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<!DOCTYPE html>
<html lang="en"><head>
<script src="ClusterProfiler_files/libs/clipboard/clipboard.min.js"></script>
<script src="ClusterProfiler_files/libs/quarto-html/tabby.min.js"></script>
<script src="ClusterProfiler_files/libs/quarto-html/popper.min.js"></script>
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<link href="ClusterProfiler_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-1.2.269">
<meta name="author" content="Graham Hamilton">
<title>Functional Analysis</title>
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
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code{white-space: pre-wrap;}
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code span { color: #003b4f; } /* Normal */
code span.al { color: #ad0000; } /* Alert */
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code span.do { color: #5e5e5e; font-style: italic; } /* Documentation */
code span.dt { color: #ad0000; } /* DataType */
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code span.er { color: #ad0000; } /* Error */
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<style type="text/css">
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margin-top: 1em;
margin-bottom: 1em;
border-radius: .25rem;
}
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border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
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}
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border-right: solid 1px silver;
border-top: solid 1px silver;
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font-size: .9rem;
font-weight: 600;
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border-bottom: none;
font-weight: 600;
opacity: 85%;
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padding-left: 0.5em;
padding-right: 0.5em;
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padding-right: 0.5em;
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width: 1rem;
display: inline-block;
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background-repeat: no-repeat;
background-size: 1rem 1rem;
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height: 0.9rem;
width: 0.9rem;
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content: "";
background-repeat: no-repeat;
background-size: 0.9rem 0.9rem;
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}
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margin-top: 1rem;
padding-right: .5rem;
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margin-bottom: 0.5rem;
}
.callout.callout-captioned .callout-icon::before {
margin-top: .5rem;
padding-right: .5rem;
}
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margin-top: 1rem;
padding-right: .5rem;
}
/* Callout Types */
div.callout-note {
border-left-color: #4582ec !important;
}
div.callout-note .callout-icon::before {
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}
div.callout-note.callout-style-default .callout-caption {
background-color: #dae6fb
}
div.callout-important {
border-left-color: #d9534f !important;
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</head>
<body class="quarto-light">
<div class="reveal">
<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">Functional Analysis</h1>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
Graham Hamilton
</div>
</div>
</div>
</section>
<section id="overview" class="slide level2">
<h2>Overview</h2>
<div class="columns">
<div class="column" style="width:50%;">
<ul>
<li>Loading packages</li>
<li>Loading results from DESeq2</li>
<li>Annotation</li>
<li>Filtering results by fold change and adjusted p value</li>
</ul>
</div><div class="column" style="width:50%;">
<ul>
<li>Gene ontology analysis</li>
<li>Pathway enrichment</li>
<li>Cell marker gene set enrichment</li>
<li>Visualisation</li>
</ul>
</div>
</div>
</section>
<section id="ingenuity-pathway-analysis" class="slide level2">
<h2>Ingenuity Pathway Analysis</h2>
<p>Qiagen Ingenuity Pathway Analysis</p>
<ul>
<li>Canonical pathways</li>
<li>Upstream analysis</li>
<li>Diseases & functions</li>
<li>Regulator effects</li>
<li><strong>Expensive annual licence</strong></li>
</ul>
</section>
<section id="david" class="slide level2">
<h2>DAVID</h2>
<ul>
<li>Web server</li>
<li>Functional enrichment analysis</li>
<li>Functional annotation</li>
<li><strong>Free to use</strong></li>
</ul>
<aside><div>
<div style="font-size: 50%;">
<p>Brad T Sherman, Ming Hao, Ju Qiu, Xiaoli Jiao, Michael W Baseler, H Clifford Lane, Tomozumi Imamichi, Weizhong Chang, DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update), Nucleic Acids Research, Volume 50, Issue W1, 5 July 2022, Pages W216–W221, https://doi.org/10.1093/nar/gkac194</p>
</div>
