Here we extract results for the log2 of the fold change of one cell line over another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): cell N061011 vs N61311 \n",
       "Wald test p-value: cell N061011 vs N61311 \n",
       "DataFrame with 29391 rows and 6 columns\n",
       "                   baseMean log2FoldChange     lfcSE         stat     pvalue\n",
       "                  <numeric>      <numeric> <numeric>    <numeric>  <numeric>\n",
       "ENSG00000000003 708.6021697     0.30632635 0.1435330  2.134187335 0.03282745\n",
       "ENSG00000000419 520.2979006    -0.05404672 0.1597205 -0.338383153 0.73507447\n",
       "ENSG00000000457 237.1630368     0.01630854 0.2030380  0.080322608 0.93598068\n",
       "ENSG00000000460  57.9326331     0.27912688 0.4007042  0.696590931 0.48605883\n",
       "ENSG00000000938   0.3180984     0.03849337 4.9975798  0.007702403 0.99385443\n",
       "...                     ...            ...       ...          ...        ...\n",
       "ENSG00000273485   1.2864477    -2.32142337 2.3530918 -0.986541761  0.3238673\n",
       "ENSG00000273486  15.4525365    -0.07271569 0.7119582 -0.102134773  0.9186497\n",
       "ENSG00000273487   8.1632350    -0.02693764 1.0051679 -0.026799147  0.9786199\n",
       "ENSG00000273488   8.5844790     1.09584871 0.8990390  1.218911152  0.2228779\n",
       "ENSG00000273489   0.2758994     0.03849337 4.9975798  0.007702403  0.9938544\n",
       "                     padj\n",
       "                <numeric>\n",
       "ENSG00000000003 0.2206411\n",
       "ENSG00000000419 0.9395460\n",
       "ENSG00000000457 0.9904710\n",
       "ENSG00000000460 0.8550869\n",
       "ENSG00000000938        NA\n",
       "...                   ...\n",
       "ENSG00000273485        NA\n",
       "ENSG00000273486 0.9865440\n",
       "ENSG00000273487 0.9958619\n",
       "ENSG00000273488 0.6482708\n",
       "ENSG00000273489        NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results(dds, contrast = c(\"cell\", \"N061011\", \"N61311\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "If results for an interaction term are desired, the name argument of results should be used. Please see the help for the results function for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Multiple testing\n",
    "\n",
    "In high-throughput biology, we are careful to not use the p values directly as evidence against the null, but to correct for multiple testing. What would happen if we were to simply threshold the p values at a low value, say 0.05? There are 5676 genes with a p value below 0.05 among the 29391 genes for which the test succeeded in reporting a p value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "5676"
      ],
      "text/latex": [
       "5676"
      ],
      "text/markdown": [
       "5676"
      ],
      "text/plain": [
       "[1] 5676"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "29391"
      ],
      "text/latex": [
       "29391"
      ],
      "text/markdown": [
       "29391"
      ],
      "text/plain": [
       "[1] 29391"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(res$pvalue < 0.05, na.rm=TRUE)\n",
    "sum(!is.na(res$pvalue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Now, assume for a moment that the null hypothesis is true for all genes, i.e., no gene is affected by the treatment with dexamethasone. Then, by the definition of the p value, we expect up to 5% of the genes to have a p value below 0.05. This amounts to 1470 genes. If we just considered the list of genes with a p value below 0.05 as differentially expressed, this list should therefore be expected to contain up to 1470 / 5676 = 26% false positives.\n",
    "\n",
    "DESeq2 uses the Benjamini-Hochberg (BH) adjustment (Benjamini and Hochberg 1995) as implemented in the base R p.adjust function; in brief, this method calculates for each gene an adjusted p value that answers the following question: if one called significant all genes with an adjusted p value less than or equal to this gene’s adjusted p value threshold, what would be the fraction of false positives (the false discovery rate, FDR) among them, in the sense of the calculation outlined above? These values, called the BH-adjusted p values, are given in the column padj of the res object.\n",
    "\n",
    "The FDR is a useful statistic for many high-throughput experiments, as we are often interested in reporting or focusing on a set of interesting genes, and we would like to put an upper bound on the percent of false positives in this set.\n",
    "\n",
    "Hence, if we consider a fraction of 10% false positives acceptable, we can consider all genes with an adjusted p value below 10% = 0.1 as significant. How many such genes are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "4825"
      ],
      "text/latex": [
       "4825"
      ],
      "text/markdown": [
       "4825"
      ],
      "text/plain": [
       "[1] 4825"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(res$padj < 0.1, na.rm=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We subset the results table to these genes and then sort it by the log2 fold change estimate to get the significant genes with the strongest down-regulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): dex trt vs untrt \n",
       "Wald test p-value: dex trt vs untrt \n",
       "DataFrame with 6 rows and 6 columns\n",
       "                 baseMean log2FoldChange     lfcSE      stat       pvalue\n",
       "                <numeric>      <numeric> <numeric> <numeric>    <numeric>\n",
       "ENSG00000128285  6.624741      -5.325912 1.2578863 -4.234017 2.295537e-05\n",
       "ENSG00000267339 26.233573      -4.611553 0.6731316 -6.850894 7.338997e-12\n",
       "ENSG00000019186 14.087605      -4.325920 0.8578247 -5.042895 4.585398e-07\n",
       "ENSG00000183454  5.804171      -4.264087 1.1669498 -3.654045 2.581412e-04\n",
       "ENSG00000146006 46.807597      -4.211875 0.5288797 -7.963767 1.668799e-15\n",
       "ENSG00000141469 53.436528      -4.124784 1.1297977 -3.650905 2.613174e-04\n",
       "                        padj\n",
       "                   <numeric>\n",
       "ENSG00000128285 2.378002e-04\n",
       "ENSG00000267339 2.053778e-10\n",
       "ENSG00000019186 6.611351e-06\n",
       "ENSG00000183454 2.048057e-03\n",
       "ENSG00000146006 7.166675e-14\n",
       "ENSG00000141469 2.069606e-03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resSig <- subset(res, padj < 0.1)\n",
    "head(resSig[ order(resSig$log2FoldChange), ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "And the strongest up-regulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): dex trt vs untrt \n",
       "Wald test p-value: dex trt vs untrt \n",
       "DataFrame with 6 rows and 6 columns\n",
       "                  baseMean log2FoldChange     lfcSE      stat       pvalue\n",
       "                 <numeric>      <numeric> <numeric> <numeric>    <numeric>\n",
       "ENSG00000179593  67.243048       9.505972 1.0545111  9.014578 1.976299e-19\n",
       "ENSG00000109906 385.071029       7.352628 0.5363902 13.707610 9.141988e-43\n",
       "ENSG00000250978  56.318194       6.327393 0.6778153  9.334981 1.010098e-20\n",
       "ENSG00000132518   5.654654       5.885113 1.3241367  4.444491 8.810031e-06\n",
       "ENSG00000127954 286.384119       5.207160 0.4930828 10.560419 4.546302e-26\n",
       "ENSG00000249364   8.839061       5.098168 1.1596852  4.396166 1.101798e-05\n",
       "                        padj\n",
       "                   <numeric>\n",
       "ENSG00000179593 1.252166e-17\n",
       "ENSG00000109906 2.253437e-40\n",
       "ENSG00000250978 7.212582e-19\n",
       "ENSG00000132518 1.000175e-04\n",
       "ENSG00000127954 5.049763e-24\n",
       "ENSG00000249364 1.223812e-04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(resSig[ order(resSig$log2FoldChange, decreasing = TRUE), ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Plotting Results\n",
    "### Counts plot\n",
    "\n",
    "A quick way to visualize the counts for a particular gene is to use the plotCounts function that takes as arguments the DESeqDataSet, a gene name, and the group over which to plot the counts (figure below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/top_gene.pdf\")\n",
    "topGene <- rownames(res)[which.min(res$padj)]\n",
    "plotCounts(dds, gene = topGene, intgroup=c(\"dex\"))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/top_gene.pdf (4.6 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/top_gene.pdf (4.6 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/top_gene.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Normalized counts for a single gene over treatment group.**\n",
    "\n",
    "We can also make custom plots using the ggplot function from the ggplot2 package (figures below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {},
     "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"
    }
   ],
   "source": [
    "pdf(\"figures/counts.pdf\")\n",
    "library(\"ggbeeswarm\")\n",
    "geneCounts <- plotCounts(dds, gene = topGene, intgroup = c(\"dex\",\"cell\"),\n",
    "                         returnData = TRUE)\n",
    "ggplot(geneCounts, aes(x = dex, y = count, color = cell)) +\n",
    "  scale_y_log10() +  geom_beeswarm(cex = 3)\n",
    "## ----ggplotcountsgroup, fig.width = 4, fig.height = 3--------------------\n",
    "ggplot(geneCounts, aes(x = dex, y = count, color = cell, group = cell)) +\n",
    "  scale_y_log10() + geom_point(size = 3) + geom_line()\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/counts.pdf (5.8 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/counts.pdf (5.8 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/counts.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Normalized counts with lines connecting cell lines.**\n",
    "Note that the DESeq test actually takes into account the cell line effect, so this figure more closely depicts the difference being tested.\n",
    "\n",
    "### MA-plot\n",
    "\n",
    "An MA-plot (R. Dudoit et al. 2002) provides a useful overview for the distribution of the estimated coefficients in the model, e.g. the comparisons of interest, across all genes. On the y-axis, the “M” stands for “minus” – subtraction of log values is equivalent to the log of the ratio – and on the x-axis, the “A” stands for “average”. You may hear this plot also referred to as a mean-difference plot, or a Bland-Altman plot.\n",
    "\n",
    "Before making the MA-plot, we use the lfcShrink function to shrink the log2 fold changes for the comparison of dex treated vs untreated samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/ma_treatment.pdf\")\n",
    "res <- lfcShrink(dds, contrast=c(\"dex\",\"trt\",\"untrt\"), res=res)\n",
