From 6a624fb37270c2c375efe12c65569ce2f16deeda Mon Sep 17 00:00:00 2001
From: Marcel Kempenaar <m.kempenaar@pl.hanze.nl>
Date: Wed, 8 May 2024 10:54:05 +0200
Subject: [PATCH] Added appendix section about mapping RNA-Seq reads using STAR

---
 docs/404.html                                 |   3 +-
 docs/EDA.html                                 |   7 +-
 docs/a1-data_loading.html                     |   3 +-
 docs/a2-annotation.html                       |  25 +-
 docs/a3-read-mapping.html                     | 565 ++++++++++++++++++
 docs/chapter-1.html                           | 249 --------
 docs/chapter-4.html                           |   3 +-
 docs/chapter-5.html                           |   3 +-
 docs/datasets.html                            |   5 +-
 .../from-reads-to-counts-a3-read-mapping.html | 565 ++++++++++++++++++
 docs/index.html                               |   3 +-
 docs/reference-keys.txt                       |  10 +
 docs/search.json                              |   2 +-
 13 files changed, 1161 insertions(+), 282 deletions(-)
 create mode 100644 docs/a3-read-mapping.html
 delete mode 100644 docs/chapter-1.html
 create mode 100644 docs/from-reads-to-counts-a3-read-mapping.html

diff --git a/docs/404.html b/docs/404.html
index 9539158..e3f6978 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -99,7 +100,7 @@ <h1>Page not found<a class="anchor" aria-label="anchor" href="#page-not-found"><
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/EDA.html b/docs/EDA.html
index a329cfb..e9e304a 100644
--- a/docs/EDA.html
+++ b/docs/EDA.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -107,7 +108,7 @@ <h2>
 <strong>Note</strong>: if the data set consists of separate files (i.e. one per sample) or for general tips on reading in data, see the <a href="a1-data_loading.html#a1-batch_data_loading">Appendix A: <em>Batch Loading Expression Data</em></a> chapter.</li>
 <li>For the remainder of the document, try to show either the contents, structure or - in this case - dimensions of relevant R objects
 <ul>
-<li>Show the first five lines of the loaded data set. Including tables in a markdown document can be done using the <code>pander</code> function from the <code>pander</code><span class="citation">(<a href="a2-annotation.html#ref-R-pander">Daróczi and Tsegelskyi 2017</a>)</span> R-library.</li>
+<li>Show the first five lines of the loaded data set. Including tables in a markdown document can be done using the <code>pander</code> function from the <code>pander</code><span class="citation">(<a href="a3-read-mapping.html#ref-R-pander">Daróczi and Tsegelskyi 2017</a>)</span> R-library.</li>
 <li>Give the dimensions (with <code><a href="https://rdrr.io/r/base/dim.html">dim()</a></code> and the structure (with <code><a href="https://rdrr.io/r/utils/str.html">str()</a></code>) of the loaded data set.</li>
 <li>Check the output of the <code>str</code> function to see if all columns are of the expected R data type (e.g. <code>values</code>, <code>factors</code>, <code>character</code>, etc.)</li>
 </ul>
@@ -308,7 +309,7 @@ <h2>
 <span class="header-section-number">3.4</span> Visualizing using <code>heatmap</code> and <code>MDS</code><a class="anchor" aria-label="anchor" href="#EDA_part2"><i class="fas fa-link"></i></a>
 </h2>
 <p>This section adds a few Exploratory Data Analysis techniques where we will measure and look at <em>distances</em> between samples based on <em>normalized</em> data. Measuring distances between two data objects (samples in our case) is a common task in cluster analysis to compare similarity (low distance indicates similar data). In this case we will calculate the distances between our samples and visualize them in a <code>heatmap</code> and using a <code>multidimensional scaling</code> (MDS) technique.</p>
-<p>In the previous section we used the raw count data. You might have one or more samples that have different values (i.e. shifted) compared to other samples. While we need the raw count data to use R packages such as <code>edgeR</code> <span class="citation">(<a href="a2-annotation.html#ref-R-edgeR">Chen et al. 2018</a>)</span> and <code>DESeq2</code> <span class="citation">(<a href="a2-annotation.html#ref-R-DESeq2">Love, Anders, and Huber 2017</a>)</span>, calculating sample distances (used in the visualizations in this section) should be done on some form of normalized data. This data can either be RPKM/FPKM/TPM/CPM or vst-transformed (raw-)read counts. A proper method of transforming raw read count data is using the <code>vst</code> method from the <code>DESeq2</code> R Bioconductor library which is shown below. This ‘<em>variance stabilizing transformation</em>’ normalized data will only be used in this chapter, in chapter 4 we will again normalize using a different technique.</p>
+<p>In the previous section we used the raw count data. You might have one or more samples that have different values (i.e. shifted) compared to other samples. While we need the raw count data to use R packages such as <code>edgeR</code> <span class="citation">(<a href="a3-read-mapping.html#ref-R-edgeR">Chen et al. 2018</a>)</span> and <code>DESeq2</code> <span class="citation">(<a href="a3-read-mapping.html#ref-R-DESeq2">Love, Anders, and Huber 2017</a>)</span>, calculating sample distances (used in the visualizations in this section) should be done on some form of normalized data. This data can either be RPKM/FPKM/TPM/CPM or vst-transformed (raw-)read counts. A proper method of transforming raw read count data is using the <code>vst</code> method from the <code>DESeq2</code> R Bioconductor library which is shown below. This ‘<em>variance stabilizing transformation</em>’ normalized data will only be used in this chapter, in chapter 4 we will again normalize using a different technique.</p>
 <p>The following code examples shows how to use this library to normalize the count data from the <strong>GSE101942</strong> experiment to vst-normalized data before we calculate a distance metric.</p>
 <ul>
 <li>new(“standardGeneric”, .Data = function (object, …)</li>
@@ -714,7 +715,7 @@ <h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/a1-data_loading.html b/docs/a1-data_loading.html
index 954df6a..eb8abe9 100644
--- a/docs/a1-data_loading.html
+++ b/docs/a1-data_loading.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="active" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -594,7 +595,7 @@ <h3>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/a2-annotation.html b/docs/a2-annotation.html
index 100bca4..5b02fcd 100644
--- a/docs/a2-annotation.html
+++ b/docs/a2-annotation.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="active" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -848,32 +849,13 @@ <h3>
 <span><span class="va">results</span><span class="op">$</span><span class="va">gene_length</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html">abs</a></span><span class="op">(</span><span class="va">results</span><span class="op">$</span><span class="va">end_position</span> <span class="op">-</span> <span class="va">results</span><span class="op">$</span><span class="va">start_position</span><span class="op">)</span></span></code></pre></div>
 <p>The <code>results</code> object is a <code>data.frame</code> with 5 columns that we can merge with our data set giving us the following annotation columns (combined from the <code>AnnotationDBI</code> and <code>biomaRt</code> libraries).</p>
 <p>This was just an example on how to use the <code>biomaRt</code> library and it comes down to selecting the correct filter and looking for interesting attributes to retrieve. Further information can be found in the documentation avaialble with `vignette(‘biomaRt’)</p>
-</div>
-</div>
-<div id="references" class="section level2" number="7.3">
-<h2>
-<span class="header-section-number">B.3</span> References<a class="anchor" aria-label="anchor" href="#references"><i class="fas fa-link"></i></a>
-</h2>
 
-<div id="refs" class="references csl-bib-body hanging-indent">
-<div id="ref-deelen15" class="csl-entry">
-al., Patrick Deelen et. 2015. <span>“Calling Genotypes from Public RNA-Sequencing Data Enables Identification of Genetic Variants That Affect Gene-Expression Levels.”</span> <em>Genome Medicine</em> 7 (30).
-</div>
-<div id="ref-R-edgeR" class="csl-entry">
-Chen, Yunshun, Aaron Lun, Davis McCarthy, Xiaobei Zhou, Mark Robinson, and Gordon Smyth. 2018. <em>edgeR: Empirical Analysis of Digital Gene Expression Data in r</em>. <a href="http://bioinf.wehi.edu.au/edgeR">http://bioinf.wehi.edu.au/edgeR</a>.
-</div>
-<div id="ref-R-pander" class="csl-entry">
-Daróczi, Gergely, and Roman Tsegelskyi. 2017. <em>Pander: An r ’Pandoc’ Writer</em>. <a href="https://CRAN.R-project.org/package=pander">https://CRAN.R-project.org/package=pander</a>.
-</div>
-<div id="ref-R-DESeq2" class="csl-entry">
-Love, Michael, Simon Anders, and Wolfgang Huber. 2017. <em>DESeq2: Differential Gene Expression Analysis Based on the Negative Binomial Distribution</em>. <a href="https://github.com/mikelove/DESeq2">https://github.com/mikelove/DESeq2</a>.
-</div>
 </div>
 </div>
 </div>
   <div class="chapter-nav">
 <div class="prev"><a href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></div>
-<div class="empty"></div>
+<div class="next"><a href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></div>
 </div></main><div class="col-md-3 col-lg-2 d-none d-md-block sidebar sidebar-chapter">
     <nav id="toc" data-toggle="toc" aria-label="On this page"><h2>On this page</h2>
       <ul class="nav navbar-nav">
@@ -885,7 +867,6 @@ <h2>
 <li><a class="nav-link" href="#using-biomart"><span class="header-section-number">B.2.2</span> Using biomaRt</a></li>
 </ul>
 </li>
-<li><a class="nav-link" href="#references"><span class="header-section-number">B.3</span> References</a></li>
 </ul>
 
       <div class="book-extra">
@@ -903,7 +884,7 @@ <h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/a3-read-mapping.html b/docs/a3-read-mapping.html
new file mode 100644
index 0000000..4af7bae
--- /dev/null
+++ b/docs/a3-read-mapping.html
@@ -0,0 +1,565 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
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+<title>C From Reads to Counts | Capstone Project - Gene Expression Analysis</title>
+<meta name="author" content="Marcel Kempenaar">
+<meta name="description" content="This chapter can be used to gain insight into how the count files are generated. Step one in any RNA-Seq data analysis is generating the gene or transcript counts for each sample. This overlaps...">
+<meta name="generator" content="bookdown 0.36 with bs4_book()">
+<meta property="og:title" content="C From Reads to Counts | Capstone Project - Gene Expression Analysis">
+<meta property="og:type" content="book">
+<meta property="og:description" content="This chapter can be used to gain insight into how the count files are generated. Step one in any RNA-Seq data analysis is generating the gene or transcript counts for each sample. This overlaps...">
+<meta name="twitter:card" content="summary">
+<meta name="twitter:title" content="C From Reads to Counts | Capstone Project - Gene Expression Analysis">
+<meta name="twitter:description" content="This chapter can be used to gain insight into how the count files are generated. Step one in any RNA-Seq data analysis is generating the gene or transcript counts for each sample. This overlaps...">
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+
+    <div class="d-flex align-items-start justify-content-between">
+      <h1>
+        <a href="index.html" title="">Capstone Project - Gene Expression Analysis</a>
+      </h1>
+      <button class="btn btn-outline-primary d-lg-none ml-2 mt-1" type="button" data-toggle="collapse" data-target="#main-nav" aria-expanded="true" aria-controls="main-nav"><i class="fas fa-bars"></i><span class="sr-only">Show table of contents</span></button>
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+      <nav aria-label="Table of contents"><h2>Table of contents</h2>
+        <ul class="book-toc list-unstyled">
+<li><a class="" href="index.html"><span class="header-section-number">1</span> Introduction</a></li>
+<li><a class="" href="datasets.html"><span class="header-section-number">2</span> Discovering Data Sets</a></li>
+<li><a class="" href="EDA.html"><span class="header-section-number">3</span> Exploratory Data Analysis</a></li>
+<li><a class="" href="chapter-4.html"><span class="header-section-number">4</span> Discovering Differentialy Expressed Genes (DEGs)</a></li>
+<li><a class="" href="chapter-5.html"><span class="header-section-number">5</span> Data Analysis and Visualization</a></li>
+<li class="book-part">Appendix</li>
+<li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
+<li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="active" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
+</ul>
+
+        <div class="book-extra">
+          <p><a id="book-repo" href="https://github.com/rstudio/bookdown-demo">View book source <i class="fab fa-github"></i></a></p>
+        </div>
+      </nav>
+</div>
+  </header><main class="col-sm-12 col-md-9 col-lg-7" id="content"><div id="a3-read-mapping" class="section level1" number="8">
+<h1>
+<span class="header-section-number">C</span> From Reads to Counts<a class="anchor" aria-label="anchor" href="#a3-read-mapping"><i class="fas fa-link"></i></a>
+</h1>
+<p>This chapter can be used to gain insight into how the count files are generated. Step one in any RNA-Seq data analysis is generating the gene or transcript counts for each sample. This overlaps partly with steps taken in a genomics workflow (that we performed in Galaxy):</p>
+<ul>
+<li>Download SRA files and extract FastQ files</li>
+<li>Perform quality control on raw reads (FastQC)</li>
+<li>Perform trimming as needed (Trimmomatic)</li>
+<li>Perform quality control post trimming (FastQC)</li>
+<li>Perform mapping against reference genome (most often STAR)</li>
+<li>Generate gene/ transcript counts (either STAR, HTseq or featurecounts)</li>
+<li>
+<em>Downstream analysis</em> (starting from chapter 3 of this document)</li>
+</ul>
+<p>This chapter shows these steps for an example data set (<code>GSE149995</code>)</p>
+<div class="rmdtip">
+<p>
+<strong>Note about storage</strong>:
+</p>
+<p>
+The fastq-files used for an RNA-Seq experiment can be fairly large
+and depending on the number of samples, the total storage required for
+downloading and processing these files can run into multiple terrabytes.
+For students there is enough storage in the <code>/students/</code>
+folder. Please create a new directory in the
+<code>/students/202x-202x/minor/</code> folder that will be used for all
+the following steps. Note that you need to replace the
+<code>/local-fs/staff/marcel/</code> path with the path to your own
+directory.
+</p>
+<p>
+Furthermore, it is adviced (or basically required) to run all
+commands on the <code>assemblix</code> server as that has the capacity
+to process multiple samples efficiently in parallel.
+</p>
+</div>
+<div id="downloading-and-extracting-fastq-files" class="section level2" number="8.1">
+<h2>
+<span class="header-section-number">C.1</span> Downloading and extracting FastQ files<a class="anchor" aria-label="anchor" href="#downloading-and-extracting-fastq-files"><i class="fas fa-link"></i></a>
+</h2>
+<p>Given the SRA identifier for this project, select all samples and download as accession file (SRX identifiers). Using these identifiers as input for the <code>prefetch</code> system command (requires the NCBI SRA-Toolkit installed) downloads the SRA files and we convert them using the <code>fasterq-dump</code> command to fastq files.</p>
+<div class="sourceCode" id="cb76"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb76-1"><a href="a3-read-mapping.html#cb76-1" tabindex="-1"></a><span class="ex">prefetch</span> <span class="va">$(</span><span class="op">&lt;</span>GSE149995_SRA_ACC_RNA-Seq.csv<span class="va">)</span> <span class="at">--output-directory</span> <span class="dt">\</span></span>
+<span id="cb76-2"><a href="a3-read-mapping.html#cb76-2" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/SRA</span></code></pre></div>
+<p>The <code>fasterq-dump</code> by default performs a <code>split 3</code> operation, see <a href="https://github.com/ncbi/sra-tools/wiki/HowTo:-fasterq-dump">the SRA toolkit manual</a> for further details.</p>
+<div class="sourceCode" id="cb77"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb77-1"><a href="a3-read-mapping.html#cb77-1" tabindex="-1"></a><span class="fu">find</span> /local-fs/staff/marcel/GSE149995/SRA/ <span class="at">-name</span> <span class="st">"*.sra"</span> <span class="kw">|</span> <span class="dt">\</span></span>
+<span id="cb77-2"><a href="a3-read-mapping.html#cb77-2" tabindex="-1"></a>  <span class="ex">parallel</span> <span class="st">'fasterq-dump -O /local-fs/staff/marcel/GSE149995/fastq/ {}'</span></span></code></pre></div>
+</div>
+<div id="optional-annotation" class="section level2" number="8.2">
+<h2>
+<span class="header-section-number">C.2</span> (Optional) Annotation<a class="anchor" aria-label="anchor" href="#optional-annotation"><i class="fas fa-link"></i></a>
+</h2>
+<p>The NCBI GEO page lists 9 samples with 3 conditions (<code>WT</code>, <code>potent</code> and <code>arf7</code>). However, this experiment includes 18 sequence files. By downloading the ‘series matrix file’ from the GEO (using the <code>GEOquery</code> library) and combining this with the run info downloadable from the SRA, we can list the mapping of run to sample.</p>
+<p><strong>Note</strong>: This is an optional step for now and is only useful when there are doubts about the group/sample setup.</p>
+<div class="sourceCode" id="cb78"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/seandavi/GEOquery">GEOquery</a></span><span class="op">)</span></span>
+<span><span class="va">gse</span> <span class="op">&lt;-</span> <span class="fu"><a href="http://seandavi.github.io/GEOquery/reference/getGEO.html">getGEO</a></span><span class="op">(</span>GEO <span class="op">=</span> <span class="st">"GSE149995"</span><span class="op">)</span></span>
+<span><span class="co"># Combine the condition with a sample number and replicate number</span></span>
+<span><span class="va">Condition</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/paste.html">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="va">gse</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">phenoData</span><span class="op">@</span><span class="va">data</span><span class="op">$</span><span class="va">`genotype/variation:ch1`</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
+<span>                  <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
+<span>                  <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">'r'</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">'_'</span><span class="op">)</span></span>
+<span><span class="va">run_info</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html">read.csv</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"data/GSE149995_Sra_RunInfo.csv"</span><span class="op">)</span></span>
+<span><span class="va">setup</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html">cbind</a></span><span class="op">(</span><span class="va">run_info</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html">%&gt;%</a></span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">Run</span>, <span class="va">Experiment</span><span class="op">)</span>, <span class="va">Condition</span><span class="op">)</span></span>
+<span><span class="fu">pander</span><span class="op">(</span><span class="va">setup</span><span class="op">)</span></span></code></pre></div>
+<div class="inline-table"><table style="width:57%;" class="table table-sm">
+<colgroup>
+<col width="19%">
+<col width="18%">
+<col width="19%">
+</colgroup>
+<thead><tr class="header">
+<th align="center">Run</th>
+<th align="center">Experiment</th>
+<th align="center">Condition</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="center">SRR11714145</td>
+<td align="center">SRX8273521</td>
+<td align="center">WT_1_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714146</td>
+<td align="center">SRX8273521</td>
+<td align="center">WT_1_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714147</td>
+<td align="center">SRX8273522</td>
+<td align="center">WT_2_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714148</td>
+<td align="center">SRX8273522</td>
+<td align="center">WT_2_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714149</td>
+<td align="center">SRX8273523</td>
+<td align="center">WT_3_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714150</td>
+<td align="center">SRX8273523</td>
+<td align="center">WT_3_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714151</td>
+<td align="center">SRX8273524</td>
+<td align="center">potent_1_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714152</td>
+<td align="center">SRX8273524</td>
+<td align="center">potent_1_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714153</td>
+<td align="center">SRX8273525</td>
+<td align="center">potent_2_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714154</td>
+<td align="center">SRX8273525</td>
+<td align="center">potent_2_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714155</td>
+<td align="center">SRX8273526</td>
+<td align="center">potent_3_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714156</td>
+<td align="center">SRX8273526</td>
+<td align="center">potent_3_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714157</td>
+<td align="center">SRX8273527</td>
+<td align="center">arf7_1_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714158</td>
+<td align="center">SRX8273527</td>
+<td align="center">arf7_1_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714159</td>
+<td align="center">SRX8273528</td>
+<td align="center">arf7_2_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714160</td>
+<td align="center">SRX8273528</td>
+<td align="center">arf7_2_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714143</td>
+<td align="center">SRX8273529</td>
+<td align="center">arf7_3_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714144</td>
+<td align="center">SRX8273529</td>
+<td align="center">arf7_3_r2</td>
+</tr>
+</tbody>
+</table></div>
+</div>
+<div id="quality-control" class="section level2" number="8.3">
+<h2>
+<span class="header-section-number">C.3</span> Quality Control<a class="anchor" aria-label="anchor" href="#quality-control"><i class="fas fa-link"></i></a>
+</h2>
+<p>Here we perform the FastQC -&gt; Trimmomatic -&gt; FastQC steps</p>
+<div id="fastqc" class="section level3" number="8.3.1">
+<h3>
+<span class="header-section-number">C.3.1</span> FastQC<a class="anchor" aria-label="anchor" href="#fastqc"><i class="fas fa-link"></i></a>
+</h3>
+<p>Running FastQC</p>
+<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fqdir</span> <span class="op">&lt;-</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fastq/"</span></span>
+<span><span class="fu">fastqc</span><span class="op">(</span>fq.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fastq/"</span>,</span>
+<span>       qc.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fqc"</span>,</span>
+<span>       fastqc.path <span class="op">=</span> <span class="st">"/usr/bin/fastqc"</span>,</span>
+<span>       threads <span class="op">=</span> <span class="fl">32</span><span class="op">)</span></span></code></pre></div>
+<p>QC-report using <code>multiqc</code></p>
+<div class="sourceCode" id="cb80"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb80-1"><a href="a3-read-mapping.html#cb80-1" tabindex="-1"></a><span class="ex">multiqc</span> <span class="dt">\</span></span>
+<span id="cb80-2"><a href="a3-read-mapping.html#cb80-2" tabindex="-1"></a>  <span class="at">--filename</span> ~/Development/2.1.2-Transcriptomics/GSE149995_FQC_multiqc_report.html <span class="dt">\</span></span>
+<span id="cb80-3"><a href="a3-read-mapping.html#cb80-3" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/fqc/ </span></code></pre></div>
+</div>
+</div>
+<div id="trimming" class="section level2" number="8.4">
+<h2>
+<span class="header-section-number">C.4</span> Trimming<a class="anchor" aria-label="anchor" href="#trimming"><i class="fas fa-link"></i></a>
+</h2>
+<p>Proper trimming requires experimentation and research regarding the used sequencing technology. The following command uses Trimmomatic (release 0.39, available on <a href="https://github.com/usadellab/Trimmomatic/releases">Github</a>) to trim all fastq files. Note that this example is for <em>single-end</em> sequencing only, use the <code>TrimmomaticPE</code> command for paired-end data processing and the <code>TruSeq</code> FASTA file ending in <code>PE</code> instead.</p>
+<div class="sourceCode" id="cb81"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb81-1"><a href="a3-read-mapping.html#cb81-1" tabindex="-1"></a><span class="fu">cat</span> data/GSE149995_Sra_RunInfo.csv <span class="kw">|</span> <span class="dt">\</span></span>
+<span id="cb81-2"><a href="a3-read-mapping.html#cb81-2" tabindex="-1"></a>  <span class="ex">parallel</span> <span class="st">'TrimmomaticSE -threads 4 '</span> <span class="dt">\</span></span>
+<span id="cb81-3"><a href="a3-read-mapping.html#cb81-3" tabindex="-1"></a>                  <span class="st">'/local-fs/staff/marcel/GSE149995/fastq/{}.fastq.gz '</span> <span class="dt">\</span></span>
+<span id="cb81-4"><a href="a3-read-mapping.html#cb81-4" tabindex="-1"></a>                  <span class="st">'/local-fs/staff/marcel/GSE149995/fastq-trimmed/{}.trimmed.fastq.gz '</span> <span class="dt">\</span></span>
+<span id="cb81-5"><a href="a3-read-mapping.html#cb81-5" tabindex="-1"></a>                  <span class="st">'ILLUMINACLIP:/homes/marcelk/Development/2.1.2-Transcriptomics/TruSeq3-SE.fa:2:30:10 '</span> <span class="dt">\</span></span>
+<span id="cb81-6"><a href="a3-read-mapping.html#cb81-6" tabindex="-1"></a>                  <span class="st">'MINLEN:40 '</span> <span class="dt">\</span></span>
+<span id="cb81-7"><a href="a3-read-mapping.html#cb81-7" tabindex="-1"></a>                  <span class="st">'SLIDINGWINDOW:4:20'</span></span></code></pre></div>
+<p>Re-running FastQC</p>
+<div class="sourceCode" id="cb82"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">fastqc</span><span class="op">(</span>fq.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fastq-trimmed/"</span>,</span>
+<span>       qc.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fqc-trimmed"</span>,</span>
+<span>       fastqc.path <span class="op">=</span> <span class="st">"/usr/bin/fastqc"</span>,</span>
+<span>       threads <span class="op">=</span> <span class="fl">32</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb83"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb83-1"><a href="a3-read-mapping.html#cb83-1" tabindex="-1"></a><span class="ex">multiqc</span> <span class="at">--filename</span> <span class="dt">\</span></span>
+<span id="cb83-2"><a href="a3-read-mapping.html#cb83-2" tabindex="-1"></a>  ~/Development/2.1.2-Transcriptomics/GSE149995_FQC_Trimmed.html <span class="dt">\</span></span>
+<span id="cb83-3"><a href="a3-read-mapping.html#cb83-3" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/fqc-trimmed/</span></code></pre></div>
+</div>
+<div id="mapping-with-star" class="section level2" number="8.5">
+<h2>
+<span class="header-section-number">C.5</span> Mapping with STAR<a class="anchor" aria-label="anchor" href="#mapping-with-star"><i class="fas fa-link"></i></a>
+</h2>
+<div id="building-star-index" class="section level3" number="8.5.1">
+<h3>
+<span class="header-section-number">C.5.1</span> Building STAR index<a class="anchor" aria-label="anchor" href="#building-star-index"><i class="fas fa-link"></i></a>
+</h3>
+<p>Other than with <code>bwa</code>, STAR requires an extra annotation file (GFF or GTF format) describing features on the genome. This data set requires the FASTA of the Arabidopsis thaliana genome and its accompanying annotation file. The <code>genomeDir</code> specifies the output location for the index.</p>
+<div class="sourceCode" id="cb84"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb84-1"><a href="a3-read-mapping.html#cb84-1" tabindex="-1"></a><span class="bu">cd</span> /local-fs/staff/marcel/GSE149995/reference</span>
+<span id="cb84-2"><a href="a3-read-mapping.html#cb84-2" tabindex="-1"></a><span class="fu">mkdir</span> star<span class="kw">;</span> <span class="bu">cd</span> star</span>
+<span id="cb84-3"><a href="a3-read-mapping.html#cb84-3" tabindex="-1"></a><span class="ex">STAR</span> <span class="dt">\</span></span>
+<span id="cb84-4"><a href="a3-read-mapping.html#cb84-4" tabindex="-1"></a>  <span class="at">--runThreadN</span> 12 <span class="dt">\</span></span>
+<span id="cb84-5"><a href="a3-read-mapping.html#cb84-5" tabindex="-1"></a>  <span class="at">--runMode</span> genomeGenerate <span class="dt">\</span></span>
+<span id="cb84-6"><a href="a3-read-mapping.html#cb84-6" tabindex="-1"></a>  <span class="at">--genomeDir</span> /local-fs/staff/marcel/GSE149995/reference/star <span class="dt">\</span></span>
+<span id="cb84-7"><a href="a3-read-mapping.html#cb84-7" tabindex="-1"></a>  <span class="at">--genomeFastaFiles</span> /local-fs/staff/marcel/GSE149995/reference/TAIR10_chr_all.fas <span class="dt">\</span></span>
+<span id="cb84-8"><a href="a3-read-mapping.html#cb84-8" tabindex="-1"></a>  <span class="at">--sjdbGTFfile</span> /local-fs/staff/marcel/GSE149995/reference/TAIR10_GTF_genes.gtf <span class="dt">\</span></span>
+<span id="cb84-9"><a href="a3-read-mapping.html#cb84-9" tabindex="-1"></a>  <span class="at">--genomeSAindexNbases</span> 12 <span class="dt">\</span></span>
+<span id="cb84-10"><a href="a3-read-mapping.html#cb84-10" tabindex="-1"></a>  <span class="at">--sjdbOverhang</span> 49</span></code></pre></div>
+</div>
+<div id="read-mapping-and-transcript-counting" class="section level3" number="8.5.2">
+<h3>
+<span class="header-section-number">C.5.2</span> Read mapping and transcript counting<a class="anchor" aria-label="anchor" href="#read-mapping-and-transcript-counting"><i class="fas fa-link"></i></a>
+</h3>
+<p>The following command performs mapping against the reference genome, saves this as a sorted BAM file and directly performs a read count for all transcripts (NOTE: requires the <code>--sjdbGTFfile</code> parameter during index building). For each input file, there is a <code>ReadsPerGenes.out.tab</code> file generated containing 4 columns:</p>
+<ul>
+<li>column 1: gene ID</li>
+<li>column 2: counts for unstranded RNA-seq</li>
+<li>column 3: counts for the 1st read strand aligned with RNA (htseq-count option -s yes)</li>
+<li>column 4: counts for the 2nd read strand aligned with RNA (htseq-count option -s reverse)</li>
+</ul>
+<p>See an interesting discussion on <a href="https://www.biostars.org/p/218995/">BioStars</a> regarding which column to use.</p>
+<p>When planning to use a different tool for calculating gene counts, you can remove the <code>quantMode</code> argument which will only generate the sorted BAM files. Please read the manual of the tool of choice to see if an indexed BAM file as the BAM file does not have an index.</p>
+<div class="sourceCode" id="cb85"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb85-1"><a href="a3-read-mapping.html#cb85-1" tabindex="-1"></a><span class="fu">find</span> /local-fs/staff/marcel/GSE149995/fastq-trimmed/ <span class="at">-name</span> <span class="st">"*.fastq.gz"</span> <span class="at">-exec</span> basename {} <span class="dt">\;</span> <span class="kw">|</span> <span class="dt">\</span></span>
+<span id="cb85-2"><a href="a3-read-mapping.html#cb85-2" tabindex="-1"></a>  <span class="fu">sed</span> <span class="st">'s/\.fastq.gz$//'</span> <span class="kw">|</span> <span class="ex">parallel</span> <span class="st">'STAR --runThreadN 6 '</span> <span class="dt">\</span></span>
+<span id="cb85-3"><a href="a3-read-mapping.html#cb85-3" tabindex="-1"></a>    <span class="st">'--genomeDir /local-fs/staff/marcel/GSE149995/reference/star '</span> <span class="dt">\</span></span>
+<span id="cb85-4"><a href="a3-read-mapping.html#cb85-4" tabindex="-1"></a>    <span class="st">'--readFilesIn /local-fs/staff/marcel/GSE149995/fastq-trimmed/{}.fastq '</span> <span class="dt">\</span></span>
+<span id="cb85-5"><a href="a3-read-mapping.html#cb85-5" tabindex="-1"></a>    <span class="st">'--outSAMtype BAM SortedByCoordinate '</span> <span class="dt">\</span></span>
+<span id="cb85-6"><a href="a3-read-mapping.html#cb85-6" tabindex="-1"></a>    <span class="st">'--quantMode GeneCounts '</span> <span class="dt">\</span></span>
+<span id="cb85-7"><a href="a3-read-mapping.html#cb85-7" tabindex="-1"></a>    <span class="st">'--outFileNamePrefix /local-fs/staff/marcel/GSE149995/mapping/star/{}_star_'</span></span></code></pre></div>
+<p>Generating MultiQC report for STAR mapping phase</p>
+<div class="sourceCode" id="cb86"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb86-1"><a href="a3-read-mapping.html#cb86-1" tabindex="-1"></a><span class="ex">~/miniconda3/bin/multiqc</span> <span class="at">--filename</span> <span class="dt">\</span></span>
+<span id="cb86-2"><a href="a3-read-mapping.html#cb86-2" tabindex="-1"></a>  ~/Development/2.1.2-Transcriptomics/STAR-mapping.html <span class="dt">\</span></span>
+<span id="cb86-3"><a href="a3-read-mapping.html#cb86-3" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/mapping/star/</span></code></pre></div>
+</div>
+<div id="combining-star-count-files" class="section level3" number="8.5.3">
+<h3>
+<span class="header-section-number">C.5.3</span> Combining STAR Count Files<a class="anchor" aria-label="anchor" href="#combining-star-count-files"><i class="fas fa-link"></i></a>
+</h3>
+<p>STAR generates a <code>[sample]_star_ReadsPerGene.out.tab</code> file for each sample, containing the gene IDs and the count numbers. We can merge these files into a single data frame for further processing (see also <a href="https://mkempenaar.github.io/gene_expression_analysis/a1-data_loading.html#a1-batch_data_loading">Appendix A1 in the manual</a>). This example takes the 2nd column for the count values. Make sure that you know which column to use based on the description above. The reason that we’re merging the data is because it might not always be guaranteed that you get the exact same transcripts in each file.</p>
+<p>Note: if you use the <code>featurecounts</code> tool, you will already get a single file containing the count values for all samples.</p>
+<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">file.names</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.files.html">list.files</a></span><span class="op">(</span><span class="st">'/local-fs/staff/marcel/GSE149995/mapping/star/'</span>,</span>
+<span>                         pattern <span class="op">=</span> <span class="st">'*_star_ReadsPerGene.out.tab'</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Function for reading in files</span></span>
+<span><span class="va">read_sample</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">file.name</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="co">## Extract the sample name for naming the column (retaining the 'SRR....' part)</span></span>
+<span>  <span class="va">sample.name</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/strsplit.html">strsplit</a></span><span class="op">(</span><span class="va">file.name</span>, <span class="st">"."</span>, fixed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span></span>
+<span>  <span class="va">sample</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html">read.table</a></span><span class="op">(</span><span class="va">file.name</span>, header <span class="op">=</span> <span class="cn">FALSE</span>, sep<span class="op">=</span><span class="st">"\t"</span>, </span>
+<span>                       row.names <span class="op">=</span> <span class="cn">NULL</span>, skip <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span>
+<span>  <span class="co">## Rename the count column</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">sample</span><span class="op">)</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">sample.name</span></span>
+<span>  <span class="co">## Return a subset containing the transcript ID and sample name columns</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>Now we read all count files and merge them into a single data frame.</p>
+<div class="sourceCode" id="cb88"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/getwd.html">setwd</a></span><span class="op">(</span><span class="st">'/local-fs/staff/marcel/GSE149995/mapping/star/'</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Read the FIRST sample</span></span>
+<span><span class="va">counts</span> <span class="op">&lt;-</span> <span class="fu">read_sample</span><span class="op">(</span><span class="va">file.names</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Read the remaining files and merge the contents</span></span>
+<span><span class="kw">for</span> <span class="op">(</span><span class="va">file.name</span> <span class="kw">in</span> <span class="va">file.names</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">file.names</span><span class="op">)</span><span class="op">]</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="va">sample</span> <span class="op">&lt;-</span> <span class="fu">read_sample</span><span class="op">(</span><span class="va">file.name</span><span class="op">)</span></span>
+<span>  <span class="va">counts</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/S4Vectors/man/Vector-merge.html">merge</a></span><span class="op">(</span><span class="va">counts</span>, <span class="va">sample</span>, by <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Set the row names to the transcript IDs</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html">rownames</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">counts</span><span class="op">$</span><span class="va">V1</span></span>
+<span><span class="va">counts</span> <span class="op">&lt;-</span> <span class="va">counts</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span></span></code></pre></div>
+<p>We now have a single data frame with count values for all samples and transcripts. The names however are nondescriptive since they are just the <code>SRR</code> identifiers. Since we already did some annotation, we’re going to rename the columns to something more meaningful:</p>
+<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># For each SRR id, find the corresponding column in counts and rename</span></span>
+<span><span class="co"># using the name in the 'Condition' column</span></span>
+<span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html">nrow</a></span><span class="op">(</span><span class="va">setup</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="va">idx</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/grep.html">grep</a></span><span class="op">(</span><span class="va">setup</span><span class="op">$</span><span class="va">Run</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span><span class="op">[</span><span class="va">idx</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">setup</span><span class="op">$</span><span class="va">Condition</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Show first 6 columns and rows</span></span>
+<span><span class="fu">pander</span><span class="op">(</span><span class="va">counts</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">6</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<div class="inline-table"><table class="table table-sm">
+<colgroup>
+<col width="20%">
+<col width="15%">
+<col width="15%">
+<col width="12%">
+<col width="12%">
+<col width="12%">
+<col width="12%">
+</colgroup>
+<thead><tr class="header">
+<th align="center"> </th>
+<th align="center">arf7_3_r1</th>
+<th align="center">arf7_3_r2</th>
+<th align="center">WT_1_r1</th>
+<th align="center">WT_1_r2</th>
+<th align="center">WT_2_r1</th>
+<th align="center">WT_2_r2</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="center"><strong>AT1G01010</strong></td>
+<td align="center">556</td>
+<td align="center">536</td>
+<td align="center">58</td>
+<td align="center">54</td>
+<td align="center">342</td>
+<td align="center">335</td>
+</tr>
+<tr class="even">
+<td align="center"><strong>AT1G01020</strong></td>
+<td align="center">24</td>
+<td align="center">26</td>
+<td align="center">68</td>
+<td align="center">62</td>
+<td align="center">161</td>
+<td align="center">145</td>
+</tr>
+<tr class="odd">
+<td align="center"><strong>AT1G01030</strong></td>
+<td align="center">8</td>
+<td align="center">4</td>
+<td align="center">1</td>
+<td align="center">3</td>
+<td align="center">2</td>
+<td align="center">4</td>
+</tr>
+<tr class="even">
+<td align="center"><strong>AT1G01040</strong></td>
+<td align="center">363</td>
+<td align="center">333</td>
+<td align="center">69</td>
+<td align="center">70</td>
+<td align="center">76</td>
+<td align="center">85</td>
+</tr>
+<tr class="odd">
+<td align="center"><strong>AT1G01046</strong></td>
+<td align="center">1</td>
+<td align="center">0</td>
+<td align="center">0</td>
+<td align="center">1</td>
+<td align="center">0</td>
+<td align="center">0</td>
+</tr>
+<tr class="even">
+<td align="center"><strong>AT1G01050</strong></td>
+<td align="center">1344</td>
+<td align="center">1336</td>
+<td align="center">959</td>
+<td align="center">935</td>
+<td align="center">2342</td>
+<td align="center">2272</td>
+</tr>
+</tbody>
+</table></div>
+</div>
+</div>
+<div id="references" class="section level2" number="8.6">
+<h2>
+<span class="header-section-number">C.6</span> References<a class="anchor" aria-label="anchor" href="#references"><i class="fas fa-link"></i></a>
+</h2>
+
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-deelen15" class="csl-entry">
+al., Patrick Deelen et. 2015. <span>“Calling Genotypes from Public RNA-Sequencing Data Enables Identification of Genetic Variants That Affect Gene-Expression Levels.”</span> <em>Genome Medicine</em> 7 (30).