</div></aside></section>
<section id="clusterprofiler" class="slide level2 smaller">
<h2>ClusterProfiler</h2>
<p>ClusterProfiler is an R package that performs functional enrichment analysis, to aide interpretation of high throughput data like RNASeq.</p>
<ul>
<li>Over representation analysis
<ul>
<li>Identify pathways or ontologies annotated be the gene set greater than expected by chance, ORA</li>
</ul></li>
<li>Gene set enrichment analysis, GSEA
<ul>
<li>Similar to ORA but with differing statistical approach</li>
</ul></li>
<li><strong>Free to use</strong></li>
</ul>
<aside><div>
<div style="font-size: 75%;">
<p>https://yulab-smu.top/biomedical-knowledge-mining-book/index.html <br><br> Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141., https://doi.org/10.1016/j.xinn.2021.100141</p>
</div>
</div></aside></section>
<section id="cran-packages" class="slide level2">
<h2>CRAN packages</h2>
<p>Install CRAN packages</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>cran.packages <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"tidyverse"</span>,</span>
<span id="cb1-2"><a href="#cb1-2"></a> <span class="st">"ggrepel"</span>,</span>
<span id="cb1-3"><a href="#cb1-3"></a> <span class="st">"kableExtra"</span>,</span>
<span id="cb1-4"><a href="#cb1-4"></a> <span class="st">"ggupset"</span>)</span>
<span id="cb1-5"><a href="#cb1-5"></a></span>
<span id="cb1-6"><a href="#cb1-6"></a>cran.load <span class="ot"><-</span> <span class="cf">function</span>(pkg){</span>
<span id="cb1-7"><a href="#cb1-7"></a> new.pkg <span class="ot"><-</span> pkg[<span class="sc">!</span>(pkg <span class="sc">%in%</span> <span class="fu">installed.packages</span>()[, <span class="st">"Package"</span>])]</span>
<span id="cb1-8"><a href="#cb1-8"></a> <span class="cf">if</span> (<span class="fu">length</span>(new.pkg)){</span>
<span id="cb1-9"><a href="#cb1-9"></a> <span class="fu">install.packages</span>(new.pkg, <span class="at">dependencies =</span> <span class="cn">TRUE</span>)</span>
<span id="cb1-10"><a href="#cb1-10"></a> }</span>
<span id="cb1-11"><a href="#cb1-11"></a> <span class="fu">sapply</span>(pkg, require, <span class="at">character.only =</span> <span class="cn">TRUE</span>)</span>
<span id="cb1-12"><a href="#cb1-12"></a>}</span>
<span id="cb1-13"><a href="#cb1-13"></a><span class="fu">cran.load</span>(cran.packages)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="bioconductor-packages" class="slide level2">
<h2>Bioconductor Packages</h2>
<p>Install Biocondustor packages</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>bioconductor.packages <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"org.Hs.eg.db"</span>,</span>
<span id="cb2-2"><a href="#cb2-2"></a> <span class="st">"biomaRt"</span>,</span>
<span id="cb2-3"><a href="#cb2-3"></a> <span class="st">"clusterProfiler"</span>,</span>
<span id="cb2-4"><a href="#cb2-4"></a> <span class="st">"enrichplot"</span>,</span>
<span id="cb2-5"><a href="#cb2-5"></a> <span class="st">"pathview"</span>)</span>
<span id="cb2-6"><a href="#cb2-6"></a></span>
<span id="cb2-7"><a href="#cb2-7"></a>bioconductor.load <span class="ot"><-</span> <span class="cf">function</span>(pkg){</span>
<span id="cb2-8"><a href="#cb2-8"></a> new.pkg <span class="ot"><-</span> pkg[<span class="sc">!</span>(pkg <span class="sc">%in%</span> <span class="fu">installed.packages</span>()[, <span class="st">"Package"</span>])]</span>
<span id="cb2-9"><a href="#cb2-9"></a> <span class="cf">if</span> (<span class="fu">length</span>(new.pkg)){</span>
<span id="cb2-10"><a href="#cb2-10"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">requireNamespace</span>(<span class="st">"BiocManager"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>))</span>
<span id="cb2-11"><a href="#cb2-11"></a> <span class="fu">install.packages</span>(<span class="st">"BiocManager"</span>)</span>
<span id="cb2-12"><a href="#cb2-12"></a> BiocManager<span class="sc">::</span><span class="fu">install</span>(new.pkg)</span>
<span id="cb2-13"><a href="#cb2-13"></a> }</span>
<span id="cb2-14"><a href="#cb2-14"></a> <span class="fu">sapply</span>(pkg, require, <span class="at">character.only =</span> <span class="cn">TRUE</span>)</span>
<span id="cb2-15"><a href="#cb2-15"></a>}</span>
<span id="cb2-16"><a href="#cb2-16"></a><span class="fu">bioconductor.load</span>(bioconductor.packages)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="statistical-cut-off-values" class="slide level2">
<h2>Statistical cut off values</h2>
<p>Create the cut off used for filtering results</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>pvalueCutoff <span class="ot"><-</span> <span class="fl">0.05</span> <span class="co"># P value</span></span>