    "plotMA(res, ylim = c(-5, 5))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/ma_treatment.pdf (203.5 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/ma_treatment.pdf (203.5 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/ma_treatment.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**An MA-plot of changes induced by treatment.** \n",
    "The log2 fold change for a particular comparison is plotted on the y-axis and the average of the counts normalized by size factor is shown on the x-axis (“M” for minus, because a log ratio is equal to log minus log, and “A” for average). Each gene is represented with a dot. Genes with an adjusted p value below a threshold (here 0.1, the default) are shown in red.\n",
    "\n",
    "The DESeq2 package uses statistical techniques to moderate log2 fold changes from genes with very low counts and highly variable counts, as can be seen by the narrowing of the vertical spread of points on the left side of the MA-plot. For a detailed explanation of the rationale of moderated fold changes, please see the DESeq2 paper (Love, Huber, and Anders 2014). This plot demonstrates that only genes with a large average normalized count contain sufficient information to yield a significant call.\n",
    "\n",
    "If we had not used statistical moderation to shrink the noisy log2 fold changes, we would have instead seen the following plot:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/ma_treatment_noshr.pdf\")\n",
    "res.noshr <- results(dds)\n",
    "plotMA(res.noshr, ylim = c(-5, 5))\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/ma_treatment_noshr.pdf (210.5 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/ma_treatment_noshr.pdf (210.5 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/ma_treatment_noshr.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "\n",
    "We can also make an MA-plot for the results table in which we raised the log2 fold change threshold (figure below). We can label individual points on the MA-plot as well. Here we use the with R function to plot a circle and text for a selected row of the results object. Within the with function, only the baseMean and log2FoldChange values for the selected rows of res are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/ma_log2.pdf\")\n",
    "plotMA(resLFC1, ylim = c(-5,5))\n",
    "topGene <- rownames(resLFC1)[which.min(resLFC1$padj)]\n",
    "with(resLFC1[topGene, ], {\n",
    "  points(baseMean, log2FoldChange, col=\"dodgerblue\", cex=2, lwd=2)\n",
    "  text(baseMean, log2FoldChange, topGene, pos=2, col=\"dodgerblue\")\n",
    "})\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/ma_log2.pdf (196.9 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/ma_log2.pdf (196.9 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/ma_log2.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**An MA-plot of a test for large log2 fold changes.** The red points indicate genes for which the log2 fold change was significantly higher than 1 or less than -1 (treatment resulting in more than doubling or less than halving of the normalized counts). The point circled in blue indicates the gene with the lowest adjusted p value.\n",
    "\n",
    "Another useful diagnostic plot is the histogram of the p values (figure below). This plot is best formed by excluding genes with very small counts, which otherwise generate spikes in the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "kernel": "R"
   },
   "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('figures/pvals.pdf')\n",
    "hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20,\n",
    "     col = \"grey50\", border = \"white\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/pvals.pdf (4.6 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/pvals.pdf (4.6 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/pvals.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Histogram of p values for genes with mean normalized count larger than 1.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Gene clustering\n",
    "\n",
    "In the sample distance heatmap made previously, the dendrogram at the side shows us a hierarchical clustering of the samples. Such a clustering can also be performed for the genes. Since the clustering is only relevant for genes that actually carry a signal, one usually would only cluster a subset of the most highly variable genes. Here, for demonstration, let us select the 20 genes with the highest variance across samples. We will work with the rlog transformed counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘genefilter’\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    rowSds, rowVars\n"
     ]
    }
   ],
   "source": [
    "library(\"genefilter\")\n",
    "topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The heatmap becomes more interesting if we do not look at absolute expression strength but rather at the amount by which each gene deviates in a specific sample from the gene’s average across all samples. Hence, we center each genes’ values across samples, and plot a heatmap (figure below). We provide a data.frame that instructs the pheatmap function how to label the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/heatmap_rlog.pdf\")\n",
    "mat  <- assay(rld)[ topVarGenes, ]\n",
    "mat  <- mat - rowMeans(mat)\n",
    "anno <- as.data.frame(colData(rld)[, c(\"cell\",\"dex\")])\n",
    "pheatmap(mat, annotation_col = anno)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/heatmap_rlog.pdf (7.6 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/heatmap_rlog.pdf (7.6 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/heatmap_rlog.pdf --pages 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Heatmap of relative rlog-transformed values across samples.** \n",
    "Treatment status and cell line information are shown with colored bars at the top of the heatmap. Blocks of genes that covary across patients. Note that a set of genes at the top of the heatmap are separating the N061011 cell line from the others. In the center of the heatmap, we see a set of genes for which the dexamethasone treated samples have higher gene expression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Independent Filtering**\n",
    "\n",
    "The MA plot highlights an important property of RNA-seq data. For weakly expressed genes, we have no chance of seeing differential expression, because the low read counts suffer from such high Poisson noise that any biological effect is drowned in the uncertainties from the sampling at a low rate. We can also show this by examining the ratio of small p values (say, less than 0.05) for genes binned by mean normalized count. We will use the results table subjected to the threshold to show what this looks like in a case when there are few tests with small p value.\n",
    "\n",
    "In the following code chunk, we create bins using the quantile function, bin the genes by base mean using cut, rename the levels of the bins using the middle point, calculate the ratio of p values less than 0.05 for each bin, and finally plot these ratios (figure below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/pval_binned.pdf\")\n",
    "## ----sensitivityovermean, fig.width=6------------------------------------\n",
    "qs <- c(0, quantile(resLFC1$baseMean[resLFC1$baseMean > 0], 0:6/6))\n",
    "bins <- cut(resLFC1$baseMean, qs)\n",
    "levels(bins) <- paste0(\"~\", round(signif((qs[-1] + qs[-length(qs)])/2, 2)))\n",
    "fractionSig <- tapply(resLFC1$pvalue, bins, function(p)\n",
    "                          mean(p < .05, na.rm = TRUE))\n",
    "barplot(fractionSig, xlab = \"mean normalized count\",\n",
    "                     ylab = \"fraction of small p values\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/pval_binned.pdf (4.3 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/pval_binned.pdf (4.3 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/pval_binned.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The ratio of small p values for genes binned by mean normalized count. The p values are from a test of log2 fold change greater than 1 or less than -1. This plot demonstrates that genes with very low mean count have little or no power, and are best excluded from testing.\n",
    "\n",
    "At first sight, there may seem to be little benefit in filtering out these genes. After all, the test found them to be non-significant anyway. However, these genes have an influence on the multiple testing adjustment, whose performance improves if such genes are removed. By removing the low count genes from the input to the FDR procedure, we can find more genes to be significant among those that we keep, and so improved the power of our test. This approach is known as independent filtering.\n",
    "\n",
    "The DESeq2 software automatically performs independent filtering that maximizes the number of genes with adjusted p value less than a critical value (by default, alpha is set to 0.1). This automatic independent filtering is performed by, and can be controlled by, the results function.\n",
    "\n",
    "The term independent highlights an important caveat. Such filtering is permissible only if the statistic that we filter on (here the mean of normalized counts across all samples) is independent of the actual test statistic (the p value) under the null hypothesis. Otherwise, the filtering would invalidate the test and consequently the assumptions of the BH procedure. The independent filtering software used inside DESeq2 comes from the GeneFilter package, that contains a reference to a paper describing the statistical foundation for independent filtering (Bourgon, Gentleman, and Huber 2010)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Annotating and exporting results\n",
    "\n",
    "Our result table so far only contains the Ensembl gene IDs, but alternative gene names may be more informative for interpretation. Bioconductor’s annotation packages help with mapping various ID schemes to each other. We load the `AnnotationDbi` package and the annotation package `org.Hs.eg.db`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "library(\"AnnotationDbi\")\n",
    "library(\"org.Hs.eg.db\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "This is the organism annotation package (“org”) for Homo sapiens (“Hs”), organized as an AnnotationDbi database package (“db”), using Entrez Gene IDs (“eg”) as primary key. To get a list of all available key types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'ACCNUM'</li>\n",
       "\t<li>'ALIAS'</li>\n",
       "\t<li>'ENSEMBL'</li>\n",
       "\t<li>'ENSEMBLPROT'</li>\n",
       "\t<li>'ENSEMBLTRANS'</li>\n",
       "\t<li>'ENTREZID'</li>\n",
       "\t<li>'ENZYME'</li>\n",
       "\t<li>'EVIDENCE'</li>\n",
       "\t<li>'EVIDENCEALL'</li>\n",
       "\t<li>'GENENAME'</li>\n",
       "\t<li>'GO'</li>\n",
       "\t<li>'GOALL'</li>\n",
       "\t<li>'IPI'</li>\n",
       "\t<li>'MAP'</li>\n",
       "\t<li>'OMIM'</li>\n",
       "\t<li>'ONTOLOGY'</li>\n",
       "\t<li>'ONTOLOGYALL'</li>\n",
       "\t<li>'PATH'</li>\n",