+</div>
+<div id="ref-R-edgeR" class="csl-entry">
+Chen, Yunshun, Aaron Lun, Davis McCarthy, Xiaobei Zhou, Mark Robinson, and Gordon Smyth. 2018. <em>edgeR: Empirical Analysis of Digital Gene Expression Data in r</em>. <a href="http://bioinf.wehi.edu.au/edgeR">http://bioinf.wehi.edu.au/edgeR</a>.
+</div>
+<div id="ref-R-pander" class="csl-entry">
+Daróczi, Gergely, and Roman Tsegelskyi. 2017. <em>Pander: An r ’Pandoc’ Writer</em>. <a href="https://CRAN.R-project.org/package=pander">https://CRAN.R-project.org/package=pander</a>.
+</div>
+<div id="ref-R-DESeq2" class="csl-entry">
+Love, Michael, Simon Anders, and Wolfgang Huber. 2017. <em>DESeq2: Differential Gene Expression Analysis Based on the Negative Binomial Distribution</em>. <a href="https://github.com/mikelove/DESeq2">https://github.com/mikelove/DESeq2</a>.
+</div>
+</div>
+</div>
+</div>
+  <div class="chapter-nav">
+<div class="prev"><a href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></div>
+<div class="empty"></div>
+</div></main><div class="col-md-3 col-lg-2 d-none d-md-block sidebar sidebar-chapter">
+    <nav id="toc" data-toggle="toc" aria-label="On this page"><h2>On this page</h2>
+      <ul class="nav navbar-nav">
+<li><a class="nav-link" href="#a3-read-mapping"><span class="header-section-number">C</span> From Reads to Counts</a></li>
+<li><a class="nav-link" href="#downloading-and-extracting-fastq-files"><span class="header-section-number">C.1</span> Downloading and extracting FastQ files</a></li>
+<li><a class="nav-link" href="#optional-annotation"><span class="header-section-number">C.2</span> (Optional) Annotation</a></li>
+<li>
+<a class="nav-link" href="#quality-control"><span class="header-section-number">C.3</span> Quality Control</a><ul class="nav navbar-nav"><li><a class="nav-link" href="#fastqc"><span class="header-section-number">C.3.1</span> FastQC</a></li></ul>
+</li>
+<li><a class="nav-link" href="#trimming"><span class="header-section-number">C.4</span> Trimming</a></li>
+<li>
+<a class="nav-link" href="#mapping-with-star"><span class="header-section-number">C.5</span> Mapping with STAR</a><ul class="nav navbar-nav">
+<li><a class="nav-link" href="#building-star-index"><span class="header-section-number">C.5.1</span> Building STAR index</a></li>
+<li><a class="nav-link" href="#read-mapping-and-transcript-counting"><span class="header-section-number">C.5.2</span> Read mapping and transcript counting</a></li>
+<li><a class="nav-link" href="#combining-star-count-files"><span class="header-section-number">C.5.3</span> Combining STAR Count Files</a></li>
+</ul>
+</li>
+<li><a class="nav-link" href="#references"><span class="header-section-number">C.6</span> References</a></li>
+</ul>
+
+      <div class="book-extra">
+        <ul class="list-unstyled">
+<li><a id="book-source" href="https://github.com/rstudio/bookdown-demo/blob/master/chapters/a3-read-mapping.Rmd">View source <i class="fab fa-github"></i></a></li>
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+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
+  </div>
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-<div id="chapter-1" class="section level1" number="1">
-<h1><span class="header-section-number"></span> Introduction</h1>
-<div id="capstone-project---analysis-of-gene-expression" class="section level2" number="1.1">
-<h2><span class="header-section-number">1.1</span> Capstone Project - Analysis of Gene Expression</h2>
-<p>The use of RNA-Sequencing techniques for measuring gene expression is relatively new and replaces <em>microarrays</em>, though in some cases microarrays are still used. Gene expression data gives valuable insights into the workings of cells in certain conditions. Especially when comparing for instance healthy and diseased samples it can become clear which genes are causal or under influence of a specific condition. Finding the genes of interest (genes showing differing expression accross conditions, called the <em>Differentially Expressed Genes</em> (DEGs)) is the goal of this project.</p>
-<p>While there is no golden standard for analyzing RNA-sequencing datasets as there are many tools (all manufacturers of sequencing equiptment also deliver software packages) we will use R combined with proven libraries for processing, visualizing and analyzing publically available datasets. While in some cases you are allowed to use the actual <em>raw</em> (read) data that is available, it is highly recommended to use the pre-processed data which often is a table with a <em>count</em> value for each gene. This count is the number of reads that was mapped to that gene which corresponds to the relative number of transcripts (mRNA sequences) of that gene present in the cell at the time of sampling.</p>
-</div>
-<div id="project-deliverables" class="section level2" number="1.2">
-<h2><span class="header-section-number">1.2</span> Project Deliverables</h2>
-<p>The end products of this course consist of two deliverables; a PDF output file from an RMarkdown ‘lab journal’ where you have logged all steps performed to get to the end result and a final report in the form of a short article. This chapter briefly describes the requirements and contents of these products and ends with instructions on how to use an RMarkdown <code>template</code> for writing the article.</p>
-</div>
-<div id="project-schedule" class="section level2" number="1.3">
-<h2><span class="header-section-number">1.3</span> Project Schedule</h2>
-<p>The aim is to keep to the below schedule during this course. Use the first two weeks to see if you need to focus more on one of the points below (depending on your dataset) and discuss changes to the planning with your teacher.</p>
-<ul>
-<li>Find a public experiment of interest [week one]
-<ul>
-<li>Using online resources (sections <a href="datasets.html#finding-public-data">2.1</a>, <a href="datasets.html#ncbi-geo">2.2</a>)</li>
-</ul></li>
-<li>Data Gathering and Literature Research [week two]
-<ul>
-<li>Make a final project choice</li>
-<li>Retrieve the accompanying publication</li>
-<li>Starting with Exploratory Data Analysis (chapter <a href="EDA.html#EDA">EDA</a>)</li>
-</ul></li>
-<li>Data Analysis [weeks three, four and five]
-<ul>
-<li>Finalizing Exploratory Data Analysis</li>
-<li>Discovering Differentially Expressed Genes (chapter <a href="chapter-4.html#chapter-4">4</a>)</li>
-<li>Data Annotation (optional; appendix <a href="a2-annotation.html#a2-annotation">a2</a>)</li>
-<li>Techniques used: R with bioconductor, the EdgeR and/ or DESeq2 packages, RMarkdown</li>
-</ul></li>
-<li>Result Analysis [week five and six]
-<ul>
-<li>Analyzing and Visualizing your results (chapter <a href="chapter-5.html#chapter-5">5</a>)</li>
-<li>Techniques used: clustering, pathway analysis, gene-enrichment analysis</li>
-</ul></li>
-<li>Finalizing analysis and start writing the final report (article) [weeks 7 and 8]</li>
-</ul>
-</div>
-<div id="grading" class="section level2" number="1.4">
-<h2><span class="header-section-number">1.4</span> Grading</h2>
-<p>The final grade consists of a weighted average of the work done for the <em>lab journal</em> containing all performed steps, their code and outputs (<strong>70%</strong>) and the <em>article</em> report (<strong>30%</strong>). A grade &gt;= <strong>5.5</strong> will give a total of 8 EC. The rest of this chapter explains the expected contents for the three graded products (each of these elements is explained in greater detail in other chapters).</p>
-</div>
-<div id="lab-journal" class="section level2" number="1.5">
-<h2><span class="header-section-number">1.5</span> <em>Lab Journal</em></h2>
-<p>As you know from previous projects and most likely from working in the laboratory, it is essential to keep a proper lab journal detailing every step you have done during the experiment. Here, the log is to be kept in an R markdown file, showing which steps have been taken in the analysis of the data set. This markdown should be <em>knitted</em> into a single PDF-file once the project is completed thus containing text detailling the steps and any decisions you’ve made, R-code (always visible!) and their resulting output/ images.</p>
-<div class="rmdtip">
-<p>
-<strong>Notes</strong>: As a general advice; do not wait with
-knitting this whole document until the project is done as knitting is
-very prone to errors and trying to fix these in a large document is not
-easy. Give each code chunk the proper attributes, including a name at
-the minimum. This helps spot errors during knitting as that process
-mentiones which chunk has been processed. Note that chunk-names
-<em>must</em> be unique. And try to make proper use of chapters and
-sections and include an (optional) table of contents.
-</p>
-</div>
-</div>
-<div id="article" class="section level2" number="1.6">
-<h2><span class="header-section-number">1.6</span> Article</h2>
-<p>The final report is written in article form which is a bit different from a usual report, mainly in its size. The article-report has a <em>maximum</em> number of pages of <strong>4</strong> including all images and references (no appendices!). Contents for this article should be extracted from the lab journal combined with part introduction and part conclusion/ discussion. The sections below describe a template that is available for writing this report and example report(s) will be made available for inspiration. Refer to this chapter again once you start writing the report.</p>
-<div id="installing-the-article-template" class="section level3" number="1.6.1">
-<h3><span class="header-section-number">1.6.1</span> Installing the Article Template</h3>
-<p>The templates are available in an R package and contains both <code>RMarkdown</code> and (another layer on top of the actual <a href="https://nl.wikipedia.org/wiki/TeX">markup language</a>, yes, another language…) files. RStudio can use templates for a number of documents, including article-templates. These templates can be installed from a package called <code>rticles</code> by running the <code>install.packages</code> function (note: might already be installed):</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="chapter-1.html#cb1-1" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">&quot;rticles&quot;</span>, <span class="at">type =</span> <span class="st">&quot;source&quot;</span>)</span></code></pre></div>
-</div>
-<div id="using-the-template" class="section level3" number="1.6.2">
-<h3><span class="header-section-number">1.6.2</span> Using the Template</h3>
-<p>Now that you have the templates package (<code>rticles</code>) installed you can download and use the template project (ZIP-file) available from <a href="https://bioinf.nl/~marcelk/capstone/course/report-template.zip">here</a>. Download this file to your project folder, extract its contents and open the <code>report-template.Rmd</code> file contained within the folder in RStudio. Verify that everything is setup correctly by hitting the <code>Knit</code> button at the top, this should create a PDF version of the report. Note that - somehow - the resulting PDF file is named <code>RJwrapper.pdf</code> instead of the expected <code>report-template.pdf</code>. If everything checks out OK you can rename the file to your liking and start editing.</p>
-<p>This template is based on the <code>R Journal Submission</code> template that you can also find in RStudio in the <code>New file -&gt; R Markdown -&gt; From Template</code> menu. Articles published in the <code>R-journal</code> are based on this template which you can browse for inspiration at the <a href="https://journal.r-project.org/archive/2021-2/">R Journal Website</a>.</p>
-<p>The available template shows an <em>example</em> of segments/ chapters and briefly describes what each section could or should contain. If you want to write your report in the Dutch language, you can create a new file from the template and change the segment names and content to Dutch. There will however still be some headings added by the template in English which is fine with me, but if you want to keep everything Dutch you can either edit the <code>RJournal.sty</code> file manually (<em>not recommended</em>) or start a new file by yourself and add some nice headings and page options.</p>
-<p>The top part of the template (between the dashes <code>---</code>) contains some settings that you need to change such as <code>title</code>, <code>authors</code> and <code>abstract</code>. Compare for instance a newly created article from this template with the one offered from the project-repository website.</p>
-
-</div>
-</div>
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diff --git a/docs/chapter-4.html b/docs/chapter-4.html
index a98d6bf..9d7519c 100644
--- a/docs/chapter-4.html
+++ b/docs/chapter-4.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -487,7 +488,7 @@ <h3>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/chapter-5.html b/docs/chapter-5.html
index 1ae9878..3774737 100644
--- a/docs/chapter-5.html
+++ b/docs/chapter-5.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -560,7 +561,7 @@ <h3>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/datasets.html b/docs/datasets.html
index e153f9e..ad30686 100644
--- a/docs/datasets.html
+++ b/docs/datasets.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -90,7 +91,7 @@ <h2>
 </h2>
 <p>This chapter describes a method of finding public data sets of interest. A <em>data set</em> in the context of this course refers to all data belonging to a certain gene-expression experiment, usually consisting of a number of sequencing-samples combined with <em>meta-data</em> describing the experiment.</p>
 <p>There are a number of very large databases online that offer access to thousands of - published - experiments and here we will focus on searching and downloading gene expression data from high-throughput (<code>NGS</code>) sequencing techniques (<code>RNA-Seq</code>).</p>
-<p>With thousands of freely available data sets it is possible to start performing research without the need of actually performing your own lab-experiment. For all common conditions, organisms and tissues you can download samples and compare them over multiple studies to find novel relations between genes and conditions or to verify experiments performed in your laboratory. For instance, the power of public data sets was demonstrated jointly by three of our alumni in an article called <em>Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels.</em><span class="citation">(<a href="a2-annotation.html#ref-deelen15">al. 2015</a>)</span>.</p>
+<p>With thousands of freely available data sets it is possible to start performing research without the need of actually performing your own lab-experiment. For all common conditions, organisms and tissues you can download samples and compare them over multiple studies to find novel relations between genes and conditions or to verify experiments performed in your laboratory. For instance, the power of public data sets was demonstrated jointly by three of our alumni in an article called <em>Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels.</em><span class="citation">(<a href="a3-read-mapping.html#ref-deelen15">al. 2015</a>)</span>.</p>
 <p>It’s method section begins with the sentence “<em>We downloaded the raw reads for all available human RNA-seq data sets …</em>” of which the amount of data and work will become clear later on. If you are interested in <em>expression Quantitative Trait Loci</em> (<code>eQTLs</code>) or <em>Allele-Specific Expression</em> (<code>ASE</code>) analysis, please read this very interesting paper.</p>
 <p>But before we start diving into large and complex databases potentially containing thousands of experiments with millions of samples and terabytes of data, we need to get a rough idea on what we would like to do once we have found something of interest in order to know what we are looking for in the first place. While there is no definitive guide or protocol that can be followed for processing and analyzing a gene-expression data set, the following goals can be considered:</p>
 <ol style="list-style-type: decimal">
@@ -190,7 +191,7 @@ <h2>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/from-reads-to-counts-a3-read-mapping.html b/docs/from-reads-to-counts-a3-read-mapping.html
new file mode 100644
index 0000000..05063ce
--- /dev/null
+++ b/docs/from-reads-to-counts-a3-read-mapping.html
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+<meta name="author" content="Marcel Kempenaar">
+<meta name="description" content="This chapter can be used to gain insight into how the count files are generated. Step one in any RNA-Seq data analysis is generating the gene or transcript counts for each sample. This overlaps...">
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+      <button class="btn btn-outline-primary d-lg-none ml-2 mt-1" type="button" data-toggle="collapse" data-target="#main-nav" aria-expanded="true" aria-controls="main-nav"><i class="fas fa-bars"></i><span class="sr-only">Show table of contents</span></button>
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+      <nav aria-label="Table of contents"><h2>Table of contents</h2>
+        <ul class="book-toc list-unstyled">
+<li><a class="" href="index.html"><span class="header-section-number">1</span> Introduction</a></li>
+<li><a class="" href="datasets.html"><span class="header-section-number">2</span> Discovering Data Sets</a></li>
+<li><a class="" href="EDA.html"><span class="header-section-number">3</span> Exploratory Data Analysis</a></li>
+<li><a class="" href="chapter-4.html"><span class="header-section-number">4</span> Discovering Differentialy Expressed Genes (DEGs)</a></li>
+<li><a class="" href="chapter-5.html"><span class="header-section-number">5</span> Data Analysis and Visualization</a></li>
+<li class="book-part">Appendix</li>
+<li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
+<li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="active" href="from-reads-to-counts-a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts {a3-read-mapping}</a></li>
+</ul>
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+  </header><main class="col-sm-12 col-md-9 col-lg-7" id="content"><div id="from-reads-to-counts-a3-read-mapping" class="section level1" number="8">
+<h1>
+<span class="header-section-number">C</span> From Reads to Counts {a3-read-mapping}<a class="anchor" aria-label="anchor" href="#from-reads-to-counts-a3-read-mapping"><i class="fas fa-link"></i></a>
+</h1>
+<p>This chapter can be used to gain insight into how the count files are generated. Step one in any RNA-Seq data analysis is generating the gene or transcript counts for each sample. This overlaps partly with steps taken in a genomics workflow (that we performed in Galaxy):</p>
+<ul>
+<li>Download SRA files and extract FastQ files</li>
+<li>Perform quality control on raw reads (FastQC)</li>
+<li>Perform trimming as needed (Trimmomatic)</li>
+<li>Perform quality control post trimming (FastQC)</li>
+<li>Perform mapping against reference genome (most often STAR)</li>
+<li>Generate gene/ transcript counts (either STAR, HTseq or featurecounts)</li>
+<li>
+<em>Downstream analysis</em> (starting from chapter 3 of this document)</li>
+</ul>
+<p>This chapter shows these steps for an example data set (<code>GSE149995</code>)</p>
+<div class="rmdtip">
+<p>
+<strong>Note about storage</strong>:
+</p>
+<p>
+The fastq-files used for an RNA-Seq experiment can be fairly large
+and depending on the number of samples, the total storage required for
+downloading and processing these files can run into multiple terrabytes.
+For students there is enough storage in the <code>/students/</code>
+folder. Please create a new directory in the
+<code>/students/202x-202x/minor/</code> folder that will be used for all
+the following steps. Note that you need to replace the
+<code>/local-fs/staff/marcel/</code> path with the path to your own
+directory.
+</p>
+<p>
+Furthermore, it is adviced (or basically required) to run all
+commands on the <code>assemblix</code> server as that has the capacity
+to process multiple samples efficiently in parallel.
+</p>
+</div>
+<div id="downloading-and-extracting-fastq-files" class="section level2" number="8.1">
+<h2>
+<span class="header-section-number">C.1</span> Downloading and extracting FastQ files<a class="anchor" aria-label="anchor" href="#downloading-and-extracting-fastq-files"><i class="fas fa-link"></i></a>
+</h2>
+<p>Given the SRA identifier for this project, select all samples and download as accession file (SRX identifiers). Using these identifiers as input for the <code>prefetch</code> system command (requires the NCBI SRA-Toolkit installed) downloads the SRA files and we convert them using the <code>fasterq-dump</code> command to fastq files.</p>
+<div class="sourceCode" id="cb76"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb76-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb76-1" tabindex="-1"></a><span class="ex">prefetch</span> <span class="va">$(</span><span class="op">&lt;</span>GSE149995_SRA_ACC_RNA-Seq.csv<span class="va">)</span> <span class="at">--output-directory</span> <span class="dt">\</span></span>
+<span id="cb76-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb76-2" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/SRA</span></code></pre></div>
+<p>The <code>fasterq-dump</code> by default performs a <code>split 3</code> operation, see <a href="https://github.com/ncbi/sra-tools/wiki/HowTo:-fasterq-dump">the SRA toolkit manual</a> for further details.</p>
+<div class="sourceCode" id="cb77"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb77-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb77-1" tabindex="-1"></a><span class="fu">find</span> /local-fs/staff/marcel/GSE149995/SRA/ <span class="at">-name</span> <span class="st">"*.sra"</span> <span class="kw">|</span> <span class="dt">\</span></span>
+<span id="cb77-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb77-2" tabindex="-1"></a>  <span class="ex">parallel</span> <span class="st">'fasterq-dump -O /local-fs/staff/marcel/GSE149995/fastq/ {}'</span></span></code></pre></div>
+</div>
+<div id="optional-annotation" class="section level2" number="8.2">
+<h2>
+<span class="header-section-number">C.2</span> (Optional) Annotation<a class="anchor" aria-label="anchor" href="#optional-annotation"><i class="fas fa-link"></i></a>
+</h2>
+<p>The NCBI GEO page lists 9 samples with 3 conditions (<code>WT</code>, <code>potent</code> and <code>arf7</code>). However, this experiment includes 18 sequence files. By downloading the ‘series matrix file’ from the GEO (using the <code>GEOquery</code> library) and combining this with the run info downloadable from the SRA, we can list the mapping of run to sample.</p>
+<p><strong>Note</strong>: This is an optional step for now and is only useful when there are doubts about the group/sample setup.</p>
+<div class="sourceCode" id="cb78"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/seandavi/GEOquery">GEOquery</a></span><span class="op">)</span></span>
+<span><span class="va">gse</span> <span class="op">&lt;-</span> <span class="fu"><a href="http://seandavi.github.io/GEOquery/reference/getGEO.html">getGEO</a></span><span class="op">(</span>GEO <span class="op">=</span> <span class="st">"GSE149995"</span><span class="op">)</span></span>
+<span><span class="co"># Combine the condition with a sample number and replicate number</span></span>
+<span><span class="va">Condition</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/paste.html">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="va">gse</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">@</span><span class="va">phenoData</span><span class="op">@</span><span class="va">data</span><span class="op">$</span><span class="va">`genotype/variation:ch1`</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
+<span>                  <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
+<span>                  <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">'r'</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">'_'</span><span class="op">)</span></span>
+<span><span class="va">run_info</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html">read.csv</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"data/GSE149995_Sra_RunInfo.csv"</span><span class="op">)</span></span>
+<span><span class="va">setup</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html">cbind</a></span><span class="op">(</span><span class="va">run_info</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html">%&gt;%</a></span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">Run</span>, <span class="va">Experiment</span><span class="op">)</span>, <span class="va">Condition</span><span class="op">)</span></span>
+<span><span class="fu">pander</span><span class="op">(</span><span class="va">setup</span><span class="op">)</span></span></code></pre></div>
+<div class="inline-table"><table style="width:57%;" class="table table-sm">
+<colgroup>
+<col width="19%">
+<col width="18%">
+<col width="19%">
+</colgroup>
+<thead><tr class="header">
+<th align="center">Run</th>
+<th align="center">Experiment</th>
+<th align="center">Condition</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="center">SRR11714145</td>
+<td align="center">SRX8273521</td>
+<td align="center">WT_1_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714146</td>
+<td align="center">SRX8273521</td>
+<td align="center">WT_1_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714147</td>
+<td align="center">SRX8273522</td>
+<td align="center">WT_2_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714148</td>
+<td align="center">SRX8273522</td>
+<td align="center">WT_2_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714149</td>
+<td align="center">SRX8273523</td>
+<td align="center">WT_3_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714150</td>
+<td align="center">SRX8273523</td>
+<td align="center">WT_3_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714151</td>
+<td align="center">SRX8273524</td>
+<td align="center">potent_1_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714152</td>
+<td align="center">SRX8273524</td>
+<td align="center">potent_1_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714153</td>
+<td align="center">SRX8273525</td>
+<td align="center">potent_2_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714154</td>
+<td align="center">SRX8273525</td>
+<td align="center">potent_2_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714155</td>
+<td align="center">SRX8273526</td>
+<td align="center">potent_3_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714156</td>
+<td align="center">SRX8273526</td>
+<td align="center">potent_3_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714157</td>
+<td align="center">SRX8273527</td>
+<td align="center">arf7_1_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714158</td>
+<td align="center">SRX8273527</td>
+<td align="center">arf7_1_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714159</td>
+<td align="center">SRX8273528</td>
+<td align="center">arf7_2_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714160</td>
+<td align="center">SRX8273528</td>
+<td align="center">arf7_2_r2</td>
+</tr>
+<tr class="odd">
+<td align="center">SRR11714143</td>
+<td align="center">SRX8273529</td>
+<td align="center">arf7_3_r1</td>
+</tr>
+<tr class="even">
+<td align="center">SRR11714144</td>
+<td align="center">SRX8273529</td>
+<td align="center">arf7_3_r2</td>
+</tr>
+</tbody>
+</table></div>
+</div>
+<div id="quality-control" class="section level2" number="8.3">
+<h2>
+<span class="header-section-number">C.3</span> Quality Control<a class="anchor" aria-label="anchor" href="#quality-control"><i class="fas fa-link"></i></a>
+</h2>
+<p>Here we perform the FastQC -&gt; Trimmomatic -&gt; FastQC steps</p>
+<div id="fastqc" class="section level3" number="8.3.1">
+<h3>
+<span class="header-section-number">C.3.1</span> FastQC<a class="anchor" aria-label="anchor" href="#fastqc"><i class="fas fa-link"></i></a>
+</h3>
+<p>Running FastQC</p>
+<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fqdir</span> <span class="op">&lt;-</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fastq/"</span></span>
+<span><span class="fu">fastqc</span><span class="op">(</span>fq.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fastq/"</span>,</span>
+<span>       qc.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fqc"</span>,</span>
+<span>       fastqc.path <span class="op">=</span> <span class="st">"/usr/bin/fastqc"</span>,</span>
+<span>       threads <span class="op">=</span> <span class="fl">32</span><span class="op">)</span></span></code></pre></div>
+<p>QC-report using <code>multiqc</code></p>
+<div class="sourceCode" id="cb80"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb80-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb80-1" tabindex="-1"></a><span class="ex">multiqc</span> <span class="dt">\</span></span>
+<span id="cb80-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb80-2" tabindex="-1"></a>  <span class="at">--filename</span> ~/Development/2.1.2-Transcriptomics/GSE149995_FQC_multiqc_report.html <span class="dt">\</span></span>
+<span id="cb80-3"><a href="from-reads-to-counts-a3-read-mapping.html#cb80-3" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/fqc/ </span></code></pre></div>
+</div>
+</div>
+<div id="trimming" class="section level2" number="8.4">
+<h2>
+<span class="header-section-number">C.4</span> Trimming<a class="anchor" aria-label="anchor" href="#trimming"><i class="fas fa-link"></i></a>
+</h2>
+<p>Proper trimming requires experimentation and research regarding the used sequencing technology. The following command uses Trimmomatic (release 0.39, available on <a href="https://github.com/usadellab/Trimmomatic/releases">Github</a>) to trim all fastq files. Note that this example is for <em>single-end</em> sequencing only, use the <code>TrimmomaticPE</code> command for paired-end data processing and the <code>TruSeq</code> FASTA file ending in <code>PE</code> instead.</p>
+<div class="sourceCode" id="cb81"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb81-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-1" tabindex="-1"></a><span class="fu">cat</span> data/GSE149995_Sra_RunInfo.csv <span class="kw">|</span> <span class="dt">\</span></span>
+<span id="cb81-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-2" tabindex="-1"></a>  <span class="ex">parallel</span> <span class="st">'TrimmomaticSE -threads 4 '</span> <span class="dt">\</span></span>
+<span id="cb81-3"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-3" tabindex="-1"></a>                  <span class="st">'/local-fs/staff/marcel/GSE149995/fastq/{}.fastq.gz '</span> <span class="dt">\</span></span>
+<span id="cb81-4"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-4" tabindex="-1"></a>                  <span class="st">'/local-fs/staff/marcel/GSE149995/fastq-trimmed/{}.trimmed.fastq.gz '</span> <span class="dt">\</span></span>
+<span id="cb81-5"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-5" tabindex="-1"></a>                  <span class="st">'ILLUMINACLIP:/homes/marcelk/Development/2.1.2-Transcriptomics/TruSeq3-SE.fa:2:30:10 '</span> <span class="dt">\</span></span>
+<span id="cb81-6"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-6" tabindex="-1"></a>                  <span class="st">'MINLEN:40 '</span> <span class="dt">\</span></span>
+<span id="cb81-7"><a href="from-reads-to-counts-a3-read-mapping.html#cb81-7" tabindex="-1"></a>                  <span class="st">'SLIDINGWINDOW:4:20'</span></span></code></pre></div>
+<p>Re-running FastQC</p>
+<div class="sourceCode" id="cb82"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">fastqc</span><span class="op">(</span>fq.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fastq-trimmed/"</span>,</span>
+<span>       qc.dir <span class="op">=</span> <span class="st">"/local-fs/staff/marcel/GSE149995/fqc-trimmed"</span>,</span>
+<span>       fastqc.path <span class="op">=</span> <span class="st">"/usr/bin/fastqc"</span>,</span>
+<span>       threads <span class="op">=</span> <span class="fl">32</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb83"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb83-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb83-1" tabindex="-1"></a><span class="ex">multiqc</span> <span class="at">--filename</span> <span class="dt">\</span></span>
+<span id="cb83-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb83-2" tabindex="-1"></a>  ~/Development/2.1.2-Transcriptomics/GSE149995_FQC_Trimmed.html <span class="dt">\</span></span>
+<span id="cb83-3"><a href="from-reads-to-counts-a3-read-mapping.html#cb83-3" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/fqc-trimmed/</span></code></pre></div>
+</div>
+<div id="mapping-with-star" class="section level2" number="8.5">
+<h2>
+<span class="header-section-number">C.5</span> Mapping with STAR<a class="anchor" aria-label="anchor" href="#mapping-with-star"><i class="fas fa-link"></i></a>
+</h2>
+<div id="building-star-index" class="section level3" number="8.5.1">
+<h3>
+<span class="header-section-number">C.5.1</span> Building STAR index<a class="anchor" aria-label="anchor" href="#building-star-index"><i class="fas fa-link"></i></a>
+</h3>
+<p>Other than with <code>bwa</code>, STAR requires an extra annotation file (GFF or GTF format) describing features on the genome. This data set requires the FASTA of the Arabidopsis thaliana genome and its accompanying annotation file. The <code>genomeDir</code> specifies the output location for the index.</p>
+<div class="sourceCode" id="cb84"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb84-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-1" tabindex="-1"></a><span class="bu">cd</span> /local-fs/staff/marcel/GSE149995/reference</span>
+<span id="cb84-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-2" tabindex="-1"></a><span class="fu">mkdir</span> star<span class="kw">;</span> <span class="bu">cd</span> star</span>
+<span id="cb84-3"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-3" tabindex="-1"></a><span class="ex">STAR</span> <span class="dt">\</span></span>
+<span id="cb84-4"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-4" tabindex="-1"></a>  <span class="at">--runThreadN</span> 12 <span class="dt">\</span></span>
+<span id="cb84-5"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-5" tabindex="-1"></a>  <span class="at">--runMode</span> genomeGenerate <span class="dt">\</span></span>
+<span id="cb84-6"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-6" tabindex="-1"></a>  <span class="at">--genomeDir</span> /local-fs/staff/marcel/GSE149995/reference/star <span class="dt">\</span></span>
+<span id="cb84-7"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-7" tabindex="-1"></a>  <span class="at">--genomeFastaFiles</span> /local-fs/staff/marcel/GSE149995/reference/TAIR10_chr_all.fas <span class="dt">\</span></span>
+<span id="cb84-8"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-8" tabindex="-1"></a>  <span class="at">--sjdbGTFfile</span> /local-fs/staff/marcel/GSE149995/reference/TAIR10_GTF_genes.gtf <span class="dt">\</span></span>
+<span id="cb84-9"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-9" tabindex="-1"></a>  <span class="at">--genomeSAindexNbases</span> 12 <span class="dt">\</span></span>
+<span id="cb84-10"><a href="from-reads-to-counts-a3-read-mapping.html#cb84-10" tabindex="-1"></a>  <span class="at">--sjdbOverhang</span> 49</span></code></pre></div>
+</div>
+<div id="read-mapping-and-transcript-counting" class="section level3" number="8.5.2">
+<h3>
+<span class="header-section-number">C.5.2</span> Read mapping and transcript counting<a class="anchor" aria-label="anchor" href="#read-mapping-and-transcript-counting"><i class="fas fa-link"></i></a>
+</h3>
+<p>The following command performs mapping against the reference genome, saves this as a sorted BAM file and directly performs a read count for all transcripts (NOTE: requires the <code>--sjdbGTFfile</code> parameter during index building). For each input file, there is a <code>ReadsPerGenes.out.tab</code> file generated containing 4 columns:</p>
+<ul>
+<li>column 1: gene ID</li>
+<li>column 2: counts for unstranded RNA-seq</li>
+<li>column 3: counts for the 1st read strand aligned with RNA (htseq-count option -s yes)</li>
+<li>column 4: counts for the 2nd read strand aligned with RNA (htseq-count option -s reverse)</li>
+</ul>
+<p>See an interesting discussion on <a href="https://www.biostars.org/p/218995/">BioStars</a> regarding which column to use.</p>
+<p>When planning to use a different tool for calculating gene counts, you can remove the <code>quantMode</code> argument which will only generate the sorted BAM files. Please read the manual of the tool of choice to see if an indexed BAM file as the BAM file does not have an index.</p>
+<div class="sourceCode" id="cb85"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb85-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-1" tabindex="-1"></a><span class="fu">find</span> /local-fs/staff/marcel/GSE149995/fastq-trimmed/ <span class="at">-name</span> <span class="st">"*.fastq.gz"</span> <span class="at">-exec</span> basename {} <span class="dt">\;</span> <span class="kw">|</span> <span class="dt">\</span></span>
+<span id="cb85-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-2" tabindex="-1"></a>  <span class="fu">sed</span> <span class="st">'s/\.fastq.gz$//'</span> <span class="kw">|</span> <span class="ex">parallel</span> <span class="st">'STAR --runThreadN 6 '</span> <span class="dt">\</span></span>
+<span id="cb85-3"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-3" tabindex="-1"></a>    <span class="st">'--genomeDir /local-fs/staff/marcel/GSE149995/reference/star '</span> <span class="dt">\</span></span>
+<span id="cb85-4"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-4" tabindex="-1"></a>    <span class="st">'--readFilesIn /local-fs/staff/marcel/GSE149995/fastq-trimmed/{}.fastq '</span> <span class="dt">\</span></span>
+<span id="cb85-5"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-5" tabindex="-1"></a>    <span class="st">'--outSAMtype BAM SortedByCoordinate '</span> <span class="dt">\</span></span>