<span id="cb3-2"><a href="#cb3-2"></a>qvalueCutoff <span class="ot"><-</span> <span class="fl">0.1</span> <span class="co"># Adjusted P value</span></span>
<span id="cb3-3"><a href="#cb3-3"></a>foldchange <span class="ot"><-</span> <span class="dv">1</span> <span class="co"># Fold change, usually fold change is log2</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="load-results" class="slide level2 smaller">
<h2>Load Results</h2>
<p>Read in the results file from DESeq2 Analysis</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>de_results <span class="ot"><-</span> <span class="fu">read_delim</span>(<span class="st">"./data/Treatment_vs_Control_results.tsv"</span>, </span>
<span id="cb4-2"><a href="#cb4-2"></a> <span class="at">delim =</span> <span class="st">"</span><span class="sc">\t</span><span class="st">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p><br></p>
<div style="font-size: 80%;">
<div class="cell">
<div class="cell-output-display">
<table class="table table-condensed" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;"> ensembl_gene_id </th>
<th style="text-align:right;"> baseMean </th>
<th style="text-align:right;"> log2FoldChange </th>
<th style="text-align:right;"> lfcSE </th>
<th style="text-align:right;"> stat </th>
<th style="text-align:right;"> pvalue </th>
<th style="text-align:right;"> padj </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> ENSG00000157191 </td>
<td style="text-align:right;"> 8171.718 </td>
<td style="text-align:right;"> -2.429669 </td>
<td style="text-align:right;"> 0.0374863 </td>
<td style="text-align:right;"> -64.81491 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000117090 </td>
<td style="text-align:right;"> 2322.100 </td>
<td style="text-align:right;"> -5.224265 </td>
<td style="text-align:right;"> 0.0775600 </td>
<td style="text-align:right;"> -67.35771 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000026751 </td>
<td style="text-align:right;"> 4495.926 </td>
<td style="text-align:right;"> -4.690935 </td>
<td style="text-align:right;"> 0.0870932 </td>
<td style="text-align:right;"> -53.86110 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<p><br></p>
<p><strong>baseMean</strong> - mean of normalized counts for all samples<br>
<strong>log2FoldChang</strong>e - log2 fold change (MAP): condition treated vs untreated<br>
<strong>lfcSE</strong> - standard error: condition treated vs untreated<br>
<strong>stat</strong> - Wald statistic: condition treated vs untreated<br>
<strong>pvalue</strong> - Wald test p-value: condition treated vs untreated<br>
<strong>padj</strong> - BH adjusted p-values</p>
</section>
<section id="annotations" class="slide level2 smaller">
<h2>Annotations</h2>
<p>The DESeq2 results file contains very little information about the genes.<br>
Annotations can be added from the GFF3 file or form the Biomart data base.<br>
Using the R package biomaRt we can access the data sets <br></p>
<div class="cell">
</div>
<div style="font-size: 50%;">
<div class="cell">
<div class="cell-output-display">
<table class="table table-condensed" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;"> dataset </th>
<th style="text-align:left;"> description </th>
<th style="text-align:left;"> version </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> abrachyrhynchus_gene_ensembl </td>
<td style="text-align:left;"> Pink-footed goose genes (ASM259213v1) </td>
<td style="text-align:left;"> ASM259213v1 </td>
</tr>
<tr>
<td style="text-align:left;"> acalliptera_gene_ensembl </td>
<td style="text-align:left;"> Eastern happy genes (fAstCal1.2) </td>
<td style="text-align:left;"> fAstCal1.2 </td>
</tr>
<tr>
<td style="text-align:left;"> acarolinensis_gene_ensembl </td>
<td style="text-align:left;"> Green anole genes (AnoCar2.0v2) </td>
<td style="text-align:left;"> AnoCar2.0v2 </td>
</tr>
<tr>
<td style="text-align:left;"> acchrysaetos_gene_ensembl </td>
<td style="text-align:left;"> Golden eagle genes (bAquChr1.2) </td>
<td style="text-align:left;"> bAquChr1.2 </td>
</tr>
<tr>
<td style="text-align:left;"> acitrinellus_gene_ensembl </td>
<td style="text-align:left;"> Midas cichlid genes (Midas_v5) </td>
<td style="text-align:left;"> Midas_v5 </td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<p><br> There are <strong>214</strong> organism datasets in Biomart.</p>
</section>
<section id="annotations-1" class="slide level2 smaller">
<h2>Annotations</h2>
<p>Here we get annotations from the Ensembl human database at Biomart</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>database <span class="ot"><-</span> <span class="st">"hsapiens_gene_ensembl"</span></span>
<span id="cb5-2"><a href="#cb5-2"></a>mart <span class="ot"><-</span> <span class="st">"genes"</span></span>