       "\t<li>'PFAM'</li>\n",
       "\t<li>'PMID'</li>\n",
       "\t<li>'PROSITE'</li>\n",
       "\t<li>'REFSEQ'</li>\n",
       "\t<li>'SYMBOL'</li>\n",
       "\t<li>'UCSCKG'</li>\n",
       "\t<li>'UNIGENE'</li>\n",
       "\t<li>'UNIPROT'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'ACCNUM'\n",
       "\\item 'ALIAS'\n",
       "\\item 'ENSEMBL'\n",
       "\\item 'ENSEMBLPROT'\n",
       "\\item 'ENSEMBLTRANS'\n",
       "\\item 'ENTREZID'\n",
       "\\item 'ENZYME'\n",
       "\\item 'EVIDENCE'\n",
       "\\item 'EVIDENCEALL'\n",
       "\\item 'GENENAME'\n",
       "\\item 'GO'\n",
       "\\item 'GOALL'\n",
       "\\item 'IPI'\n",
       "\\item 'MAP'\n",
       "\\item 'OMIM'\n",
       "\\item 'ONTOLOGY'\n",
       "\\item 'ONTOLOGYALL'\n",
       "\\item 'PATH'\n",
       "\\item 'PFAM'\n",
       "\\item 'PMID'\n",
       "\\item 'PROSITE'\n",
       "\\item 'REFSEQ'\n",
       "\\item 'SYMBOL'\n",
       "\\item 'UCSCKG'\n",
       "\\item 'UNIGENE'\n",
       "\\item 'UNIPROT'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'ACCNUM'\n",
       "2. 'ALIAS'\n",
       "3. 'ENSEMBL'\n",
       "4. 'ENSEMBLPROT'\n",
       "5. 'ENSEMBLTRANS'\n",
       "6. 'ENTREZID'\n",
       "7. 'ENZYME'\n",
       "8. 'EVIDENCE'\n",
       "9. 'EVIDENCEALL'\n",
       "10. 'GENENAME'\n",
       "11. 'GO'\n",
       "12. 'GOALL'\n",
       "13. 'IPI'\n",
       "14. 'MAP'\n",
       "15. 'OMIM'\n",
       "16. 'ONTOLOGY'\n",
       "17. 'ONTOLOGYALL'\n",
       "18. 'PATH'\n",
       "19. 'PFAM'\n",
       "20. 'PMID'\n",
       "21. 'PROSITE'\n",
       "22. 'REFSEQ'\n",
       "23. 'SYMBOL'\n",
       "24. 'UCSCKG'\n",
       "25. 'UNIGENE'\n",
       "26. 'UNIPROT'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"ACCNUM\"       \"ALIAS\"        \"ENSEMBL\"      \"ENSEMBLPROT\"  \"ENSEMBLTRANS\"\n",
       " [6] \"ENTREZID\"     \"ENZYME\"       \"EVIDENCE\"     \"EVIDENCEALL\"  \"GENENAME\"    \n",
       "[11] \"GO\"           \"GOALL\"        \"IPI\"          \"MAP\"          \"OMIM\"        \n",
       "[16] \"ONTOLOGY\"     \"ONTOLOGYALL\"  \"PATH\"         \"PFAM\"         \"PMID\"        \n",
       "[21] \"PROSITE\"      \"REFSEQ\"       \"SYMBOL\"       \"UCSCKG\"       \"UNIGENE\"     \n",
       "[26] \"UNIPROT\"     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns(org.Hs.eg.db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We can use the mapIds function to add individual columns to our results table. We provide the row names of our results table as a key, and specify that keytype=ENSEMBL. The column argument tells the mapIds function which information we want, and the multiVals argument tells the function what to do if there are multiple possible values for a single input value. Here we ask to just give us back the first one that occurs in the database. To add the gene symbol and Entrez ID, we call mapIds twice.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'select()' returned 1:many mapping between keys and columns\n",
      "'select()' returned 1:many mapping between keys and columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "log2 fold change (MAP): dex trt vs untrt \n",
       "Wald test p-value: dex trt vs untrt \n",
       "DataFrame with 6 rows and 7 columns\n",
       "                  baseMean log2FoldChange      stat        pvalue          padj\n",
       "                 <numeric>      <numeric> <numeric>     <numeric>     <numeric>\n",
       "ENSG00000152583   997.4398       4.313968  24.85607 2.223017e-136 4.000096e-132\n",
       "ENSG00000165995   495.0929       3.186818  24.71198 7.951622e-135 7.154074e-131\n",
       "ENSG00000120129  3409.0294       2.871488  24.27480 3.619142e-130 2.170761e-126\n",
       "ENSG00000101347 12703.3871       3.618751  24.23532 9.444882e-130 4.248780e-126\n",
       "ENSG00000189221  2341.7673       3.230386  23.65337 1.089779e-123 3.921897e-120\n",
       "ENSG00000211445 12285.6151       3.553363  22.49579 4.563626e-112 1.368631e-108\n",
       "                     symbol      entrez\n",
       "                <character> <character>\n",
       "ENSG00000152583     SPARCL1        8404\n",
       "ENSG00000165995      CACNB2         783\n",
       "ENSG00000120129       DUSP1        1843\n",
       "ENSG00000101347      SAMHD1       25939\n",
       "ENSG00000189221        MAOA        4128\n",
       "ENSG00000211445        GPX3        2878"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res$symbol <- mapIds(org.Hs.eg.db,\n",
    "                     keys=row.names(res),\n",
    "                     column=\"SYMBOL\",\n",
    "                     keytype=\"ENSEMBL\",\n",
    "                     multiVals=\"first\")\n",
    "res$entrez <- mapIds(org.Hs.eg.db,\n",
    "                     keys=row.names(res),\n",
    "                     column=\"ENTREZID\",\n",
    "                     keytype=\"ENSEMBL\",\n",
    "                     multiVals=\"first\")\n",
    "resOrdered <- res[order(res$pvalue),]\n",
    "head(resOrdered)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Exporting results\n",
    "\n",
    "You can easily save the results table in a CSV file that you can then share or load with a spreadsheet program such as Excel. The call to as.data.frame is necessary to convert the DataFrame object (IRanges package) to a data.frame object that can be processed by write.csv. Here, we take just the top 100 genes for demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "resOrderedDF <- as.data.frame(resOrdered)[1:100, ]\n",
    "write.csv(resOrderedDF, file = \"results.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "A more sophisticated way for exporting results the Bioconductor package ReportingTools (Huntley et al. 2013). ReportingTools will automatically generate dynamic HTML documents, including links to external databases using gene identifiers and boxplots summarizing the normalized counts across groups. See the ReportingTools vignettes for full details. The simplest version of creating a dynamic ReportingTools report is performed with the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "library(\"ReportingTools\")\n",
    "htmlRep <- HTMLReport(shortName=\"report\", title=\"My report\",\n",
    "                      reportDirectory=\"figures\")\n",
    "publish(resOrderedDF, htmlRep)\n",
    "url <- finish(htmlRep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> report/report.html (20.5 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> report/report.html (20.5 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html xmlns=\"http://www.w3.org/1999/xhtml\" xmlns=\"http://www.w3.org/1999/xhtml\" xml:lang=\"en\" lang=\"en\" xml:lang=\"en\">\n",
       "  <head>\n",
       "    <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
       "    <title>My report</title>\n",
       "    <script language=\"JavaScript\" src=\"jslib/jquery-1.8.0.min.js\"></script>\n",
       "    <script language=\"JavaScript\" src=\"jslib/jquery.dataTables-1.9.3.js\"></script>\n",
       "    <script language=\"JavaScript\" src=\"jslib/jquery.dataTables.columnFilter.js\"></script>\n",
       "    <script language=\"JavaScript\" src=\"jslib/jquery.dataTables.plugins.js\"></script>\n",
       "    <script language=\"JavaScript\" src=\"jslib/jquery.dataTables.reprise.js\"></script>\n",
       "    <script language=\"JavaScript\" src=\"jslib/bootstrap.js\"></script>\n",
       "    <link rel=\"stylesheet\" type=\"text/css\" href=\"csslib/bootstrap.css\" />\n",
       "    <link rel=\"stylesheet\" type=\"text/css\" href=\"csslib/reprise.table.bootstrap.css\" />\n",
       "  </head>\n",
       "  <body> \n",
       "<div class=\"container\"><h2>My report</h2></div><div id=\"id2\" class=\"ReportingTools container\"><div class=\"container\" style=\"margin-top: 10px\"> <p class=\"page-header\"></p><table cellpadding=\"0\" cellspacing=\"0\" border=\"0\" class=\"dataTable table table-hover table-condensed\"><thead><tr><th class=\"filter-num sort-num-robust top-header-row\">baseMean</th><th class=\"filter-num sort-num-robust top-header-row\">log2FoldChange</th><th class=\"filter-num sort-num-robust top-header-row\">stat</th><th class=\"filter-num sort-num-robust top-header-row\">pvalue</th><th class=\"filter-num sort-num-robust top-header-row\">padj</th><th class=\"sort-string-robust top-header-row no-print\">symbol</th><th class=\"sort-string-robust top-header-row no-print\">entrez</th></tr><tr><th class=\"filter-num sort-num-robust bottom-header-row\">baseMean</th><th class=\"filter-num sort-num-robust bottom-header-row\">log2FoldChange</th><th class=\"filter-num sort-num-robust bottom-header-row\">stat</th><th class=\"filter-num sort-num-robust bottom-header-row\">pvalue</th><th class=\"filter-num sort-num-robust bottom-header-row\">padj</th><th class=\"sort-string-robust bottom-header-row\">symbol</th><th class=\"sort-string-robust bottom-header-row\">entrez</th></tr></thead><tbody><tr><td class=\"\">   997</td><td class=\"\"> 4.310</td><td class=\"\"> 24.9</td><td class=\"\">2.22e-136</td><td class=\"\">4.00e-132</td><td class=\"\">SPARCL1</td><td class=\"\">8404</td></tr><tr><td class=\"\">   495</td><td class=\"\"> 3.190</td><td class=\"\"> 24.7</td><td class=\"\">7.95e-135</td><td class=\"\">7.15e-131</td><td class=\"\">CACNB2</td><td class=\"\">783</td></tr><tr><td class=\"\">  3410</td><td class=\"\"> 2.870</td><td class=\"\"> 24.3</td><td class=\"\">3.62e-130</td><td class=\"\">2.17e-126</td><td class=\"\">DUSP1</td><td class=\"\">1843</td></tr><tr><td class=\"\"> 12700</td><td class=\"\"> 3.620</td><td class=\"\"> 24.2</td><td class=\"\">9.44e-130</td><td class=\"\">4.25e-126</td><td class=\"\">SAMHD1</td><td class=\"\">25939</td></tr><tr><td class=\"\">  2340</td><td class=\"\"> 3.230</td><td class=\"\"> 23.7</td><td class=\"\">1.09e-123</td><td class=\"\">3.92e-120</td><td class=\"\">MAOA</td><td class=\"\">4128</td></tr><tr><td class=\"\"> 12300</td><td class=\"\"> 3.550</td><td class=\"\"> 22.5</td><td class=\"\">4.56e-112</td><td class=\"\">1.37e-108</td><td class=\"\">GPX3</td><td class=\"\">2878</td></tr><tr><td class=\"\">  3010</td><td class=\"\"> 1.950</td><td class=\"\"> 22.0</td><td class=\"\">5.69e-107</td><td class=\"\">1.46e-103</td><td class=\"\">STEAP2</td><td class=\"\">261729</td></tr><tr><td class=\"\">  5390</td><td class=\"\"> 2.000</td><td class=\"\"> 21.6</td><td class=\"\">1.30e-103</td><td class=\"\">2.93e-100</td><td class=\"\">NEXN</td><td class=\"\">91624</td></tr><tr><td class=\"\">  3660</td><td class=\"\"> 2.170</td><td class=\"\"> 20.9</td><td class=\"\"> 2.87e-97</td><td class=\"\"> 5.74e-94</td><td class=\"\">MT2A</td><td class=\"\">4502</td></tr><tr><td class=\"\"> 30300</td><td class=\"\"> 2.290</td><td class=\"\"> 20.3</td><td class=\"\"> 3.22e-91</td><td class=\"\"> 5.79e-88</td><td class=\"\">ADAMTS1</td><td class=\"\">9510</td></tr><tr><td class=\"\">  1220</td><td class=\"\"> 2.180</td><td