+<span id="cb85-6"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-6" tabindex="-1"></a>    <span class="st">'--quantMode GeneCounts '</span> <span class="dt">\</span></span>
+<span id="cb85-7"><a href="from-reads-to-counts-a3-read-mapping.html#cb85-7" tabindex="-1"></a>    <span class="st">'--outFileNamePrefix /local-fs/staff/marcel/GSE149995/mapping/star/{}_star_'</span></span></code></pre></div>
+<p>Generating MultiQC report for STAR mapping phase</p>
+<div class="sourceCode" id="cb86"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb86-1"><a href="from-reads-to-counts-a3-read-mapping.html#cb86-1" tabindex="-1"></a><span class="ex">~/miniconda3/bin/multiqc</span> <span class="at">--filename</span> <span class="dt">\</span></span>
+<span id="cb86-2"><a href="from-reads-to-counts-a3-read-mapping.html#cb86-2" tabindex="-1"></a>  ~/Development/2.1.2-Transcriptomics/STAR-mapping.html <span class="dt">\</span></span>
+<span id="cb86-3"><a href="from-reads-to-counts-a3-read-mapping.html#cb86-3" tabindex="-1"></a>  /local-fs/staff/marcel/GSE149995/mapping/star/</span></code></pre></div>
+</div>
+<div id="combining-star-count-files" class="section level3" number="8.5.3">
+<h3>
+<span class="header-section-number">C.5.3</span> Combining STAR Count Files<a class="anchor" aria-label="anchor" href="#combining-star-count-files"><i class="fas fa-link"></i></a>
+</h3>
+<p>STAR generates a <code>[sample]_star_ReadsPerGene.out.tab</code> file for each sample, containing the gene IDs and the count numbers. We can merge these files into a single data frame for further processing (see also <a href="https://mkempenaar.github.io/gene_expression_analysis/a1-data_loading.html#a1-batch_data_loading">Appendix A1 in the manual</a>). This example takes the 2nd column for the count values. Make sure that you know which column to use based on the description above. The reason that we’re merging the data is because it might not always be guaranteed that you get the exact same transcripts in each file.</p>
+<p>Note: if you use the <code>featurecounts</code> tool, you will already get a single file containing the count values for all samples.</p>
+<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">file.names</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.files.html">list.files</a></span><span class="op">(</span><span class="st">'/local-fs/staff/marcel/GSE149995/mapping/star/'</span>,</span>
+<span>                         pattern <span class="op">=</span> <span class="st">'*_star_ReadsPerGene.out.tab'</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Function for reading in files</span></span>
+<span><span class="va">read_sample</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">file.name</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="co">## Extract the sample name for naming the column (retaining the 'SRR....' part)</span></span>
+<span>  <span class="va">sample.name</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/strsplit.html">strsplit</a></span><span class="op">(</span><span class="va">file.name</span>, <span class="st">"."</span>, fixed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span></span>
+<span>  <span class="va">sample</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html">read.table</a></span><span class="op">(</span><span class="va">file.name</span>, header <span class="op">=</span> <span class="cn">FALSE</span>, sep<span class="op">=</span><span class="st">"\t"</span>, </span>
+<span>                       row.names <span class="op">=</span> <span class="cn">NULL</span>, skip <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span>
+<span>  <span class="co">## Rename the count column</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">sample</span><span class="op">)</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">sample.name</span></span>
+<span>  <span class="co">## Return a subset containing the transcript ID and sample name columns</span></span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html">return</a></span><span class="op">(</span><span class="va">sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>Now we read all count files and merge them into a single data frame.</p>
+<div class="sourceCode" id="cb88"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/getwd.html">setwd</a></span><span class="op">(</span><span class="st">'/local-fs/staff/marcel/GSE149995/mapping/star/'</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Read the FIRST sample</span></span>
+<span><span class="va">counts</span> <span class="op">&lt;-</span> <span class="fu">read_sample</span><span class="op">(</span><span class="va">file.names</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">## Read the remaining files and merge the contents</span></span>
+<span><span class="kw">for</span> <span class="op">(</span><span class="va">file.name</span> <span class="kw">in</span> <span class="va">file.names</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">file.names</span><span class="op">)</span><span class="op">]</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="va">sample</span> <span class="op">&lt;-</span> <span class="fu">read_sample</span><span class="op">(</span><span class="va">file.name</span><span class="op">)</span></span>
+<span>  <span class="va">counts</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/S4Vectors/man/Vector-merge.html">merge</a></span><span class="op">(</span><span class="va">counts</span>, <span class="va">sample</span>, by <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Set the row names to the transcript IDs</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html">rownames</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">counts</span><span class="op">$</span><span class="va">V1</span></span>
+<span><span class="va">counts</span> <span class="op">&lt;-</span> <span class="va">counts</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span></span></code></pre></div>
+<p>We now have a single data frame with count values for all samples and transcripts. The names however are nondescriptive since they are just the <code>SRR</code> identifiers. Since we already did some annotation, we’re going to rename the columns to something more meaningful:</p>
+<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># For each SRR id, find the corresponding column in counts and rename</span></span>
+<span><span class="co"># using the name in the 'Condition' column</span></span>
+<span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html">nrow</a></span><span class="op">(</span><span class="va">setup</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="va">idx</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/grep.html">grep</a></span><span class="op">(</span><span class="va">setup</span><span class="op">$</span><span class="va">Run</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span><span class="op">[</span><span class="va">idx</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">setup</span><span class="op">$</span><span class="va">Condition</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Show first 6 columns and rows</span></span>
+<span><span class="fu">pander</span><span class="op">(</span><span class="va">counts</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">6</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<div class="inline-table"><table class="table table-sm">
+<colgroup>
+<col width="20%">
+<col width="15%">
+<col width="15%">
+<col width="12%">
+<col width="12%">
+<col width="12%">
+<col width="12%">
+</colgroup>
+<thead><tr class="header">
+<th align="center"> </th>
+<th align="center">arf7_3_r1</th>
+<th align="center">arf7_3_r2</th>
+<th align="center">WT_1_r1</th>
+<th align="center">WT_1_r2</th>
+<th align="center">WT_2_r1</th>
+<th align="center">WT_2_r2</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="center"><strong>AT1G01010</strong></td>
+<td align="center">556</td>
+<td align="center">536</td>
+<td align="center">58</td>
+<td align="center">54</td>
+<td align="center">342</td>
+<td align="center">335</td>
+</tr>
+<tr class="even">
+<td align="center"><strong>AT1G01020</strong></td>
+<td align="center">24</td>
+<td align="center">26</td>
+<td align="center">68</td>
+<td align="center">62</td>
+<td align="center">161</td>
+<td align="center">145</td>
+</tr>
+<tr class="odd">
+<td align="center"><strong>AT1G01030</strong></td>
+<td align="center">8</td>
+<td align="center">4</td>
+<td align="center">1</td>
+<td align="center">3</td>
+<td align="center">2</td>
+<td align="center">4</td>
+</tr>
+<tr class="even">
+<td align="center"><strong>AT1G01040</strong></td>
+<td align="center">363</td>
+<td align="center">333</td>
+<td align="center">69</td>
+<td align="center">70</td>
+<td align="center">76</td>
+<td align="center">85</td>
+</tr>
+<tr class="odd">
+<td align="center"><strong>AT1G01046</strong></td>
+<td align="center">1</td>
+<td align="center">0</td>
+<td align="center">0</td>
+<td align="center">1</td>
+<td align="center">0</td>
+<td align="center">0</td>
+</tr>
+<tr class="even">
+<td align="center"><strong>AT1G01050</strong></td>
+<td align="center">1344</td>
+<td align="center">1336</td>
+<td align="center">959</td>
+<td align="center">935</td>
+<td align="center">2342</td>
+<td align="center">2272</td>
+</tr>
+</tbody>
+</table></div>
+</div>
+</div>
+<div id="references" class="section level2" number="8.6">
+<h2>
+<span class="header-section-number">C.6</span> References<a class="anchor" aria-label="anchor" href="#references"><i class="fas fa-link"></i></a>
+</h2>
+
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-deelen15" class="csl-entry">
+al., Patrick Deelen et. 2015. <span>“Calling Genotypes from Public RNA-Sequencing Data Enables Identification of Genetic Variants That Affect Gene-Expression Levels.”</span> <em>Genome Medicine</em> 7 (30).
+</div>
+<div id="ref-R-edgeR" class="csl-entry">
+Chen, Yunshun, Aaron Lun, Davis McCarthy, Xiaobei Zhou, Mark Robinson, and Gordon Smyth. 2018. <em>edgeR: Empirical Analysis of Digital Gene Expression Data in r</em>. <a href="http://bioinf.wehi.edu.au/edgeR">http://bioinf.wehi.edu.au/edgeR</a>.
+</div>
+<div id="ref-R-pander" class="csl-entry">
+Daróczi, Gergely, and Roman Tsegelskyi. 2017. <em>Pander: An r ’Pandoc’ Writer</em>. <a href="https://CRAN.R-project.org/package=pander">https://CRAN.R-project.org/package=pander</a>.
+</div>
+<div id="ref-R-DESeq2" class="csl-entry">
+Love, Michael, Simon Anders, and Wolfgang Huber. 2017. <em>DESeq2: Differential Gene Expression Analysis Based on the Negative Binomial Distribution</em>. <a href="https://github.com/mikelove/DESeq2">https://github.com/mikelove/DESeq2</a>.
+</div>
+</div>
+</div>
+</div>
+  <div class="chapter-nav">
+<div class="prev"><a href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></div>
+<div class="empty"></div>
+</div></main><div class="col-md-3 col-lg-2 d-none d-md-block sidebar sidebar-chapter">
+    <nav id="toc" data-toggle="toc" aria-label="On this page"><h2>On this page</h2>
+      <ul class="nav navbar-nav">
+<li><a class="nav-link" href="#from-reads-to-counts-a3-read-mapping"><span class="header-section-number">C</span> From Reads to Counts {a3-read-mapping}</a></li>
+<li><a class="nav-link" href="#downloading-and-extracting-fastq-files"><span class="header-section-number">C.1</span> Downloading and extracting FastQ files</a></li>
+<li><a class="nav-link" href="#optional-annotation"><span class="header-section-number">C.2</span> (Optional) Annotation</a></li>
+<li>
+<a class="nav-link" href="#quality-control"><span class="header-section-number">C.3</span> Quality Control</a><ul class="nav navbar-nav"><li><a class="nav-link" href="#fastqc"><span class="header-section-number">C.3.1</span> FastQC</a></li></ul>
+</li>
+<li><a class="nav-link" href="#trimming"><span class="header-section-number">C.4</span> Trimming</a></li>
+<li>
+<a class="nav-link" href="#mapping-with-star"><span class="header-section-number">C.5</span> Mapping with STAR</a><ul class="nav navbar-nav">
+<li><a class="nav-link" href="#building-star-index"><span class="header-section-number">C.5.1</span> Building STAR index</a></li>
+<li><a class="nav-link" href="#read-mapping-and-transcript-counting"><span class="header-section-number">C.5.2</span> Read mapping and transcript counting</a></li>
+<li><a class="nav-link" href="#combining-star-count-files"><span class="header-section-number">C.5.3</span> Combining STAR Count Files</a></li>
+</ul>
+</li>
+<li><a class="nav-link" href="#references"><span class="header-section-number">C.6</span> References</a></li>
+</ul>
+
+      <div class="book-extra">
+        <ul class="list-unstyled">
+<li><a id="book-source" href="https://github.com/rstudio/bookdown-demo/blob/master/chapters/a3-read-mapping.Rmd">View source <i class="fab fa-github"></i></a></li>
+          <li><a id="book-edit" href="https://github.com/rstudio/bookdown-demo/edit/master/chapters/a3-read-mapping.Rmd">Edit this page <i class="fab fa-github"></i></a></li>
+        </ul>
+</div>
+    </nav>
+</div>
+
+</div>
+</div> <!-- .container -->
+
+<footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
+
+  <div class="col-12 col-md-6 mt-3">
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
+  </div>
+
+  <div class="col-12 col-md-6 mt-3">
+    <p>This book was built by the <a class="text-light" href="https://bookdown.org">bookdown</a> R package.</p>
+  </div>
+
+</div></div>
+</footer><!-- dynamically load mathjax for compatibility with self-contained --><script>
+  (function () {
+    var script = document.createElement("script");
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+</html>
diff --git a/docs/index.html b/docs/index.html
index 0c2eee8..52171f5 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -73,6 +73,7 @@ <h1>
 <li class="book-part">Appendix</li>
 <li><a class="" href="a1-data_loading.html"><span class="header-section-number">A</span> Loading Expression Data in R</a></li>
 <li><a class="" href="a2-annotation.html"><span class="header-section-number">B</span> Annotating an RNA-Seq Experiment</a></li>
+<li><a class="" href="a3-read-mapping.html"><span class="header-section-number">C</span> From Reads to Counts</a></li>
 </ul>
 
         <div class="book-extra">
@@ -216,7 +217,7 @@ <h3>
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-04-23.</p>
+    <p>"<strong>Capstone Project - Gene Expression Analysis</strong>" was written by Marcel Kempenaar. It was last built on 2024-05-08.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">
diff --git a/docs/reference-keys.txt b/docs/reference-keys.txt
index 07b9717..bfcfd32 100644
--- a/docs/reference-keys.txt
+++ b/docs/reference-keys.txt
@@ -64,4 +64,14 @@ download-geo
 manually-annotate
 annotationDBI
 using-biomart
+a3-read-mapping
+downloading-and-extracting-fastq-files
+optional-annotation
+quality-control
+fastqc
+trimming
+mapping-with-star
+building-star-index
+read-mapping-and-transcript-counting
+combining-star-count-files
 references
diff --git a/docs/search.json b/docs/search.json
index 1cd99c4..bee5f0f 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"index.html","id":"chapter-1","chapter":" Introduction","heading":" Introduction","text":"","code":""},{"path":"index.html","id":"capstone-project---analysis-of-gene-expression","chapter":" Introduction","heading":"1.1 Capstone Project - Analysis of Gene Expression","text":"use RNA-Sequencing techniques measuring gene expression relatively new replaces microarrays, though cases microarrays still used. Gene expression data gives valuable insights workings cells certain conditions. Especially comparing instance healthy diseased samples can become clear genes causal influence specific condition. Finding genes interest (genes showing differing expression accross conditions, called Differentially Expressed Genes (DEGs)) goal project.golden standard analyzing RNA-sequencing datasets many tools (manufacturers sequencing equiptment also deliver software packages) use R combined proven libraries processing, visualizing analyzing publically available datasets. cases allowed use actual raw (read) data available, highly recommended use pre-processed data often table count value gene. count number reads mapped gene corresponds relative number transcripts (mRNA sequences) gene present cell time sampling.","code":""},{"path":"index.html","id":"project-deliverables","chapter":" Introduction","heading":"1.2 Project Deliverables","text":"end products course consist two deliverables; PDF output file RMarkdown ‘lab journal’ logged steps performed get end result final report form short article. chapter briefly describes requirements contents products ends instructions use RMarkdown template writing article.","code":""},{"path":"index.html","id":"project-schedule","chapter":" Introduction","heading":"1.3 Project Schedule","text":"aim keep schedule course. Use first two weeks see need focus one points (depending dataset) discuss changes planning teacher.Find public experiment interest [week one]\nUsing online resources (sections 2.1, 2.2)\nUsing online resources (sections 2.1, 2.2)Data Gathering Literature Research [week two]\nMake final project choice\nRetrieve accompanying publication\nStarting Exploratory Data Analysis (chapter EDA)\nMake final project choiceRetrieve accompanying publicationStarting Exploratory Data Analysis (chapter EDA)Data Analysis [weeks three, four five]\nFinalizing Exploratory Data Analysis\nDiscovering Differentially Expressed Genes (chapter 4)\nData Annotation (optional; appendix a2)\nTechniques used: R bioconductor, EdgeR / DESeq2 packages, RMarkdown\nFinalizing Exploratory Data AnalysisDiscovering Differentially Expressed Genes (chapter 4)Data Annotation (optional; appendix a2)Techniques used: R bioconductor, EdgeR / DESeq2 packages, RMarkdownResult Analysis [week five six]\nAnalyzing Visualizing results (chapter 5)\nTechniques used: clustering, pathway analysis, gene-enrichment analysis\nAnalyzing Visualizing results (chapter 5)Techniques used: clustering, pathway analysis, gene-enrichment analysisFinalizing analysis start writing final report (article) [weeks 7 8]","code":""},{"path":"index.html","id":"grading","chapter":" Introduction","heading":"1.4 Grading","text":"final grade consists weighted average work done lab journal containing performed steps, code outputs (70%) article report (30%). grade >= 5.5 give total 8 EC. rest chapter explains expected contents three graded products (elements explained greater detail chapters).","code":""},{"path":"index.html","id":"lab-journal","chapter":" Introduction","heading":"1.5 Lab Journal","text":"know previous projects likely working laboratory, essential keep proper lab journal detailing every step done experiment. , log kept R markdown file, showing steps taken analysis data set. markdown knitted single PDF-file project completed thus containing text detailling steps decisions ’ve made, R-code (always visible!) resulting output/ images.\nNotes: general advice; wait \nknitting whole document project done knitting \nprone errors trying fix large document \neasy. Give code chunk proper attributes, including name \nminimum. helps spot errors knitting process\nmentiones chunk processed. Note chunk-names\nmust unique. try make proper use chapters \nsections include (optional) table contents.\n","code":""},{"path":"index.html","id":"article","chapter":" Introduction","heading":"1.6 Article","text":"final report written article form bit different usual report, mainly size. article-report maximum number pages 4 including images references (appendices!). Contents article extracted lab journal combined part introduction part conclusion/ discussion. sections describe template available writing report example report(s) made available inspiration. Refer chapter start writing report.","code":""},{"path":"index.html","id":"installing-the-article-template","chapter":" Introduction","heading":"1.6.1 Installing the Article Template","text":"templates available R package contains RMarkdown (another layer top actual markup language, yes, another language…) files. RStudio can use templates number documents, including article-templates. templates can installed package called rticles running install.packages function (note: might already installed):","code":"\ninstall.packages(\"rticles\", type = \"source\")"},{"path":"index.html","id":"using-the-template","chapter":" Introduction","heading":"1.6.2 Using the Template","text":"Now templates package (rticles) installed can download use template project (ZIP-file) available . Download file project folder, extract contents open report-template.Rmd file contained within folder RStudio. Verify everything setup correctly hitting Knit button top, create PDF version report. Note - somehow - resulting PDF file named RJwrapper.pdf instead expected report-template.pdf. everything checks OK can rename file liking start editing.template based R Journal Submission template can also find RStudio New file -> R Markdown -> Template menu. Articles published R-journal based template can browse inspiration R Journal Website.available template shows example segments/ chapters briefly describes section contain. want write report Dutch language, can create new file template change segment names content Dutch. however still headings added template English fine , want keep everything Dutch can either edit RJournal.sty file manually (recommended) start new file add nice headings page options.top part template (dashes ---) contains settings need change title, authors abstract. Compare instance newly created article template one offered project-repository website.","code":""},{"path":"datasets.html","id":"datasets","chapter":" Discovering Data Sets","heading":" Discovering Data Sets","text":"","code":""},{"path":"datasets.html","id":"finding-public-data","chapter":" Discovering Data Sets","heading":"2.1 Finding Public Data Sets of Interest","text":"chapter describes method finding public data sets interest. data set context course refers data belonging certain gene-expression experiment, usually consisting number sequencing-samples combined meta-data describing experiment.number large databases online offer access thousands - published - experiments focus searching downloading gene expression data high-throughput (NGS) sequencing techniques (RNA-Seq).thousands freely available data sets possible start performing research without need actually performing lab-experiment. common conditions, organisms tissues can download samples compare multiple studies find novel relations genes conditions verify experiments performed laboratory. instance, power public data sets demonstrated jointly three alumni article called Calling genotypes public RNA-sequencing data enables identification genetic variants affect gene-expression levels.(al. 2015).’s method section begins sentence “downloaded raw reads available human RNA-seq data sets …” amount data work become clear later . interested expression Quantitative Trait Loci (eQTLs) Allele-Specific Expression (ASE) analysis, please read interesting paper.start diving large complex databases potentially containing thousands experiments millions samples terabytes data, need get rough idea like found something interest order know looking first place. definitive guide protocol can followed processing analyzing gene-expression data set, following goals can considered:Re-(part ) analysis described accompanying publication.\ndata sets - except ones added recently - accompanied publication, can gather lot information regarding experimental design results just paper. often, researchers already performed interesting research data set therefore good exercise try reproduce results. Challenges getting proper understanding techniques used researchers (article) translating analysis steps. Often though, details clear article requires interpretation results performing analysis steps arrive conclusion.\ndata sets - except ones added recently - accompanied publication, can gather lot information regarding experimental design results just paper. often, researchers already performed interesting research data set therefore good exercise try reproduce results. Challenges getting proper understanding techniques used researchers (article) translating analysis steps. Often though, details clear article requires interpretation results performing analysis steps arrive conclusion.Alternatively can come ideas (biological) question(s) try answer given data set instead redoing published work.\ncourse challenging suitable data sets.\nmight notice publications focus small set genes instead whole transcriptome. instance, perform experiment yeast want measure activity genes involved alcohol fermentation, researchers might look genes Glycolysis pathway. leaves 6000+ yeast genes explore possibly come novel relations gene expression experimental setup.\nanalysis approach explained paper focusing pre-selected list genes, depending experiment might come comparisons done original research. instance, experiment maternal age (birth) correlated autism offspring, paternal age also known addressed publication. subject research (spoiler alert; clear conclusion can drawn including extra factor…).\ncourse challenging suitable data sets.might notice publications focus small set genes instead whole transcriptome. instance, perform experiment yeast want measure activity genes involved alcohol fermentation, researchers might look genes Glycolysis pathway. leaves 6000+ yeast genes explore possibly come novel relations gene expression experimental setup.analysis approach explained paper focusing pre-selected list genes, depending experiment might come comparisons done original research. instance, experiment maternal age (birth) correlated autism offspring, paternal age also known addressed publication. subject research (spoiler alert; clear conclusion can drawn including extra factor…).Another type project evaluate different methods either normalization statistical analysis either confirm published results find novel genes involved experiment.\ncan viewed technical research subject ok pursue, however final report mainly concern biological impact findings.\nmeans accompanying publication shows conclusive results, probably come acknowledging results (therefore, extend point 1 list). conclusive result hope find results case might become hard formulate good conclusion end.\nHowever, publication isn’t specific states conclusion ‘Benzene exposure shows increased risk leukemia’ followed list genes might involved, try see can find genes might involved changing analysis approach.\n\ncan viewed technical research subject ok pursue, however final report mainly concern biological impact findings.\nmeans accompanying publication shows conclusive results, probably come acknowledging results (therefore, extend point 1 list). conclusive result hope find results case might become hard formulate good conclusion end.\nHowever, publication isn’t specific states conclusion ‘Benzene exposure shows increased risk leukemia’ followed list genes might involved, try see can find genes might involved changing analysis approach.\nmeans accompanying publication shows conclusive results, probably come acknowledging results (therefore, extend point 1 list). conclusive result hope find results case might become hard formulate good conclusion end.However, publication isn’t specific states conclusion ‘Benzene exposure shows increased risk leukemia’ followed list genes might involved, try see can find genes might involved changing analysis approach.Although last project goal many risks involved need sound project proposal, might result interesting project. shown linked article, possible combine data multiple experiments.\ninstance find two related experiments (.e. researching effect certain drug) measuring expression different tissue types (.e. liver brain tissue). analyzing experiments, present set genes show effect tissues - interestingly - genes showing effect one tissue.\ninstance find two related experiments (.e. researching effect certain drug) measuring expression different tissue types (.e. liver brain tissue). analyzing experiments, present set genes show effect tissues - interestingly - genes showing effect one tissue.list guideline kept mind browsing suitable data sets extended week three. Also, don’t worry terms unclear, getting good understanding used terminology part first weeks","code":""},{"path":"datasets.html","id":"ncbi-geo","chapter":" Discovering Data Sets","heading":"2.2 The NCBI Gene Expression Omnibus","text":"easiest method exploring finding interesting data sets simply using web-browser access data-repository. One repository NCBI Gene Expression Omnibus (GEO). repository primarily used store micro array data sets describe experiments, linking raw data, processed data accompanying publication. however many sources data browsable GEO, project limit searches experiments using RNA-Seq technology 60.000 experiments listed. Following “Expression profiling high throughput sequencing” link summary table get complete listing can find interesting experiment. Besides finding experiment interested , important requirements need account browsing listed . Note point need download data yet, described chapter 3.experiment must published publication must fully available, free otherwise Hanze University library.sample group, minimum three replicates must present. one groups less three replicates, group used (rest data might still usable minimum two groups 3 replicates).\nReplicates often easy spot. lucky samples named “WildType - rep 2” instance often need read “Overall design” part page click samples (“GSM…” ID) make sure.\nReplicates often easy spot. lucky samples named “WildType - rep 2” instance often need read “Overall design” part page click samples (“GSM…” ID) make sure.available data must contain least count data.\nData offered multiple formats, always including RAW data (actual reads don’t use), also processed data FPKM, RPKM TPM (see interesting blog-post discussing use data). tools R analysis libraries use downstream analysis rely unnormalized unprocessed data count data. counts simply represent number reads mapped transcript.\noften hardest part assess; sometimes clear provided data actual raw count values. Usually, can find (sometimes rather cryptic) information clicking sample ID ‘Data processing’ section. paragraph text often mention ‘Supplementary_files_format_and_content’ describes actual content downloadable files. Following number examples valid invalid data provided experiment. single-sample pages can find description. Unfortunately, authors experiment free explain data please, likely encounter exact descriptions:\nValid: Global effects SUPT4H1 RNAi gene expression HEK293 cells; description: Supplementary_files_format_and_content: tab-delimited, gzip-compressed text file matrix raw quantitation results sample. Mentions raw-anything likely valid.\nValid: Transcriptome profiling normal transformed mouse urothelial cells; description: Supplementary_files_format_and_content: raw_counts_unfiltered.txt.gz: Raw counts.. Even though 4 different file types available download, one contains actual raw count data.\nValid: Quantitative Analysis PPARD RNA-Seq Transcriptomes Mouse Gastric Corpus Epithenial Cells Next Generation Sequencing (NGS); description: Supplementary_files_format_and_content: raw counts sequencing reads genes. Single file download containing raw counts.\nValid: Impact shRNA-mediated KLF4 -modulation transcriptome profile human keratinocyte precursor cells; description: Supplementary_files_format_and_content: raw normalized counts.\nValid: Role TAF5L TAF6L controlling mouse ESC state; description: Supplementary_files_format_and_content: Comma-separated text files containing raw counts, normalized counts, FPKM expression values TPM expression values.. common include normalized raw values.\nInvalid: Co-chaperone Mzb1 key effector Blimp1 plasma cell differentiation b1 integrin function; description: Supplementary_files_format_and_content: normalized gene expression values (FPKM) calculated Cuffnorm (v2.2.1) provided TXT format. clearly states normalized data (FPKM values) available.\nInvalid: EGFR-targeted therapy-induced resistance mechanism glioblastoma; description: Supplementary_files_format_and_content: .txt file including gene name, RefSeq id, locus, RPKM value gene sample. RPKM values available normalized raw count values, usable.\nInvalid: RNA-seq subcellular fractions nESC EpiLC; description: Supplementary_files_format_and_content: _genes.fpkm_table.txt: Tab-delimited text files include FPKM values replicate.*. FPKM data available.\n\nData offered multiple formats, always including RAW data (actual reads don’t use), also processed data FPKM, RPKM TPM (see interesting blog-post discussing use data). tools R analysis libraries use downstream analysis rely unnormalized unprocessed data count data. counts simply represent number reads mapped transcript.often hardest part assess; sometimes clear provided data actual raw count values. Usually, can find (sometimes rather cryptic) information clicking sample ID ‘Data processing’ section. paragraph text often mention ‘Supplementary_files_format_and_content’ describes actual content downloadable files. Following number examples valid invalid data provided experiment. single-sample pages can find description. Unfortunately, authors experiment free explain data please, likely encounter exact descriptions:\nValid: Global effects SUPT4H1 RNAi gene expression HEK293 cells; description: Supplementary_files_format_and_content: tab-delimited, gzip-compressed text file matrix raw quantitation results sample. Mentions raw-anything likely valid.\nValid: Transcriptome profiling normal transformed mouse urothelial cells; description: Supplementary_files_format_and_content: raw_counts_unfiltered.txt.gz: Raw counts.. Even though 4 different file types available download, one contains actual raw count data.\nValid: Quantitative Analysis PPARD RNA-Seq Transcriptomes Mouse Gastric Corpus Epithenial Cells Next Generation Sequencing (NGS); description: Supplementary_files_format_and_content: raw counts sequencing reads genes. Single file download containing raw counts.\nValid: Impact shRNA-mediated KLF4 -modulation transcriptome profile human keratinocyte precursor cells; description: Supplementary_files_format_and_content: raw normalized counts.\nValid: Role TAF5L TAF6L controlling mouse ESC state; description: Supplementary_files_format_and_content: Comma-separated text files containing raw counts, normalized counts, FPKM expression values TPM expression values.. common include normalized raw values.\nInvalid: Co-chaperone Mzb1 key effector Blimp1 plasma cell differentiation b1 integrin function; description: Supplementary_files_format_and_content: normalized gene expression values (FPKM) calculated Cuffnorm (v2.2.1) provided TXT format. clearly states normalized data (FPKM values) available.\nInvalid: EGFR-targeted therapy-induced resistance mechanism glioblastoma; description: Supplementary_files_format_and_content: .txt file including gene name, RefSeq id, locus, RPKM value gene sample. RPKM values available normalized raw count values, usable.\nInvalid: RNA-seq subcellular fractions nESC EpiLC; description: Supplementary_files_format_and_content: _genes.fpkm_table.txt: Tab-delimited text files include FPKM values replicate.*. FPKM data available.\nValid: Global effects SUPT4H1 RNAi gene expression HEK293 cells; description: Supplementary_files_format_and_content: tab-delimited, gzip-compressed text file matrix raw quantitation results sample. Mentions raw-anything likely valid.Valid: Transcriptome profiling normal transformed mouse urothelial cells; description: Supplementary_files_format_and_content: raw_counts_unfiltered.txt.gz: Raw counts.. Even though 4 different file types available download, one contains actual raw count data.Valid: Quantitative Analysis PPARD RNA-Seq Transcriptomes Mouse Gastric Corpus Epithenial Cells Next Generation Sequencing (NGS); description: Supplementary_files_format_and_content: raw counts sequencing reads genes. Single file download containing raw counts.Valid: Impact shRNA-mediated KLF4 -modulation transcriptome profile human keratinocyte precursor cells; description: Supplementary_files_format_and_content: raw normalized counts.Valid: Role TAF5L TAF6L controlling mouse ESC state; description: Supplementary_files_format_and_content: Comma-separated text files containing raw counts, normalized counts, FPKM expression values TPM expression values.. common include normalized raw values.Invalid: Co-chaperone Mzb1 key effector Blimp1 plasma cell differentiation b1 integrin function; description: Supplementary_files_format_and_content: normalized gene expression values (FPKM) calculated Cuffnorm (v2.2.1) provided TXT format. clearly states normalized data (FPKM values) available.Invalid: EGFR-targeted therapy-induced resistance mechanism glioblastoma; description: Supplementary_files_format_and_content: .txt file including gene name, RefSeq id, locus, RPKM value gene sample. RPKM values available normalized raw count values, usable.Invalid: RNA-seq subcellular fractions nESC EpiLC; description: Supplementary_files_format_and_content: _genes.fpkm_table.txt: Tab-delimited text files include FPKM values replicate.*. FPKM data available.","code":""},{"path":"EDA.html","id":"EDA","chapter":" Exploratory Data Analysis","heading":" Exploratory Data Analysis","text":"start performing exploratory data analysis steps goal getting grips chosen data set properly identify strategy actual analysis steps. exploration also keep eye quality data. Even though downloadable data ‘processed’, might samples present deviate much group samples (called outlier). Creating basic visualizations data give necessary insight continue.","code":""},{"path":"EDA.html","id":"loading-data","chapter":" Exploratory Data Analysis","heading":"3.1 Loading data into R","text":"one column based text files can read R simply using read.table() function.Follow following steps read data start exploratory data analysis. resulting document treated lab journal log process loading data final analysis steps.Open RStudioCreate new R Markdown document\nGive proper title select PDF format\nGive proper title select PDF formatGive document structure; e.g. create segment (using single hash #) called Exploratory Data Analysis.Whenever add code document make sure readable (keep maximum line length < 100 possible) sufficient documentation either text around code ‘chunks’ (preferred) using comments within code chunk.Read data file(s)\nPreferably use read.table function carefully set arguments. Open file text editor first check contents; header? can set row.names? columns needed? etc.