<span id="cb5-3"><a href="#cb5-3"></a>filt <span class="ot"><-</span> <span class="st">"ensembl_gene_id"</span></span>
<span id="cb5-4"><a href="#cb5-4"></a></span>
<span id="cb5-5"><a href="#cb5-5"></a>ensembl <span class="ot"><-</span> <span class="fu">useEnsembl</span>(<span class="at">biomart =</span> mart, <span class="at">dataset =</span> database)</span>
<span id="cb5-6"><a href="#cb5-6"></a></span>
<span id="cb5-7"><a href="#cb5-7"></a>att <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"ensembl_gene_id"</span>,<span class="st">"external_gene_name"</span>,<span class="st">"chromosome_name"</span>,<span class="st">"start_position"</span>,<span class="st">"end_position"</span>,<span class="st">"gene_biotype"</span>,<span class="st">"entrezgene_id"</span>)</span>
<span id="cb5-8"><a href="#cb5-8"></a></span>
<span id="cb5-9"><a href="#cb5-9"></a>annotations <span class="ot"><-</span> <span class="fu">getBM</span>(<span class="at">attributes=</span>att,<span class="at">filter=</span>filt,<span class="at">values=</span>de_results<span class="sc">$</span>ensembl_gene_id,<span class="at">mart=</span>ensembl) <span class="sc">%>%</span> </span>
<span id="cb5-10"><a href="#cb5-10"></a> <span class="fu">distinct</span>(ensembl_gene_id, <span class="at">.keep_all =</span> <span class="cn">TRUE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="annotations-2" class="slide level2">
<h2>Annotations</h2>
<p><br></p>
<div style="font-size: 40%;">
<div class="cell">
<div class="cell-output-display">
<table class="table table-condensed" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;"> ensembl_gene_id </th>
<th style="text-align:left;"> external_gene_name </th>
<th style="text-align:left;"> chromosome_name </th>
<th style="text-align:right;"> start_position </th>
<th style="text-align:right;"> end_position </th>
<th style="text-align:left;"> gene_biotype </th>
<th style="text-align:right;"> entrezgene_id </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> ENSG00000000460 </td>
<td style="text-align:left;"> FIRRM </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 169662007 </td>
<td style="text-align:right;"> 169854080 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 55732 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000000938 </td>
<td style="text-align:left;"> FGR </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 27612064 </td>
<td style="text-align:right;"> 27635185 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 2268 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000001084 </td>
<td style="text-align:left;"> GCLC </td>
<td style="text-align:left;"> 6 </td>
<td style="text-align:right;"> 53497341 </td>
<td style="text-align:right;"> 53616970 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 2729 </td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</section>
<section id="add-annotations" class="slide level2">
<h2>Add Annotations</h2>
<p>The annotations can be merged with our results file</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>de_results <span class="ot"><-</span> de_results <span class="sc">%>%</span></span>
<span id="cb6-2"><a href="#cb6-2"></a> <span class="fu">left_join</span>(annotations, <span class="at">by =</span> <span class="st">"ensembl_gene_id"</span>)<span class="sc">%>%</span> <span class="co"># merge annotations by ensembl gene id</span></span>
<span id="cb6-3"><a href="#cb6-3"></a> <span class="fu">arrange</span>(padj) <span class="sc">%>%</span> <span class="co"># Sort the dataframe by the p adjusted value ascending</span></span>
<span id="cb6-4"><a href="#cb6-4"></a> <span class="fu">replace_na</span>(<span class="fu">list</span>(<span class="at">log2FoldChange =</span> <span class="fl">0.001</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p><br></p>
<div style="font-size: 50%;">
<div class="cell">
<div class="cell-output-display">
<table class="table table-condensed" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;"> ensembl_gene_id </th>
<th style="text-align:right;"> baseMean </th>
<th style="text-align:right;"> log2FoldChange </th>
<th style="text-align:right;"> lfcSE </th>
<th style="text-align:right;"> stat </th>
<th style="text-align:right;"> pvalue </th>
<th style="text-align:right;"> padj </th>
<th style="text-align:left;"> external_gene_name </th>
<th style="text-align:left;"> chromosome_name </th>
<th style="text-align:right;"> start_position </th>
<th style="text-align:right;"> end_position </th>
<th style="text-align:left;"> gene_biotype </th>
<th style="text-align:right;"> entrezgene_id </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> ENSG00000157191 </td>
<td style="text-align:right;"> 8171.718 </td>
<td style="text-align:right;"> -2.429669 </td>