class=\"\"> 19.8</td><td class=\"\"> 7.56e-87</td><td class=\"\"> 1.24e-83</td><td class=\"\">FGD4</td><td class=\"\">121512</td></tr><tr><td class=\"\">  1100</td><td class=\"\"> 1.860</td><td class=\"\"> 19.7</td><td class=\"\"> 8.76e-87</td><td class=\"\"> 1.31e-83</td><td class=\"\">PDPN</td><td class=\"\">10630</td></tr><tr><td class=\"\">  5510</td><td class=\"\"> 2.150</td><td class=\"\"> 19.6</td><td class=\"\"> 1.44e-85</td><td class=\"\"> 1.99e-82</td><td class=\"\">SORT1</td><td class=\"\">6272</td></tr><tr><td class=\"\">   777</td><td class=\"\"> 3.040</td><td class=\"\"> 19.5</td><td class=\"\"> 2.13e-84</td><td class=\"\"> 2.73e-81</td><td class=\"\">PER1</td><td class=\"\">5187</td></tr><tr><td class=\"\">   508</td><td class=\"\">-3.450</td><td class=\"\">-19.4</td><td class=\"\"> 5.68e-84</td><td class=\"\"> 6.81e-81</td><td class=\"\">VCAM1</td><td class=\"\">7412</td></tr><tr><td class=\"\">   561</td><td class=\"\"> 4.070</td><td class=\"\"> 19.0</td><td class=\"\"> 2.24e-80</td><td class=\"\"> 2.51e-77</td><td class=\"\">KLF15</td><td class=\"\">28999</td></tr><tr><td class=\"\">  2650</td><td class=\"\">-2.450</td><td class=\"\">-18.8</td><td class=\"\"> 6.63e-79</td><td class=\"\"> 7.01e-76</td><td class=\"\">KCTD12</td><td class=\"\">115207</td></tr><tr><td class=\"\">  2060</td><td class=\"\"> 2.780</td><td class=\"\"> 18.1</td><td class=\"\"> 3.56e-73</td><td class=\"\"> 3.56e-70</td><td class=\"\">CCDC69</td><td class=\"\">26112</td></tr><tr><td class=\"\"> 25100</td><td class=\"\">-1.880</td><td class=\"\">-18.1</td><td class=\"\"> 4.73e-73</td><td class=\"\"> 4.48e-70</td><td class=\"\">CXCL12</td><td class=\"\">6387</td></tr><tr><td class=\"\">  1370</td><td class=\"\">-1.780</td><td class=\"\">-18.1</td><td class=\"\"> 6.79e-73</td><td class=\"\"> 6.11e-70</td><td class=\"\">ADAM12</td><td class=\"\">8038</td></tr><tr><td class=\"\">   314</td><td class=\"\">-2.640</td><td class=\"\">-17.8</td><td class=\"\"> 7.82e-71</td><td class=\"\"> 6.70e-68</td><td class=\"\">PRSS35</td><td class=\"\">167681</td></tr><tr><td class=\"\">   885</td><td class=\"\">-1.730</td><td class=\"\">-17.6</td><td class=\"\"> 1.93e-69</td><td class=\"\"> 1.58e-66</td><td class=\"\">DNM1</td><td class=\"\">1759</td></tr><tr><td class=\"\">   670</td><td class=\"\"> 1.860</td><td class=\"\"> 17.5</td><td class=\"\"> 2.49e-68</td><td class=\"\"> 1.95e-65</td><td class=\"\">ACSS1</td><td class=\"\">84532</td></tr><tr><td class=\"\">  1220</td><td class=\"\">-2.120</td><td class=\"\">-17.4</td><td class=\"\"> 3.53e-68</td><td class=\"\"> 2.65e-65</td><td class=\"\">CCKAR</td><td class=\"\">886</td></tr><tr><td class=\"\">  1040</td><td class=\"\"> 1.940</td><td class=\"\"> 17.4</td><td class=\"\"> 1.27e-67</td><td class=\"\"> 9.15e-65</td><td class=\"\">TRNP1</td><td class=\"\">388610</td></tr><tr><td class=\"\">  8570</td><td class=\"\"> 2.190</td><td class=\"\"> 17.3</td><td class=\"\"> 6.33e-67</td><td class=\"\"> 4.38e-64</td><td class=\"\">PTX3</td><td class=\"\">5806</td></tr><tr><td class=\"\">  1470</td><td class=\"\"> 1.500</td><td class=\"\"> 17.3</td><td class=\"\"> 7.72e-67</td><td class=\"\"> 5.14e-64</td><td class=\"\">DNAJB4</td><td class=\"\">11080</td></tr><tr><td class=\"\"> 11200</td><td class=\"\"> 1.430</td><td class=\"\"> 17.2</td><td class=\"\"> 4.64e-66</td><td class=\"\"> 2.98e-63</td><td class=\"\">STOM</td><td class=\"\">2040</td></tr><tr><td class=\"\">  1280</td><td class=\"\">-2.320</td><td class=\"\">-17.1</td><td class=\"\"> 8.87e-66</td><td class=\"\"> 5.50e-63</td><td class=\"\">SOX4</td><td class=\"\">6659</td></tr><tr><td class=\"\">  1970</td><td class=\"\"> 2.240</td><td class=\"\"> 17.1</td><td class=\"\"> 1.54e-65</td><td class=\"\"> 9.22e-63</td><td class=\"\">ADARB1</td><td class=\"\">104</td></tr><tr><td class=\"\">  8010</td><td class=\"\"> 1.710</td><td class=\"\"> 16.9</td><td class=\"\"> 5.50e-64</td><td class=\"\"> 3.19e-61</td><td class=\"\">SYNPO2</td><td class=\"\">171024</td></tr><tr><td class=\"\">  3230</td><td class=\"\"> 2.340</td><td class=\"\"> 16.8</td><td class=\"\"> 2.25e-63</td><td class=\"\"> 1.26e-60</td><td class=\"\">ERRFI1</td><td class=\"\">54206</td></tr><tr><td class=\"\">   419</td><td class=\"\"> 3.210</td><td class=\"\"> 16.5</td><td class=\"\"> 2.56e-61</td><td class=\"\"> 1.39e-58</td><td class=\"\">INHBB</td><td class=\"\">3625</td></tr><tr><td class=\"\">  7430</td><td class=\"\"> 2.120</td><td class=\"\"> 16.3</td><td class=\"\"> 1.68e-59</td><td class=\"\"> 8.91e-57</td><td class=\"\">NNMT</td><td class=\"\">4837</td></tr><tr><td class=\"\">  2130</td><td class=\"\"> 1.780</td><td class=\"\"> 16.0</td><td class=\"\"> 1.03e-57</td><td class=\"\"> 5.29e-55</td><td class=\"\">FSTL3</td><td class=\"\">10272</td></tr><tr><td class=\"\">   333</td><td class=\"\">-2.850</td><td class=\"\">-15.9</td><td class=\"\"> 6.93e-57</td><td class=\"\"> 3.46e-54</td><td class=\"\">WNT2</td><td class=\"\">7472</td></tr><tr><td class=\"\">   943</td><td class=\"\"> 2.210</td><td class=\"\"> 15.8</td><td class=\"\"> 3.16e-56</td><td class=\"\"> 1.53e-53</td><td class=\"\">RASL11A</td><td class=\"\">387496</td></tr><tr><td class=\"\">  5030</td><td class=\"\"> 1.880</td><td class=\"\"> 15.8</td><td class=\"\"> 4.83e-56</td><td class=\"\"> 2.29e-53</td><td class=\"\">TJP2</td><td class=\"\">9414</td></tr><tr><td class=\"\">  3700</td><td class=\"\"> 1.440</td><td class=\"\"> 15.7</td><td class=\"\"> 8.20e-56</td><td class=\"\"> 3.78e-53</td><td class=\"\">TEX2</td><td class=\"\">55852</td></tr><tr><td class=\"\"> 17200</td><td class=\"\"> 1.970</td><td class=\"\"> 15.3</td><td class=\"\"> 7.31e-53</td><td class=\"\"> 3.29e-50</td><td class=\"\">LAMA2</td><td class=\"\">3908</td></tr><tr><td class=\"\">101000</td><td class=\"\">-1.290</td><td class=\"\">-15.3</td><td class=\"\"> 1.26e-52</td><td class=\"\"> 5.52e-50</td><td class=\"\">COL1A1</td><td class=\"\">1277</td></tr><tr><td class=\"\">   847</td><td class=\"\"> 2.610</td><td class=\"\"> 15.1</td><td class=\"\"> 8.99e-52</td><td class=\"\"> 3.85e-49</td><td class=\"\">PLXNA4</td><td class=\"\">91584</td></tr><tr><td class=\"\">  2240</td><td class=\"\"> 1.810</td><td class=\"\"> 15.1</td><td class=\"\"> 1.20e-51</td><td class=\"\"> 5.01e-49</td><td class=\"\">SUN2</td><td class=\"\">25777</td></tr><tr><td class=\"\">  2020</td><td class=\"\">-1.230</td><td class=\"\">-15.1</td><td class=\"\"> 1.31e-51</td><td class=\"\"> 5.36e-49</td><td class=\"\">LHFPL2</td><td class=\"\">10184</td></tr><tr><td class=\"\">  2740</td><td class=\"\"> 1.370</td><td class=\"\"> 15.1</td><td class=\"\"> 1.40e-51</td><td class=\"\"> 5.59e-49</td><td class=\"\">PHC2</td><td class=\"\">1912</td></tr><tr><td class=\"\">   756</td><td class=\"\">-1.540</td><td class=\"\">-15.1</td><td class=\"\"> 2.07e-51</td><td class=\"\"> 8.12e-49</td><td class=\"\">GXYLT2</td><td class=\"\">727936</td></tr><tr><td class=\"\">  5400</td><td class=\"\"> 1.190</td><td class=\"\"> 15.0</td><td class=\"\"> 3.61e-51</td><td class=\"\"> 1.38e-48</td><td class=\"\">TSC22D1</td><td class=\"\">8848</td></tr><tr><td class=\"\">   364</td><td class=\"\"> 2.300</td><td class=\"\"> 15.0</td><td class=\"\"> 6.40e-51</td><td class=\"\"> 2.40e-48</td><td class=\"\">COL11A1</td><td class=\"\">1301</td></tr><tr><td class=\"\">   530</td><td class=\"\"> 2.580</td><td class=\"\"> 15.0</td><td class=\"\"> 7.93e-51</td><td class=\"\"> 2.91e-48</td><td class=\"\">KCNK6</td><td class=\"\">9424</td></tr><tr><td class=\"\">   449</td><td class=\"\"> 2.340</td><td class=\"\"> 15.0</td><td class=\"\"> 1.51e-50</td><td class=\"\"> 5.45e-48</td><td class=\"\">HSPA2</td><td class=\"\">3306</td></tr><tr><td class=\"\">   168</td><td class=\"\"> 2.750</td><td class=\"\"> 14.9</td><td class=\"\"> 2.15e-50</td><td class=\"\"> 7.59e-48</td><td class=\"\">TIMP4</td><td class=\"\">7079</td></tr><tr><td class=\"\">  3060</td><td class=\"\"> 1.510</td><td class=\"\"> 14.8</td><td class=\"\"> 7.09e-50</td><td class=\"\"> 2.45e-47</td><td class=\"\">TCEAL4</td><td class=\"\">79921</td></tr><tr><td class=\"\">   800</td><td class=\"\">-2.370</td><td class=\"\">-14.8</td><td class=\"\"> 9.35e-50</td><td class=\"\"> 3.17e-47</td><td class=\"\">ADAMTS14</td><td class=\"\">140766</td></tr><tr><td class=\"\">  3080</td><td class=\"\"> 2.500</td><td class=\"\"> 14.6</td><td class=\"\"> 1.41e-48</td><td class=\"\"> 4.70e-46</td><td class=\"\">CRISPLD2</td><td class=\"\">83716</td></tr><tr><td class=\"\">   740</td><td class=\"\">-2.050</td><td class=\"\">-14.6</td><td class=\"\"> 1.78e-48</td><td class=\"\"> 5.84e-46</td><td class=\"\">SLC6A6</td><td class=\"\">6533</td></tr><tr><td class=\"\">  1600</td><td class=\"\"> 1.840</td><td class=\"\"> 14.6</td><td class=\"\"> 3.03e-48</td><td class=\"\"> 9.73e-46</td><td class=\"\"> </td><td class=\"\"> </td></tr><tr><td class=\"\">   160</td><td class=\"\"> 3.980</td><td class=\"\"> 14.6</td><td class=\"\"> 3.86e-48</td><td class=\"\"> 1.22e-45</td><td class=\"\">FAM107A</td><td class=\"\">11170</td></tr><tr><td class=\"\">  1360</td><td class=\"\"> 1.480</td><td class=\"\"> 14.6</td><td class=\"\"> 4.10e-48</td><td class=\"\"> 1.27e-45</td><td class=\"\">CDC42EP3</td><td class=\"\">10602</td></tr><tr><td class=\"\">   197</td><td class=\"\"> 2.390</td><td class=\"\"> 14.5</td><td class=\"\"> 9.57e-48</td><td class=\"\"> 2.92e-45</td><td class=\"\">HIF3A</td><td class=\"\">64344</td></tr><tr><td class=\"\">   628</td><td class=\"\"> 2.230</td><td class=\"\"> 14.5</td><td class=\"\"> 1.87e-47</td><td class=\"\"> 5.62e-45</td><td class=\"\">IMPA2</td><td class=\"\">3613</td></tr><tr><td class=\"\">   371</td><td class=\"\">-2.090</td><td class=\"\">-14.4</td><td class=\"\"> 2.79e-47</td><td class=\"\"> 8.23e-45</td><td class=\"\">IGDCC4</td><td class=\"\">57722</td></tr><tr><td class=\"\">  2750</td><td class=\"\">-1.780</td><td class=\"\">-14.4</td><td class=\"\"> 5.25e-47</td><td class=\"\"> 1.52e-44</td><td class=\"\">TBX18</td><td class=\"\">9096</td></tr><tr><td class=\"\">  1920</td><td class=\"\"> 1.530</td><td class=\"\"> 14.3</td><td class=\"\"> 1.44e-46</td><td class=\"\"> 4.12e-44</td><td class=\"\">STEAP1</td><td class=\"\">26872</td></tr><tr><td class=\"\">  1250</td><td class=\"\"> 1.360</td><td class=\"\"> 14.2</td><td class=\"\"> 5.79e-46</td><td class=\"\"> 1.63e-43</td><td class=\"\">INPP5A</td><td class=\"\">3632</td></tr><tr><td class=\"\">  1630</td><td class=\"\"> 1.510</td><td class=\"\"> 14.1</td><td class=\"\"> 2.24e-45</td><td class=\"\"> 6.19e-43</td><td class=\"\">RAB11FIP1</td><td class=\"\">80223</td></tr><tr><td class=\"\">   436</td><td class=\"\">-2.410</td><td class=\"\">-14.0</td><td class=\"\"> 2.52e-44</td><td class=\"\"> 6.87e-42</td><td class=\"\">NOV</td><td class=\"\">4856</td></tr><tr><td class=\"\"> 12800</td><td class=\"\"> 1.340</td><td class=\"\"> 13.9</td><td class=\"\"> 3.28e-44</td><td class=\"\"> 8.82e-42</td><td class=\"\">VGLL3</td><td class=\"\">389136</td></tr><tr><td