\nNote: data set consists separate files (.e. one per sample) general tips reading data, see Appendix : Batch Loading Expression Data chapter.\nremainder document, try show either contents, structure - case - dimensions relevant R objects\nShow first five lines loaded data set. Including tables markdown document can done using pander function pander(Daróczi Tsegelskyi 2017) R-library.\nGive dimensions (dim() structure (str()) loaded data set.\nCheck output str function see columns expected R data type (e.g. values, factors, character, etc.)\n\nPreferably use read.table function carefully set arguments. Open file text editor first check contents; header? can set row.names? columns needed? etc.Note: data set consists separate files (.e. one per sample) general tips reading data, see Appendix : Batch Loading Expression Data chapter.remainder document, try show either contents, structure - case - dimensions relevant R objects\nShow first five lines loaded data set. Including tables markdown document can done using pander function pander(Daróczi Tsegelskyi 2017) R-library.\nGive dimensions (dim() structure (str()) loaded data set.\nCheck output str function see columns expected R data type (e.g. values, factors, character, etc.)\nShow first five lines loaded data set. Including tables markdown document can done using pander function pander(Daróczi Tsegelskyi 2017) R-library.Give dimensions (dim() structure (str()) loaded data set.Check output str function see columns expected R data type (e.g. values, factors, character, etc.)Examine samples included experiment create many R character objects needed store classification.\nNote: important item often overlooked. See last 4 lines code chunk section Example Data .\ninstance, eight samples divided case/ control columns create variable called case store column indices (numbers!) respective columns data set variable called control remaining four data column indices. later use.\nvariables allows us repeatedly access data rest course, .e. code boxplot(counts[case]) immediately shows plotting case samples counts data clear reading boxplot(counts[c(5:8)]) gives reference indices mean can lead typing mistakes.\nNote: important item often overlooked. See last 4 lines code chunk section Example Data .instance, eight samples divided case/ control columns create variable called case store column indices (numbers!) respective columns data set variable called control remaining four data column indices. later use.variables allows us repeatedly access data rest course, .e. code boxplot(counts[case]) immediately shows plotting case samples counts data clear reading boxplot(counts[c(5:8)]) gives reference indices mean can lead typing mistakes.\ndata sets order samples listed \nloaded data set different order shown GEO\nwebsite. often, names different lacking \ndescription non descriptive IDs. creating\nvariables control need point \ncontrol samples, need make sure right columns\ndata frame. Go Appendix A2;\nannotation order unclear just large number\nsamples might supporting data available can help\nmake sense sample layout.\nwant include external images log better control properties height width individual images can use following code. Note: code example need replace img_to_include.png file path actual image.):See Knitr chunk options page full listing chunk options.","code":"\n# Add the following to the code chunk header to control figure height, width and add a caption:\n#    {r, fig.height=5, fig.width=8, echo=FALSE, fig.cap=\"Image description\"}\n# The `dpi` argument is to remove any scaling\nknitr::include_graphics(\"figures/img_to_include.png\", dpi = NA)"},{"path":"EDA.html","id":"ch3_example_data","chapter":" Exploratory Data Analysis","heading":"3.2 Example Data","text":"section lists (publically available) data set(s) used chapter. chapter contains section new data sets used . Note examples, data different examples one challenges course translating examples data. Keep mind simple copy-pasting code fail reason. examples therefore print input data comparison data.section Normalization onwards, experiment identifier GSE101942 used visualizing normalized raw count values (earlier sections explore unnormalized data). experiment titled “Transcriptome analysis genetically matched human induced pluripotent stem cells disomic trisomic chromosome 21” experimental setup described follows:“12 total polyA selected samples. 6 IPSC samples 3 biological repeats trisomic samples 3 biological repeats disomic samples. 6 IPSC derived neuronal samples 3 biological repeats trisomic samples 3 biological repeats disomic samples.”following code shows load two available files containing raw-count data (one file per 6 samples), stored locally data/gse101942/ folder. simple ‘cleanup’ steps performed aren’t required helpful demonstration purposes (.e., renaming samples identify groups).GSE101942_IPSC_rawCounts.txt: raw count data induced pluripotent stem cells (iPSCs), trisomic disomicGSE101942_Neuron_rawCounts.txt: raw count data IPSC-derived Neurons, trisomic disomic. cells treated remove chromosome 21 iPSCs, described treatment protocol : “Targeted removal CHR 21 IPSC using TKNEO transgene”.See Table 1 Normalization section resulting contents counts dataframe.","code":"\n# Load the two sets of 6 samples\nipsc.data <- read.table('./data/gse101942/GSE101942_IPSC_rawCounts.txt',\n                        header = TRUE)\nneuron.data <- read.table('./data/gse101942/GSE101942_Neuron_rawCounts.txt',\n                          header=TRUE)\n# Merge by rowname (by = 0, see the help of 'merge')\ncounts <- merge(ipsc.data, neuron.data, by = 0, all.x = TRUE, all.y = TRUE,\n                sort = FALSE)\n# Change all NA's introduced by the merge to zeros\ncounts[is.na(counts)] <- 0\n# Show column names coming from the input files\nprint(names(counts))##  [1] \"Row.names\" \"c244A\"     \"c244B\"     \"c243\"      \"c2A\"       \"c2B\"      \n##  [7] \"c2C\"       \"C3_1\"      \"C3_2\"      \"C3_4\"      \"C2_2_1\"    \"C2_2_2\"   \n## [13] \"C2_2_3\"\n# Set the row names to the gene IDs stored in the 'Row.names' column\nrow.names(counts) <- counts$Row.names\n# Remove the gene ID column\ncounts <- counts[-1]\n# Rename samples to include their group name\nnames(counts) <- c(paste0('di_IPSC_r', 1:3),  # Disomic IPSC, replicates 1-3\n                   paste0('tri_IPSC_r', 1:3), # Trisomic IPSC, replicates 1-3\n                   paste0('di_NEUR_r', 1:3),  # Disomic Neuron, replicates 1-3\n                   paste0('tri_NEUR_r', 1:3)) # Trisomic Neuron, replicates 1-3\n# Show results of renaming the samples\nprint(names(counts))##  [1] \"di_IPSC_r1\"  \"di_IPSC_r2\"  \"di_IPSC_r3\"  \"tri_IPSC_r1\" \"tri_IPSC_r2\"\n##  [6] \"tri_IPSC_r3\" \"di_NEUR_r1\"  \"di_NEUR_r2\"  \"di_NEUR_r3\"  \"tri_NEUR_r1\"\n## [11] \"tri_NEUR_r2\" \"tri_NEUR_r3\"\n# Assign column -indices- to variables for later use (i.e. counts[di_NEUR] selects the relevant columns)\ndi_IPSC <- 1:3\ntri_IPSC <- 4:6\ndi_NEUR <- 7:9\ntri_NEUR <- 10:12"},{"path":"EDA.html","id":"EDA_part1","chapter":" Exploratory Data Analysis","heading":"3.3 Visualizing using boxplot and density plot","text":"segment describes basic steps visualization can performed Exploratory Data Analysis. start inspecting unnormalized count data. next steps normalization applied shown Normalization section . mentioned , focus EDA get overview/ perform bit Quality Control data set often requires visualizing data, figures need pretty. Simple figures perfectly fine stage. Try create figures data (keep log) add small description figure pointing anything different expect.Also note steps just selection. Furthermore, make sure every visualization make, add proper axis-labels containing measurement units (important!). might able see, values log-transformed using log2 function often numerical values high range ‘hide’ details plots. See section Fold Change value chapter 4 details. fine use non-log-transformed (simply raw-)data, otherwise use instance boxplot(log2(dataset)) plotting.\nInstead using basic R-plotting library\n(.e. plot, boxplot, hist, etc.)\ncan also opt using (challenging) ggplot2\nlibrary also used boxplot following section.\nconstructing ggplot2 plot feels like learning yet\nanother language, many resources available online can\nfollow.\n","code":""},{"path":"EDA.html","id":"eda_statistics","chapter":" Exploratory Data Analysis","heading":"3.3.1 Statistics","text":"Even basic statistics can give insight data performing 6-number-statistic data columns using summary() function. Note: can also use pander function pretty-print summary knitted output. notice look numbers produced executing function complete data set?","code":""},{"path":"EDA.html","id":"eda_boxplots","chapter":" Exploratory Data Analysis","heading":"3.3.2 Boxplots","text":"visual representation values can shown using boxplot. Boxplots easy create R data frame object just passing data columns. following boxplot shows data experiment separate boxplot sample. allows quick overview spotting irregularities (.e. checking replicates within sample-group show similar expression profiles). course, consider amount data single plot, can hint problems, need look much detail form quality control. Creating boxplot dataframe easy, saw using summary function; data large range maximum average values far apart. create lot outliers plot interesting later , boxplot can either:hide completely using outline = FALSE argument boxplot() (say figure description!), orperform log2() transformation can see .\ncreating plots expression data without performing\nlog-transformations see (instance boxplot) \nrange data large (0 1.000.000) lot\noutliers upper range. makes informative\nfigures, placing simple log2() function call around\ndata almost resolves issue.\n\nHowever, might introduce another problem (taking boxplot \nexample) log-transformation results data \ncontains negative infinity (-Inf) values. \ncaused 0-values data since\nlog2(0) == -Inf. circumvent issue, can add \npseudo count data simply adding value\n1 count values since log2(1) == 0;\nlog2(counts + 1).\n\nsituations boxplot adding \npseudo count value complete dataset can useful. Always add \npseudocount -place, meaning within plotting code instead\noverwriting original data set pseudo count added \nsteps require . cases (see density plot ) can useful \nclearly separate 0-values rest case \n0 < pseudo count < 1 value, 0.1\ncan used generates negative value (-3.3) \nfar away rest data. count values always\n>0 result positive log2 value.\n\nFigure 3.1: Boxplot comparing basic statistics genes across multiple samples\n","code":""},{"path":"EDA.html","id":"eda_density_plot","chapter":" Exploratory Data Analysis","heading":"3.3.3 Density Plots","text":"Another form visualizing data using density plot. method shows distribution (log2-transformed) count data samples allows easy spotting problems. plot commonly used analysing microarrays, still useful comparing complete dataset. code figure show example distribution 12 samples. things note figure huge peak exactly -3.321928 can ignored value calculated log2(0.1) explained box . peak consists 0-values (inactive genes) isn’t important us now. Therefore, added vertical line indicate left-part little interest. Note: lot code extra, simple inspection using line plotDensity(log2(counts + 0.1)) enough, rest extra example code.assess quality data look peaks plot. heigth peak doesn’t matter much, shifted peaks however indicate either lower higher amount reads sequenced sample. can confirmed first plot demonstrated next section library-sizes visualized.\nFigure 3.2: Density plot comparing count distribution 12 samples\n","code":"\n## The affy library has a density plotting function\nlibrary(affy)\n\n## Create a list of 4 colors to use which are the same used throughout this chapter \nlibrary(scales)\nmyColors <- hue_pal()(4)\n\n## Plot the log2-transformed data with a 0.1 pseudocount\nplotDensity(log2(counts + 0.1), col=rep(myColors, each=3),\n            lty=c(1:ncol(counts)), xlab='Log2(count)',\n            main='Expression Distribution')\n\n## Add a legend and vertical line\nlegend('topright', names(counts), lty=c(1:ncol(counts)),\n       col=rep(myColors, each=3))\nabline(v=-1.5, lwd=1, col='red', lty=2)"},{"path":"EDA.html","id":"EDA_part2","chapter":" Exploratory Data Analysis","heading":"3.4 Visualizing using heatmap and MDS","text":"section adds Exploratory Data Analysis techniques measure look distances samples based normalized data. Measuring distances two data objects (samples case) common task cluster analysis compare similarity (low distance indicates similar data). case calculate distances samples visualize heatmap using multidimensional scaling (MDS) technique.previous section used raw count data. might one samples different values (.e. shifted) compared samples. need raw count data use R packages edgeR (Chen et al. 2018) DESeq2 (Love, Anders, Huber 2017), calculating sample distances (used visualizations section) done form normalized data. data can either RPKM/FPKM/TPM/CPM vst-transformed (raw-)read counts. proper method transforming raw read count data using vst method DESeq2 R Bioconductor library shown . ‘variance stabilizing transformation’ normalized data used chapter, chapter 4 normalize using different technique.following code examples shows use library normalize count data GSE101942 experiment vst-normalized data calculate distance metric.new(“standardGeneric”, .Data = function (object, …)standardGeneric(“counts”), generic = structure(“counts”, package = “BiocGenerics”),package = \"BiocGenerics\", group = list(), valueClass = character(0),signature = \"object\", default = NULL, skeleton = (function (object,    ...)stop(gettextf(\"invalid call method dispatch '%s' (default method)\",look Table 1 immediately see huge difference groups genes. gene (A1BG) shows >100 expression tri_NEUR group compared di_IPSC group, within groups high variation indicating might actual expression different. One simple quick method inspection looking total number mapped reads per sample (sum sample/ column), sequencing-depth might vary across samples.Hint:Simple version plot can achieved barplot(colSums(counts) / 1e6)Table 2; Mapped reads per sample (millions) (continued )\nFigure 3.3: Library sizes samples GSE101942\nnumbers shown Table 2 barplot immediately clarify just experimental condition might caused large difference sample counts sequencing depth shows substantial difference . minimum maximum sequencing depth within trisomic neuron group (purple barplot). average expression A1BG gene within group 820, minimum 461 maximum 1010 lot variation. one reason telling us normalize data. following code chunks show DESeq2’s vst method (variance stabilizing transformation). Check Packages tab Rstudio see DESeq2 package installed load library command.","code":"package = \"BiocGenerics\", group = list(), valueClass = character(0),signature = \"object\", default = NULL, skeleton = (function (object,    ...)stop(gettextf(\"invalid call in method dispatch to '%s' (no default method)\","},{"path":"EDA.html","id":"normalization","chapter":" Exploratory Data Analysis","heading":"3.4.1 Normalization","text":"use vst function DESeq2 library must construct DESeqDataSet-object consisting count data combined sample annotation. Since want use (now) performing vst-transformation use basic form simple design sample names annotation (colData argument):now proper DESeqDataSet object can see , containing 56640 rows 12 columns (genes samples) gene symbols row names. Usually object hold data, requirement perform vst transformation good enough now. Next step performing transformation (can take depending size experiment results Large DESeqTransform object) retrieving actual data assay function object contains lot meta-data. table shows updated values now comparable across genes whereas raw count data harder compare.Table 2; VST transformed disomic IPSC count dataNote looking A1BG gene (one displayed figure 3.8), normalized expression values show much less variation across samples (shown, also within sample group). Therefore, assume large difference expression observed earlier might non-existent (might still significant difference though!).","code":"\n# Load the library\nlibrary('DESeq2')\n\n# DESeq2 will construct a SummarizedExperiment object and combine this \n# into a 'DESeqDataSet' object. The 'design' argument usually indicates the \n# experimental design using the condition(s) names as a 'factor', for now we use just '~ 1'\n(ddsMat <- DESeqDataSetFromMatrix(countData = counts,\n                                  colData = data.frame(samples = names(counts)),\n                                  design = ~ 1))## class: DESeqDataSet \n## dim: 56640 12 \n## metadata(1): version\n## assays(1): counts\n## rownames(56640): 5S_rRNA 7SK ... C1orf220 C2orf15\n## rowData names(0):\n## colnames(12): di_IPSC_r1 di_IPSC_r2 ... tri_NEUR_r2 tri_NEUR_r3\n## colData names(1): samples\n# Perform normalization\nrld.dds <- vst(ddsMat)\n# 'Extract' normalized values\nrld <- assay(rld.dds)"},{"path":"EDA.html","id":"eda_distance","chapter":" Exploratory Data Analysis","heading":"3.4.2 Distance Calculation","text":"now normalized data can use distance calculation. standard procedure many data analysis tasks calculates distance metric combination samples use check variation within sample groups. first need transpose matrix (rld) normalized values using t-function, dist function expects different samples rows genes columns. Note output matrix symmetric. Table 3 shows calculated distances within disomic IPSC group distance samples varies 100 140. maximum distance samples 380 demonstrated visualizations next sections.Table 3; Sample distances disomic IPSCs (Euclidean method)","code":"\n# Calculate basic distance metric (using euclidean distance, see '?dist')\nsampledists <- dist( t( rld ))"},{"path":"EDA.html","id":"eda_heatmap","chapter":" Exploratory Data Analysis","heading":"3.4.3 Sample Distances using a Heatmap","text":"(raw-)count data normalized format (TPM, RPKM, etc.), can optionally follow procedure count data create heatmap formats see makes difference. reason RNA-Seq method exists 10 years, still ongoing discussions subject data processing, especially regarding subjects like normalization technque use data analysis.following code block creates heatmap using pheatmap library offers many available heatmap functions. Using annotation data frame (can inspect contents ) identify samples based cell type ploidy. clustering shown heatmap clearly separates data based cell type differences ploidy seems minimal. Looking still, differences within single group minimal meaning - yet - inclined remove outlier-samples. Since 3 samples per category, also lose statistical power eventually remove one samples (also, always check article see remove samples prior data analysis).\nFigure 3.4: Heatmap showing Euclidean distances samples\nresulting heatmap shows interesting comparison across samples order samples purely determined using distance often different order data set. Ideally, want sample groups clustered together see data. particular heatmap clearly shows large difference overal expression induced pluripotent stem cells (iPSCs) differentiated cortical neurons difference - within cell type - disomic trisomic much lower.","code":"\n# We use the 'pheatmap' library (install with install.packages('pheatmap'))\nlibrary(pheatmap)\n\n# Convert the 'dist' object into a matrix for creating a heatmap\nsampleDistMatrix <- as.matrix(sampledists)\n\n# The annotation is an extra layer that will be plotted above the heatmap columns\n# indicating the cell type\nannotation <- data.frame(Cell = factor(rep(1:2, each = 6), \n                                          labels = c(\"IPSC\", \"Neuron\")),\n                         Ploidy = factor(rep(rep(1:2, each = 3), 2),\n                                         labels = c(\"disomic\", \"trisomic\")))\n                         \n# Set the rownames of the annotation dataframe to the sample names (required)\nrownames(annotation) <- names(counts)\n\npheatmap(sampleDistMatrix, show_colnames = FALSE,\n         annotation_col = annotation,\n         clustering_distance_rows = sampledists,\n         clustering_distance_cols = sampledists,\n         main = \"Euclidean Sample Distances\")"},{"path":"EDA.html","id":"eda_mds","chapter":" Exploratory Data Analysis","heading":"3.4.4 Multi-Dimensional Scaling","text":"following code example shows perform Multi-Dimensional Scaling (MDS) displays similarly calculated distances 2D-plot. experiment like two groups samples, hope see two clearly separated clusters formed, however ’ve seen heatmap, sample KO1B showed large deviation also see (confirm) using MDS.\nfigure plotted using ggplot2, \nadvanced method plotting R. plots preferred \nbase-R plotting, always sufficient use just can \nchallenging alter example code shown section. \ndata object plotted shown case contains simple X- \nY-coordinates.\nMDS use slightly different distance metric (Poisson instead default euclidean used dist function). following code shows calculate fitting distance sequencing data called Poisson Distance number optimizations available PoiClaClu library. library specifically designed handle read count data less influenced read-count differences across samples. Note , code usable count data function requires:n--p data matrix observations rows, p features columns. (,j) element x number reads observation mapped feature (e.g. gene exon) j.Table 5: MDS coordinates used plotting distances using Poisson Distance\nFigure 3.5: Multi Dimensional Scaling - Poisson Distance samples\nclearly see separation cell type less clear separation ploidy. spread within disomic groups much lower compared trisomic groups. Note y-axis range 4th x-axis range. sample di_NEUR_r3 shows large difference MDS plot. set genes identified differentially expressed repeat step expectation clear clustering.","code":"\nlibrary('PoiClaClu')\n# Note: uses the raw-count data, PoissonDistance performs normalization\n# set by the 'type' parameter (uses DESeq)\ndds <- assay(ddsMat)\npoisd <- PoissonDistance( t(dds), type = \"deseq\")\n# Extract the matrix with distances\nsamplePoisDistMatrix <- as.matrix(poisd$dd)\n# Calculate the MDS and get the X- and Y-coordinates\nmdsPoisData <- data.frame( cmdscale(samplePoisDistMatrix) )\n\n# And set some better readable names for the columns\nnames(mdsPoisData) <- c('x_coord', 'y_coord')\n# Separate the annotation factor (as the variable name is used as label)\ngroups <- factor(rep(1:4, each=3), \n                 labels = c(\"di_IPSC\", \"tri_IPSC\", \"di_NEUR\", \"tri_NEUR\"))\ncoldata <- names(counts)\n\n# Create the plot using ggplot\nggplot(mdsPoisData, aes(x_coord, y_coord, color = groups, label = coldata)) + \n  geom_text(size = 4) +\n  ggtitle('Multi Dimensional Scaling') +\n  labs(x = \"Poisson Distance\", y = \"Poisson Distance\") +\n  theme_bw()\n## Using base-R plot and legend functions:\n# plot(mdsPoisData, col=rep(myColors, each=3), pch=20, lwd=2)\n# legend(x=-20000, y=11000, legend = levels(groups), col=myColors, pch=20)"},{"path":"EDA.html","id":"cleaning-data","chapter":" Exploratory Data Analysis","heading":"3.5 Cleaning Data","text":"Conclude chapter cleaning dataset visualizations show necessity . Cleaning case means removing complete samples can classified outlier within group. tasks course, clear advice decide remove one samples. clear rule group must retain least three samples. one three samples visibly deviates replicates, even normalization, stay data set mentioned analysis report/ final article noise may introduced sample.\nNote: satisfied data reported findings, return steps next chapters. mentions creating heatmap instance, means heatmap expression data instead sample distances ’ve shown chapter.","code":""},{"path":"chapter-4.html","id":"chapter-4","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":" Discovering Differentialy Expressed Genes (DEGs)","text":"first important ‘real’ analysis step finding genes show difference expression sample groups; differentially expressed genes (DEGs). concept might sound rather simple; calculate ratios genes samples determine fold-change (FC) denoting factor change expression groups. , filter genes actually show difference. give list genes showing different behaviour across samples, need focus genes show difference, observed difference also statistically significant!determine whether gene can classified significant DEG going use existing libraries, one article might refer . ’re going brief manual examination count data make sure properly understand ’re looking : significance Fold-Change value.","code":""},{"path":"chapter-4.html","id":"deg-preprocessing","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.1 Pre-processing","text":"Given results exploratory data analysis performed chapter 3, might concluded one samples show () deviating expression patterns compared samples group. mentioned , enough (> 3) samples group, might opt remove sample reduce noise statistical tests sensitive . Since performing analysis steps programatically also easy test DEGs without sample(s) question see removal results lower p-values (= higher significance). Remember always properly document choice reasoning!perform manual steps data using libraries, re-normalization different technique. simple method normalizing count data calculate fragments per million mapped fragments (FPM, equal CPM) value transform log2. Opposed FPKM RPKM include gene-length calculation (likely don’t ). dataset also includes FPKM RPKM (pre-normalized), allowed use data .FPM CPM simply dividing count values sample total number reads sample divided 1 million shown :Another step can - might guided article - filter (partially) inactive genes. required using edgeR library (DESeq2 includes low-count filtering methods), good exercise manually use calculating lFC values. data sets available contains lot 0-measurements (see instance density plot previous chapter); genes reads mapped. R-studio Environment tab, click data set (perform View(data) command click one sample columns order data ascending. now likely (unless bacterial data instance) see lot zero values columns.experiment two groups, three replicates , three six samples 0-reads mapped, often advised remove gene completely. can subjective experiment, might expected (thus important) comparing different tissues knockout experiments. Also, genes () low read count (< 5) can give high (artificial) FC value (see left hand side MA-plot). Comparing two samples one value 2 11, reads -regulated gene factor 5.5 might actually just noise!Assignment: Search article advice filter zero values low count genes. nothing stated subject, think tactic (search literature/ online!). perfectly fine discuss peers.\ndescribe aspect; filter data, clearly explain (likely article stated proper reason)\nPerform filtering data set. likely need use one apply, rowSums, rowMeans functions, combined maybe , functions.\nManually verify rows removed correctly filtered.\nProperly document many genes filtered !\ndescribe aspect; filter data, clearly explain (likely article stated proper reason)Perform filtering data set. likely need use one apply, rowSums, rowMeans functions, combined maybe , functions.Manually verify rows removed correctly filtered.Properly document many genes filtered !","code":"\n# Perform a naive FPM normalization\n# Note: log transformation includes a pseudocount of 1\ncounts.fpm <- log2( (counts / (colSums(counts) / 1e6)) + 1 )"},{"path":"chapter-4.html","id":"deg-fc","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.2 The Fold Change Value","text":"FC usually given calculated log2 case/control ratio. example, gene average expression 30 mapped reads control group 88 reads experiment group, ratio case/control 2.93. Ratio values > 1 indicate increased expression experiment relation control values 0 1 indicate lower expression. log2 transformed value ratio calculated log2(2.93) results 1.55. counts reversed, ratio 30/88, 0.34. previously calculated value 2.93 means 3-fold regulation 0.34 value means 3-fold regulation can see range numbers different. Comparing log2 values 1.55 -1.55 compares much better.easy calculate FC genes , simple FC value doesn’t mean much, yet! still need use power lie within replicates sample group. Using replicates, want determine observed FC just biological noise sequencing error.Assignment: Create histogram log2-FC values genes. Apply following steps FPM-normalized data least one comparison (need possible combinations):\naverage replicates two groups separately add new columns FPM-normalized data set,\ncalculate log Fold Change (lFC); simply subtract one group (averaged-column) another, usually experiment-control\nNote since already log-transformed data, calculate ratio (experiment/control). Also, Logarithm quotient rule, divide average columns ratio calculation, subtract: \\(log_b{(x/y)} = log_b{(x)} - log_b{(y)}\\)\n\nplot data using hist(logFC_column, breaks=60) function (change breaks argument needed),\nAdd two vertical lines -1 1 (using abline(v=...)) indicate significance (2-fold change).\ndata shows low fold-change values might see genes outside boundary. mean find significantly changed genes (DEGs).\n\naverage replicates two groups separately add new columns FPM-normalized data set,calculate log Fold Change (lFC); simply subtract one group (averaged-column) another, usually experiment-control\nNote since already log-transformed data, calculate ratio (experiment/control). Also, Logarithm quotient rule, divide average columns ratio calculation, subtract: \\(log_b{(x/y)} = log_b{(x)} - log_b{(y)}\\)\nNote since already log-transformed data, calculate ratio (experiment/control). Also, Logarithm quotient rule, divide average columns ratio calculation, subtract: \\(log_b{(x/y)} = log_b{(x)} - log_b{(y)}\\)plot data using hist(logFC_column, breaks=60) function (change breaks argument needed),Add two vertical lines -1 1 (using abline(v=...)) indicate significance (2-fold change).\ndata shows low fold-change values might see genes outside boundary. mean find significantly changed genes (DEGs).\ndata shows low fold-change values might see genes outside boundary. mean find significantly changed genes (DEGs).calculated FC shows large change expression groups means nothing variation within group high. reason use form statistical test checks variation group difference groups samples. simplest form usually comes using t-test:“can used determine two sets data significantly different ”often instead manually performing t-tests ANOVA analysis, article mentions use one R libraries software packages finding DEGs. exactly also chapter. output finding DEG’s always includes - limited - list p-values; one p-value per gene. value indicates whether gene statistically-significant differentially expressed gene (SDEG) find genes interest need get genes p-value threshold (.e. 0.05).","code":""},{"path":"chapter-4.html","id":"deg-bioconductor","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3 Using Bioconductor Packages","text":"section demonstrates use two packages perform DEG-analysis count data. many packages available Bioconductor RNA-Seq analysis, DSS, EBSeq, NOISeq BaySeq, focus edgeR DESeq2 processing count-based data. Chances one two packages mentioned article described use R statistical analysis widely accepted methods processing gene expression data.packages apply normalization methods (described sections ) therefore work raw count data. data consists expected count data, need round values (using floor function). can choose one two packages use, use since increase statistical power hard use understand model data ’ll show next.Assignment: Perform data-analysis using least one two packages. however article mentioned different R package analysis (instance limma package) allowed use one instead. non-R packages can done extra (dataset can always analyzed using edgeR DESeq2). Optionally, use one package can compare results packages.\nStart documenting relevant information article, also used software analysis.\nStart documenting relevant information article, also used software analysis.two listed packages come complete extensive manual. Therefore, course manual (document) guide exactly use ; find . manuals can found PDF BioConductor website, opened within R using vignette function. see available manuals (note; different function-help documentation) execute function without arguments: vignette(). lists vignettes edgeR DESeq2 :vignette(\"DESeq2\") (whole course partly designed document!)vignette(\"edgeR\")\n","code":""},{"path":"chapter-4.html","id":"deg-design","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3.1 The Design (matrix)","text":"packages need properly specify samples grouped. seen examples using R factor object heatmap MDS visualizations tell groups samples group sample belongs. Reading documentation packages shows important part performing DE-analysis. example, following code shown edgeR documentation page 8 two sample groups defined (numbered 1 2), placed factor object used input model.matrix function.can often complex part seems theoretical, practice ’s difficult. really grasp topic strongly adviced watch two videos define design:StatQuest YouTube: Using Linear Models t-tests ANOVA, Clearly ExplainedStatQuest YouTube: Design Matrices Linear Models, Clearly ExplainedThese videos expects basic understanding topics already discussed statistics course, otherwise mentioned topic videos videos available explaining . See video-index handy overview topics discussed channel.mentioned need think question want ask; difference want know? two sample groups shown question rather easy; ‘genes show effect case/ control samples?’. two sample groups however question becomes complex. experiment comparing influence three kinds drugs (thus three groups) combined effect time, want focus influence drugs time? valid questions define way create design matrix.Documentation subject plenty, however often contains overwhelming information. page contains valuable details (can safely start reading Choice design), including following text based example used edgeR documentation:“examples cover , use linear models make comparisons different groups. Hence, design matrices ultimately work least two columns: intercept column, consists column 1’s, second column, specifies samples second group. case, two coefficients fit linear model: intercept, represents population average first group, second coefficient, represents difference population averages second group first group. latter typically coefficient interested performing statistical tests: want know difference two groups.”two sample groups want change question (.e. test influence different group), read section Releveling factor.","code":"\ngroup <- factor(c(1,1,2,2), labels = c(\"Control\", \"Case\"))\n(design <- model.matrix( ~ group))##   (Intercept) groupCase\n## 1           1         0\n## 2           1         0\n## 3           1         1\n## 4           1         1\n## attr(,\"assign\")\n## [1] 0 1\n## attr(,\"contrasts\")\n## attr(,\"contrasts\")$group\n## [1] \"contr.treatment\""},{"path":"chapter-4.html","id":"deg-deseq2","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3.2 DESeq2","text":"used DESeq2 library DEG analysis re-use DESeqDataSet object adviced create new object proper design formula instead ~ 1 used .need normalize data using previously used vst function DESeq2 library normalize count data follows:“DESeq computes scaling factor given sample computing median ratio, gene, read count geometric mean across samples. uses assumption genes DE uses median ratios obtain scaling factor associated sample.”example data four sample groups re-use annotation data frame created EDA: Heatmap section (printed ). allows us comparisons based celltype ploidy samples. One minor note importance first level factor taken reference, expression analysis, control group. example (see table ) IPSC control-celltype disomic control-ploidy. simple case/ control study, first level (check levels() function) control group. Use relevel() function levels need switched.proper DESeqDataSet need run DESeq function object. , using results function can extract DEGs DESeqResults object. Applying summary function object(s) shows number regulated DEGs can seen lists impact trisomy IPSC cell type (~800 DEGs) comparing IPSC Neuron disomic (control) cell types (can seen, huge amount genes affected differentiation stem cell cortical neurons):output results function contains following columns gene:call results function depends heavily experiment. can see output summary function, details given comparison shown (also, default p-value 0.1 used instead 0.05).Depending design used create DESeqDataSet , one comparisons can made (applying DESeq function calculates filter results function). Read help results function carefully; especially regarding contrast argument define comparison retrieve. following code can used example experiment get DEGs comparing celltypes using adjusted p-value 0.05:DESeq2 library contains number plotting functions can applied DESeqResults object (output results function), notable plotMA function. Note first MA-plot shown high range log fold changes (-10, 10) maximum value 22.4 (shown triangle stating outside plotting range). log fold change 22 means >5 million increased expression seems artificially high. DESeq2 includes function perform downstream processing estimated log fold change values called lfcShrink advised always run afterwards. reason executing function described vignette :“useful visualize MA-plot shrunken log2 fold changes, remove noise associated log2 fold changes low count genes without requiring arbitrary filtering thresholds.”