<td style="text-align:right;"> 0.0374863 </td>
<td style="text-align:right;"> -64.81491 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:left;"> NECAP2 </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 16440721 </td>
<td style="text-align:right;"> 16460078 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 55707 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000117090 </td>
<td style="text-align:right;"> 2322.100 </td>
<td style="text-align:right;"> -5.224265 </td>
<td style="text-align:right;"> 0.0775600 </td>
<td style="text-align:right;"> -67.35771 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:left;"> SLAMF1 </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 160608106 </td>
<td style="text-align:right;"> 160647044 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 6504 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000026751 </td>
<td style="text-align:right;"> 4495.926 </td>
<td style="text-align:right;"> -4.690935 </td>
<td style="text-align:right;"> 0.0870932 </td>
<td style="text-align:right;"> -53.86110 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:left;"> SLAMF7 </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 160739057 </td>
<td style="text-align:right;"> 160754821 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 57823 </td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</section>
<section id="results-directory" class="slide level2">
<h2>Results Directory</h2>
<p>Create a results directory for the ClusterProfiler results files</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>results_directory <span class="ot"><-</span> <span class="st">"ClusterProfiler"</span></span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="fu">dir.create</span>(results_directory, <span class="at">showWarnings =</span> <span class="cn">FALSE</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="filter" class="slide level2 smaller">
<h2>Filter</h2>
<p>Filter the results table for genes that have a p adjusted value of 0.1 and a log 2 fold change of 1.</p>
<p><strong>NOTE:</strong> The use of the absolute value for the log2 fold change.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>filtered_de_results <span class="ot"><-</span> de_results <span class="sc">%>%</span></span>
<span id="cb8-2"><a href="#cb8-2"></a> <span class="fu">filter</span>(padj <span class="sc"><</span> qvalueCutoff,</span>
<span id="cb8-3"><a href="#cb8-3"></a> <span class="fu">abs</span>(log2FoldChange) <span class="sc">></span> foldchange) <span class="sc">%>%</span></span>
<span id="cb8-4"><a href="#cb8-4"></a> <span class="fu">drop_na</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p><br></p>
<div style="font-size: 50%;">
<div class="cell">
<div class="cell-output-display">
<table class="table table-condensed" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;"> ensembl_gene_id </th>
<th style="text-align:right;"> baseMean </th>
<th style="text-align:right;"> log2FoldChange </th>
<th style="text-align:right;"> lfcSE </th>
<th style="text-align:right;"> stat </th>
<th style="text-align:right;"> pvalue </th>
<th style="text-align:right;"> padj </th>
<th style="text-align:left;"> external_gene_name </th>
<th style="text-align:left;"> chromosome_name </th>
<th style="text-align:right;"> start_position </th>
<th style="text-align:right;"> end_position </th>
<th style="text-align:left;"> gene_biotype </th>
<th style="text-align:right;"> entrezgene_id </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> ENSG00000157191 </td>
<td style="text-align:right;"> 8171.718 </td>
<td style="text-align:right;"> -2.429669 </td>
<td style="text-align:right;"> 0.0374863 </td>
<td style="text-align:right;"> -64.81491 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:left;"> NECAP2 </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 16440721 </td>
<td style="text-align:right;"> 16460078 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 55707 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000117090 </td>
<td style="text-align:right;"> 2322.100 </td>
<td style="text-align:right;"> -5.224265 </td>
<td style="text-align:right;"> 0.0775600 </td>
<td style="text-align:right;"> -67.35771 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:left;"> SLAMF1 </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 160608106 </td>
<td style="text-align:right;"> 160647044 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 6504 </td>
</tr>
<tr>
<td style="text-align:left;"> ENSG00000026751 </td>
<td style="text-align:right;"> 4495.926 </td>
<td style="text-align:right;"> -4.690935 </td>
<td style="text-align:right;"> 0.0870932 </td>
<td style="text-align:right;"> -53.86110 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:right;"> 0 </td>