class=\"\">  4620</td><td class=\"\">-1.140</td><td class=\"\">-13.9</td><td class=\"\"> 5.58e-44</td><td class=\"\"> 1.48e-41</td><td class=\"\">C14orf132</td><td class=\"\">56967</td></tr><tr><td class=\"\">  2910</td><td class=\"\">-1.750</td><td class=\"\">-13.8</td><td class=\"\"> 1.91e-43</td><td class=\"\"> 4.99e-41</td><td class=\"\">FST</td><td class=\"\">10468</td></tr><tr><td class=\"\">   230</td><td class=\"\">-2.130</td><td class=\"\">-13.8</td><td class=\"\"> 2.00e-43</td><td class=\"\"> 5.14e-41</td><td class=\"\">SLC6A9</td><td class=\"\">6536</td></tr><tr><td class=\"\">  8020</td><td class=\"\">-0.998</td><td class=\"\">-13.8</td><td class=\"\"> 3.08e-43</td><td class=\"\"> 7.80e-41</td><td class=\"\">CDKN1A</td><td class=\"\">1026</td></tr><tr><td class=\"\">  2030</td><td class=\"\"> 1.860</td><td class=\"\"> 13.7</td><td class=\"\"> 6.24e-43</td><td class=\"\"> 1.56e-40</td><td class=\"\">ENDOD1</td><td class=\"\">23052</td></tr><tr><td class=\"\">   385</td><td class=\"\"> 4.850</td><td class=\"\"> 13.7</td><td class=\"\"> 9.14e-43</td><td class=\"\"> 2.25e-40</td><td class=\"\">ZBTB16</td><td class=\"\">7704</td></tr><tr><td class=\"\">  3890</td><td class=\"\"> 2.150</td><td class=\"\"> 13.7</td><td class=\"\"> 1.25e-42</td><td class=\"\"> 3.03e-40</td><td class=\"\">METTL7A</td><td class=\"\">25840</td></tr><tr><td class=\"\">  1810</td><td class=\"\"> 1.240</td><td class=\"\"> 13.7</td><td class=\"\"> 1.99e-42</td><td class=\"\"> 4.77e-40</td><td class=\"\">EOGT</td><td class=\"\">285203</td></tr><tr><td class=\"\">   232</td><td class=\"\"> 2.660</td><td class=\"\"> 13.6</td><td class=\"\"> 2.84e-42</td><td class=\"\"> 6.72e-40</td><td class=\"\">PPP1R14A</td><td class=\"\">94274</td></tr><tr><td class=\"\">  4910</td><td class=\"\"> 1.170</td><td class=\"\"> 13.6</td><td class=\"\"> 4.22e-42</td><td class=\"\"> 9.85e-40</td><td class=\"\">ANXA4</td><td class=\"\">307</td></tr><tr><td class=\"\">   677</td><td class=\"\">-1.790</td><td class=\"\">-13.6</td><td class=\"\"> 5.74e-42</td><td class=\"\"> 1.32e-39</td><td class=\"\">MEST</td><td class=\"\">4232</td></tr><tr><td class=\"\">   386</td><td class=\"\">-2.240</td><td class=\"\">-13.6</td><td class=\"\"> 7.56e-42</td><td class=\"\"> 1.72e-39</td><td class=\"\">AMOT</td><td class=\"\">154796</td></tr><tr><td class=\"\">  1460</td><td class=\"\"> 1.560</td><td class=\"\"> 13.5</td><td class=\"\"> 9.48e-42</td><td class=\"\"> 2.13e-39</td><td class=\"\">ACSL1</td><td class=\"\">2180</td></tr><tr><td class=\"\">   679</td><td class=\"\"> 2.130</td><td class=\"\"> 13.5</td><td class=\"\"> 1.35e-41</td><td class=\"\"> 3.00e-39</td><td class=\"\">FOXO1</td><td class=\"\">2308</td></tr><tr><td class=\"\">   451</td><td class=\"\">-1.800</td><td class=\"\">-13.5</td><td class=\"\"> 1.94e-41</td><td class=\"\"> 4.25e-39</td><td class=\"\">GDF15</td><td class=\"\">9518</td></tr><tr><td class=\"\">  1960</td><td class=\"\"> 1.470</td><td class=\"\"> 13.5</td><td class=\"\"> 1.98e-41</td><td class=\"\"> 4.29e-39</td><td class=\"\">CEBPD</td><td class=\"\">1052</td></tr><tr><td class=\"\">   554</td><td class=\"\"> 2.110</td><td class=\"\"> 13.3</td><td class=\"\"> 1.30e-40</td><td class=\"\"> 2.79e-38</td><td class=\"\">MT1E</td><td class=\"\">4493</td></tr><tr><td class=\"\"> 12600</td><td class=\"\"> 1.150</td><td class=\"\"> 13.3</td><td class=\"\"> 1.43e-40</td><td class=\"\"> 3.02e-38</td><td class=\"\">TMEM47</td><td class=\"\">83604</td></tr><tr><td class=\"\">  8440</td><td class=\"\"> 2.070</td><td class=\"\"> 13.3</td><td class=\"\"> 2.20e-40</td><td class=\"\"> 4.60e-38</td><td class=\"\">USP53</td><td class=\"\">54532</td></tr><tr><td class=\"\">  1970</td><td class=\"\"> 2.030</td><td class=\"\"> 13.3</td><td class=\"\"> 3.13e-40</td><td class=\"\"> 6.47e-38</td><td class=\"\">IRS2</td><td class=\"\">8660</td></tr><tr><td class=\"\">   244</td><td class=\"\">-2.640</td><td class=\"\">-13.2</td><td class=\"\"> 9.48e-40</td><td class=\"\"> 1.94e-37</td><td class=\"\">SLC7A14</td><td class=\"\">57709</td></tr><tr><td class=\"\">  1020</td><td class=\"\"> 1.390</td><td class=\"\"> 13.1</td><td class=\"\"> 1.99e-39</td><td class=\"\"> 4.03e-37</td><td class=\"\">RAI2</td><td class=\"\">10742</td></tr><tr><td class=\"\">  1940</td><td class=\"\"> 1.630</td><td class=\"\"> 13.0</td><td class=\"\"> 7.06e-39</td><td class=\"\"> 1.41e-36</td><td class=\"\">DAAM2</td><td class=\"\">23500</td></tr><tr><td class=\"\">   655</td><td class=\"\"> 1.880</td><td class=\"\"> 13.0</td><td class=\"\"> 8.42e-39</td><td class=\"\"> 1.66e-36</td><td class=\"\">TRPC6</td><td class=\"\">7225</td></tr><tr><td class=\"\">  3110</td><td class=\"\"> 1.190</td><td class=\"\"> 13.0</td><td class=\"\"> 1.03e-38</td><td class=\"\"> 2.02e-36</td><td class=\"\">SAMD4A</td><td class=\"\">23034</td></tr><tr><td class=\"\">  1680</td><td class=\"\"> 1.990</td><td class=\"\"> 13.0</td><td class=\"\"> 1.67e-38</td><td class=\"\"> 3.24e-36</td><td class=\"\">KLF9</td><td class=\"\">687</td></tr><tr><td class=\"\">  6850</td><td class=\"\">-1.160</td><td class=\"\">-13.0</td><td class=\"\"> 1.93e-38</td><td class=\"\"> 3.69e-36</td><td class=\"\">PDE5A</td><td class=\"\">8654</td></tr><tr><td class=\"\">  1310</td><td class=\"\"> 1.560</td><td class=\"\"> 13.0</td><td class=\"\"> 2.19e-38</td><td class=\"\"> 4.14e-36</td><td class=\"\">PDLIM5</td><td class=\"\">10611</td></tr><tr><td class=\"\">  3660</td><td class=\"\"> 1.560</td><td class=\"\"> 12.9</td><td class=\"\"> 5.97e-38</td><td class=\"\"> 1.12e-35</td><td class=\"\">LMCD1</td><td class=\"\">29995</td></tr><tr><td class=\"\">  4770</td><td class=\"\">-1.300</td><td class=\"\">-12.8</td><td class=\"\"> 1.14e-37</td><td class=\"\"> 2.11e-35</td><td class=\"\">MXRA5</td><td class=\"\">25878</td></tr><tr><td class=\"\"> 25900</td><td class=\"\">-0.991</td><td class=\"\">-12.8</td><td class=\"\"> 1.71e-37</td><td class=\"\"> 3.14e-35</td><td class=\"\">PLPP3</td><td class=\"\">8613</td></tr><tr><td class=\"\"> 13200</td><td class=\"\"> 1.420</td><td class=\"\"> 12.7</td><td class=\"\"> 4.78e-37</td><td class=\"\"> 8.69e-35</td><td class=\"\">TGFBR2</td><td class=\"\">7048</td></tr><tr><td class=\"\">  2160</td><td class=\"\"> 1.960</td><td class=\"\"> 12.7</td><td class=\"\"> 5.58e-37</td><td class=\"\"> 1.00e-34</td><td class=\"\">LINC00968</td><td class=\"\">100507632</td></tr></tbody></table><foot><tr><th class=\"filter-num sort-num-robust bottom-header-row\">baseMean</th><th class=\"filter-num sort-num-robust bottom-header-row\">log2FoldChange</th><th class=\"filter-num sort-num-robust bottom-header-row\">stat</th><th class=\"filter-num sort-num-robust bottom-header-row\">pvalue</th><th class=\"filter-num sort-num-robust bottom-header-row\">padj</th><th class=\"sort-string-robust bottom-header-row\">symbol</th><th class=\"sort-string-robust bottom-header-row\">entrez</th></tr></foot></div></div><div id=\".RTsplash\" class=\"ReportingTools container\"><br /><br /><font size=\"-2\">(Page generated on Fri Sep 22 16:18:12 2017 by <a href=\"http://research-pub.gene.com/ReportingTools/\">ReportingTools</a> 2.16.0 and <a href=\"http://www.ebi.ac.uk/~gpau/hwriter/\">hwriter</a> )</font></div></body>\n",
       "</html>"
      ],
      "text/plain": [
       "My report My report \n",
       "\n",
       "\n",
       "\n",
       "baseMean log2FoldChange stat pvalue padj symbol entrez baseMean log2FoldChange stat pvalue padj symbol entrez 997 4.310 24.9 2.22e-136 4.00e-132 SPARCL1 8404 495 3.190 24.7 7.95e-135 7.15e-131 CACNB2 783 3410 2.870 24.3 3.62e-130 2.17e-126 DUSP1 1843 12700 3.620 24.2 9.44e-130 4.25e-126 SAMHD1 25939 2340 3.230 23.7 1.09e-123 3.92e-120 MAOA 4128 12300 3.550 22.5 4.56e-112 1.37e-108 GPX3 2878 3010 1.950 22.0 5.69e-107 1.46e-103 STEAP2 261729 5390 2.000 21.6 1.30e-103 2.93e-100 NEXN 91624 3660 2.170 20.9 2.87e-97 5.74e-94 MT2A 4502 30300 2.290 20.3 3.22e-91 5.79e-88 ADAMTS1 9510 1220 2.180 19.8 7.56e-87 1.24e-83 FGD4 121512 1100 1.860 19.7 8.76e-87 1.31e-83 PDPN 10630 5510 2.150 19.6 1.44e-85 1.99e-82 SORT1 6272 777 3.040 19.5 2.13e-84 2.73e-81 PER1 5187 508 -3.450 -19.4 5.68e-84 6.81e-81 VCAM1 7412 561 4.070 19.0 2.24e-80 2.51e-77 KLF15 28999 2650 -2.450 -18.8 6.63e-79 7.01e-76 KCTD12 115207 2060 2.780 18.1 3.56e-73 3.56e-70 CCDC69 26112 25100 -1.880 -18.1 4.73e-73 4.48e-70 CXCL12 6387 1370 -1.780 -18.1 6.79e-73 6.11e-70 ADAM12 8038 314 -2.640 -17.8 7.82e-71 6.70e-68 PRSS35 167681 885 -1.730 -17.6 1.93e-69 1.58e-66 DNM1 1759 670 1.860 17.5 2.49e-68 1.95e-65 ACSS1 84532 1220 -2.120 -17.4 3.53e-68 2.65e-65 CCKAR 886 1040 1.940 17.4 1.27e-67 9.15e-65 TRNP1 388610 8570 2.190 17.3 6.33e-67 4.38e-64 PTX3 5806 1470 1.500 17.3 7.72e-67 5.14e-64 DNAJB4 11080 11200 1.430 17.2 4.64e-66 2.98e-63 STOM 2040 1280 -2.320 -17.1 8.87e-66 5.50e-63 SOX4 6659 1970 2.240 17.1 1.54e-65 9.22e-63 ADARB1 104 8010 1.710 16.9 5.50e-64 3.19e-61 SYNPO2 171024 3230 2.340 16.8 2.25e-63 1.26e-60 ERRFI1 54206 419 3.210 16.5 2.56e-61 1.39e-58 INHBB 3625 7430 2.120 16.3 1.68e-59 8.91e-57 NNMT 4837 2130 1.780 16.0 1.03e-57 5.29e-55 FSTL3 10272 333 -2.850 -15.9 6.93e-57 3.46e-54 WNT2 7472 943 2.210 15.8 3.16e-56 1.53e-53 RASL11A 387496 5030 1.880 15.8 4.83e-56 2.29e-53 TJP2 9414 3700 1.440 15.7 8.20e-56 3.78e-53 TEX2 55852 17200 1.970 15.3 7.31e-53 3.29e-50 LAMA2 3908 101000 -1.290 -15.3 1.26e-52 5.52e-50 COL1A1 1277 847 2.610 15.1 8.99e-52 3.85e-49 PLXNA4 91584 2240 1.810 15.1 1.20e-51 5.01e-49 SUN2 25777 2020 -1.230 -15.1 1.31e-51 5.36e-49 LHFPL2 10184 2740 1.370 15.1 1.40e-51 5.59e-49 PHC2 1912 756 -1.540 -15.1 2.07e-51 8.12e-49 GXYLT2 727936 5400 1.190 15.0 3.61e-51 1.38e-48 TSC22D1 8848 364 2.300 15.0 6.40e-51 2.40e-48 COL11A1 1301 530 2.580 15.0 7.93e-51 2.91e-48 KCNK6 9424 449 2.340 15.0 1.51e-50 5.45e-48 HSPA2 3306 168 2.750 14.9 2.15e-50 7.59e-48 TIMP4 7079 3060 1.510 14.8 7.09e-50 2.45e-47 TCEAL4 79921 800 -2.370 -14.8 9.35e-50 3.17e-47 ADAMTS14 140766 3080 2.500 14.6 1.41e-48 4.70e-46 CRISPLD2 83716 740 -2.050 -14.6 1.78e-48 5.84e-46 SLC6A6 6533 1600 1.840 14.6 3.03e-48 9.73e-46 160 3.980 14.6 3.86e-48 1.22e-45 FAM107A 11170 1360 1.480 14.6 4.10e-48 1.27e-45 CDC42EP3 10602 197 2.390 14.5 9.57e-48 2.92e-45 HIF3A 64344 628 2.230 14.5 1.87e-47 5.62e-45 IMPA2 3613 371 -2.090 -14.4 2.79e-47 8.23e-45 IGDCC4 57722 2750 -1.780 -14.4 5.25e-47 1.52e-44 TBX18 9096 1920 1.530 14.3 1.44e-46 4.12e-44 STEAP1 26872 1250 1.360 14.2 5.79e-46 1.63e-43 INPP5A 3632 1630 1.510 14.1 2.24e-45 6.19e-43 RAB11FIP1 80223 436 -2.410 -14.0 2.52e-44 6.87e-42 NOV 4856 12800 1.340 13.9 3.28e-44 8.82e-42 VGLL3 389136 4620 -1.140 -13.9 5.58e-44 1.48e-41 C14orf132 56967 2910 -1.750 -13.8 1.91e-43 4.99e-41 FST 10468 230 -2.130 -13.8 2.00e-43 5.14e-41 SLC6A9 6536 8020 -0.998 -13.8 3.08e-43 7.80e-41 CDKN1A 1026 2030 1.860 13.7 6.24e-43 1.56e-40 ENDOD1 23052 385 4.850 13.7 9.14e-43 2.25e-40 ZBTB16 7704 3890 2.150 13.7 1.25e-42 3.03e-40 METTL7A 25840 1810 1.240 13.7 1.99e-42 4.77e-40 EOGT 285203 232 2.660 13.6 2.84e-42 6.72e-40 PPP1R14A 94274 4910 1.170 13.6 4.22e-42 9.85e-40 ANXA4 307 677 -1.790 -13.6 5.74e-42 1.32e-39 MEST 4232 386 -2.240 -13.6 7.56e-42 1.72e-39 AMOT 154796 1460 1.560 13.5 9.48e-42 2.13e-39 ACSL1 2180 679 2.130 13.5 1.35e-41 3.00e-39 FOXO1 2308 451 -1.800 -13.5 1.94e-41 4.25e-39 GDF15 9518 1960 1.470 13.5 1.98e-41 4.29e-39 CEBPD 1052 554 2.110 13.3 1.30e-40 2.79e-38 MT1E 4493 12600 1.150 13.3 1.43e-40 3.02e-38 TMEM47 83604 8440 2.070 13.3 2.20e-40 4.60e-38 USP53 54532 1970 2.030 13.3 3.13e-40 6.47e-38 IRS2 8660 244 -2.640 -13.2 9.48e-40 1.94e-37 SLC7A14 57709 1020 1.390 13.1 1.99e-39 4.03e-37 RAI2 10742 1940 1.630 13.0 7.06e-39 1.41e-36 DAAM2 23500 655 1.880 13.0 8.42e-39 1.66e-36 TRPC6 7225 3110 1.190 13.0 1.03e-38 2.02e-36 SAMD4A 23034 1680 1.990 13.0 1.67e-38 3.24e-36 KLF9 687 6850 -1.160 -13.0 1.93e-38 3.69e-36 PDE5A 8654 1310 1.560 13.0 2.19e-38 4.14e-36 PDLIM5 10611 3660 1.560 12.9 5.97e-38 1.12e-35 LMCD1 29995 4770 -1.300 -12.8 1.14e-37 2.11e-35 MXRA5 25878 25900 -0.991 -12.8 1.71e-37 3.14e-35 PLPP3 8613 13200 1.420 12.7 4.78e-37 8.69e-35 TGFBR2 7048 2160 1.960 12.7 5.58e-37 1.00e-34 LINC00968 100507632 baseMean log2FoldChange stat pvalue padj symbol entrez \n",