\nFigure 4.1: MA-plots standard DESeq2 output (left) shrinking ‘lfcShrink’ (right)\nLinksAnalyzing RNA-seq data DESeq2 comprehensive guide analyzing RNA-Seq data using DESeq2 (part document used material !). adviced read first sections guide take good look index document many interesting sections might help later.Publication, accompanying article showing differences performance compared methods packages.","code":"\nout of 44504 with nonzero total read count\nadjusted p-value < 0.05\nLFC > 0 (up)       : 432, 0.97%\nLFC < 0 (down)     : 417, 0.94%\noutliers [1]       : 219, 0.49%\nlow counts [2]     : 17004, 38%\n(mean count < 6)\n[1] see 'cooksCutoff' argument of ?results\n[2] see 'independentFiltering' argument of ?results\n\n\nout of 44504 with nonzero total read count\nadjusted p-value < 0.05\nLFC > 0 (up)       : 8224, 18%\nLFC < 0 (down)     : 9594, 22%\noutliers [1]       : 219, 0.49%\nlow counts [2]     : 11906, 27%\n(mean count < 2)\n[1] see 'cooksCutoff' argument of ?results\n[2] see 'independentFiltering' argument of ?results\n\n\nres <- results(dds, contrast = c(\"Cell\", \"IPSC\", \"Neuron\"),\n               alpha = 0.05)"},{"path":"chapter-4.html","id":"deg-edger","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3.3 edgeR","text":"One mature libraries RNA-Seq data analysis edgeR library available Bioconductor. complete (sometimes bit complex) manual available need read Chapter 2 focus 2.1 2.7, 2.9 - complex design - 2.10. Section 1.4 (Quickstart) shows code example steps needed DEG analysis using count data using two glm methods (quasi-likelihood F-tests likelihood ratio tests). steps shown identical non-glm method calculating fit object can replaced performing exactTest function shown section 2.9.2.edgeR library normalize count data follows:“trimmed means M values (TMM) Robinson Oshlack, implemented edgeR, computes scaling factor two experiments using weighted average subset genes excluding genes exhibit high average read counts genes large differences expression.”Running edgeR requires raw count data together grouping-factor packaged DGEList object (DGEList() function). Furthermore, proper model.matrix object (see section design) needed input estimateDisp function. exact steps take (variations DESeq2) must searched documentation linked .analysis done can retrieve actual results topTags function:Quitting lines 696-762 [.edger-TEST] (gene_expression.Rmd)\nError h():\n! error evaluating argument ‘x’ selecting method function ‘.data.frame’: object ‘qlf’ found\nBacktrace:\n1. pander::pander(.data.frame(topTags(qlf)), caption = “significant genes given edgeR”)\n4. base::.handleSimpleError(<fn>, “object ‘qlf’ found”, base::quote(topTags(qlf)))\n5. base (local) h(simpleError(msg, call))package also contains plotting methods can use intermediate steps analysis. instance, calculating normalization factors (calcNormFactors), can perform multi-dimensional scaling plotMDS function similar ’ve done manually chapter three:\nFigure 4.2: edgeR MDS plot based calculated log2 fold changes\ndispersion running estimateDisp function plotBCV function:\nFigure 4.3: edgeR plot several dispersion methods\nlog-fold changes genes, output exactTest function (output et DGEExact object) plotSmear function. abline shows log-FC threshold:LinksDifferential Expression Analysis using edgeR tutorialAnother tutorial hosted GitHub","code":"\nplotMDS(y)\nplotBCV(y)\ndeGenes <- decideTestsDGE(qlf, p=0.05)\ndeGenes <- rownames(qlf)[as.logical(deGenes)]\nplotSmear(qlf, de.tags=deGenes)\nabline(h=c(-1, 1), col=2)"},{"path":"chapter-5.html","id":"chapter-5","chapter":" Data Analysis and Visualization","heading":" Data Analysis and Visualization","text":"point one sets DEGs experiment. might just one set might large number comparisons made set DEGs per comparison / results multiple approaches (manual, packages). chapter offers guidance visualize, summarize - finally - connect biological relevance results. Browse chapter first choosing visualization(s) make data! article contains relevant figures like try reproduce (given enough data available) can done well instead visualizations shown . Note however Volcano plot described required.","code":""},{"path":"chapter-5.html","id":"volcano","chapter":" Data Analysis and Visualization","heading":"5.1 Volcano Plot","text":"volcano plot often first visualization data statistical tests completed. plot shows data genes highlight genes considered DEG using thresholds (adjusted) p-value fold-change. Many articles describe values used thresholds methods section, otherwise good default 0.05 adjusted p-value around 1.0 log-FC.use EnhancedVolcano library creates ggplot2 volcano plot shown . packaged function deseq.volcano just create two figures need change input data set plot title. resulting scatterplot places -log10(pvalue) values y-axis log-FC x-axis. often results plot representing volcanic ‘eruption’ fold-change influences spread significance height. Coloring done based thresholds (-log10(0.05) adjusted log2(2) log-FC) follows:(dark) gray: significant; FC adjusted p-value thresholds.green: high FC, adjusted p-value threshold.blue: adjusted p-value indicates significance, FC thresholdred: significant DEGYou can adjust pCutoff FCcutoff values depending requirements, see elaborate documentation examples GitHub.Notes:used edgeR instead need change names columns (.e. log2FoldChange == logFC padj == FDR) used example.example many DEGs annotate name plots. EnhancedVolcano plot function many options annotating significant genes, see linked manual information.section rewritten use EnhancedVolcano function instead creating manual ggplot2 plot can seen old version course manual.","code":"\nlibrary(EnhancedVolcano)\n\n## Simple function for plotting a Volcano plot, returns a ggplot object\ndeseq.volcano <- function(res, datasetName) {\n  return(EnhancedVolcano(res, x = 'log2FoldChange', y = 'padj',\n                         lab=rownames(res),\n                         title = paste(datasetName, \"trisomic vs disomic\"),\n                         subtitle = bquote(italic('FDR <= 0.05 and absolute FC >= 2')),\n                         # Change text and icon sizes\n                         labSize = 3, pointSize = 1.5, axisLabSize=10, titleLabSize=12,\n                         subtitleLabSize=8, captionLabSize=10,\n                         # Disable legend\n                         legendPosition = \"none\",\n                         # Set cutoffs\n                         pCutoff = 0.05, FCcutoff = 2))\n}\n\n## Note: input data is the corrected DESeq2 output using the 'lfcShrink' function (see chapter 4)\ndeseq.volcano(res = res.ipsc.lfc, datasetName = \"IPSC\")\ndeseq.volcano(res = res.neuron.lfc, datasetName = \"Neuron\")\n# Note that the most simplest case is:\nEnhancedVolcano(res.ipsc, x = 'log2FoldChange', y = 'padj', lab = rownames(res.ipsc))"},{"path":"chapter-5.html","id":"venn-diagram","chapter":" Data Analysis and Visualization","heading":"5.2 Venn Diagram","text":"Another common visualization Venn-diagram. data set ’ve shown two comparisons; trisomic vs disomic two cell types. Using Venn-diagram can show shared unique DEGS IPSC trisomic vs IPSC disomic NEUR trisomic vs NEUR disomic comparisons.Creating Venn-diagram can good method selecting (sub)set DEGs, instance selecting set DEGs shared groups (Intersection, see Wikipedia page Set Theory way around, DEGs unique group (Difference). Keep mind continuing clustering section likely useful cluster combinations might .Code examples:Data preparation (first code-chunk): creating vectors DEGs packages. DEGs gene names (taken row names DEG-analysis).\noutput DESeq2 results lfcShrink functions stored res.ipsc.lfc res.ipsc.neuron objects.\noutput DESeq2 results lfcShrink functions stored res.ipsc.lfc res.ipsc.neuron objects.Using VennDiagram library (second code-chunk): good complex/ large number comparison, highly configurable difficult use. see examples, run following function R console: example('draw.quad.venn')Using gplots library (third code-chunk): Easier use support complex Venn diagrams, good adjusting appearance.See blog post “VENN DIAGRAM R RSTUDIO: MILLION WAYS” methods creating Venn diagrams.two created vectors gene names can used either taking lengths (VennDiagram library) just names (gplots library).\nFigure 5.1: Venn diagram comparing edgeR DESeq2 DEGs using ‘VennDiagram’\nmentioned, following easier alternative (especially > 2 groups) venn function gplots library. needs list containing either row-numbers gene-names DEGs easier (offers less adjustability make prettier).\nFigure 5.2: Venn diagram comparing edgeR DESeq2 DEGs using ‘gplots’\nanalysis result similar amount DEGS (849 IPSC 769 Neuron cell types) 96 DEGs shared cell types thus unique.","code":"\n## Data preparation\npval_threshold <- 0.05\nipsc.degs <- row.names(res.ipsc.lfc[which(res.ipsc.lfc$padj <= pval_threshold), ])\nneur.degs <- row.names(res.neuron.lfc[which(res.neuron.lfc$padj <= pval_threshold), ])\n## Venn-diagram using the `VennDiagram` library (see below for alternative method)\nlibrary(VennDiagram)## Loading required package: futile.logger\n# Arguments for a pairwise (two-sets) venn-diagram are sizes for set1, set2 and overlap (intersect)\n# Many more functions are available for triple, quad and quantuple diagrams (starting with 'draw.***')\nvenn.plot <- draw.pairwise.venn(length(ipsc.degs),\n                                length(neur.degs),\n                                # Calculate the intersection of the two sets\n                                length( intersect(ipsc.degs, neur.degs) ),\n                                category = c(\"IPSC Trisomic vs Disomic\", \"NEURON Trisomic vs Disomic\"), scaled = F,\n                                fill = c(\"light blue\", \"pink\"), alpha = rep(0.5, 2),\n                                cat.pos = c(0, 0))\n\n# Actually plot the plot\ngrid.draw(venn.plot)\n## Alternative method using the `gplots` library\nlibrary(gplots)\n# Create a Venn-diagram given just the list of gene-names for both sets\nvenn(list(\"IPSC Trisomic vs Disomic\" = ipsc.degs,\n          \"NEURON Trisomic vs Disomic\" = neur.degs), )"},{"path":"chapter-5.html","id":"clustering","chapter":" Data Analysis and Visualization","heading":"5.3 Clustering","text":", can create heatmap found DEGs samples colors show fold-change value. Usually heatmap shown two color gradient; red (downregulated) green (upregulated). however optional using default colors pheatmap example perfectly fine long proper legend present.multiple methods creating heatmap (one ’ve already used) directly apply clustering visualization. clustering can applied expression pattern gene (row-clustering), expression pattern sample (column-clustering) (default pheatmap).following example commonly found publications shows log2 Fold Change values comparison done. first fifth columns show log2(FC) calculated DESeq2 two trisomic vs disomic comparisons. remaining columns log fold changes separate trisomic replicates vs average disomic expression (manually calculating Log(FC) done section 4.1 using DESeq2 normalized count data rld.dds object). DESeq2 calculated FC values show greater changes compared manually calculated FC values. value showing manually calculated values see replicates show patterns within groups. however optional unless one comparison create heatmap single column.Note column-clustering turned type plot setting cluster_cols = FALSE (replicates group, clustering add useful information).\nFigure 5.3: Heatmap showing fold changes Trisomic vs Disomic comparisons cell types. shown genes shared DEGS (96 shown Venn-diagrams) also filtered absolute log2(FC) value >2.\nlimitation heatmap show 100 DEGs found; just doesn’t fit well single figure causes clustering especially gene names/ labels unreadable. therefore always adviced filter adjusted p-value, also log-FC value reduce number genes shown. Alternatively data properly annotated, instead showing gene names row, showing GO-term clustering might reveal expression patterns certain gene groups.","code":""},{"path":"chapter-5.html","id":"pathway-analysis","chapter":" Data Analysis and Visualization","heading":"5.4 Pathway Analysis","text":"cases ’ll see hundreds even thousands DEGs result analysis. large amounts DEGs much visualization approaches easily say something biological context many genes.One approach tackling large set DEGs pathway analysis different methods genes grouped pathway get overview affected pathways experiment.section demonstrate two methods analysis, one using online platform gene-annotation enrichment analysis R-method signaling pathway impact analysis.","code":""},{"path":"chapter-5.html","id":"gene-functional-classification-using-david-100-degs","chapter":" Data Analysis and Visualization","heading":"5.4.1 Gene Functional Classification using DAVID (>100 DEGs)","text":"online Database Annotation, Visualization Integrated Discovery DAVID tool suite allows multiple - functional - annotation methods, one can used enrichment analysis based gene annotation. use DAVID tools need list gene names, ID’s identifyable information (need p-values fold-changes). basics method consist grouping genes pathways, count many genes pathway compare expect take random sample genome size number DEGs.example, RNA transport pathway contains total 50 genes total 25.000 whole mouse genome. means 0.2% mouse genes role pathway.uploaded total 1000 DEGs analysis, expect 0.2% = 2 genes DEG list placed pathway. , seems 10 genes RNA transport pathway set 1000 genes, 5 times much expected. difference expected observed gene counts per pathway allows say particular pathway enriched.DAVID provides single interface perform multiple different annotation pipelines, see table available types use.following example code writes gene symbols analysis approaches file upload DAVID.Upload dataClick Start Analysis button top DAVID website. , copy genes () upload file (B) select proper identifier type (OFFICIAL_GENE_SYMBOL example). Select Gene List option Step 3 click Submit List button Step 4.usually results popup mentions IDs found multiple organisms. Close popup select organism (Mus musculus example) Species list. 940 uploaded gene-symbols, 881 found mouse select followed clicking Select Species button.useful analysis Functional Annotation Chart, selectable Start Analysis page uploading set genes. output table shows many (case ~560) chart records genes grouped. records orginate various sources, GO-terms, Interpro, KEGG-pathways, SMART, etc. See image quick explaination displayed chart (mostly just table..).interesting statistic add table Fold Enrichment shows - -represented group . Note based gene-expression deviation amount genes provided certain group expected given total number uploaded gene IDs.Now, looking article random example set GSE ID GSE80128, one conclusions mentions following gene--research (Igf2as):suggested Igf2as play role energy metabolism, cell cycle, histone acetylation muscle contraction pathways.almost 1000 DEGs grouped 560 different categories, can find ‘evidence’ conclusions difficulty finding truly important pathways. Adviced check KEGG-pathways listed table, sort corrected p-value inspect pathways look interesting. example, amongst others, following pathways listed significant (p-value < 0.05):Metabolic pathways: includes many pathways, 100 genes total 1.6-fold enrichmentCitrate cycle (TCA): major metabolic pathway, 9 genes 5.7-fold enrichmentCardiac Muscle contraction: 12 genes 3.3-fold enrichmentClicking Term table KEGG-pathway shows graphical representation pathway indicating found DEGs (blinking, even), see .","code":"\n# Write gene names to a file\nwrite.table(edgeR.degs, file = \"../data/edger-deg-names.txt\",\n            row.names = FALSE, quote = FALSE, col.names = FALSE)\nwrite.table(DESeq.degs, file = \"../data/deseq-deg-names.txt\",\n            row.names = FALSE, quote = FALSE, col.names = FALSE)"},{"path":"chapter-5.html","id":"kegg-pathway-visualization-10-degs","chapter":" Data Analysis and Visualization","heading":"5.4.2 KEGG-pathway Visualization (>10 DEGs)","text":"Knowing interesting KEGG-pathway(s) upfront (.e. listed article) allows visualizations applied selected pathway. can use pathview website Bioconductor library:“… maps renders wide variety biological data relevant pathway graphs. users need supply data specify target pathway. Pathview automatically downloads pathway graph data, parses data file, maps user data pathway, render pathway graph mapped data.”section demonstrates use bioconductor package visualizing DEGs onto pathways, also change expression gene.Table continues belowSYMBOL, GENENAME, ENSEMBL, ENSEMBLPROT, UNIGENE, UNIPROT, ACCNUM, ENSEMBLTRANS, REFSEQ, ENZYME, TAIR, PROSITE ORFIt fine use webtool purpose however notacibly easier use R package.","code":"\n## Bioconductor library (install with bioclite() if missing)\nlibrary(pathview)\n\n## Example pathway IDs (for human, change organism key for other organisms)\ndata(\"paths.hsa\")\npander(head(paths.hsa, n=5))\n## Check 'gene.idtype' argument possibilities\ndata(gene.idtype.list); \npander(gene.idtype.list)\n## Prepare data for visualization\ndeseq.degs.logfc <- subset(deseq.results, padj < pval_threshold, select = log2FoldChange)\n\npander(head(deseq.degs.logfc))\npathview(gene.data=deseq.degs.logfc,\n         pathway.id=\"00020\", # TCA-cycle\n         species=\"hsa\"     # Organism key\n)"},{"path":"chapter-5.html","id":"spia","chapter":" Data Analysis and Visualization","heading":"5.4.3 Signaling Pathway Impact Analysis (>20 DEGs)","text":"also look topology genes interact , can use SPIA Bioconductor package. package takes list Entrez gene IDs DEGs complete list Entrez IDs present genes evaluates - per pathway - genes involved pathway either inhibited activated.code uses data objects always shown , see tables contents.input sig_genes named vector contents:all_genes vector Entrez IDs input (note contain expression information, just IDs):start processing pathways looking influenced signal pathway, execute spia function proper organism (Mus Musculus, mmu case):Running function print list (> 100) pathways analyze, take :resulting data frame ordered p-value contains following information:Top signaling pathways (continued )get overal picture can create plot pathway point placed according spia calculated -representation p-value pNDE (probability observe least nDE genes pathway). case obvious Alzheimer’s Parkinson’s pathways ID’s 05010 05012 respectively influenced experiment, according method.\nFigure 5.4: SPIA two-way evidence plot showing influenced pathways\n","code":"\n# Bioconductor package for \"Signaling Pathway Impact Analysis\"\n# (install with biocLite(\"SPIA\") if it is not installed)\nlibrary(SPIA)\n# Only used for the `subset` function\nlibrary(dplyr)\n\n# Get a vector of log(FC) values for all significant genes\nsig_genes <- subset(deseq.results, padj < pval_threshold, select = log2FoldChange)[[1]]\n# Make it a named vector by assigning the Entrez ID's to each log(FC) value\nnames(sig_genes) <- subset(deseq.results, padj < pval_threshold, select = EntrezID)[[1]]\n# A complete list of Entrez IDs for all genes in this experiment\nall_genes <- deseq.results$EntrezID\npander(all_genes[which(idx)[100:110]])\n# Process all signaling pathways to see if they are inhibited or activated\nspia_result <- spia(de=sig_genes, all=all_genes, organism=\"hsa\", plots=TRUE)Done pathway 1 : RNA transport..\nDone pathway 2 : RNA degradation..\nDone pathway 3 : PPAR signaling pathway..\nDone pathway 4 : Fanconi anemia pathway..\nDone pathway 5 : MAPK signaling pathway..\nDone pathway 6 : ErbB signaling pathway..\nDone pathway 7 : Calcium signaling pathway..\nDone pathway 8 : Cytokine-cytokine receptor int..\nDone pathway 9 : Chemokine signaling pathway..\nDone pathway 10 : NF-kappa B signaling pathway..\nDone pathway 11 : Phosphatidylinositol signaling..\nDone pathway 12 : Neuroactive ligand-receptor in..\nDone pathway 13 : Cell cycle..\n...\nDone pathway 132 : Graft-versus-host disease..\nDone pathway 133 : Arrhythmogenic right ventricul..\nDone pathway 134 : Dilated cardiomyopathy..\nDone pathway 135 : Viral myocarditis..## Loading required package: KEGGgraph## \n## Attaching package: 'KEGGgraph'## The following object is masked from 'package:pathview':\n## \n##     KEGGEdgeSubtype## The following object is masked from 'package:graphics':\n## \n##     plot## The following object is masked from 'package:base':\n## \n##     plot\nplotP(spia_result)"},{"path":"a1-data_loading.html","id":"a1-data_loading","chapter":"A Loading Expression Data in R","heading":"A Loading Expression Data in R","text":"","code":""},{"path":"a1-data_loading.html","id":"expl-data","chapter":"A Loading Expression Data in R","heading":"A.1 Exploring the Available Project Data","text":"Whether ’ve found dataset SRA GEO, want get data R start working . now, download count data likely stored TXT XLS(X) file format:Data formats:TXT: simple text file containing minimum two columns (either tab comma separated containing .e. gene / transcript identifier one mentioned data types).\nHowever can also contain 10 data columns either including information regarding gene/ transcript (.e. gene ID, name, symbol, chromosome, start/ stop position, length, etc.) numerical columns (.e. raw read count, normalized readcount, FPKM, etc.).\nHowever can also contain 10 data columns either including information regarding gene/ transcript (.e. gene ID, name, symbol, chromosome, start/ stop position, length, etc.) numerical columns (.e. raw read count, normalized readcount, FPKM, etc.).XLS(X): Microsoft Excel file containing data columns mentioned TXT files.Data types:Read Count (required; simple raw count mapped reads certain gene transcript).FPKM (Fragments Per Kilobase Million, F Fragment, paired-end sequencing),RPKM (Reads Per Kilobase Million, R Read, single-end sequencing),TPM (Transcripts Per Kilobase Million, T Transcript, technique independent),LayoutsEither one files per sample one data types orOne file containing data types samples (samples data columns file)Please watch video read page found RNA-Seq blog regarding meaning calculation mentioned expression data formats technical document found farrago blog page.GEO can see data might availalbe Supplementary column, shown :\nFigure .1: Finding experiment GEO TXT file supplementary data\noverview GEO contains many links direct links items dataset, can used filter browsing results. want actually download data, click GSE identifier (first column) brings overview experiment containing lot information experiment (subject, research institute, publication, design, etc.) links individual sample (GSM identifier). Following link sample shows information sample retrieved often many (lab) protocols used. Sometimes segment regarding “Data Processing” refers techniques software used full analysis might contain something like:… Differential expression testing sample groups performed Rstudio (v. 1.0.36) using DESeq2 (v.1.14.1) …Back experiment overview page ’ll see (variable) number links data files belonging experiment, see \\(\\ref{fig:GEO_Data-download}\\). now interested count-data stored TXT-file (see column File type/resource) called GSE97406_RAW.tar. file contains data need, even though 220Kb size experiment started 5Gb read-data small bacteria:\nFigure .2: Finding supplementary data GEO record\nbioinformatician love compress files particular example, download tar-archive file, extract find folder another archive file sample. extracting files end 12 TXT-files just two columns; gene identifier raw count value (~4500 rows data per sample):Table .1:  Contents TXT-file sample GSM2563998Other experiments combine samples single file column represents sample. get experiment one file per sample, can programmatically (yes, even R) load data batch.","code":""},{"path":"a1-data_loading.html","id":"a1-batch_data_loading","chapter":"A Loading Expression Data in R","heading":"A.2 Batch Loading Expression Data","text":"code example shows batch-load multiple files containing expression (count) data single sample. data example can found GEO ID GSE109798. Downloading data experiment GEO gives us single .tar file called GSE109798_RAW.tar. Extracting archive file nets us folder following files:","code":"\nfile.names <- list.files('./data/GSE109798_RAW/')"},{"path":"a1-data_loading.html","id":"decompressing","chapter":"A Loading Expression Data in R","heading":"A.2.1 Decompressing","text":"file extension files .txt.gz means files compressed using gzip need unpacked can loaded. easiest method using system gunzip command files can done within R applying gunzip command using system function file.Now can update file.names variable since file name changed.","code":"\n## Change directory to where the files are stored\nsetwd('./data/GSE109798_RAW/')\nsapply(file.names, FUN = function(file.name) {\n  system(paste(\"gunzip\", file.name)) \n  })\nfile.names <- list.files('./data/GSE109798_RAW/')"},{"path":"a1-data_loading.html","id":"determining-data-format","chapter":"A Loading Expression Data in R","heading":"A.2.2 Determining Data Format","text":"Next, can inspect contents files, assuming layout/ column names etc. decide need use analysis.files contain (much) just count value gene can see columns (transcript) length, TPM, FPKM, etc. Also, count-column called expected_count raises questions well.\nexpected count value usable contains \ninformation - compared raw count - actually require. \nexpected part results multimapped reads \nsingle read mapped multiple positions genome. \ntranscript originates one location, multimapped read \nusually discarded. expected count though, instead \ndiscarding read completely estimated \noriginates added fraction count\nvalue. value 1.32 see line 5 \nexample means true count 1 (uniquely mapped\nread) .32 (estimated part) results \nalgorithm can mean multiple things.\n\nmentioned , require integer count data use \npackages DESeq2 edgeR \ntwo methods convert expected count raw count data: +\nround value nearest integer (widely accepted method\nwell within expected sampling variation), + discard \nfraction part using example floor()\nfunction.\n","code":"\n## Call the system 'head' tool to 'peek' inside the file\nsystem(paste0(\"head \", \"./data/GSE109798_RAW/\", file.names[1]))"},{"path":"a1-data_loading.html","id":"loading-data-1","chapter":"A Loading Expression Data in R","heading":"A.2.3 Loading Data","text":"columns want keep transcript_id expected_count columns ignore rest (might interested data later though). need lead file separately can define function reads data, keeping columns interest returning dataframe data. Note first line file used header, check setting header argument TRUE, sometimes expression data starts line 1. file name also used name column dataframe know column sample. done splitting file name (using strsplit) using dot (‘.’) keeping first part (.e. ‘GSM2970156_HCC1806E224A’) discarding second part (‘isoforms.results.txt’). strsplit function however always returns list, case containing vector 5 splitted elements:Applying read_sample function file names gives us set data frames can merge together using merge function. merge data based transcript id defined = 1 argument pointing first column. start reading just one file ‘base’ dataframe merge files.processing seemed data set divided two groups also listed GEO website project:GPL11154 Illumina HiSeq 2000 (Homo sapiens)GPL13112 Illumina HiSeq 2000 (Mus musculus)first 6 files human source last 6 mouse. Therefore, following code shows read first 6 samples merge single dataframe. Repeating process 6 files result another dataframe samples.Table continues belowThe dataset variable now contains data first 6 samples experiment. advisable compare number rows data set number rows single sample. guaranteed samples exactly number genes/transcripts present (.e., 0-values might discarded) results final data set many rows smallest sample. See help merge case argument can used introduce extra rows missing data.","code":"\n## String splitting in R\n## (the fixed = TRUE is required as the dot is a special character, see '?strsplit')\nstrsplit('GSM2970155_HCC1806E224B.isoforms.results.txt.gz', '.', fixed = TRUE)## [[1]]\n## [1] \"GSM2970155_HCC1806E224B\" \"isoforms\"               \n## [3] \"results\"                 \"txt\"                    \n## [5] \"gz\"\n## Keeping the sample identifier\nstrsplit('GSM2970155_HCC1806E224B.isoforms.results.txt.gz', '.', fixed = TRUE)[[1]][1]## [1] \"GSM2970155_HCC1806E224B\"\n## Function for reading in files\nread_sample <- function(file.name) {\n  ## Extract the sample name for naming the column\n  sample.name <- strsplit(file.name, \".\", fixed = TRUE)[[1]][1]\n  ## Read the data, setting the 'transcript_id' as row.names (column 1)\n  sample <- read.table(file.name, header = TRUE, sep=\"\\t\", row.names = NULL)\n  ## Rename the count column\n  names(sample)[5] <- sample.name\n  ## Return a subset containing the 'transcript_id' and sample name columns\n  return(sample[c(1, 5)])\n}\nsetwd('./data/GSE109798_RAW/')\n\n## Read the FIRST sample\ndataset <- read_sample(file.names[1])\n\n## Read first sample group (6)\nfor (file.name in file.names[2:6]) {\n  sample <- read_sample(file.name)\n  dataset <- merge(dataset, sample, by = 1)\n}\n\npander(head(dataset))"},{"path":"a2-annotation.html","id":"a2-annotation","chapter":"B Annotating an RNA-Seq Experiment","heading":"B Annotating an RNA-Seq Experiment","text":"chapter describes annotate data, meaning assigning names functions Differentially Expressed Genes. step can either easy bit challenging depending data source project. first example relevant data set originated NCBI GEO, ‘’ always annotated default. found data set elsewhere (GEO identifier project), skip Manually annotate data section .goal chapter - least - find Gene Symbol common ID (NCBI/ Ensembl) gene. Using information much easier find relevant information online sources say something functionality impact DEGs.Even though might lucky either already data simple GEO query results everything want/ need, tasked also perform manual annotation phase even use found annotation data. written code access sources good excercise using complex Bioconductor packages. Also, can always use find information require later .","code":""},{"path":"a2-annotation.html","id":"download-geo","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.1 Downloading annotation data from GEO","text":"Given GSE**** id experiment can download available (annotation)data GEO using GEOquery library. example experiment used previously, following code downloads two files; series_matrix.txt.gz file also available download NCBI GEO website. Note destdir argument storing known location instead /tmp folder later use.downloaded, can load data using:gse object may contain useful information samples genes. Unfortunately example data set information available regarding samples can access phenoData slot. Slots R objects method storing multiple data objects single R object, case object called ExpressionSet (see ?ExpressionSet description contents structure). storing information experiment handy; can store data frame actual expression data, list containing laboratory information, chunk text abstract, etc. single object.common data sets found ExpressionSet object:\n* @assayData: actual expression data. common microarray experiments () rare RNA-seq experiments\n* @phenoData: sample information, present GEO data set\n* @featureData: data frame holding feature (=gene) information, slightly rare RNA-seq data sets\n* @experimentData: information lab performed experimentYou already know access column data frame using $ notation, accessing complete data frame gse object done using @ symbol:see sample described using sample identifier (GSM****), information sample group (title column) number characteristics. actual information included depends data set.Contents gse@phenoData@data information (selection) (continued )data set properly annotated gene information, accessible @featureData slot. instance, data set GSE20489 annotation available. loaded, see following annotation columns present gene featureData slot (following small subset, total can well 20 columns information per gene). Also, metadata listing shows many features (=genes, 54675) samples (54) expression data present (@assayData slot):Gene information stored ExpressionSet object experiment GSE20489 (continued )","code":"\nlibrary(GEOquery)\ngetGEO(GEO = 'GSE101942', destdir = \"./data/\")\nlibrary(GEOquery)\ngse <- getGEO(filename = './data/GSE101942_series_matrix.txt.gz')\n# Access a data frame containing phenotype information\ngse@phenoData@data\n# This gets a list with a single 'ExpressionSet' object\nGSE20489 <- getGEO(GEO = \"GSE20489\", destdir = './data')\n# Print some metadata of the featureData slot\nGSE20489[[1]]## ExpressionSet (storageMode: lockedEnvironment)\n## assayData: 54675 features, 54 samples \n##   element names: exprs \n## protocolData: none\n## phenoData\n##   sampleNames: GSM514737 GSM514738 ... GSM514790 (54 total)\n##   varLabels: title geo_accession ... tissue:ch1 (39 total)\n##   varMetadata: labelDescription\n## featureData\n##   featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (54675 total)\n##   fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)\n##   fvarMetadata: Column Description labelDescription\n## experimentData: use 'experimentData(object)'\n##   pubMedIds: 23883607 \n## Annotation: GPL570"},{"path":"a2-annotation.html","id":"manually-annotate","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.2 Manual Data Annotation","text":"Luckily us, R offers number libraries automatically retrieve information large sets genes, unless chosen organism much data available. section demonstrates use two libraries, starting AnnotationDbi followed biomaRt.AnnotationDbi library downloads local copy organism-specific database gene information biomaRt uses online databases retrieve data given query. biomaRt offers far data (1000 data fields per organism) complex use.Since biomaRt also relying online databases might good strategy annotate genes interest (DEGs) instead querying >20.000 genes might retain 20 statistical analysis. planning use biomaRt, skip Discovering Differentialy Expressed Genes (DEGs) chapter first return Using R Bioconductors biomaRt section annotate data.","code":""},{"path":"a2-annotation.html","id":"annotationDBI","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.2.1 Using AnnotationDBI","text":"AnnotationDbi offers data sets many organisms form installable libraries depending experiment need find proper library. example experiment contains samples house mouse (Mus musculus) therefore select data set.following information types available database (use columns function inspect).Available fields databaseThe table shows data available information types given randomly chosen EntrezID ‘1080’. Note table split show 24 data types values.Example values field ‘org.Hs.eg.db’Retrieving data locally stored annotation database can done using mapIds function takes number arguments:x: local database querykeys: IDs data set, often ENSEMBL IDs gene SYMBOLS. example data rownames data set contain gene SYMBOLS (see Table 1 page 2)column: data column retrieve databasekeytype: type data provide. example data gene SYMBOLmultiVals: number columns contain one entry single gene, case want store first one.output mapIds function single character vector can add data frame containing DEGs (case DEGs IPSC trisomic vs disomic comparison containing 828 genes stored ipsc.tri.vs.di data frame).Unfortunately, organisms offer access information information always complete. instance, following table shows number available records Ensembl, Entrez en KEGG IDs downloaded:Statistics annotation columns using AnnotationDbiFor data sets might luck, otherwise continue biomaRt method explained .","code":"\n# Load the AnnotationDbi interface library\nlibrary(AnnotationDbi)\n# Load the Bioconductor installation library (contains 'biocLite()')\nlibrary(BiocInstaller)\n\n# Install and load the organism specific gene database\n# 'org' for Organism\n# 'Hs' for Homo sapiens\n# 'eg' for Entrez Gene IDs\n# (try to load (using the library function) first before installing, it might already be present)\nbiocLite('org.Hs.eg.db')\nlibrary(org.Hs.eg.db)\n.Hs.data.table <- cbind(.Hs.data[1:12,], .Hs.data[13:24,])\ncolnames(.Hs.data.table) <- c('Data Type', 'Example Value',\n                              'Data Type', 'Example Value')\npander(.Hs.data.table, caption=\"Example values for each field in 'org.Hs.eg.db'\")\n# Retrieve the ENSEMBL gene ID, e.g. 'ENSG00000001626'\nipsc.tri.vs.di$Ensembl <- mapIds(x = org.Hs.eg.db,\n                           keys=row.names(ipsc.tri.vs.di),\n                           column=\"ENSEMBL\",\n                           keytype=\"SYMBOL\",\n                           multiVals=\"first\")\n\n# Retrieve the ENTREZ gene ID, e.g. '12323'\nipsc.tri.vs.di$EntrezID <- mapIds(x = org.Hs.eg.db,\n                            keys=row.names(ipsc.tri.vs.di),\n                            column=\"ENTREZID\",\n                            keytype=\"SYMBOL\",\n                            multiVals=\"first\")\n\n# Retrieve the KEGG enzyme code, e.g. 2.7.11.17\nipsc.tri.vs.di$Enzyme <- mapIds(x = org.Hs.eg.db,\n                           keys=row.names(ipsc.tri.vs.di),\n                           column=\"ENZYME\",\n                           keytype=\"SYMBOL\",\n                           multiVals=\"first\")"},{"path":"a2-annotation.html","id":"using-biomart","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.2.2 Using biomaRt","text":"short explanation biomart (source: Wikipedia):“purpose BioMarts Ensembl Genomes allow user mine download tables containing genes single species, genes specific region chromosome genes one region chromosome associated InterPro domain. BioMarts also include filters refine data extracted attributes (Variant ID, Chromosome name, Ensembl ID, location, etc.) appear final table file can selected user.”text mentiones species, attributes filters, need combine elements query biomart databases annotation. following code ‘chunks’ show possible values elements gather store relevant data.biomaRt library interfaces biomart.org online database. Sometimes biomart website (offers browsable database) due maintenance, many mirrors can still used, instance Ensembl.required explore number objects given example code likely suitable data/ organism. project well documented Bioconductor biomaRt website links biomaRt users guide.Using listDatasets function can get full list available datasets. Store list R object ‘browse’ object RStudio see organism included. Copy name dataset, species element use.Subset 214 datasets availableNext deciding filter. can use listFilters function ensembl object, storing full list filters. wise view RStudio find filter use.filter specifies use search . instance, AnnotationDbi queries gave us Ensembl gene ID’s use ensembl_gene_id filter. viewing list filters RStudio can use search text-box top-right view, example ensembl look filters use type identifier.Subset 437 available filtersFinally get point determine data like get database. attributes can get listAttributes function ensembl object. - especially attributes since often >1000 selectable options - store attributes view RStudio look data want.Subset 3172 available attributesIf want gene chromosome, start- end-position well description (note, just example, , interesting information available !) combine character vector (attrs.get, see ). one caveat though, order get back results input order! means simple combine data original data set need merge together. However, able merge data need know record belongs gene therefore add selected filter (case ensembl_gene_id) list attributes get. , get results dataframe can use merge function R combine gene information actual data:now three needed elements (species, filter attributes) ready query database getBM function (read help using ?getBM).example retrieves data using set five Ensembl gene IDs since genes Ensembl ID ’ve seen , filter first use values parameter .results object data.frame 5 columns can merge data set giving us following annotation columns (combined AnnotationDBI biomaRt libraries).just example use biomaRt library comes selecting correct filter looking interesting attributes retrieve. information can found documentation avaialble `vignette(‘biomaRt’)","code":"\n# Load the library\nlibrary(biomaRt)\n\n# Use an alternative database server as the regular one sometimes has issues..\nensembl=useMart(\"ENSEMBL_MART_ENSEMBL\", host=\"https://www.ensembl.org\")\n# Select the 'ensembl' database\nensembl <- useMart(\"ensembl\")\nmart.datasets <- listDatasets(ensembl)\n\n# Select the correct dataset, for the example data we select the 'hsapiens_gene_ensembl' \nensembl <- useDataset('hsapiens_gene_ensembl', mart = ensembl)\nfilters <- listFilters(ensembl)\nattributes <- listAttributes(ensembl)\nmerge(x = ipsc.tri.vs.di, y = results, by.x = 'Ensembl', by.y = 'ensembl_gene_id')\n# Set the 'attributes' values\nattrs.get <- c(\"ensembl_gene_id\", \"chromosome_name\", \n               \"start_position\",\"end_position\", \"description\")\n\n# Perform a biomaRt query using 'getBM'\nresults <- getBM(attributes = attrs.get,\n                 filters = \"ensembl_gene_id\",\n                 values = ipsc.tri.vs.di$Ensembl[1:5], \n                 mart = ensembl)\n\nresults$gene_length <- abs(results$end_position - results$start_position)"},{"path":"a2-annotation.html","id":"references","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.3 References","text":"","code":""}]
+[{"path":"index.html","id":"chapter-1","chapter":" Introduction","heading":" Introduction","text":"","code":""},{"path":"index.html","id":"capstone-project---analysis-of-gene-expression","chapter":" Introduction","heading":"1.1 Capstone Project - Analysis of Gene Expression","text":"use RNA-Sequencing techniques measuring gene expression relatively new replaces microarrays, though cases microarrays still used. Gene expression data gives valuable insights workings cells certain conditions. Especially comparing instance healthy diseased samples can become clear genes causal influence specific condition. Finding genes interest (genes showing differing expression accross conditions, called Differentially Expressed Genes (DEGs)) goal project.golden standard analyzing RNA-sequencing datasets many tools (manufacturers sequencing equiptment also deliver software packages) use R combined proven libraries processing, visualizing analyzing publically available datasets. cases allowed use actual raw (read) data available, highly recommended use pre-processed data often table count value gene. count number reads mapped gene corresponds relative number transcripts (mRNA sequences) gene present cell time sampling.","code":""},{"path":"index.html","id":"project-deliverables","chapter":" Introduction","heading":"1.2 Project Deliverables","text":"end products course consist two deliverables; PDF output file RMarkdown ‘lab journal’ logged steps performed get end result final report form short article. chapter briefly describes requirements contents products ends instructions use RMarkdown template writing article.","code":""},{"path":"index.html","id":"project-schedule","chapter":" Introduction","heading":"1.3 Project Schedule","text":"aim keep schedule course. Use first two weeks see need focus one points (depending dataset) discuss changes planning teacher.Find public experiment interest [week one]\nUsing online resources (sections 2.1, 2.2)\nUsing online resources (sections 2.1, 2.2)Data Gathering Literature Research [week two]\nMake final project choice\nRetrieve accompanying publication\nStarting Exploratory Data Analysis (chapter EDA)\nMake final project choiceRetrieve accompanying publicationStarting Exploratory Data Analysis (chapter EDA)Data Analysis [weeks three, four five]\nFinalizing Exploratory Data Analysis\nDiscovering Differentially Expressed Genes (chapter 4)\nData Annotation (optional; appendix a2)\nTechniques used: R bioconductor, EdgeR / DESeq2 packages, RMarkdown\nFinalizing Exploratory Data AnalysisDiscovering Differentially Expressed Genes (chapter 4)Data Annotation (optional; appendix a2)Techniques used: R bioconductor, EdgeR / DESeq2 packages, RMarkdownResult Analysis [week five six]\nAnalyzing Visualizing results (chapter 5)\nTechniques used: clustering, pathway analysis, gene-enrichment analysis\nAnalyzing Visualizing results (chapter 5)Techniques used: clustering, pathway analysis, gene-enrichment analysisFinalizing analysis start writing final report (article) [weeks 7 8]","code":""},{"path":"index.html","id":"grading","chapter":" Introduction","heading":"1.4 Grading","text":"final grade consists weighted average work done lab journal containing performed steps, code outputs (70%) article report (30%). grade >= 5.5 give total 8 EC. rest chapter explains expected contents three graded products (elements explained greater detail chapters).","code":""},{"path":"index.html","id":"lab-journal","chapter":" Introduction","heading":"1.5 Lab Journal","text":"know previous projects likely working laboratory, essential keep proper lab journal detailing every step done experiment. , log kept R markdown file, showing steps taken analysis data set. markdown knitted single PDF-file project completed thus containing text detailling steps decisions ’ve made, R-code (always visible!) resulting output/ images.\nNotes: general advice; wait \nknitting whole document project done knitting \nprone errors trying fix large document \neasy. Give code chunk proper attributes, including name \nminimum. helps spot errors knitting process\nmentiones chunk processed. Note chunk-names\nmust unique. try make proper use chapters \nsections include (optional) table contents.\n","code":""},{"path":"index.html","id":"article","chapter":" Introduction","heading":"1.6 Article","text":"final report written article form bit different usual report, mainly size. article-report maximum number pages 4 including images references (appendices!). Contents article extracted lab journal combined part introduction part conclusion/ discussion. sections describe template available writing report example report(s) made available inspiration. Refer chapter start writing report.","code":""},{"path":"index.html","id":"installing-the-article-template","chapter":" Introduction","heading":"1.6.1 Installing the Article Template","text":"templates available R package contains RMarkdown (another layer top actual markup language, yes, another language…) files. RStudio can use templates number documents, including article-templates. templates can installed package called rticles running install.packages function (note: might already installed):","code":"\ninstall.packages(\"rticles\", type = \"source\")"},{"path":"index.html","id":"using-the-template","chapter":" Introduction","heading":"1.6.2 Using the Template","text":"Now templates package (rticles) installed can download use template project (ZIP-file) available . Download file project folder, extract contents open report-template.Rmd file contained within folder RStudio. Verify everything setup correctly hitting Knit button top, create PDF version report. Note - somehow - resulting PDF file named RJwrapper.pdf instead expected report-template.pdf. everything checks OK can rename file liking start editing.template based R Journal Submission template can also find RStudio New file -> R Markdown -> Template menu. Articles published R-journal based template can browse inspiration R Journal Website.available template shows example segments/ chapters briefly describes section contain. want write report Dutch language, can create new file template change segment names content Dutch. however still headings added template English fine , want keep everything Dutch can either edit RJournal.sty file manually (recommended) start new file add nice headings page options.top part template (dashes ---) contains settings need change title, authors abstract. Compare instance newly created article template one offered project-repository website.","code":""},{"path":"datasets.html","id":"datasets","chapter":" Discovering Data Sets","heading":" Discovering Data Sets","text":"","code":""},{"path":"datasets.html","id":"finding-public-data","chapter":" Discovering Data Sets","heading":"2.1 Finding Public Data Sets of Interest","text":"chapter describes method finding public data sets interest. data set context course refers data belonging certain gene-expression experiment, usually consisting number sequencing-samples combined meta-data describing experiment.number large databases online offer access thousands - published - experiments focus searching downloading gene expression data high-throughput (NGS) sequencing techniques (RNA-Seq).thousands freely available data sets possible start performing research without need actually performing lab-experiment. common conditions, organisms tissues can download samples compare multiple studies find novel relations genes conditions verify experiments performed laboratory. instance, power public data sets demonstrated jointly three alumni article called Calling genotypes public RNA-sequencing data enables identification genetic variants affect gene-expression levels.(al. 2015).’s method section begins sentence “downloaded raw reads available human RNA-seq data sets …” amount data work become clear later . interested expression Quantitative Trait Loci (eQTLs) Allele-Specific Expression (ASE) analysis, please read interesting paper.start diving large complex databases potentially containing thousands experiments millions samples terabytes data, need get rough idea like found something interest order know looking first place. definitive guide protocol can followed processing analyzing gene-expression data set, following goals can considered:Re-(part ) analysis described accompanying publication.\ndata sets - except ones added recently - accompanied publication, can gather lot information regarding experimental design results just paper. often, researchers already performed interesting research data set therefore good exercise try reproduce results. Challenges getting proper understanding techniques used researchers (article) translating analysis steps. Often though, details clear article requires interpretation results performing analysis steps arrive conclusion.\ndata sets - except ones added recently - accompanied publication, can gather lot information regarding experimental design results just paper. often, researchers already performed interesting research data set therefore good exercise try reproduce results. Challenges getting proper understanding techniques used researchers (article) translating analysis steps. Often though, details clear article requires interpretation results performing analysis steps arrive conclusion.Alternatively can come ideas (biological) question(s) try answer given data set instead redoing published work.\ncourse challenging suitable data sets.\nmight notice publications focus small set genes instead whole transcriptome. instance, perform experiment yeast want measure activity genes involved alcohol fermentation, researchers might look genes Glycolysis pathway. leaves 6000+ yeast genes explore possibly come novel relations gene expression experimental setup.\nanalysis approach explained paper focusing pre-selected list genes, depending experiment might come comparisons done original research. instance, experiment maternal age (birth) correlated autism offspring, paternal age also known addressed publication. subject research (spoiler alert; clear conclusion can drawn including extra factor…).\ncourse challenging suitable data sets.might notice publications focus small set genes instead whole transcriptome. instance, perform experiment yeast want measure activity genes involved alcohol fermentation, researchers might look genes Glycolysis pathway. leaves 6000+ yeast genes explore possibly come novel relations gene expression experimental setup.analysis approach explained paper focusing pre-selected list genes, depending experiment might come comparisons done original research. instance, experiment maternal age (birth) correlated autism offspring, paternal age also known addressed publication. subject research (spoiler alert; clear conclusion can drawn including extra factor…).Another type project evaluate different methods either normalization statistical analysis either confirm published results find novel genes involved experiment.\ncan viewed technical research subject ok pursue, however final report mainly concern biological impact findings.\nmeans accompanying publication shows conclusive results, probably come acknowledging results (therefore, extend point 1 list). conclusive result hope find results case might become hard formulate good conclusion end.\nHowever, publication isn’t specific states conclusion ‘Benzene exposure shows increased risk leukemia’ followed list genes might involved, try see can find genes might involved changing analysis approach.\n\ncan viewed technical research subject ok pursue, however final report mainly concern biological impact findings.\nmeans accompanying publication shows conclusive results, probably come acknowledging results (therefore, extend point 1 list). conclusive result hope find results case might become hard formulate good conclusion end.\nHowever, publication isn’t specific states conclusion ‘Benzene exposure shows increased risk leukemia’ followed list genes might involved, try see can find genes might involved changing analysis approach.\nmeans accompanying publication shows conclusive results, probably come acknowledging results (therefore, extend point 1 list). conclusive result hope find results case might become hard formulate good conclusion end.However, publication isn’t specific states conclusion ‘Benzene exposure shows increased risk leukemia’ followed list genes might involved, try see can find genes might involved changing analysis approach.Although last project goal many risks involved need sound project proposal, might result interesting project. shown linked article, possible combine data multiple experiments.\ninstance find two related experiments (.e. researching effect certain drug) measuring expression different tissue types (.e. liver brain tissue). analyzing experiments, present set genes show effect tissues - interestingly - genes showing effect one tissue.\ninstance find two related experiments (.e. researching effect certain drug) measuring expression different tissue types (.e. liver brain tissue). analyzing experiments, present set genes show effect tissues - interestingly - genes showing effect one tissue.list guideline kept mind browsing suitable data sets extended week three. Also, don’t worry terms unclear, getting good understanding used terminology part first weeks","code":""},{"path":"datasets.html","id":"ncbi-geo","chapter":" Discovering Data Sets","heading":"2.2 The NCBI Gene Expression Omnibus","text":"easiest method exploring finding interesting data sets simply using web-browser access data-repository. One repository NCBI Gene Expression Omnibus (GEO). repository primarily used store micro array data sets describe experiments, linking raw data, processed data accompanying publication. however many sources data browsable GEO, project limit searches experiments using RNA-Seq technology 60.000 experiments listed. Following “Expression profiling high throughput sequencing” link summary table get complete listing can find interesting experiment. Besides finding experiment interested , important requirements need account browsing listed . Note point need download data yet, described chapter 3.experiment must published publication must fully available, free otherwise Hanze University library.sample group, minimum three replicates must present. one groups less three replicates, group used (rest data might still usable minimum two groups 3 replicates).\nReplicates often easy spot. lucky samples named “WildType - rep 2” instance often need read “Overall design” part page click samples (“GSM…” ID) make sure.\nReplicates often easy spot. lucky samples named “WildType - rep 2” instance often need read “Overall design” part page click samples (“GSM…” ID) make sure.available data must contain least count data.\nData offered multiple formats, always including RAW data (actual reads don’t use), also processed data FPKM, RPKM TPM (see interesting blog-post discussing use data). tools R analysis libraries use downstream analysis rely unnormalized unprocessed data count data. counts simply represent number reads mapped transcript.\noften hardest part assess; sometimes clear provided data actual raw count values. Usually, can find (sometimes rather cryptic) information clicking sample ID ‘Data processing’ section. paragraph text often mention ‘Supplementary_files_format_and_content’ describes actual content downloadable files. Following number examples valid invalid data provided experiment. single-sample pages can find description. Unfortunately, authors experiment free explain data please, likely encounter exact descriptions:\nValid: Global effects SUPT4H1 RNAi gene expression HEK293 cells; description: Supplementary_files_format_and_content: tab-delimited, gzip-compressed text file matrix raw quantitation results sample. Mentions raw-anything likely valid.\nValid: Transcriptome profiling normal transformed mouse urothelial cells; description: Supplementary_files_format_and_content: raw_counts_unfiltered.txt.gz: Raw counts.. Even though 4 different file types available download, one contains actual raw count data.\nValid: Quantitative Analysis PPARD RNA-Seq Transcriptomes Mouse Gastric Corpus Epithenial Cells Next Generation Sequencing (NGS); description: Supplementary_files_format_and_content: raw counts sequencing reads genes. Single file download containing raw counts.\nValid: Impact shRNA-mediated KLF4 -modulation transcriptome profile human keratinocyte precursor cells; description: Supplementary_files_format_and_content: raw normalized counts.\nValid: Role TAF5L TAF6L controlling mouse ESC state; description: Supplementary_files_format_and_content: Comma-separated text files containing raw counts, normalized counts, FPKM expression values TPM expression values.. common include normalized raw values.\nInvalid: Co-chaperone Mzb1 key effector Blimp1 plasma cell differentiation b1 integrin function; description: Supplementary_files_format_and_content: normalized gene expression values (FPKM) calculated Cuffnorm (v2.2.1) provided TXT format. clearly states normalized data (FPKM values) available.\nInvalid: EGFR-targeted therapy-induced resistance mechanism glioblastoma; description: Supplementary_files_format_and_content: .txt file including gene name, RefSeq id, locus, RPKM value gene sample. RPKM values available normalized raw count values, usable.\nInvalid: RNA-seq subcellular fractions nESC EpiLC; description: Supplementary_files_format_and_content: _genes.fpkm_table.txt: Tab-delimited text files include FPKM values replicate.*. FPKM data available.\n\nData offered multiple formats, always including RAW data (actual reads don’t use), also processed data FPKM, RPKM TPM (see interesting blog-post discussing use data). tools R analysis libraries use downstream analysis rely unnormalized unprocessed data count data. counts simply represent number reads mapped transcript.often hardest part assess; sometimes clear provided data actual raw count values. Usually, can find (sometimes rather cryptic) information clicking sample ID ‘Data processing’ section. paragraph text often mention ‘Supplementary_files_format_and_content’ describes actual content downloadable files. Following number examples valid invalid data provided experiment. single-sample pages can find description. Unfortunately, authors experiment free explain data please, likely encounter exact descriptions:\nValid: Global effects SUPT4H1 RNAi gene expression HEK293 cells; description: Supplementary_files_format_and_content: tab-delimited, gzip-compressed text file matrix raw quantitation results sample. Mentions raw-anything likely valid.\nValid: Transcriptome profiling normal transformed mouse urothelial cells; description: Supplementary_files_format_and_content: raw_counts_unfiltered.txt.gz: Raw counts.. Even though 4 different file types available download, one contains actual raw count data.\nValid: Quantitative Analysis PPARD RNA-Seq Transcriptomes Mouse Gastric Corpus Epithenial Cells Next Generation Sequencing (NGS); description: Supplementary_files_format_and_content: raw counts sequencing reads genes. Single file download containing raw counts.\nValid: Impact shRNA-mediated KLF4 -modulation transcriptome profile human keratinocyte precursor cells; description: Supplementary_files_format_and_content: raw normalized counts.\nValid: Role TAF5L TAF6L controlling mouse ESC state; description: Supplementary_files_format_and_content: Comma-separated text files containing raw counts, normalized counts, FPKM expression values TPM expression values.. common include normalized raw values.\nInvalid: Co-chaperone Mzb1 key effector Blimp1 plasma cell differentiation b1 integrin function; description: Supplementary_files_format_and_content: normalized gene expression values (FPKM) calculated Cuffnorm (v2.2.1) provided TXT format. clearly states normalized data (FPKM values) available.\nInvalid: EGFR-targeted therapy-induced resistance mechanism glioblastoma; description: Supplementary_files_format_and_content: .txt file including gene name, RefSeq id, locus, RPKM value gene sample. RPKM values available normalized raw count values, usable.\nInvalid: RNA-seq subcellular fractions nESC EpiLC; description: Supplementary_files_format_and_content: _genes.fpkm_table.txt: Tab-delimited text files include FPKM values replicate.*. FPKM data available.\nValid: Global effects SUPT4H1 RNAi gene expression HEK293 cells; description: Supplementary_files_format_and_content: tab-delimited, gzip-compressed text file matrix raw quantitation results sample. Mentions raw-anything likely valid.Valid: Transcriptome profiling normal transformed mouse urothelial cells; description: Supplementary_files_format_and_content: raw_counts_unfiltered.txt.gz: Raw counts.. Even though 4 different file types available download, one contains actual raw count data.Valid: Quantitative Analysis PPARD RNA-Seq Transcriptomes Mouse Gastric Corpus Epithenial Cells Next Generation Sequencing (NGS); description: Supplementary_files_format_and_content: raw counts sequencing reads genes. Single file download containing raw counts.Valid: Impact shRNA-mediated KLF4 -modulation transcriptome profile human keratinocyte precursor cells; description: Supplementary_files_format_and_content: raw normalized counts.Valid: Role TAF5L TAF6L controlling mouse ESC state; description: Supplementary_files_format_and_content: Comma-separated text files containing raw counts, normalized counts, FPKM expression values TPM expression values.. common include normalized raw values.Invalid: Co-chaperone Mzb1 key effector Blimp1 plasma cell differentiation b1 integrin function; description: Supplementary_files_format_and_content: normalized gene expression values (FPKM) calculated Cuffnorm (v2.2.1) provided TXT format. clearly states normalized data (FPKM values) available.Invalid: EGFR-targeted therapy-induced resistance mechanism glioblastoma; description: Supplementary_files_format_and_content: .txt file including gene name, RefSeq id, locus, RPKM value gene sample. RPKM values available normalized raw count values, usable.Invalid: RNA-seq subcellular fractions nESC EpiLC; description: Supplementary_files_format_and_content: _genes.fpkm_table.txt: Tab-delimited text files include FPKM values replicate.*. FPKM data available.","code":""},{"path":"EDA.html","id":"EDA","chapter":" Exploratory Data Analysis","heading":" Exploratory Data Analysis","text":"start performing exploratory data analysis steps goal getting grips chosen data set properly identify strategy actual analysis steps. exploration also keep eye quality data. Even though downloadable data ‘processed’, might samples present deviate much group samples (called outlier). Creating basic visualizations data give necessary insight continue.","code":""},{"path":"EDA.html","id":"loading-data","chapter":" Exploratory Data Analysis","heading":"3.1 Loading data into R","text":"one column based text files can read R simply using read.table() function.Follow following steps read data start exploratory data analysis. resulting document treated lab journal log process loading data final analysis steps.Open RStudioCreate new R Markdown document\nGive proper title select PDF format\nGive proper title select PDF formatGive document structure; e.g. create segment (using single hash #) called Exploratory Data Analysis.Whenever add code document make sure readable (keep maximum line length < 100 possible) sufficient documentation either text around code ‘chunks’ (preferred) using comments within code chunk.Read data file(s)\nPreferably use read.table function carefully set arguments. Open file text editor first check contents; header? can set row.names? columns needed? etc.\nNote: data set consists separate files (.e. one per sample) general tips reading data, see Appendix : Batch Loading Expression Data chapter.\nremainder document, try show either contents, structure - case - dimensions relevant R objects\nShow first five lines loaded data set. Including tables markdown document can done using pander function pander(Daróczi Tsegelskyi 2017) R-library.\nGive dimensions (dim() structure (str()) loaded data set.\nCheck output str function see columns expected R data type (e.g. values, factors, character, etc.)\n\nPreferably use read.table function carefully set arguments. Open file text editor first check contents; header? can set row.names? columns needed? etc.Note: data set consists separate files (.e. one per sample) general tips reading data, see Appendix : Batch Loading Expression Data chapter.remainder document, try show either contents, structure - case - dimensions relevant R objects\nShow first five lines loaded data set. Including tables markdown document can done using pander function pander(Daróczi Tsegelskyi 2017) R-library.\nGive dimensions (dim() structure (str()) loaded data set.\nCheck output str function see columns expected R data type (e.g. values, factors, character, etc.)\nShow first five lines loaded data set. Including tables markdown document can done using pander function pander(Daróczi Tsegelskyi 2017) R-library.Give dimensions (dim() structure (str()) loaded data set.Check output str function see columns expected R data type (e.g. values, factors, character, etc.)Examine samples included experiment create many R character objects needed store classification.\nNote: important item often overlooked. See last 4 lines code chunk section Example Data .\ninstance, eight samples divided case/ control columns create variable called case store column indices (numbers!) respective columns data set variable called control remaining four data column indices. later use.\nvariables allows us repeatedly access data rest course, .e. code boxplot(counts[case]) immediately shows plotting case samples counts data clear reading boxplot(counts[c(5:8)]) gives reference indices mean can lead typing mistakes.\nNote: important item often overlooked. See last 4 lines code chunk section Example Data .instance, eight samples divided case/ control columns create variable called case store column indices (numbers!) respective columns data set variable called control remaining four data column indices. later use.variables allows us repeatedly access data rest course, .e. code boxplot(counts[case]) immediately shows plotting case samples counts data clear reading boxplot(counts[c(5:8)]) gives reference indices mean can lead typing mistakes.\ndata sets order samples listed \nloaded data set different order shown GEO\nwebsite. often, names different lacking \ndescription non descriptive IDs. creating\nvariables control need point \ncontrol samples, need make sure right columns\ndata frame. Go Appendix A2;\nannotation order unclear just large number\nsamples might supporting data available can help\nmake sense sample layout.\nwant include external images log better control properties height width individual images can use following code. Note: code example need replace img_to_include.png file path actual image.):See Knitr chunk options page full listing chunk options.","code":"\n# Add the following to the code chunk header to control figure height, width and add a caption:\n#    {r, fig.height=5, fig.width=8, echo=FALSE, fig.cap=\"Image description\"}\n# The `dpi` argument is to remove any scaling\nknitr::include_graphics(\"figures/img_to_include.png\", dpi = NA)"},{"path":"EDA.html","id":"ch3_example_data","chapter":" Exploratory Data Analysis","heading":"3.2 Example Data","text":"section lists (publically available) data set(s) used chapter. chapter contains section new data sets used . Note examples, data different examples one challenges course translating examples data. Keep mind simple copy-pasting code fail reason. examples therefore print input data comparison data.section Normalization onwards, experiment identifier GSE101942 used visualizing normalized raw count values (earlier sections explore unnormalized data). experiment titled “Transcriptome analysis genetically matched human induced pluripotent stem cells disomic trisomic chromosome 21” experimental setup described follows:“12 total polyA selected samples. 6 IPSC samples 3 biological repeats trisomic samples 3 biological repeats disomic samples. 6 IPSC derived neuronal samples 3 biological repeats trisomic samples 3 biological repeats disomic samples.”following code shows load two available files containing raw-count data (one file per 6 samples), stored locally data/gse101942/ folder. simple ‘cleanup’ steps performed aren’t required helpful demonstration purposes (.e., renaming samples identify groups).GSE101942_IPSC_rawCounts.txt: raw count data induced pluripotent stem cells (iPSCs), trisomic disomicGSE101942_Neuron_rawCounts.txt: raw count data IPSC-derived Neurons, trisomic disomic. cells treated remove chromosome 21 iPSCs, described treatment protocol : “Targeted removal CHR 21 IPSC using TKNEO transgene”.See Table 1 Normalization section resulting contents counts dataframe.","code":"\n# Load the two sets of 6 samples\nipsc.data <- read.table('./data/gse101942/GSE101942_IPSC_rawCounts.txt',\n                        header = TRUE)\nneuron.data <- read.table('./data/gse101942/GSE101942_Neuron_rawCounts.txt',\n                          header=TRUE)\n# Merge by rowname (by = 0, see the help of 'merge')\ncounts <- merge(ipsc.data, neuron.data, by = 0, all.x = TRUE, all.y = TRUE,\n                sort = FALSE)\n# Change all NA's introduced by the merge to zeros\ncounts[is.na(counts)] <- 0\n# Show column names coming from the input files\nprint(names(counts))##  [1] \"Row.names\" \"c244A\"     \"c244B\"     \"c243\"      \"c2A\"       \"c2B\"      \n##  [7] \"c2C\"       \"C3_1\"      \"C3_2\"      \"C3_4\"      \"C2_2_1\"    \"C2_2_2\"   \n## [13] \"C2_2_3\"\n# Set the row names to the gene IDs stored in the 'Row.names' column\nrow.names(counts) <- counts$Row.names\n# Remove the gene ID column\ncounts <- counts[-1]\n# Rename samples to include their group name\nnames(counts) <- c(paste0('di_IPSC_r', 1:3),  # Disomic IPSC, replicates 1-3\n                   paste0('tri_IPSC_r', 1:3), # Trisomic IPSC, replicates 1-3\n                   paste0('di_NEUR_r', 1:3),  # Disomic Neuron, replicates 1-3\n                   paste0('tri_NEUR_r', 1:3)) # Trisomic Neuron, replicates 1-3\n# Show results of renaming the samples\nprint(names(counts))##  [1] \"di_IPSC_r1\"  \"di_IPSC_r2\"  \"di_IPSC_r3\"  \"tri_IPSC_r1\" \"tri_IPSC_r2\"\n##  [6] \"tri_IPSC_r3\" \"di_NEUR_r1\"  \"di_NEUR_r2\"  \"di_NEUR_r3\"  \"tri_NEUR_r1\"\n## [11] \"tri_NEUR_r2\" \"tri_NEUR_r3\"\n# Assign column -indices- to variables for later use (i.e. counts[di_NEUR] selects the relevant columns)\ndi_IPSC <- 1:3\ntri_IPSC <- 4:6\ndi_NEUR <- 7:9\ntri_NEUR <- 10:12"},{"path":"EDA.html","id":"EDA_part1","chapter":" Exploratory Data Analysis","heading":"3.3 Visualizing using boxplot and density plot","text":"segment describes basic steps visualization can performed Exploratory Data Analysis. start inspecting unnormalized count data. next steps normalization applied shown Normalization section . mentioned , focus EDA get overview/ perform bit Quality Control data set often requires visualizing data, figures need pretty. Simple figures perfectly fine stage. Try create figures data (keep log) add small description figure pointing anything different expect.Also note steps just selection. Furthermore, make sure every visualization make, add proper axis-labels containing measurement units (important!). might able see, values log-transformed using log2 function often numerical values high range ‘hide’ details plots. See section Fold Change value chapter 4 details. fine use non-log-transformed (simply raw-)data, otherwise use instance boxplot(log2(dataset)) plotting.\nInstead using basic R-plotting library\n(.e. plot, boxplot, hist, etc.)\ncan also opt using (challenging) ggplot2\nlibrary also used boxplot following section.\nconstructing ggplot2 plot feels like learning yet\nanother language, many resources available online can\nfollow.\n","code":""},{"path":"EDA.html","id":"eda_statistics","chapter":" Exploratory Data Analysis","heading":"3.3.1 Statistics","text":"Even basic statistics can give insight data performing 6-number-statistic data columns using summary() function. Note: can also use pander function pretty-print summary knitted output. notice look numbers produced executing function complete data set?","code":""},{"path":"EDA.html","id":"eda_boxplots","chapter":" Exploratory Data Analysis","heading":"3.3.2 Boxplots","text":"visual representation values can shown using boxplot. Boxplots easy create R data frame object just passing data columns. following boxplot shows data experiment separate boxplot sample. allows quick overview spotting irregularities (.e. checking replicates within sample-group show similar expression profiles). course, consider amount data single plot, can hint problems, need look much detail form quality control. Creating boxplot dataframe easy, saw using summary function; data large range maximum average values far apart. create lot outliers plot interesting later , boxplot can either:hide completely using outline = FALSE argument boxplot() (say figure description!), orperform log2() transformation can see .\ncreating plots expression data without performing\nlog-transformations see (instance boxplot) \nrange data large (0 1.000.000) lot\noutliers upper range. makes informative\nfigures, placing simple log2() function call around\ndata almost resolves issue.\n\nHowever, might introduce another problem (taking boxplot \nexample) log-transformation results data \ncontains negative infinity (-Inf) values. \ncaused 0-values data since\nlog2(0) == -Inf. circumvent issue, can add \npseudo count data simply adding value\n1 count values since log2(1) == 0;\nlog2(counts + 1).\n\nsituations boxplot adding \npseudo count value complete dataset can useful. Always add \npseudocount -place, meaning within plotting code instead\noverwriting original data set pseudo count added \nsteps require . cases (see density plot ) can useful \nclearly separate 0-values rest case \n0 < pseudo count < 1 value, 0.1\ncan used generates negative value (-3.3) \nfar away rest data. count values always\n>0 result positive log2 value.\n\nFigure 3.1: Boxplot comparing basic statistics genes across multiple samples\n","code":""},{"path":"EDA.html","id":"eda_density_plot","chapter":" Exploratory Data Analysis","heading":"3.3.3 Density Plots","text":"Another form visualizing data using density plot. method shows distribution (log2-transformed) count data samples allows easy spotting problems. plot commonly used analysing microarrays, still useful comparing complete dataset. code figure show example distribution 12 samples. things note figure huge peak exactly -3.321928 can ignored value calculated log2(0.1) explained box . peak consists 0-values (inactive genes) isn’t important us now. Therefore, added vertical line indicate left-part little interest. Note: lot code extra, simple inspection using line plotDensity(log2(counts + 0.1)) enough, rest extra example code.assess quality data look peaks plot. heigth peak doesn’t matter much, shifted peaks however indicate either lower higher amount reads sequenced sample. can confirmed first plot demonstrated next section library-sizes visualized.