       "\n",
       "\n",
       "\n",
       "(Page generated on Fri Sep 22 16:18:12 2017 by ReportingTools 2.16.0 and hwriter )"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview figures/report.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "### Plotting fold changes in genomic space\n",
    "\n",
    "If we have used the summarizeOverlaps function to count the reads, then our DESeqDataSet object is built on top of ready-to-use Bioconductor objects specifying the genomic coordinates of the genes. We can therefore easily plot our differential expression results in genomic space. While the results function by default returns a DataFrame, using the format argument, we can ask for GRanges or GRangesList output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "rowRanges is GRangesList, performing unlist(range(x)) on the rowRanges\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GRanges object with 29391 ranges and 6 metadata columns:\n",
       "                  seqnames                 ranges strand |          baseMean\n",
       "                     <Rle>              <IRanges>  <Rle> |         <numeric>\n",
       "  ENSG00000000003        X [ 99883667,  99894988]      - |  708.602169691234\n",
       "  ENSG00000000419       20 [ 49551404,  49575092]      - |  520.297900552084\n",
       "  ENSG00000000457        1 [169818772, 169863408]      - |  237.163036796015\n",
       "  ENSG00000000460        1 [169631245, 169823221]      + |  57.9326331250967\n",
       "  ENSG00000000938        1 [ 27938575,  27961788]      - | 0.318098378392895\n",
       "              ...      ...                    ...    ... .               ...\n",
       "  ENSG00000273485       10 [105209953, 105210609]      + |  1.28644765243289\n",
       "  ENSG00000273486        3 [136556180, 136557863]      - |  15.4525365439045\n",
       "  ENSG00000273487        1 [ 92654794,  92656264]      + |   8.1632349843654\n",
       "  ENSG00000273488        3 [100080031, 100080481]      + |  8.58447903624707\n",
       "  ENSG00000273489        7 [131178723, 131182453]      - | 0.275899382507797\n",
       "                       log2FoldChange             lfcSE               stat\n",
       "                            <numeric>         <numeric>          <numeric>\n",
       "  ENSG00000000003  -0.381253879698557 0.100655966717578                  0\n",
       "  ENSG00000000419   0.206812707891904 0.112221794731045                  0\n",
       "  ENSG00000000457  0.0379205016307932 0.143453214684348                  0\n",
       "  ENSG00000000460 -0.0881632238895022  0.28716770484323                  0\n",
       "  ENSG00000000938     -1.378233965407  3.49987526565862 -0.108070698724123\n",
       "              ...                 ...               ...                ...\n",
       "  ENSG00000273485  -0.127136302041973  1.60055705383304                  0\n",
       "  ENSG00000273486   -0.15099444383732 0.486549011015488                  0\n",
       "  ENSG00000273487    1.04641689863008 0.699033601462932 0.0664015270981841\n",
       "  ENSG00000273488   0.107819010749087 0.638164612529114                  0\n",
       "  ENSG00000273489    1.48372584344306  3.51394515550545   0.13765890531478\n",
       "                             pvalue      padj\n",
       "                          <numeric> <numeric>\n",
       "  ENSG00000000003                 1         1\n",
       "  ENSG00000000419                 1         1\n",
       "  ENSG00000000457                 1         1\n",
       "  ENSG00000000460                 1         1\n",
       "  ENSG00000000938 0.913939611026568      <NA>\n",
       "              ...               ...       ...\n",
       "  ENSG00000273485                 1      <NA>\n",
       "  ENSG00000273486                 1         1\n",
       "  ENSG00000273487 0.947058154439633         1\n",
       "  ENSG00000273488                 1         1\n",
       "  ENSG00000273489 0.890509998916736      <NA>\n",
       "  -------\n",
       "  seqinfo: 722 sequences (1 circular) from an unspecified genome"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resGR <- results(dds, lfcThreshold = 1, format = \"GRanges\")\n",
    "resGR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We need to add the symbol again for labeling the genes on the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'select()' returned 1:many mapping between keys and columns\n"
     ]
    }
   ],
   "source": [
    "resGR$symbol <- mapIds(org.Hs.eg.db, names(resGR), \"SYMBOL\", \"ENSEMBL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We will use the Gviz package for plotting the GRanges and associated metadata: the log fold changes due to dexamethasone treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "suppressPackageStartupMessages(library(\"Gviz\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The following code chunk specifies a window of 1 million base pairs upstream and downstream from the gene with the smallest p value. We create a subset of our full results, for genes within the window. We add the gene symbol as a name if the symbol exists and is not duplicated in our subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "window <- resGR[topGene] + 1e6\n",
    "strand(window) <- \"*\"\n",
    "\n",
    "resGRsub <- resGR[resGR %over% window]\n",
    "naOrDup <- is.na(resGRsub$symbol) | duplicated(resGRsub$symbol)\n",
    "resGRsub$group <- ifelse(naOrDup, names(resGRsub), resGRsub$symbol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We create a vector specifying if the genes in this subset had a low value of `padj`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "status <- factor(ifelse(resGRsub$padj < 0.1 & !is.na(resGRsub$padj),\n",
    "                     \"sig\", \"notsig\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We can then plot the results using Gviz functions (figure below). We create an axis track specifying our location in the genome, a track that will show the genes and their names, colored by significance, and a data track that will draw vertical bars showing the moderated log fold change produced by DESeq2, which we know are only large when the effect is well supported by the information in the counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "kernel": "R"
   },
   "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(\"figures/gviz_genes.pdf\")\n",
    "## ----gvizplot------------------------------------------------------------\n",
    "options(ucscChromosomeNames = FALSE)\n",
    "g <- GenomeAxisTrack()\n",
    "a <- AnnotationTrack(resGRsub, name = \"gene ranges\", feature = status)\n",
    "d <- DataTrack(resGRsub, data = \"log2FoldChange\", baseline = 0,\n",
    "               type = \"h\", name = \"log2 fold change\", strand = \"+\")\n",
    "plotTracks(list(g, d, a), groupAnnotation = \"group\",\n",
    "           notsig = \"grey\", sig = \"hotpink\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/gviz_genes.pdf (6.5 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/gviz_genes.pdf (6.5 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/gviz_genes.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**log2 fold changes in genomic region surrounding the gene with smallest adjusted p value.** \n",
    "Genes highlighted in pink have adjusted p value less than 0.1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Removing hidden batch effects\n",
    "\n",
    "Suppose we did not know that there were different cell lines involved in the experiment, only that there was treatment with dexamethasone. The cell line effect on the counts then would represent some hidden and unwanted variation that might be affecting many or all of the genes in the dataset. We can use statistical methods designed for RNA-seq from the SVA package (Leek 2014) to detect such groupings of the samples, and then we can add these to the DESeqDataSet design, in order to account for them. The SVA package uses the term surrogate variables for the estimated variables that we want to account for in our analysis. Another package for detecting hidden batches is the RUVSeq package (Risso et al. 2014), with the acronym “Remove Unwanted Variation”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Below we obtain a matrix of normalized counts for which the average count across samples is larger than 1. As we described above, we are trying to recover any hidden batch effects, supposing that we do not know the cell line information. So we use a full model matrix with the dex variable, and a reduced, or null, model matrix with only an intercept term. Finally we specify that we want to estimate 2 surrogate variables. For more information read the manual page for the sva-seq function by typing *?svaseq*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of significant surrogate variables is:  2 \n",
      "Iteration (out of 5 ):1  2  3  4  5  "
     ]
    }
   ],
   "source": [
    "suppressPackageStartupMessages(library(\"sva\"))\n",
    "dat  <- counts(dds, normalized = TRUE)\n",
    "idx  <- rowMeans(dat) > 1\n",
    "dat  <- dat[idx, ]\n",
    "mod  <- model.matrix(~ dex, colData(dds))\n",
    "mod0 <- model.matrix(~   1, colData(dds))\n",
    "svseq <- svaseq(dat, mod, mod0, n.sv = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td> 0.2481108 </td><td>-0.52600157</td></tr>\n",
       "\t<tr><td> 0.2629867 </td><td>-0.58115433</td></tr>\n",
       "\t<tr><td> 0.1502704 </td><td> 0.27428267</td></tr>\n",
       "\t<tr><td> 0.2023800 </td><td> 0.38419545</td></tr>\n",
       "\t<tr><td>-0.6086586 </td><td>-0.07854931</td></tr>\n",
       "\t<tr><td>-0.6101210 </td><td>-0.02923693</td></tr>\n",
       "\t<tr><td> 0.1788509 </td><td> 0.25708985</td></tr>\n",