\nFigure 3.2: Density plot comparing count distribution 12 samples\n","code":"\n## The affy library has a density plotting function\nlibrary(affy)\n\n## Create a list of 4 colors to use which are the same used throughout this chapter \nlibrary(scales)\nmyColors <- hue_pal()(4)\n\n## Plot the log2-transformed data with a 0.1 pseudocount\nplotDensity(log2(counts + 0.1), col=rep(myColors, each=3),\n            lty=c(1:ncol(counts)), xlab='Log2(count)',\n            main='Expression Distribution')\n\n## Add a legend and vertical line\nlegend('topright', names(counts), lty=c(1:ncol(counts)),\n       col=rep(myColors, each=3))\nabline(v=-1.5, lwd=1, col='red', lty=2)"},{"path":"EDA.html","id":"EDA_part2","chapter":" Exploratory Data Analysis","heading":"3.4 Visualizing using heatmap and MDS","text":"section adds Exploratory Data Analysis techniques measure look distances samples based normalized data. Measuring distances two data objects (samples case) common task cluster analysis compare similarity (low distance indicates similar data). case calculate distances samples visualize heatmap using multidimensional scaling (MDS) technique.previous section used raw count data. might one samples different values (.e. shifted) compared samples. need raw count data use R packages edgeR (Chen et al. 2018) DESeq2 (Love, Anders, Huber 2017), calculating sample distances (used visualizations section) done form normalized data. data can either RPKM/FPKM/TPM/CPM vst-transformed (raw-)read counts. proper method transforming raw read count data using vst method DESeq2 R Bioconductor library shown . ‘variance stabilizing transformation’ normalized data used chapter, chapter 4 normalize using different technique.following code examples shows use library normalize count data GSE101942 experiment vst-normalized data calculate distance metric.new(“standardGeneric”, .Data = function (object, …)standardGeneric(“counts”), generic = structure(“counts”, package = “BiocGenerics”),package = \"BiocGenerics\", group = list(), valueClass = character(0),signature = \"object\", default = NULL, skeleton = (function (object,    ...)stop(gettextf(\"invalid call method dispatch '%s' (default method)\",look Table 1 immediately see huge difference groups genes. gene (A1BG) shows >100 expression tri_NEUR group compared di_IPSC group, within groups high variation indicating might actual expression different. One simple quick method inspection looking total number mapped reads per sample (sum sample/ column), sequencing-depth might vary across samples.Hint:Simple version plot can achieved barplot(colSums(counts) / 1e6)Table 2; Mapped reads per sample (millions) (continued )\nFigure 3.3: Library sizes samples GSE101942\nnumbers shown Table 2 barplot immediately clarify just experimental condition might caused large difference sample counts sequencing depth shows substantial difference . minimum maximum sequencing depth within trisomic neuron group (purple barplot). average expression A1BG gene within group 820, minimum 461 maximum 1010 lot variation. one reason telling us normalize data. following code chunks show DESeq2’s vst method (variance stabilizing transformation). Check Packages tab Rstudio see DESeq2 package installed load library command.","code":"package = \"BiocGenerics\", group = list(), valueClass = character(0),signature = \"object\", default = NULL, skeleton = (function (object,    ...)stop(gettextf(\"invalid call in method dispatch to '%s' (no default method)\","},{"path":"EDA.html","id":"normalization","chapter":" Exploratory Data Analysis","heading":"3.4.1 Normalization","text":"use vst function DESeq2 library must construct DESeqDataSet-object consisting count data combined sample annotation. Since want use (now) performing vst-transformation use basic form simple design sample names annotation (colData argument):now proper DESeqDataSet object can see , containing 56640 rows 12 columns (genes samples) gene symbols row names. Usually object hold data, requirement perform vst transformation good enough now. Next step performing transformation (can take depending size experiment results Large DESeqTransform object) retrieving actual data assay function object contains lot meta-data. table shows updated values now comparable across genes whereas raw count data harder compare.Table 2; VST transformed disomic IPSC count dataNote looking A1BG gene (one displayed figure 3.8), normalized expression values show much less variation across samples (shown, also within sample group). Therefore, assume large difference expression observed earlier might non-existent (might still significant difference though!).","code":"\n# Load the library\nlibrary('DESeq2')\n\n# DESeq2 will construct a SummarizedExperiment object and combine this \n# into a 'DESeqDataSet' object. The 'design' argument usually indicates the \n# experimental design using the condition(s) names as a 'factor', for now we use just '~ 1'\n(ddsMat <- DESeqDataSetFromMatrix(countData = counts,\n                                  colData = data.frame(samples = names(counts)),\n                                  design = ~ 1))## class: DESeqDataSet \n## dim: 56640 12 \n## metadata(1): version\n## assays(1): counts\n## rownames(56640): 5S_rRNA 7SK ... C1orf220 C2orf15\n## rowData names(0):\n## colnames(12): di_IPSC_r1 di_IPSC_r2 ... tri_NEUR_r2 tri_NEUR_r3\n## colData names(1): samples\n# Perform normalization\nrld.dds <- vst(ddsMat)\n# 'Extract' normalized values\nrld <- assay(rld.dds)"},{"path":"EDA.html","id":"eda_distance","chapter":" Exploratory Data Analysis","heading":"3.4.2 Distance Calculation","text":"now normalized data can use distance calculation. standard procedure many data analysis tasks calculates distance metric combination samples use check variation within sample groups. first need transpose matrix (rld) normalized values using t-function, dist function expects different samples rows genes columns. Note output matrix symmetric. Table 3 shows calculated distances within disomic IPSC group distance samples varies 100 140. maximum distance samples 380 demonstrated visualizations next sections.Table 3; Sample distances disomic IPSCs (Euclidean method)","code":"\n# Calculate basic distance metric (using euclidean distance, see '?dist')\nsampledists <- dist( t( rld ))"},{"path":"EDA.html","id":"eda_heatmap","chapter":" Exploratory Data Analysis","heading":"3.4.3 Sample Distances using a Heatmap","text":"(raw-)count data normalized format (TPM, RPKM, etc.), can optionally follow procedure count data create heatmap formats see makes difference. reason RNA-Seq method exists 10 years, still ongoing discussions subject data processing, especially regarding subjects like normalization technque use data analysis.following code block creates heatmap using pheatmap library offers many available heatmap functions. Using annotation data frame (can inspect contents ) identify samples based cell type ploidy. clustering shown heatmap clearly separates data based cell type differences ploidy seems minimal. Looking still, differences within single group minimal meaning - yet - inclined remove outlier-samples. Since 3 samples per category, also lose statistical power eventually remove one samples (also, always check article see remove samples prior data analysis).\nFigure 3.4: Heatmap showing Euclidean distances samples\nresulting heatmap shows interesting comparison across samples order samples purely determined using distance often different order data set. Ideally, want sample groups clustered together see data. particular heatmap clearly shows large difference overal expression induced pluripotent stem cells (iPSCs) differentiated cortical neurons difference - within cell type - disomic trisomic much lower.","code":"\n# We use the 'pheatmap' library (install with install.packages('pheatmap'))\nlibrary(pheatmap)\n\n# Convert the 'dist' object into a matrix for creating a heatmap\nsampleDistMatrix <- as.matrix(sampledists)\n\n# The annotation is an extra layer that will be plotted above the heatmap columns\n# indicating the cell type\nannotation <- data.frame(Cell = factor(rep(1:2, each = 6), \n                                          labels = c(\"IPSC\", \"Neuron\")),\n                         Ploidy = factor(rep(rep(1:2, each = 3), 2),\n                                         labels = c(\"disomic\", \"trisomic\")))\n                         \n# Set the rownames of the annotation dataframe to the sample names (required)\nrownames(annotation) <- names(counts)\n\npheatmap(sampleDistMatrix, show_colnames = FALSE,\n         annotation_col = annotation,\n         clustering_distance_rows = sampledists,\n         clustering_distance_cols = sampledists,\n         main = \"Euclidean Sample Distances\")"},{"path":"EDA.html","id":"eda_mds","chapter":" Exploratory Data Analysis","heading":"3.4.4 Multi-Dimensional Scaling","text":"following code example shows perform Multi-Dimensional Scaling (MDS) displays similarly calculated distances 2D-plot. experiment like two groups samples, hope see two clearly separated clusters formed, however ’ve seen heatmap, sample KO1B showed large deviation also see (confirm) using MDS.\nfigure plotted using ggplot2, \nadvanced method plotting R. plots preferred \nbase-R plotting, always sufficient use just can \nchallenging alter example code shown section. \ndata object plotted shown case contains simple X- \nY-coordinates.\nMDS use slightly different distance metric (Poisson instead default euclidean used dist function). following code shows calculate fitting distance sequencing data called Poisson Distance number optimizations available PoiClaClu library. library specifically designed handle read count data less influenced read-count differences across samples. Note , code usable count data function requires:n--p data matrix observations rows, p features columns. (,j) element x number reads observation mapped feature (e.g. gene exon) j.Table 5: MDS coordinates used plotting distances using Poisson Distance\nFigure 3.5: Multi Dimensional Scaling - Poisson Distance samples\nclearly see separation cell type less clear separation ploidy. spread within disomic groups much lower compared trisomic groups. Note y-axis range 4th x-axis range. sample di_NEUR_r3 shows large difference MDS plot. set genes identified differentially expressed repeat step expectation clear clustering.","code":"\nlibrary('PoiClaClu')\n# Note: uses the raw-count data, PoissonDistance performs normalization\n# set by the 'type' parameter (uses DESeq)\ndds <- assay(ddsMat)\npoisd <- PoissonDistance( t(dds), type = \"deseq\")\n# Extract the matrix with distances\nsamplePoisDistMatrix <- as.matrix(poisd$dd)\n# Calculate the MDS and get the X- and Y-coordinates\nmdsPoisData <- data.frame( cmdscale(samplePoisDistMatrix) )\n\n# And set some better readable names for the columns\nnames(mdsPoisData) <- c('x_coord', 'y_coord')\n# Separate the annotation factor (as the variable name is used as label)\ngroups <- factor(rep(1:4, each=3), \n                 labels = c(\"di_IPSC\", \"tri_IPSC\", \"di_NEUR\", \"tri_NEUR\"))\ncoldata <- names(counts)\n\n# Create the plot using ggplot\nggplot(mdsPoisData, aes(x_coord, y_coord, color = groups, label = coldata)) + \n  geom_text(size = 4) +\n  ggtitle('Multi Dimensional Scaling') +\n  labs(x = \"Poisson Distance\", y = \"Poisson Distance\") +\n  theme_bw()\n## Using base-R plot and legend functions:\n# plot(mdsPoisData, col=rep(myColors, each=3), pch=20, lwd=2)\n# legend(x=-20000, y=11000, legend = levels(groups), col=myColors, pch=20)"},{"path":"EDA.html","id":"cleaning-data","chapter":" Exploratory Data Analysis","heading":"3.5 Cleaning Data","text":"Conclude chapter cleaning dataset visualizations show necessity . Cleaning case means removing complete samples can classified outlier within group. tasks course, clear advice decide remove one samples. clear rule group must retain least three samples. one three samples visibly deviates replicates, even normalization, stay data set mentioned analysis report/ final article noise may introduced sample.\nNote: satisfied data reported findings, return steps next chapters. mentions creating heatmap instance, means heatmap expression data instead sample distances ’ve shown chapter.","code":""},{"path":"chapter-4.html","id":"chapter-4","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":" Discovering Differentialy Expressed Genes (DEGs)","text":"first important ‘real’ analysis step finding genes show difference expression sample groups; differentially expressed genes (DEGs). concept might sound rather simple; calculate ratios genes samples determine fold-change (FC) denoting factor change expression groups. , filter genes actually show difference. give list genes showing different behaviour across samples, need focus genes show difference, observed difference also statistically significant!determine whether gene can classified significant DEG going use existing libraries, one article might refer . ’re going brief manual examination count data make sure properly understand ’re looking : significance Fold-Change value.","code":""},{"path":"chapter-4.html","id":"deg-preprocessing","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.1 Pre-processing","text":"Given results exploratory data analysis performed chapter 3, might concluded one samples show () deviating expression patterns compared samples group. mentioned , enough (> 3) samples group, might opt remove sample reduce noise statistical tests sensitive . Since performing analysis steps programatically also easy test DEGs without sample(s) question see removal results lower p-values (= higher significance). Remember always properly document choice reasoning!perform manual steps data using libraries, re-normalization different technique. simple method normalizing count data calculate fragments per million mapped fragments (FPM, equal CPM) value transform log2. Opposed FPKM RPKM include gene-length calculation (likely don’t ). dataset also includes FPKM RPKM (pre-normalized), allowed use data .FPM CPM simply dividing count values sample total number reads sample divided 1 million shown :Another step can - might guided article - filter (partially) inactive genes. required using edgeR library (DESeq2 includes low-count filtering methods), good exercise manually use calculating lFC values. data sets available contains lot 0-measurements (see instance density plot previous chapter); genes reads mapped. R-studio Environment tab, click data set (perform View(data) command click one sample columns order data ascending. now likely (unless bacterial data instance) see lot zero values columns.experiment two groups, three replicates , three six samples 0-reads mapped, often advised remove gene completely. can subjective experiment, might expected (thus important) comparing different tissues knockout experiments. Also, genes () low read count (< 5) can give high (artificial) FC value (see left hand side MA-plot). Comparing two samples one value 2 11, reads -regulated gene factor 5.5 might actually just noise!Assignment: Search article advice filter zero values low count genes. nothing stated subject, think tactic (search literature/ online!). perfectly fine discuss peers.\ndescribe aspect; filter data, clearly explain (likely article stated proper reason)\nPerform filtering data set. likely need use one apply, rowSums, rowMeans functions, combined maybe , functions.\nManually verify rows removed correctly filtered.\nProperly document many genes filtered !\ndescribe aspect; filter data, clearly explain (likely article stated proper reason)Perform filtering data set. likely need use one apply, rowSums, rowMeans functions, combined maybe , functions.Manually verify rows removed correctly filtered.Properly document many genes filtered !","code":"\n# Perform a naive FPM normalization\n# Note: log transformation includes a pseudocount of 1\ncounts.fpm <- log2( (counts / (colSums(counts) / 1e6)) + 1 )"},{"path":"chapter-4.html","id":"deg-fc","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.2 The Fold Change Value","text":"FC usually given calculated log2 case/control ratio. example, gene average expression 30 mapped reads control group 88 reads experiment group, ratio case/control 2.93. Ratio values > 1 indicate increased expression experiment relation control values 0 1 indicate lower expression. log2 transformed value ratio calculated log2(2.93) results 1.55. counts reversed, ratio 30/88, 0.34. previously calculated value 2.93 means 3-fold regulation 0.34 value means 3-fold regulation can see range numbers different. Comparing log2 values 1.55 -1.55 compares much better.easy calculate FC genes , simple FC value doesn’t mean much, yet! still need use power lie within replicates sample group. Using replicates, want determine observed FC just biological noise sequencing error.Assignment: Create histogram log2-FC values genes. Apply following steps FPM-normalized data least one comparison (need possible combinations):\naverage replicates two groups separately add new columns FPM-normalized data set,\ncalculate log Fold Change (lFC); simply subtract one group (averaged-column) another, usually experiment-control\nNote since already log-transformed data, calculate ratio (experiment/control). Also, Logarithm quotient rule, divide average columns ratio calculation, subtract: \\(log_b{(x/y)} = log_b{(x)} - log_b{(y)}\\)\n\nplot data using hist(logFC_column, breaks=60) function (change breaks argument needed),\nAdd two vertical lines -1 1 (using abline(v=...)) indicate significance (2-fold change).\ndata shows low fold-change values might see genes outside boundary. mean find significantly changed genes (DEGs).\n\naverage replicates two groups separately add new columns FPM-normalized data set,calculate log Fold Change (lFC); simply subtract one group (averaged-column) another, usually experiment-control\nNote since already log-transformed data, calculate ratio (experiment/control). Also, Logarithm quotient rule, divide average columns ratio calculation, subtract: \\(log_b{(x/y)} = log_b{(x)} - log_b{(y)}\\)\nNote since already log-transformed data, calculate ratio (experiment/control). Also, Logarithm quotient rule, divide average columns ratio calculation, subtract: \\(log_b{(x/y)} = log_b{(x)} - log_b{(y)}\\)plot data using hist(logFC_column, breaks=60) function (change breaks argument needed),Add two vertical lines -1 1 (using abline(v=...)) indicate significance (2-fold change).\ndata shows low fold-change values might see genes outside boundary. mean find significantly changed genes (DEGs).\ndata shows low fold-change values might see genes outside boundary. mean find significantly changed genes (DEGs).calculated FC shows large change expression groups means nothing variation within group high. reason use form statistical test checks variation group difference groups samples. simplest form usually comes using t-test:“can used determine two sets data significantly different ”often instead manually performing t-tests ANOVA analysis, article mentions use one R libraries software packages finding DEGs. exactly also chapter. output finding DEG’s always includes - limited - list p-values; one p-value per gene. value indicates whether gene statistically-significant differentially expressed gene (SDEG) find genes interest need get genes p-value threshold (.e. 0.05).","code":""},{"path":"chapter-4.html","id":"deg-bioconductor","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3 Using Bioconductor Packages","text":"section demonstrates use two packages perform DEG-analysis count data. many packages available Bioconductor RNA-Seq analysis, DSS, EBSeq, NOISeq BaySeq, focus edgeR DESeq2 processing count-based data. Chances one two packages mentioned article described use R statistical analysis widely accepted methods processing gene expression data.packages apply normalization methods (described sections ) therefore work raw count data. data consists expected count data, need round values (using floor function). can choose one two packages use, use since increase statistical power hard use understand model data ’ll show next.Assignment: Perform data-analysis using least one two packages. however article mentioned different R package analysis (instance limma package) allowed use one instead. non-R packages can done extra (dataset can always analyzed using edgeR DESeq2). Optionally, use one package can compare results packages.\nStart documenting relevant information article, also used software analysis.\nStart documenting relevant information article, also used software analysis.two listed packages come complete extensive manual. Therefore, course manual (document) guide exactly use ; find . manuals can found PDF BioConductor website, opened within R using vignette function. see available manuals (note; different function-help documentation) execute function without arguments: vignette(). lists vignettes edgeR DESeq2 :vignette(\"DESeq2\") (whole course partly designed document!)vignette(\"edgeR\")\n","code":""},{"path":"chapter-4.html","id":"deg-design","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3.1 The Design (matrix)","text":"packages need properly specify samples grouped. seen examples using R factor object heatmap MDS visualizations tell groups samples group sample belongs. Reading documentation packages shows important part performing DE-analysis. example, following code shown edgeR documentation page 8 two sample groups defined (numbered 1 2), placed factor object used input model.matrix function.can often complex part seems theoretical, practice ’s difficult. really grasp topic strongly adviced watch two videos define design:StatQuest YouTube: Using Linear Models t-tests ANOVA, Clearly ExplainedStatQuest YouTube: Design Matrices Linear Models, Clearly ExplainedThese videos expects basic understanding topics already discussed statistics course, otherwise mentioned topic videos videos available explaining . See video-index handy overview topics discussed channel.mentioned need think question want ask; difference want know? two sample groups shown question rather easy; ‘genes show effect case/ control samples?’. two sample groups however question becomes complex. experiment comparing influence three kinds drugs (thus three groups) combined effect time, want focus influence drugs time? valid questions define way create design matrix.Documentation subject plenty, however often contains overwhelming information. page contains valuable details (can safely start reading Choice design), including following text based example used edgeR documentation:“examples cover , use linear models make comparisons different groups. Hence, design matrices ultimately work least two columns: intercept column, consists column 1’s, second column, specifies samples second group. case, two coefficients fit linear model: intercept, represents population average first group, second coefficient, represents difference population averages second group first group. latter typically coefficient interested performing statistical tests: want know difference two groups.”two sample groups want change question (.e. test influence different group), read section Releveling factor.","code":"\ngroup <- factor(c(1,1,2,2), labels = c(\"Control\", \"Case\"))\n(design <- model.matrix( ~ group))##   (Intercept) groupCase\n## 1           1         0\n## 2           1         0\n## 3           1         1\n## 4           1         1\n## attr(,\"assign\")\n## [1] 0 1\n## attr(,\"contrasts\")\n## attr(,\"contrasts\")$group\n## [1] \"contr.treatment\""},{"path":"chapter-4.html","id":"deg-deseq2","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3.2 DESeq2","text":"used DESeq2 library DEG analysis re-use DESeqDataSet object adviced create new object proper design formula instead ~ 1 used .need normalize data using previously used vst function DESeq2 library normalize count data follows:“DESeq computes scaling factor given sample computing median ratio, gene, read count geometric mean across samples. uses assumption genes DE uses median ratios obtain scaling factor associated sample.”example data four sample groups re-use annotation data frame created EDA: Heatmap section (printed ). allows us comparisons based celltype ploidy samples. One minor note importance first level factor taken reference, expression analysis, control group. example (see table ) IPSC control-celltype disomic control-ploidy. simple case/ control study, first level (check levels() function) control group. Use relevel() function levels need switched.proper DESeqDataSet need run DESeq function object. , using results function can extract DEGs DESeqResults object. Applying summary function object(s) shows number regulated DEGs can seen lists impact trisomy IPSC cell type (~800 DEGs) comparing IPSC Neuron disomic (control) cell types (can seen, huge amount genes affected differentiation stem cell cortical neurons):output results function contains following columns gene:call results function depends heavily experiment. can see output summary function, details given comparison shown (also, default p-value 0.1 used instead 0.05).Depending design used create DESeqDataSet , one comparisons can made (applying DESeq function calculates filter results function). Read help results function carefully; especially regarding contrast argument define comparison retrieve. following code can used example experiment get DEGs comparing celltypes using adjusted p-value 0.05:DESeq2 library contains number plotting functions can applied DESeqResults object (output results function), notable plotMA function. Note first MA-plot shown high range log fold changes (-10, 10) maximum value 22.4 (shown triangle stating outside plotting range). log fold change 22 means >5 million increased expression seems artificially high. DESeq2 includes function perform downstream processing estimated log fold change values called lfcShrink advised always run afterwards. reason executing function described vignette :“useful visualize MA-plot shrunken log2 fold changes, remove noise associated log2 fold changes low count genes without requiring arbitrary filtering thresholds.”\nFigure 4.1: MA-plots standard DESeq2 output (left) shrinking ‘lfcShrink’ (right)\nLinksAnalyzing RNA-seq data DESeq2 comprehensive guide analyzing RNA-Seq data using DESeq2 (part document used material !). adviced read first sections guide take good look index document many interesting sections might help later.Publication, accompanying article showing differences performance compared methods packages.","code":"\nout of 44504 with nonzero total read count\nadjusted p-value < 0.05\nLFC > 0 (up)       : 432, 0.97%\nLFC < 0 (down)     : 417, 0.94%\noutliers [1]       : 219, 0.49%\nlow counts [2]     : 17004, 38%\n(mean count < 6)\n[1] see 'cooksCutoff' argument of ?results\n[2] see 'independentFiltering' argument of ?results\n\n\nout of 44504 with nonzero total read count\nadjusted p-value < 0.05\nLFC > 0 (up)       : 8224, 18%\nLFC < 0 (down)     : 9594, 22%\noutliers [1]       : 219, 0.49%\nlow counts [2]     : 11906, 27%\n(mean count < 2)\n[1] see 'cooksCutoff' argument of ?results\n[2] see 'independentFiltering' argument of ?results\n\n\nres <- results(dds, contrast = c(\"Cell\", \"IPSC\", \"Neuron\"),\n               alpha = 0.05)"},{"path":"chapter-4.html","id":"deg-edger","chapter":" Discovering Differentialy Expressed Genes (DEGs)","heading":"4.3.3 edgeR","text":"One mature libraries RNA-Seq data analysis edgeR library available Bioconductor. complete (sometimes bit complex) manual available need read Chapter 2 focus 2.1 2.7, 2.9 - complex design - 2.10. Section 1.4 (Quickstart) shows code example steps needed DEG analysis using count data using two glm methods (quasi-likelihood F-tests likelihood ratio tests). steps shown identical non-glm method calculating fit object can replaced performing exactTest function shown section 2.9.2.edgeR library normalize count data follows:“trimmed means M values (TMM) Robinson Oshlack, implemented edgeR, computes scaling factor two experiments using weighted average subset genes excluding genes exhibit high average read counts genes large differences expression.”Running edgeR requires raw count data together grouping-factor packaged DGEList object (DGEList() function). Furthermore, proper model.matrix object (see section design) needed input estimateDisp function. exact steps take (variations DESeq2) must searched documentation linked .analysis done can retrieve actual results topTags function:Quitting lines 696-762 [.edger-TEST] (gene_expression.Rmd)\nError h():\n! error evaluating argument ‘x’ selecting method function ‘.data.frame’: object ‘qlf’ found\nBacktrace:\n1. pander::pander(.data.frame(topTags(qlf)), caption = “significant genes given edgeR”)\n4. base::.handleSimpleError(<fn>, “object ‘qlf’ found”, base::quote(topTags(qlf)))\n5. base (local) h(simpleError(msg, call))package also contains plotting methods can use intermediate steps analysis. instance, calculating normalization factors (calcNormFactors), can perform multi-dimensional scaling plotMDS function similar ’ve done manually chapter three:\nFigure 4.2: edgeR MDS plot based calculated log2 fold changes\ndispersion running estimateDisp function plotBCV function:\nFigure 4.3: edgeR plot several dispersion methods\nlog-fold changes genes, output exactTest function (output et DGEExact object) plotSmear function. abline shows log-FC threshold:LinksDifferential Expression Analysis using edgeR tutorialAnother tutorial hosted GitHub","code":"\nplotMDS(y)\nplotBCV(y)\ndeGenes <- decideTestsDGE(qlf, p=0.05)\ndeGenes <- rownames(qlf)[as.logical(deGenes)]\nplotSmear(qlf, de.tags=deGenes)\nabline(h=c(-1, 1), col=2)"},{"path":"chapter-5.html","id":"chapter-5","chapter":" Data Analysis and Visualization","heading":" Data Analysis and Visualization","text":"point one sets DEGs experiment. might just one set might large number comparisons made set DEGs per comparison / results multiple approaches (manual, packages). chapter offers guidance visualize, summarize - finally - connect biological relevance results. Browse chapter first choosing visualization(s) make data! article contains relevant figures like try reproduce (given enough data available) can done well instead visualizations shown . Note however Volcano plot described required.","code":""},{"path":"chapter-5.html","id":"volcano","chapter":" Data Analysis and Visualization","heading":"5.1 Volcano Plot","text":"volcano plot often first visualization data statistical tests completed. plot shows data genes highlight genes considered DEG using thresholds (adjusted) p-value fold-change. Many articles describe values used thresholds methods section, otherwise good default 0.05 adjusted p-value around 1.0 log-FC.use EnhancedVolcano library creates ggplot2 volcano plot shown . packaged function deseq.volcano just create two figures need change input data set plot title. resulting scatterplot places -log10(pvalue) values y-axis log-FC x-axis. often results plot representing volcanic ‘eruption’ fold-change influences spread significance height. Coloring done based thresholds (-log10(0.05) adjusted log2(2) log-FC) follows:(dark) gray: significant; FC adjusted p-value thresholds.green: high FC, adjusted p-value threshold.blue: adjusted p-value indicates significance, FC thresholdred: significant DEGYou can adjust pCutoff FCcutoff values depending requirements, see elaborate documentation examples GitHub.Notes:used edgeR instead need change names columns (.e. log2FoldChange == logFC padj == FDR) used example.example many DEGs annotate name plots. EnhancedVolcano plot function many options annotating significant genes, see linked manual information.section rewritten use EnhancedVolcano function instead creating manual ggplot2 plot can seen old version course manual.","code":"\nlibrary(EnhancedVolcano)\n\n## Simple function for plotting a Volcano plot, returns a ggplot object\ndeseq.volcano <- function(res, datasetName) {\n  return(EnhancedVolcano(res, x = 'log2FoldChange', y = 'padj',\n                         lab=rownames(res),\n                         title = paste(datasetName, \"trisomic vs disomic\"),\n                         subtitle = bquote(italic('FDR <= 0.05 and absolute FC >= 2')),\n                         # Change text and icon sizes\n                         labSize = 3, pointSize = 1.5, axisLabSize=10, titleLabSize=12,\n                         subtitleLabSize=8, captionLabSize=10,\n                         # Disable legend\n                         legendPosition = \"none\",\n                         # Set cutoffs\n                         pCutoff = 0.05, FCcutoff = 2))\n}\n\n## Note: input data is the corrected DESeq2 output using the 'lfcShrink' function (see chapter 4)\ndeseq.volcano(res = res.ipsc.lfc, datasetName = \"IPSC\")\ndeseq.volcano(res = res.neuron.lfc, datasetName = \"Neuron\")\n# Note that the most simplest case is:\nEnhancedVolcano(res.ipsc, x = 'log2FoldChange', y = 'padj', lab = rownames(res.ipsc))"},{"path":"chapter-5.html","id":"venn-diagram","chapter":" Data Analysis and Visualization","heading":"5.2 Venn Diagram","text":"Another common visualization Venn-diagram. data set ’ve shown two comparisons; trisomic vs disomic two cell types. Using Venn-diagram can show shared unique DEGS IPSC trisomic vs IPSC disomic NEUR trisomic vs NEUR disomic comparisons.Creating Venn-diagram can good method selecting (sub)set DEGs, instance selecting set DEGs shared groups (Intersection, see Wikipedia page Set Theory way around, DEGs unique group (Difference). Keep mind continuing clustering section likely useful cluster combinations might .Code examples:Data preparation (first code-chunk): creating vectors DEGs packages. DEGs gene names (taken row names DEG-analysis).\noutput DESeq2 results lfcShrink functions stored res.ipsc.lfc res.ipsc.neuron objects.\noutput DESeq2 results lfcShrink functions stored res.ipsc.lfc res.ipsc.neuron objects.Using VennDiagram library (second code-chunk): good complex/ large number comparison, highly configurable difficult use. see examples, run following function R console: example('draw.quad.venn')Using gplots library (third code-chunk): Easier use support complex Venn diagrams, good adjusting appearance.See blog post “VENN DIAGRAM R RSTUDIO: MILLION WAYS” methods creating Venn diagrams.two created vectors gene names can used either taking lengths (VennDiagram library) just names (gplots library).\nFigure 5.1: Venn diagram comparing edgeR DESeq2 DEGs using ‘VennDiagram’\nmentioned, following easier alternative (especially > 2 groups) venn function gplots library. needs list containing either row-numbers gene-names DEGs easier (offers less adjustability make prettier).\nFigure 5.2: Venn diagram comparing edgeR DESeq2 DEGs using ‘gplots’\nanalysis result similar amount DEGS (849 IPSC 769 Neuron cell types) 96 DEGs shared cell types thus unique.","code":"\n## Data preparation\npval_threshold <- 0.05\nipsc.degs <- row.names(res.ipsc.lfc[which(res.ipsc.lfc$padj <= pval_threshold), ])\nneur.degs <- row.names(res.neuron.lfc[which(res.neuron.lfc$padj <= pval_threshold), ])\n## Venn-diagram using the `VennDiagram` library (see below for alternative method)\nlibrary(VennDiagram)## Loading required package: futile.logger\n# Arguments for a pairwise (two-sets) venn-diagram are sizes for set1, set2 and overlap (intersect)\n# Many more functions are available for triple, quad and quantuple diagrams (starting with 'draw.***')\nvenn.plot <- draw.pairwise.venn(length(ipsc.degs),\n                                length(neur.degs),\n                                # Calculate the intersection of the two sets\n                                length( intersect(ipsc.degs, neur.degs) ),\n                                category = c(\"IPSC Trisomic vs Disomic\", \"NEURON Trisomic vs Disomic\"), scaled = F,\n                                fill = c(\"light blue\", \"pink\"), alpha = rep(0.5, 2),\n                                cat.pos = c(0, 0))\n\n# Actually plot the plot\ngrid.draw(venn.plot)\n## Alternative method using the `gplots` library\nlibrary(gplots)\n# Create a Venn-diagram given just the list of gene-names for both sets\nvenn(list(\"IPSC Trisomic vs Disomic\" = ipsc.degs,\n          \"NEURON Trisomic vs Disomic\" = neur.degs), )"},{"path":"chapter-5.html","id":"clustering","chapter":" Data Analysis and Visualization","heading":"5.3 Clustering","text":", can create heatmap found DEGs samples colors show fold-change value. Usually heatmap shown two color gradient; red (downregulated) green (upregulated). however optional using default colors pheatmap example perfectly fine long proper legend present.multiple methods creating heatmap (one ’ve already used) directly apply clustering visualization. clustering can applied expression pattern gene (row-clustering), expression pattern sample (column-clustering) (default pheatmap).following example commonly found publications shows log2 Fold Change values comparison done. first fifth columns show log2(FC) calculated DESeq2 two trisomic vs disomic comparisons. remaining columns log fold changes separate trisomic replicates vs average disomic expression (manually calculating Log(FC) done section 4.1 using DESeq2 normalized count data rld.dds object). DESeq2 calculated FC values show greater changes compared manually calculated FC values. value showing manually calculated values see replicates show patterns within groups. however optional unless one comparison create heatmap single column.Note column-clustering turned type plot setting cluster_cols = FALSE (replicates group, clustering add useful information).\nFigure 5.3: Heatmap showing fold changes Trisomic vs Disomic comparisons cell types. shown genes shared DEGS (96 shown Venn-diagrams) also filtered absolute log2(FC) value >2.\nlimitation heatmap show 100 DEGs found; just doesn’t fit well single figure causes clustering especially gene names/ labels unreadable. therefore always adviced filter adjusted p-value, also log-FC value reduce number genes shown. Alternatively data properly annotated, instead showing gene names row, showing GO-term clustering might reveal expression patterns certain gene groups.","code":""},{"path":"chapter-5.html","id":"pathway-analysis","chapter":" Data Analysis and Visualization","heading":"5.4 Pathway Analysis","text":"cases ’ll see hundreds even thousands DEGs result analysis. large amounts DEGs much visualization approaches easily say something biological context many genes.One approach tackling large set DEGs pathway analysis different methods genes grouped pathway get overview affected pathways experiment.section demonstrate two methods analysis, one using online platform gene-annotation enrichment analysis R-method signaling pathway impact analysis.","code":""},{"path":"chapter-5.html","id":"gene-functional-classification-using-david-100-degs","chapter":" Data Analysis and Visualization","heading":"5.4.1 Gene Functional Classification using DAVID (>100 DEGs)","text":"online Database Annotation, Visualization Integrated Discovery DAVID tool suite allows multiple - functional - annotation methods, one can used enrichment analysis based gene annotation. use DAVID tools need list gene names, ID’s identifyable information (need p-values fold-changes). basics method consist grouping genes pathways, count many genes pathway compare expect take random sample genome size number DEGs.example, RNA transport pathway contains total 50 genes total 25.000 whole mouse genome. means 0.2% mouse genes role pathway.uploaded total 1000 DEGs analysis, expect 0.2% = 2 genes DEG list placed pathway. , seems 10 genes RNA transport pathway set 1000 genes, 5 times much expected. difference expected observed gene counts per pathway allows say particular pathway enriched.DAVID provides single interface perform multiple different annotation pipelines, see table available types use.following example code writes gene symbols analysis approaches file upload DAVID.Upload dataClick Start Analysis button top DAVID website. , copy genes () upload file (B) select proper identifier type (OFFICIAL_GENE_SYMBOL example). Select Gene List option Step 3 click Submit List button Step 4.usually results popup mentions IDs found multiple organisms. Close popup select organism (Mus musculus example) Species list. 940 uploaded gene-symbols, 881 found mouse select followed clicking Select Species button.useful analysis Functional Annotation Chart, selectable Start Analysis page uploading set genes. output table shows many (case ~560) chart records genes grouped. records orginate various sources, GO-terms, Interpro, KEGG-pathways, SMART, etc. See image quick explaination displayed chart (mostly just table..).interesting statistic add table Fold Enrichment shows - -represented group . Note based gene-expression deviation amount genes provided certain group expected given total number uploaded gene IDs.Now, looking article random example set GSE ID GSE80128, one conclusions mentions following gene--research (Igf2as):suggested Igf2as play role energy metabolism, cell cycle, histone acetylation muscle contraction pathways.almost 1000 DEGs grouped 560 different categories, can find ‘evidence’ conclusions difficulty finding truly important pathways. Adviced check KEGG-pathways listed table, sort corrected p-value inspect pathways look interesting. example, amongst others, following pathways listed significant (p-value < 0.05):Metabolic pathways: includes many pathways, 100 genes total 1.6-fold enrichmentCitrate cycle (TCA): major metabolic pathway, 9 genes 5.7-fold enrichmentCardiac Muscle contraction: 12 genes 3.3-fold enrichmentClicking Term table KEGG-pathway shows graphical representation pathway indicating found DEGs (blinking, even), see .","code":"\n# Write gene names to a file\nwrite.table(edgeR.degs, file = \"../data/edger-deg-names.txt\",\n            row.names = FALSE, quote = FALSE, col.names = FALSE)\nwrite.table(DESeq.degs, file = \"../data/deseq-deg-names.txt\",\n            row.names = FALSE, quote = FALSE, col.names = FALSE)"},{"path":"chapter-5.html","id":"kegg-pathway-visualization-10-degs","chapter":" Data Analysis and Visualization","heading":"5.4.2 KEGG-pathway Visualization (>10 DEGs)","text":"Knowing interesting KEGG-pathway(s) upfront (.e. listed article) allows visualizations applied selected pathway. can use pathview website Bioconductor library:“… maps renders wide variety biological data relevant pathway graphs. users need supply data specify target pathway. Pathview automatically downloads pathway graph data, parses data file, maps user data pathway, render pathway graph mapped data.”section demonstrates use bioconductor package visualizing DEGs onto pathways, also change expression gene.Table continues belowSYMBOL, GENENAME, ENSEMBL, ENSEMBLPROT, UNIGENE, UNIPROT, ACCNUM, ENSEMBLTRANS, REFSEQ, ENZYME, TAIR, PROSITE ORFIt fine use webtool purpose however notacibly easier use R package.","code":"\n## Bioconductor library (install with bioclite() if missing)\nlibrary(pathview)\n\n## Example pathway IDs (for human, change organism key for other organisms)\ndata(\"paths.hsa\")\npander(head(paths.hsa, n=5))\n## Check 'gene.idtype' argument possibilities\ndata(gene.idtype.list); \npander(gene.idtype.list)\n## Prepare data for visualization\ndeseq.degs.logfc <- subset(deseq.results, padj < pval_threshold, select = log2FoldChange)\n\npander(head(deseq.degs.logfc))\npathview(gene.data=deseq.degs.logfc,\n         pathway.id=\"00020\", # TCA-cycle\n         species=\"hsa\"     # Organism key\n)"},{"path":"chapter-5.html","id":"spia","chapter":" Data Analysis and Visualization","heading":"5.4.3 Signaling Pathway Impact Analysis (>20 DEGs)","text":"also look topology genes interact , can use SPIA Bioconductor package. package takes list Entrez gene IDs DEGs complete list Entrez IDs present genes evaluates - per pathway - genes involved pathway either inhibited activated.code uses data objects always shown , see tables contents.input sig_genes named vector contents:all_genes vector Entrez IDs input (note contain expression information, just IDs):start processing pathways looking influenced signal pathway, execute spia function proper organism (Mus Musculus, mmu case):Running function print list (> 100) pathways analyze, take :resulting data frame ordered p-value contains following information:Top signaling pathways (continued )get overal picture can create plot pathway point placed according spia calculated -representation p-value pNDE (probability observe least nDE genes pathway). case obvious Alzheimer’s Parkinson’s pathways ID’s 05010 05012 respectively influenced experiment, according method.\nFigure 5.4: SPIA two-way evidence plot showing influenced pathways\n","code":"\n# Bioconductor package for \"Signaling Pathway Impact Analysis\"\n# (install with biocLite(\"SPIA\") if it is not installed)\nlibrary(SPIA)\n# Only used for the `subset` function\nlibrary(dplyr)\n\n# Get a vector of log(FC) values for all significant genes\nsig_genes <- subset(deseq.results, padj < pval_threshold, select = log2FoldChange)[[1]]\n# Make it a named vector by assigning the Entrez ID's to each log(FC) value\nnames(sig_genes) <- subset(deseq.results, padj < pval_threshold, select = EntrezID)[[1]]\n# A complete list of Entrez IDs for all genes in this experiment\nall_genes <- deseq.results$EntrezID\npander(all_genes[which(idx)[100:110]])\n# Process all signaling pathways to see if they are inhibited or activated\nspia_result <- spia(de=sig_genes, all=all_genes, organism=\"hsa\", plots=TRUE)Done pathway 1 : RNA transport..\nDone pathway 2 : RNA degradation..\nDone pathway 3 : PPAR signaling pathway..\nDone pathway 4 : Fanconi anemia pathway..\nDone pathway 5 : MAPK signaling pathway..\nDone pathway 6 : ErbB signaling pathway..\nDone pathway 7 : Calcium signaling pathway..\nDone pathway 8 : Cytokine-cytokine receptor int..\nDone pathway 9 : Chemokine signaling pathway..\nDone pathway 10 : NF-kappa B signaling pathway..\nDone pathway 11 : Phosphatidylinositol signaling..\nDone pathway 12 : Neuroactive ligand-receptor in..\nDone pathway 13 : Cell cycle..\n...\nDone pathway 132 : Graft-versus-host disease..\nDone pathway 133 : Arrhythmogenic right ventricul..\nDone pathway 134 : Dilated cardiomyopathy..\nDone pathway 135 : Viral myocarditis..## Loading required package: KEGGgraph## \n## Attaching package: 'KEGGgraph'## The following object is masked from 'package:pathview':\n## \n##     KEGGEdgeSubtype## The following object is masked from 'package:graphics':\n## \n##     plot## The following object is masked from 'package:base':\n## \n##     plot\nplotP(spia_result)"},{"path":"a1-data_loading.html","id":"a1-data_loading","chapter":"A Loading Expression Data in R","heading":"A Loading Expression Data in R","text":"","code":""},{"path":"a1-data_loading.html","id":"expl-data","chapter":"A Loading Expression Data in R","heading":"A.1 Exploring the Available Project Data","text":"Whether ’ve found dataset SRA GEO, want get data R start working . now, download count data likely stored TXT XLS(X) file format:Data formats:TXT: simple text file containing minimum two columns (either tab comma separated containing .e. gene / transcript identifier one mentioned data types).\nHowever can also contain 10 data columns either including information regarding gene/ transcript (.e. gene ID, name, symbol, chromosome, start/ stop position, length, etc.) numerical columns (.e. raw read count, normalized readcount, FPKM, etc.).\nHowever can also contain 10 data columns either including information regarding gene/ transcript (.e. gene ID, name, symbol, chromosome, start/ stop position, length, etc.) numerical columns (.e. raw read count, normalized readcount, FPKM, etc.).XLS(X): Microsoft Excel file containing data columns mentioned TXT files.Data types:Read Count (required; simple raw count mapped reads certain gene transcript).FPKM (Fragments Per Kilobase Million, F Fragment, paired-end sequencing),RPKM (Reads Per Kilobase Million, R Read, single-end sequencing),TPM (Transcripts Per Kilobase Million, T Transcript, technique independent),LayoutsEither one files per sample one data types orOne file containing data types samples (samples data columns file)Please watch video read page found RNA-Seq blog regarding meaning calculation mentioned expression data formats technical document found farrago blog page.GEO can see data might availalbe Supplementary column, shown :\nFigure .1: Finding experiment GEO TXT file supplementary data\noverview GEO contains many links direct links items dataset, can used filter browsing results. want actually download data, click GSE identifier (first column) brings overview experiment containing lot information experiment (subject, research institute, publication, design, etc.) links individual sample (GSM identifier). Following link sample shows information sample retrieved often many (lab) protocols used. Sometimes segment regarding “Data Processing” refers techniques software used full analysis might contain something like:… Differential expression testing sample groups performed Rstudio (v. 1.0.36) using DESeq2 (v.1.14.1) …Back experiment overview page ’ll see (variable) number links data files belonging experiment, see \\(\\ref{fig:GEO_Data-download}\\). now interested count-data stored TXT-file (see column File type/resource) called GSE97406_RAW.tar. file contains data need, even though 220Kb size experiment started 5Gb read-data small bacteria:\nFigure .2: Finding supplementary data GEO record\nbioinformatician love compress files particular example, download tar-archive file, extract find folder another archive file sample. extracting files end 12 TXT-files just two columns; gene identifier raw count value (~4500 rows data per sample):Table .1:  Contents TXT-file sample GSM2563998Other experiments combine samples single file column represents sample. get experiment one file per sample, can programmatically (yes, even R) load data batch.","code":""},{"path":"a1-data_loading.html","id":"a1-batch_data_loading","chapter":"A Loading Expression Data in R","heading":"A.2 Batch Loading Expression Data","text":"code example shows batch-load multiple files containing expression (count) data single sample. data example can found GEO ID GSE109798. Downloading data experiment GEO gives us single .tar file called GSE109798_RAW.tar. Extracting archive file nets us folder following files:","code":"\nfile.names <- list.files('./data/GSE109798_RAW/')"},{"path":"a1-data_loading.html","id":"decompressing","chapter":"A Loading Expression Data in R","heading":"A.2.1 Decompressing","text":"file extension files .txt.gz means files compressed using gzip need unpacked can loaded. easiest method using system gunzip command files can done within R applying gunzip command using system function file.Now can update file.names variable since file name changed.","code":"\n## Change directory to where the files are stored\nsetwd('./data/GSE109798_RAW/')\nsapply(file.names, FUN = function(file.name) {\n  system(paste(\"gunzip\", file.name)) \n  })\nfile.names <- list.files('./data/GSE109798_RAW/')"},{"path":"a1-data_loading.html","id":"determining-data-format","chapter":"A Loading Expression Data in R","heading":"A.2.2 Determining Data Format","text":"Next, can inspect contents files, assuming layout/ column names etc. decide need use analysis.files contain (much) just count value gene can see columns (transcript) length, TPM, FPKM, etc. Also, count-column called expected_count raises questions well.\nexpected count value usable contains \ninformation - compared raw count - actually require. \nexpected part results multimapped reads \nsingle read mapped multiple positions genome. \ntranscript originates one location, multimapped read \nusually discarded. expected count though, instead \ndiscarding read completely estimated \noriginates added fraction count\nvalue. value 1.32 see line 5 \nexample means true count 1 (uniquely mapped\nread) .32 (estimated part) results \nalgorithm can mean multiple things.\n\nmentioned , require integer count data use \npackages DESeq2 edgeR \ntwo methods convert expected count raw count data: +\nround value nearest integer (widely accepted method\nwell within expected sampling variation), + discard \nfraction part using example floor()\nfunction.\n","code":"\n## Call the system 'head' tool to 'peek' inside the file\nsystem(paste0(\"head \", \"./data/GSE109798_RAW/\", file.names[1]))"},{"path":"a1-data_loading.html","id":"loading-data-1","chapter":"A Loading Expression Data in R","heading":"A.2.3 Loading Data","text":"columns want keep transcript_id expected_count columns ignore rest (might interested data later though). need lead file separately can define function reads data, keeping columns interest returning dataframe data. Note first line file used header, check setting header argument TRUE, sometimes expression data starts line 1. file name also used name column dataframe know column sample. done splitting file name (using strsplit) using dot (‘.’) keeping first part (.e. ‘GSM2970156_HCC1806E224A’) discarding second part (‘isoforms.results.txt’). strsplit function however always returns list, case containing vector 5 splitted elements:Applying read_sample function file names gives us set data frames can merge together using merge function. merge data based transcript id defined = 1 argument pointing first column. start reading just one file ‘base’ dataframe merge files.processing seemed data set divided two groups also listed GEO website project:GPL11154 Illumina HiSeq 2000 (Homo sapiens)GPL13112 Illumina HiSeq 2000 (Mus musculus)first 6 files human source last 6 mouse. Therefore, following code shows read first 6 samples merge single dataframe. Repeating process 6 files result another dataframe samples.Table continues belowThe dataset variable now contains data first 6 samples experiment. advisable compare number rows data set number rows single sample. guaranteed samples exactly number genes/transcripts present (.e., 0-values might discarded) results final data set many rows smallest sample. See help merge case argument can used introduce extra rows missing data.","code":"\n## String splitting in R\n## (the fixed = TRUE is required as the dot is a special character, see '?strsplit')\nstrsplit('GSM2970155_HCC1806E224B.isoforms.results.txt.gz', '.', fixed = TRUE)## [[1]]\n## [1] \"GSM2970155_HCC1806E224B\" \"isoforms\"               \n## [3] \"results\"                 \"txt\"                    \n## [5] \"gz\"\n## Keeping the sample identifier\nstrsplit('GSM2970155_HCC1806E224B.isoforms.results.txt.gz', '.', fixed = TRUE)[[1]][1]## [1] \"GSM2970155_HCC1806E224B\"\n## Function for reading in files\nread_sample <- function(file.name) {\n  ## Extract the sample name for naming the column\n  sample.name <- strsplit(file.name, \".\", fixed = TRUE)[[1]][1]\n  ## Read the data, setting the 'transcript_id' as row.names (column 1)\n  sample <- read.table(file.name, header = TRUE, sep=\"\\t\", row.names = NULL)\n  ## Rename the count column\n  names(sample)[5] <- sample.name\n  ## Return a subset containing the 'transcript_id' and sample name columns\n  return(sample[c(1, 5)])\n}\nsetwd('./data/GSE109798_RAW/')\n\n## Read the FIRST sample\ndataset <- read_sample(file.names[1])\n\n## Read first sample group (6)\nfor (file.name in file.names[2:6]) {\n  sample <- read_sample(file.name)\n  dataset <- merge(dataset, sample, by = 1)\n}\n\npander(head(dataset))"},{"path":"a2-annotation.html","id":"a2-annotation","chapter":"B Annotating an RNA-Seq Experiment","heading":"B Annotating an RNA-Seq Experiment","text":"chapter describes annotate data, meaning assigning names functions Differentially Expressed Genes. step can either easy bit challenging depending data source project. first example relevant data set originated NCBI GEO, ‘’ always annotated default. found data set elsewhere (GEO identifier project), skip Manually annotate data section .goal chapter - least - find Gene Symbol common ID (NCBI/ Ensembl) gene. Using information much easier find relevant information online sources say something functionality impact DEGs.Even though might lucky either already data simple GEO query results everything want/ need, tasked also perform manual annotation phase even use found annotation data. written code access sources good excercise using complex Bioconductor packages. Also, can always use find information require later .","code":""},{"path":"a2-annotation.html","id":"download-geo","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.1 Downloading annotation data from GEO","text":"Given GSE**** id experiment can download available (annotation)data GEO using GEOquery library. example experiment used previously, following code downloads two files; series_matrix.txt.gz file also available download NCBI GEO website. Note destdir argument storing known location instead /tmp folder later use.downloaded, can load data using:gse object may contain useful information samples genes. Unfortunately example data set information available regarding samples can access phenoData slot. Slots R objects method storing multiple data objects single R object, case object called ExpressionSet (see ?ExpressionSet description contents structure). storing information experiment handy; can store data frame actual expression data, list containing laboratory information, chunk text abstract, etc. single object.common data sets found ExpressionSet object:\n* @assayData: actual expression data. common microarray experiments () rare RNA-seq experiments\n* @phenoData: sample information, present GEO data set\n* @featureData: data frame holding feature (=gene) information, slightly rare RNA-seq data sets\n* @experimentData: information lab performed experimentYou already know access column data frame using $ notation, accessing complete data frame gse object done using @ symbol:see sample described using sample identifier (GSM****), information sample group (title column) number characteristics. actual information included depends data set.Contents gse@phenoData@data information (selection) (continued )data set properly annotated gene information, accessible @featureData slot. instance, data set GSE20489 annotation available. loaded, see following annotation columns present gene featureData slot (following small subset, total can well 20 columns information per gene). Also, metadata listing shows many features (=genes, 54675) samples (54) expression data present (@assayData slot):Gene information stored ExpressionSet object experiment GSE20489 (continued )","code":"\nlibrary(GEOquery)\ngetGEO(GEO = 'GSE101942', destdir = \"./data/\")\nlibrary(GEOquery)\ngse <- getGEO(filename = './data/GSE101942_series_matrix.txt.gz')\n# Access a data frame containing phenotype information\ngse@phenoData@data\n# This gets a list with a single 'ExpressionSet' object\nGSE20489 <- getGEO(GEO = \"GSE20489\", destdir = './data')\n# Print some metadata of the featureData slot\nGSE20489[[1]]## ExpressionSet (storageMode: lockedEnvironment)\n## assayData: 54675 features, 54 samples \n##   element names: exprs \n## protocolData: none\n## phenoData\n##   sampleNames: GSM514737 GSM514738 ... GSM514790 (54 total)\n##   varLabels: title geo_accession ... tissue:ch1 (39 total)\n##   varMetadata: labelDescription\n## featureData\n##   featureNames: 1007_s_at 1053_at ... AFFX-TrpnX-M_at (54675 total)\n##   fvarLabels: ID GB_ACC ... Gene Ontology Molecular Function (16 total)\n##   fvarMetadata: Column Description labelDescription\n## experimentData: use 'experimentData(object)'\n##   pubMedIds: 23883607 \n## Annotation: GPL570"},{"path":"a2-annotation.html","id":"manually-annotate","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.2 Manual Data Annotation","text":"Luckily us, R offers number libraries automatically retrieve information large sets genes, unless chosen organism much data available. section demonstrates use two libraries, starting AnnotationDbi followed biomaRt.AnnotationDbi library downloads local copy organism-specific database gene information biomaRt uses online databases retrieve data given query. biomaRt offers far data (1000 data fields per organism) complex use.Since biomaRt also relying online databases might good strategy annotate genes interest (DEGs) instead querying >20.000 genes might retain 20 statistical analysis. planning use biomaRt, skip Discovering Differentialy Expressed Genes (DEGs) chapter first return Using R Bioconductors biomaRt section annotate data.","code":""},{"path":"a2-annotation.html","id":"annotationDBI","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.2.1 Using AnnotationDBI","text":"AnnotationDbi offers data sets many organisms form installable libraries depending experiment need find proper library. example experiment contains samples house mouse (Mus musculus) therefore select data set.following information types available database (use columns function inspect).Available fields databaseThe table shows data available information types given randomly chosen EntrezID ‘1080’. Note table split show 24 data types values.Example values field ‘org.Hs.eg.db’Retrieving data locally stored annotation database can done using mapIds function takes number arguments:x: local database querykeys: IDs data set, often ENSEMBL IDs gene SYMBOLS. example data rownames data set contain gene SYMBOLS (see Table 1 page 2)column: data column retrieve databasekeytype: type data provide. example data gene SYMBOLmultiVals: number columns contain one entry single gene, case want store first one.output mapIds function single character vector can add data frame containing DEGs (case DEGs IPSC trisomic vs disomic comparison containing 828 genes stored ipsc.tri.vs.di data frame).Unfortunately, organisms offer access information information always complete. instance, following table shows number available records Ensembl, Entrez en KEGG IDs downloaded:Statistics annotation columns using AnnotationDbiFor data sets might luck, otherwise continue biomaRt method explained .","code":"\n# Load the AnnotationDbi interface library\nlibrary(AnnotationDbi)\n# Load the Bioconductor installation library (contains 'biocLite()')\nlibrary(BiocInstaller)\n\n# Install and load the organism specific gene database\n# 'org' for Organism\n# 'Hs' for Homo sapiens\n# 'eg' for Entrez Gene IDs\n# (try to load (using the library function) first before installing, it might already be present)\nbiocLite('org.Hs.eg.db')\nlibrary(org.Hs.eg.db)\n.Hs.data.table <- cbind(.Hs.data[1:12,], .Hs.data[13:24,])\ncolnames(.Hs.data.table) <- c('Data Type', 'Example Value',\n                              'Data Type', 'Example Value')\npander(.Hs.data.table, caption=\"Example values for each field in 'org.Hs.eg.db'\")\n# Retrieve the ENSEMBL gene ID, e.g. 'ENSG00000001626'\nipsc.tri.vs.di$Ensembl <- mapIds(x = org.Hs.eg.db,\n                           keys=row.names(ipsc.tri.vs.di),\n                           column=\"ENSEMBL\",\n                           keytype=\"SYMBOL\",\n                           multiVals=\"first\")\n\n# Retrieve the ENTREZ gene ID, e.g. '12323'\nipsc.tri.vs.di$EntrezID <- mapIds(x = org.Hs.eg.db,\n                            keys=row.names(ipsc.tri.vs.di),\n                            column=\"ENTREZID\",\n                            keytype=\"SYMBOL\",\n                            multiVals=\"first\")\n\n# Retrieve the KEGG enzyme code, e.g. 2.7.11.17\nipsc.tri.vs.di$Enzyme <- mapIds(x = org.Hs.eg.db,\n                           keys=row.names(ipsc.tri.vs.di),\n                           column=\"ENZYME\",\n                           keytype=\"SYMBOL\",\n                           multiVals=\"first\")"},{"path":"a2-annotation.html","id":"using-biomart","chapter":"B Annotating an RNA-Seq Experiment","heading":"B.2.2 Using biomaRt","text":"short explanation biomart (source: Wikipedia):“purpose BioMarts Ensembl Genomes allow user mine download tables containing genes single species, genes specific region chromosome genes one region chromosome associated InterPro domain. BioMarts also include filters refine data extracted attributes (Variant ID, Chromosome name, Ensembl ID, location, etc.) appear final table file can selected user.”text mentiones species, attributes filters, need combine elements query biomart databases annotation. following code ‘chunks’ show possible values elements gather store relevant data.biomaRt library interfaces biomart.org online database. Sometimes biomart website (offers browsable database) due maintenance, many mirrors can still used, instance Ensembl.required explore number objects given example code likely suitable data/ organism. project well documented Bioconductor biomaRt website links biomaRt users guide.Using listDatasets function can get full list available datasets. Store list R object ‘browse’ object RStudio see organism included. Copy name dataset, species element use.Subset 214 datasets availableNext deciding filter. can use listFilters function ensembl object, storing full list filters. wise view RStudio find filter use.filter specifies use search . instance, AnnotationDbi queries gave us Ensembl gene ID’s use ensembl_gene_id filter. viewing list filters RStudio can use search text-box top-right view, example ensembl look filters use type identifier.Subset 437 available filtersFinally get point determine data like get database. attributes can get listAttributes function ensembl object. - especially attributes since often >1000 selectable options - store attributes view RStudio look data want.Subset 3172 available attributesIf want gene chromosome, start- end-position well description (note, just example, , interesting information available !) combine character vector (attrs.get, see ). one caveat though, order get back results input order! means simple combine data original data set need merge together. However, able merge data need know record belongs gene therefore add selected filter (case ensembl_gene_id) list attributes get. , get results dataframe can use merge function R combine gene information actual data:now three needed elements (species, filter attributes) ready query database getBM function (read help using ?getBM).example retrieves data using set five Ensembl gene IDs since genes Ensembl ID ’ve seen , filter first use values parameter .results object data.frame 5 columns can merge data set giving us following annotation columns (combined AnnotationDBI biomaRt libraries).just example use biomaRt library comes selecting correct filter looking interesting attributes retrieve. information can found documentation avaialble `vignette(‘biomaRt’)","code":"\n# Load the library\nlibrary(biomaRt)\n\n# Use an alternative database server as the regular one sometimes has issues..\nensembl=useMart(\"ENSEMBL_MART_ENSEMBL\", host=\"https://www.ensembl.org\")\n# Select the 'ensembl' database\nensembl <- useMart(\"ensembl\")\nmart.datasets <- listDatasets(ensembl)\n\n# Select the correct dataset, for the example data we select the 'hsapiens_gene_ensembl' \nensembl <- useDataset('hsapiens_gene_ensembl', mart = ensembl)\nfilters <- listFilters(ensembl)\nattributes <- listAttributes(ensembl)\nmerge(x = ipsc.tri.vs.di, y = results, by.x = 'Ensembl', by.y = 'ensembl_gene_id')\n# Set the 'attributes' values\nattrs.get <- c(\"ensembl_gene_id\", \"chromosome_name\", \n               \"start_position\",\"end_position\", \"description\")\n\n# Perform a biomaRt query using 'getBM'\nresults <- getBM(attributes = attrs.get,\n                 filters = \"ensembl_gene_id\",\n                 values = ipsc.tri.vs.di$Ensembl[1:5], \n                 mart = ensembl)\n\nresults$gene_length <- abs(results$end_position - results$start_position)"},{"path":"a3-read-mapping.html","id":"a3-read-mapping","chapter":"C From Reads to Counts","heading":"C From Reads to Counts","text":"chapter can used gain insight count files generated. Step one RNA-Seq data analysis generating gene transcript counts sample. overlaps partly steps taken genomics workflow (performed Galaxy):Download SRA files extract FastQ filesPerform quality control raw reads (FastQC)Perform trimming needed (Trimmomatic)Perform quality control post trimming (FastQC)Perform mapping reference genome (often STAR)Generate gene/ transcript counts (either STAR, HTseq featurecounts)Downstream analysis (starting chapter 3 document)chapter shows steps example data set (GSE149995)\nNote storage:\n\nfastq-files used RNA-Seq experiment can fairly large\ndepending number samples, total storage required \ndownloading processing files can run multiple terrabytes.\nstudents enough storage /students/\nfolder. Please create new directory \n/students/202x-202x/minor/ folder used \nfollowing steps. Note need replace \n/local-fs/staff/marcel/ path path \ndirectory.\n\nFurthermore, adviced (basically required) run \ncommands assemblix server capacity\nprocess multiple samples efficiently parallel.\n","code":""},{"path":"a3-read-mapping.html","id":"downloading-and-extracting-fastq-files","chapter":"C From Reads to Counts","heading":"C.1 Downloading and extracting FastQ files","text":"Given SRA identifier project, select samples download accession file (SRX identifiers). Using identifiers input prefetch system command (requires NCBI SRA-Toolkit installed) downloads SRA files convert using fasterq-dump command fastq files.fasterq-dump default performs split 3 operation, see SRA toolkit manual details.","code":"prefetch $(<GSE149995_SRA_ACC_RNA-Seq.csv) --output-directory \\\n  /local-fs/staff/marcel/GSE149995/SRAfind /local-fs/staff/marcel/GSE149995/SRA/ -name \"*.sra\" | \\\n  parallel 'fasterq-dump -O /local-fs/staff/marcel/GSE149995/fastq/ {}'"},{"path":"a3-read-mapping.html","id":"optional-annotation","chapter":"C From Reads to Counts","heading":"C.2 (Optional) Annotation","text":"NCBI GEO page lists 9 samples 3 conditions (WT, potent arf7). However, experiment includes 18 sequence files. downloading ‘series matrix file’ GEO (using GEOquery library) combining run info downloadable SRA, can list mapping run sample.Note: optional step now useful doubts group/sample setup.","code":"\nlibrary(GEOquery)\ngse <- getGEO(GEO = \"GSE149995\")\n# Combine the condition with a sample number and replicate number\nCondition = paste(rep(gse[[1]]@phenoData@data$`genotype/variation:ch1`, each = 2),\n                  rep(1:3, each = 2),\n                  paste0('r', rep(1:2, 9)), sep = '_')\nrun_info <- read.csv(file = \"data/GSE149995_Sra_RunInfo.csv\")\nsetup <- cbind(run_info %>% dplyr::select(Run, Experiment), Condition)\npander(setup)"},{"path":"a3-read-mapping.html","id":"quality-control","chapter":"C From Reads to Counts","heading":"C.3 Quality Control","text":"perform FastQC -> Trimmomatic -> FastQC steps","code":""},{"path":"a3-read-mapping.html","id":"fastqc","chapter":"C From Reads to Counts","heading":"C.3.1 FastQC","text":"Running FastQCQC-report using multiqc","code":"\nfqdir <- \"/local-fs/staff/marcel/GSE149995/fastq/\"\nfastqc(fq.dir = \"/local-fs/staff/marcel/GSE149995/fastq/\",\n       qc.dir = \"/local-fs/staff/marcel/GSE149995/fqc\",\n       fastqc.path = \"/usr/bin/fastqc\",\n       threads = 32)multiqc \\\n  --filename ~/Development/2.1.2-Transcriptomics/GSE149995_FQC_multiqc_report.html \\\n  /local-fs/staff/marcel/GSE149995/fqc/ "},{"path":"a3-read-mapping.html","id":"trimming","chapter":"C From Reads to Counts","heading":"C.4 Trimming","text":"Proper trimming requires experimentation research regarding used sequencing technology. following command uses Trimmomatic (release 0.39, available Github) trim fastq files. Note example single-end sequencing , use TrimmomaticPE command paired-end data processing TruSeq FASTA file ending PE instead.Re-running FastQC","code":"cat data/GSE149995_Sra_RunInfo.csv | \\\n  parallel 'TrimmomaticSE -threads 4 ' \\\n                  '/local-fs/staff/marcel/GSE149995/fastq/{}.fastq.gz ' \\\n                  '/local-fs/staff/marcel/GSE149995/fastq-trimmed/{}.trimmed.fastq.gz ' \\\n                  'ILLUMINACLIP:/homes/marcelk/Development/2.1.2-Transcriptomics/TruSeq3-SE.fa:2:30:10 ' \\\n                  'MINLEN:40 ' \\\n                  'SLIDINGWINDOW:4:20'\nfastqc(fq.dir = \"/local-fs/staff/marcel/GSE149995/fastq-trimmed/\",\n       qc.dir = \"/local-fs/staff/marcel/GSE149995/fqc-trimmed\",\n       fastqc.path = \"/usr/bin/fastqc\",\n       threads = 32)multiqc --filename \\\n  ~/Development/2.1.2-Transcriptomics/GSE149995_FQC_Trimmed.html \\\n  /local-fs/staff/marcel/GSE149995/fqc-trimmed/"},{"path":"a3-read-mapping.html","id":"mapping-with-star","chapter":"C From Reads to Counts","heading":"C.5 Mapping with STAR","text":"","code":""},{"path":"a3-read-mapping.html","id":"building-star-index","chapter":"C From Reads to Counts","heading":"C.5.1 Building STAR index","text":"bwa, STAR requires extra annotation file (GFF GTF format) describing features genome. data set requires FASTA Arabidopsis thaliana genome accompanying annotation file. genomeDir specifies output location index.","code":"cd /local-fs/staff/marcel/GSE149995/reference\nmkdir star; cd star\nSTAR \\\n  --runThreadN 12 \\\n  --runMode genomeGenerate \\\n  --genomeDir /local-fs/staff/marcel/GSE149995/reference/star \\\n  --genomeFastaFiles /local-fs/staff/marcel/GSE149995/reference/TAIR10_chr_all.fas \\\n  --sjdbGTFfile /local-fs/staff/marcel/GSE149995/reference/TAIR10_GTF_genes.gtf \\\n  --genomeSAindexNbases 12 \\\n  --sjdbOverhang 49"},{"path":"a3-read-mapping.html","id":"read-mapping-and-transcript-counting","chapter":"C From Reads to Counts","heading":"C.5.2 Read mapping and transcript counting","text":"following command performs mapping reference genome, saves sorted BAM file directly performs read count transcripts (NOTE: requires --sjdbGTFfile parameter index building). input file, ReadsPerGenes..tab file generated containing 4 columns:column 1: gene IDcolumn 2: counts unstranded RNA-seqcolumn 3: counts 1st read strand aligned RNA (htseq-count option -s yes)column 4: counts 2nd read strand aligned RNA (htseq-count option -s reverse)See interesting discussion BioStars regarding column use.planning use different tool calculating gene counts, can remove quantMode argument generate sorted BAM files. Please read manual tool choice see indexed BAM file BAM file index.Generating MultiQC report STAR mapping phase","code":"find /local-fs/staff/marcel/GSE149995/fastq-trimmed/ -name \"*.fastq.gz\" -exec basename {} \\; | \\\n  sed 's/\\.fastq.gz$//' | parallel 'STAR --runThreadN 6 ' \\\n    '--genomeDir /local-fs/staff/marcel/GSE149995/reference/star ' \\\n    '--readFilesIn /local-fs/staff/marcel/GSE149995/fastq-trimmed/{}.fastq ' \\\n    '--outSAMtype BAM SortedByCoordinate ' \\\n    '--quantMode GeneCounts ' \\\n    '--outFileNamePrefix /local-fs/staff/marcel/GSE149995/mapping/star/{}_star_'~/miniconda3/bin/multiqc --filename \\\n  ~/Development/2.1.2-Transcriptomics/STAR-mapping.html \\\n  /local-fs/staff/marcel/GSE149995/mapping/star/"},{"path":"a3-read-mapping.html","id":"combining-star-count-files","chapter":"C From Reads to Counts","heading":"C.5.3 Combining STAR Count Files","text":"STAR generates [sample]_star_ReadsPerGene..tab file sample, containing gene IDs count numbers. can merge files single data frame processing (see also Appendix A1 manual). example takes 2nd column count values. Make sure know column use based description . reason ’re merging data might always guaranteed get exact transcripts file.Note: use featurecounts tool, already get single file containing count values samples.Now read count files merge single data frame.now single data frame count values samples transcripts. names however nondescriptive since just SRR identifiers. Since already annotation, ’re going rename columns something meaningful:","code":"\nfile.names <- list.files('/local-fs/staff/marcel/GSE149995/mapping/star/',\n                         pattern = '*_star_ReadsPerGene.out.tab')\n\n## Function for reading in files\nread_sample <- function(file.name) {\n  ## Extract the sample name for naming the column (retaining the 'SRR....' part)\n  sample.name <- strsplit(file.name, \".\", fixed = TRUE)[[1]][1]\n  sample <- read.table(file.name, header = FALSE, sep=\"\\t\", \n                       row.names = NULL, skip = 4)\n  ## Rename the count column\n  names(sample)[2] <- sample.name\n  ## Return a subset containing the transcript ID and sample name columns\n  return(sample[c(1, 2)])\n}\nsetwd('/local-fs/staff/marcel/GSE149995/mapping/star/')\n\n## Read the FIRST sample\ncounts <- read_sample(file.names[1])\n\n## Read the remaining files and merge the contents\nfor (file.name in file.names[2:length(file.names)]) {\n  sample <- read_sample(file.name)\n  counts <- merge(counts, sample, by = 1)\n}\n\n# Set the row names to the transcript IDs\nrownames(counts) <- counts$V1\ncounts <- counts[-1]\n# For each SRR id, find the corresponding column in counts and rename\n# using the name in the 'Condition' column\nfor (i in 1:nrow(setup)) {\n  idx <- grep(setup$Run[i], names(counts))\n  names(counts)[idx] <- setup$Condition[i]\n}\n\n# Show first 6 columns and rows\npander(counts[1:6, 1:6])"},{"path":"a3-read-mapping.html","id":"references","chapter":"C From Reads to Counts","heading":"C.6 References","text":"","code":""}]