       "\t<tr><td> 0.1761807 </td><td> 0.29937417</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  0.2481108  & -0.52600157\\\\\n",
       "\t  0.2629867  & -0.58115433\\\\\n",
       "\t  0.1502704  &  0.27428267\\\\\n",
       "\t  0.2023800  &  0.38419545\\\\\n",
       "\t -0.6086586  & -0.07854931\\\\\n",
       "\t -0.6101210  & -0.02923693\\\\\n",
       "\t  0.1788509  &  0.25708985\\\\\n",
       "\t  0.1761807  &  0.29937417\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  0.2481108  | -0.52600157 | \n",
       "|  0.2629867  | -0.58115433 | \n",
       "|  0.1502704  |  0.27428267 | \n",
       "|  0.2023800  |  0.38419545 | \n",
       "| -0.6086586  | -0.07854931 | \n",
       "| -0.6101210  | -0.02923693 | \n",
       "|  0.1788509  |  0.25708985 | \n",
       "|  0.1761807  |  0.29937417 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]       [,2]       \n",
       "[1,]  0.2481108 -0.52600157\n",
       "[2,]  0.2629867 -0.58115433\n",
       "[3,]  0.1502704  0.27428267\n",
       "[4,]  0.2023800  0.38419545\n",
       "[5,] -0.6086586 -0.07854931\n",
       "[6,] -0.6101210 -0.02923693\n",
       "[7,]  0.1788509  0.25708985\n",
       "[8,]  0.1761807  0.29937417"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svseq$sv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "Because we actually do know the cell lines, we can see how well the SVA method did at recovering these variables (figure below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>png:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{png:} 2"
      ],
      "text/markdown": [
       "**png:** 2"
      ],
      "text/plain": [
       "png \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(\"figures/sva.pdf\")\n",
    "par(mfrow = c(2, 1), mar = c(3,5,3,1))\n",
    "for (i in 1:2) {\n",
    "  stripchart(svseq$sv[, i] ~ dds$cell, vertical = TRUE, main = paste0(\"SV\", i))\n",
    "  abline(h = 0)\n",
    " }\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/sva.pdf (4.7 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/sva.pdf (4.7 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/sva.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Surrogate variables 1 and 2 plotted over cell line.** \n",
    "Here, we know the hidden source of variation (cell line), and therefore can see how the SVA procedure is able to identify a source of variation which is correlated with cell line.\n",
    "\n",
    "Finally, in order to use SVA to remove any effect on the counts from our surrogate variables, we simply add these two surrogate variables as columns to the DESeqDataSet and then add them to the design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "ddssva <- dds\n",
    "ddssva$SV1 <- svseq$sv[,1]\n",
    "ddssva$SV2 <- svseq$sv[,2]\n",
    "design(ddssva) <- ~ SV1 + SV2 + dex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We could then produce results controlling for surrogate variables by running DESeq with the new design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "using pre-existing size factors\n",
      "estimating dispersions\n",
      "found already estimated dispersions, replacing these\n",
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n",
      "fitting model and testing\n"
     ]
    }
   ],
   "source": [
    "library(\"magrittr\")\n",
    "ddssva %<>% DESeq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Time course experiments\n",
    "\n",
    "DESeq2 can be used to analyze time course experiments, for example to find those genes that react in a condition-specific manner over time, compared to a set of baseline samples. Here we demonstrate a basic time course analysis with the `fission` data package, which contains gene counts for an RNA-seq time course of `fission` yeast (Leong et al. 2014). The yeast were exposed to oxidative stress, and half of the samples contained a deletion of the gene atf21. We use a design formula that models the strain difference at time 0, the difference over time, and any strain-specific differences over time (the interaction term strain:minute)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true,
    "kernel": "R"
   },
   "outputs": [],
   "source": [
    "library(\"fission\")\n",
    "data(\"fission\")\n",
    "ddsTC <- DESeqDataSet(fission, ~ strain + minute + strain:minute)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "The following chunk of code performs a likelihood ratio test for differential analysis, where we remove the strain-specific differences over time. Genes with small p values from this test are those which at one or more time points after time 0 showed a strain-specific effect. Note therefore that this will not give small p values to genes that moved up or down over time in the same way in both strains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "kernel": "R"
   },
   "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": [
    "ddsTC <- DESeq(ddsTC, test=\"LRT\", reduced = ~ strain + minute)\n",
    "resTC <- results(ddsTC)\n",
    "resTC$symbol <- mcols(ddsTC)$symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): strainmut.minute180 \n",
       "LRT p-value: '~ strain + minute + strain:minute' vs '~ strain + minute' \n",
       "DataFrame with 4 rows and 7 columns\n",
       "              baseMean log2FoldChange     lfcSE      stat       pvalue\n",
       "             <numeric>      <numeric> <numeric> <numeric>    <numeric>\n",
       "SPBC2F12.09c  174.6712    -2.65671953 0.7522613  97.28339 1.974151e-19\n",
       "SPAC1002.18   444.5050    -0.05093214 0.2042995  56.95360 5.169552e-11\n",
       "SPAC1002.19   336.3732    -0.39274898 0.5734940  43.53391 2.879804e-08\n",
       "SPAC1002.17c  261.7731    -1.13876477 0.6061288  39.31584 2.051371e-07\n",
       "                     padj      symbol\n",
       "                <numeric> <character>\n",
       "SPBC2F12.09c 1.334526e-15       atf21\n",
       "SPAC1002.18  1.747308e-07        urg3\n",
       "SPAC1002.19  6.489157e-05        urg1\n",
       "SPAC1002.17c 3.466817e-04        urg2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(resTC[order(resTC$padj),], 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "This is just one of the tests that can be applied to time series data. Another option would be to model the counts as a smooth function of time, and to include an interaction term of the condition with the smooth function. It is possible to build such a model using spline basis functions within R, and another, more modern approach is using Gaussian processes (Tonner et al. 2017).\n",
    "\n",
    "We can plot the counts for the groups over time using ggplot2, for the gene with the smallest adjusted p value, testing for condition-dependent time profile and accounting for differences at time 0 (figure below). Keep in mind that the interaction terms are the difference between the two groups at a given time after accounting for the difference at time 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {},
     "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"
    }
   ],
   "source": [
    "pdf(\"figures/fission_counts.pdf\")\n",
    "fiss <- plotCounts(ddsTC, which.min(resTC$padj), \n",
    "                   intgroup = c(\"minute\",\"strain\"), returnData = TRUE)\n",
    "ggplot(fiss,\n",
    "  aes(x = as.numeric(minute), y = count, color = strain, group = strain)) + \n",
    "  geom_point() + geom_smooth(se = FALSE, method = \"loess\") + scale_y_log10()\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "kernel": "SoS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/fission_counts.pdf (6.0 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/fission_counts.pdf (6.0 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/fission_counts.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Normalized counts for a gene with condition-specific changes over time.**\n",
    "\n",
    "Wald tests for the log2 fold changes at individual time points can be investigated using the test argument to results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Intercept'</li>\n",
       "\t<li>'strain_mut_vs_wt'</li>\n",
       "\t<li>'minute_15_vs_0'</li>\n",
       "\t<li>'minute_30_vs_0'</li>\n",
       "\t<li>'minute_60_vs_0'</li>\n",
       "\t<li>'minute_120_vs_0'</li>\n",
       "\t<li>'minute_180_vs_0'</li>\n",
       "\t<li>'strainmut.minute15'</li>\n",
       "\t<li>'strainmut.minute30'</li>\n",
       "\t<li>'strainmut.minute60'</li>\n",
       "\t<li>'strainmut.minute120'</li>\n",
       "\t<li>'strainmut.minute180'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Intercept'\n",
       "\\item 'strain\\_mut\\_vs\\_wt'\n",
       "\\item 'minute\\_15\\_vs\\_0'\n",
       "\\item 'minute\\_30\\_vs\\_0'\n",
       "\\item 'minute\\_60\\_vs\\_0'\n",
       "\\item 'minute\\_120\\_vs\\_0'\n",
       "\\item 'minute\\_180\\_vs\\_0'\n",
       "\\item 'strainmut.minute15'\n",
       "\\item 'strainmut.minute30'\n",
       "\\item 'strainmut.minute60'\n",
       "\\item 'strainmut.minute120'\n",
       "\\item 'strainmut.minute180'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Intercept'\n",
       "2. 'strain_mut_vs_wt'\n",
       "3. 'minute_15_vs_0'\n",
       "4. 'minute_30_vs_0'\n",
       "5. 'minute_60_vs_0'\n",
       "6. 'minute_120_vs_0'\n",
       "7. 'minute_180_vs_0'\n",
       "8. 'strainmut.minute15'\n",
       "9. 'strainmut.minute30'\n",
       "10. 'strainmut.minute60'\n",
       "11. 'strainmut.minute120'\n",
       "12. 'strainmut.minute180'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Intercept\"           \"strain_mut_vs_wt\"    \"minute_15_vs_0\"     \n",
       " [4] \"minute_30_vs_0\"      \"minute_60_vs_0\"      \"minute_120_vs_0\"    \n",
       " [7] \"minute_180_vs_0\"     \"strainmut.minute15\"  \"strainmut.minute30\" \n",
       "[10] \"strainmut.minute60\"  \"strainmut.minute120\" \"strainmut.minute180\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resultsNames(ddsTC)\n",
    "res30 <- results(ddsTC, name=\"strainmut.minute30\", test=\"Wald\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "kernel": "R",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): strainmut.minute30 \n",
       "Wald test p-value: strainmut.minute30 \n",
       "DataFrame with 1 row and 6 columns\n",
       "              baseMean log2FoldChange     lfcSE      stat       pvalue\n",
       "             <numeric>      <numeric> <numeric> <numeric>    <numeric>\n",
       "SPBC2F12.09c  174.6712      -2.600469 0.6343429 -4.099469 4.140994e-05\n",
       "                  padj\n",
       "             <numeric>\n",
       "SPBC2F12.09c 0.2799312"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res30[which.min(resTC$padj),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We can furthermore cluster significant genes by their profiles. We extract a matrix of the shrunken log2 fold changes using the coef function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Intercept'</li>\n",
       "\t<li>'strain_mut_vs_wt'</li>\n",
       "\t<li>'minute_15_vs_0'</li>\n",
       "\t<li>'minute_30_vs_0'</li>\n",
       "\t<li>'minute_60_vs_0'</li>\n",
       "\t<li>'minute_120_vs_0'</li>\n",
       "\t<li>'minute_180_vs_0'</li>\n",
       "\t<li>'strainmut.minute15'</li>\n",
       "\t<li>'strainmut.minute30'</li>\n",
       "\t<li>'strainmut.minute60'</li>\n",
       "\t<li>'strainmut.minute120'</li>\n",
       "\t<li>'strainmut.minute180'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Intercept'\n",
       "\\item 'strain\\_mut\\_vs\\_wt'\n",
       "\\item 'minute\\_15\\_vs\\_0'\n",
       "\\item 'minute\\_30\\_vs\\_0'\n",
       "\\item 'minute\\_60\\_vs\\_0'\n",
       "\\item 'minute\\_120\\_vs\\_0'\n",
       "\\item 'minute\\_180\\_vs\\_0'\n",
       "\\item 'strainmut.minute15'\n",
       "\\item 'strainmut.minute30'\n",
       "\\item 'strainmut.minute60'\n",
       "\\item 'strainmut.minute120'\n",
       "\\item 'strainmut.minute180'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Intercept'\n",
       "2. 'strain_mut_vs_wt'\n",
       "3. 'minute_15_vs_0'\n",
       "4. 'minute_30_vs_0'\n",
       "5. 'minute_60_vs_0'\n",
       "6. 'minute_120_vs_0'\n",
       "7. 'minute_180_vs_0'\n",
       "8. 'strainmut.minute15'\n",
       "9. 'strainmut.minute30'\n",
       "10. 'strainmut.minute60'\n",
       "11. 'strainmut.minute120'\n",
       "12. 'strainmut.minute180'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Intercept\"           \"strain_mut_vs_wt\"    \"minute_15_vs_0\"     \n",
       " [4] \"minute_30_vs_0\"      \"minute_60_vs_0\"      \"minute_120_vs_0\"    \n",
       " [7] \"minute_180_vs_0\"     \"strainmut.minute15\"  \"strainmut.minute30\" \n",
       "[10] \"strainmut.minute60\"  \"strainmut.minute120\" \"strainmut.minute180\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "betas <- coef(ddsTC)\n",
    "colnames(betas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "We can now plot the log2 fold changes in a heatmap (figure below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "kernel": "R"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found more than one class \"unit\" in cache; using the first, from namespace 'hexbin'\n",
      "Also defined by 'ggbio'\n",
      "Found more than one class \"unit\" in cache; using the first, from namespace 'hexbin'\n",
      "Also defined by 'ggbio'\n",
      "Found more than one class \"unit\" in cache; using the first, from namespace 'hexbin'\n",
      "Also defined by 'ggbio'\n",
      "Found more than one class \"unit\" in cache; using the first, from namespace 'hexbin'\n",
      "Also defined by 'ggbio'\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": [
    "pdf(\"figures/fission_heatmap.pdf\")\n",
    "topGenes <- head(order(resTC$padj),20)\n",
    "mat <- betas[topGenes, -c(1,2)]\n",
    "thr <- 3 \n",
    "mat[mat < -thr] <- -thr\n",
    "mat[mat > thr] <- thr\n",
    "pheatmap(mat, breaks=seq(from=-thr, to=thr, length=101),\n",
    "         cluster_col=FALSE)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"sos_hint\">> figures/fission_heatmap.pdf (7.4 KiB):</div>"
      ],
      "text/plain": [
       "\n",
       "> figures/fission_heatmap.pdf (7.4 KiB):"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%preview -s png figures/fission_heatmap.pdf --pages 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "**Heatmap of log2 fold changes for genes with smallest adjusted p value.**\n",
    "\n",
    "The bottom set of genes show strong induction of expression for the baseline samples in minutes 15-60 (red boxes in the bottom left corner), but then have slight differences for the mutant strain (shown in the boxes in the bottom right corner)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "kernel": "SoS"
   },
   "source": [
    "## Session Information\n",
    "\n",
    "Similar to R's `sessionInfo()` function, SoS notebook has a `%sessioninfo` magic that documents the languages and version of packages it uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "kernel": "SoS",
    "tags": [
     "report_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p class=\"session_section\">SoS</p>\n",
       "<table class=\"session_info\">\n",
       "<tr>\n",
       "<th>SoS Version</th><td><pre>0.9.9.1</pre></td>\n",
       "</tr>\n",
       "</table>\n",
       "<p class=\"session_section\">R</p>\n",
       "<table class=\"session_info\">\n",
       "<tr>\n",
       "<th>Kernel</th><td><pre>ir</pre></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<th>Language</th><td><pre>R</pre></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td colspan=\"2\"><pre>R version 3.4.0 (2017-04-21)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: BunsenLabs GNU/Linux 8.9 (Hydrogen)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS: /usr/lib64/microsoft-r/3.4/lib64/R/lib/libRblas.so\n",
       "LAPACK: /usr/lib64/microsoft-r/3.4/lib64/R/lib/libRlapack.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       " [1] grid      parallel  stats4    stats     graphics  grDevices utils    \n",
       " [8] datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       " [1] fission_0.110.0            magrittr_1.5              \n",
       " [3] sva_3.24.4                 mgcv_1.8-17               \n",
       " [5] nlme_3.1-131               Gviz_1.20.0               \n",
       " [7] ReportingTools_2.16.0      knitr_1.17                \n",
       " [9] org.Hs.eg.db_3.4.1         genefilter_1.58.1         \n",
       "[11] ggbeeswarm_0.5.3           PoiClaClu_1.0.2           \n",
       "[13] RColorBrewer_1.1-2         pheatmap_1.0.8            \n",
       "[15] ggplot2_2.2.1              dplyr_0.7.3               \n",
       "[17] hexbin_1.27.1              DESeq2_1.16.1             \n",
       "[19] BiocParallel_1.10.1        GenomicAlignments_1.12.2  \n",
       "[21] GenomicFeatures_1.28.5     AnnotationDbi_1.38.2      \n",
       "[23] Rsamtools_1.28.0           Biostrings_2.44.2         \n",
       "[25] XVector_0.16.0             airway_0.110.0            \n",
       "[27] SummarizedExperiment_1.6.4 DelayedArray_0.2.7        \n",
       "[29] matrixStats_0.52.2         Biobase_2.36.2            \n",
       "[31] GenomicRanges_1.28.5       GenomeInfoDb_1.12.2       \n",
       "[33] IRanges_2.10.3             S4Vectors_0.14.4          \n",
       "[35] BiocGenerics_0.22.0        RevoUtilsMath_10.0.0      \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] uuid_0.1-2                    backports_1.1.0              \n",
       "  [3] GOstats_2.42.0                Hmisc_4.0-3                  \n",
       "  [5] AnnotationHub_2.8.2           plyr_1.8.4                   \n",
       "  [7] repr_0.12.0                   lazyeval_0.2.0               \n",
       "  [9] GSEABase_1.38.2               splines_3.4.0                \n",
       " [11] digest_0.6.12                 BiocInstaller_1.26.1         \n",
       " [13] ensembldb_2.0.4               htmltools_0.3.6              \n",
       " [15] GO.db_3.4.1                   checkmate_1.8.3              \n",
       " [17] memoise_1.1.0                 BSgenome_1.44.1              \n",
       " [19] cluster_2.0.6                 limma_3.32.6                 \n",
       " [21] annotate_1.54.0               R.utils_2.5.0                \n",
       " [23] ggbio_1.24.1                  colorspace_1.3-2             \n",
       " [25] blob_1.1.0                    crayon_1.3.4                 \n",
       " [27] RCurl_1.95-4.8                jsonlite_1.4                 \n",
       " [29] graph_1.54.0                  bindr_0.1                    \n",
       " [31] survival_2.41-3               VariantAnnotation_1.22.3     \n",
       " [33] glue_1.1.1                    RevoUtils_10.0.4             \n",
       " [35] gtable_0.2.0                  zlibbioc_1.22.0              \n",
       " [37] scales_0.5.0                  vsn_3.44.0                   \n",
       " [39] DBI_0.7                       GGally_1.3.2                 \n",
       " [41] edgeR_3.18.1                  Rcpp_0.12.12                 \n",
       " [43] xtable_1.8-2                  htmlTable_1.9                \n",
       " [45] foreign_0.8-67                bit_1.1-12                   \n",
       " [47] OrganismDbi_1.18.0            preprocessCore_1.38.1        \n",
       " [49] Formula_1.2-2                 AnnotationForge_1.18.2       \n",
       " [51] htmlwidgets_0.9               httr_1.3.1                   \n",
       " [53] acepack_1.4.1                 pkgconfig_2.0.1              \n",
       " [55] reshape_0.8.7                 XML_3.98-1.9                 \n",
       " [57] R.methodsS3_1.7.1             nnet_7.3-12                  \n",
       " [59] locfit_1.5-9.1                labeling_0.3                 \n",
       " [61] rlang_0.1.2                   reshape2_1.4.2               \n",
       " [63] munsell_0.4.3                 tools_3.4.0                  \n",
       " [65] RSQLite_2.0                   evaluate_0.10.1              \n",
       " [67] stringr_1.2.0                 yaml_2.1.14                  \n",
       " [69] bit64_0.9-7                   AnnotationFilter_1.0.0       \n",
       " [71] bindrcpp_0.2                  RBGL_1.52.0                  \n",
       " [73] mime_0.5                      R.oo_1.21.0                  \n",
       " [75] biomaRt_2.32.1                compiler_3.4.0               \n",
       " [77] beeswarm_0.2.3                curl_2.8.1                   \n",
       " [79] interactiveDisplayBase_1.14.0 affyio_1.46.0                \n",
       " [81] PFAM.db_3.4.1                 tibble_1.3.4                 \n",
       " [83] geneplotter_1.54.0            stringi_1.1.5                \n",
       " [85] lattice_0.20-35               IRdisplay_0.4.4              \n",
       " [87] ProtGenerics_1.8.0            Matrix_1.2-9                 \n",
       " [89] data.table_1.10.4             bitops_1.0-6                 \n",
       " [91] httpuv_1.3.3                  rtracklayer_1.36.4           \n",
       " [93] R6_2.2.0                      latticeExtra_0.6-28          \n",
       " [95] affy_1.54.0                   hwriter_1.3.2                \n",
       " [97] gridExtra_2.3                 vipor_0.4.5                  \n",
       " [99] dichromat_2.0-0               assertthat_0.2.0             \n",
       "[101] Category_2.42.1               GenomeInfoDbData_0.99.0      \n",
       "[103] rpart_4.1-11                  IRkernel_0.8.7.9000          \n",
       "[105] biovizBase_1.24.0             pbdZMQ_0.2-6                 \n",
       "[107] shiny_1.0.3                   base64enc_0.1-3              </pre></td>\n",
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    "\n",
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    "\n",
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    "\n"
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