<td style="text-align:left;"> SLAMF7 </td>
<td style="text-align:left;"> 1 </td>
<td style="text-align:right;"> 160739057 </td>
<td style="text-align:right;"> 160754821 </td>
<td style="text-align:left;"> protein_coding </td>
<td style="text-align:right;"> 57823 </td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<p>There are <strong>4954</strong> genes remaining after filtering.</p>
</section>
<section id="gene-ontology" class="slide level2 smaller">
<h2>Gene Ontology</h2>
<div style="font-size: 75%;">
<ul>
<li>Ontology is a formal representation of knowledgebase for the functions of genes, computer and human readable</li>
<li>GO split into three domains
<ul>
<li>Molecular Function
<ul>
<li>Molecular-level activities performed by gene products e.g. catalysis or transport</li>
</ul></li>
<li>Cellular Component
<ul>
<li>Location relative to cellular structure of gene function e.g. mitochondrian or ribosome</li>
</ul></li>
<li>Biological Process
<ul>
<li>Larger processes accomplished by multiple molecular activities e.g DNA repair or signal transduction</li>
</ul></li>
</ul></li>
<li><strong>GO does not describe pathways</strong></li>
</ul>
</div>
</section>
<section id="over-representation-analysis" class="slide level2 smaller">
<h2>Over Representation Analysis</h2>
<div style="font-size: 75%;">
<ul>
<li>Widely used approach to determine whether known biological functions or processes are over-represented in a set of differentially expressed genes</li>
</ul>
<p>Example - 57 genes were differentially expressed out of a total of 17,980. Among the differentially expressed genes, 28 are annotated to a gene set</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>d <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">genes_not_differentially_expressed=</span><span class="fu">c</span>(<span class="dv">2613</span>, <span class="dv">15310</span>), <span class="at">genes_differentially_expressed=</span><span class="fu">c</span>(<span class="dv">28</span>, <span class="dv">29</span>))</span>
<span id="cb9-2"><a href="#cb9-2"></a><span class="fu">row.names</span>(d) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"In_GO_term"</span>, <span class="st">"Not_in_GO_term"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p><br></p>
<div class="cell">
<div class="cell-output-display">
<table class="table table-condensed" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;"> </th>
<th style="text-align:right;"> genes_not_differentially_expressed </th>
<th style="text-align:right;"> genes_differentially_expressed </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> In_GO_term </td>
<td style="text-align:right;"> 2613 </td>
<td style="text-align:right;"> 28 </td>
</tr>
<tr>
<td style="text-align:left;"> Not_in_GO_term </td>
<td style="text-align:right;"> 15310 </td>
<td style="text-align:right;"> 29 </td>
</tr>
</tbody>
</table>
</div>
</div>
<p><br></p>
<div class="cell">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>f_test <span class="ot"><-</span> <span class="fu">fisher.test</span>(d)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Test significance using hypergeometric distribution, one-sided Fisher’s exact test. The un-corrected P value <strong>7.879194e-10</strong></p>
</div>
<aside><div>
<div style="font-size: 75%;">
<p>Boyle, Elizabeth I, Shuai Weng, Jeremy Gollub, Heng Jin, David Botstein, J Michael Cherry, and Gavin Sherlock. 2004. “GO::TermFinder–open Source Software for Accessing Gene Ontology Information and Finding Significantly Enriched Gene Ontology Terms Associated with a List of Genes.” Bioinformatics (Oxford, England) 20 (18): 3710–15. https://doi.org/10.1093/bioinformatics/bth456.</p>
</div>
</div></aside></section>
<section id="gene-set-enrichment-analysis" class="slide level2 smaller">
<h2>Gene Set Enrichment Analysis</h2>
<ul>
<li>Over representation analysis find genes where the difference is large but will fail where the difference is small.</li>
<li>Gene Set Enrichment Analysis (GSEA) directly addresses this limitation.
<ol type="1">
<li>Calculate Enrichment Score (ES)</li>
<li>Estimation of Significance Level of ES</li>
<li>Adjustment for Multiple Hypothesis Testing</li>
</ol></li>
</ul>
<aside><div>
<div style="font-size: 75%;">
<p>Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences of the United States of America 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.</p>
</div>
</div></aside></section>
<section id="gene-ontology-analysis" class="slide level2">
<h2>Gene Ontology Analysis</h2>
<p>Over representation analysis</p>
<p>List of entrez ids for the differentially expressed genes, using the <strong>pull()</strong> command from dplyr</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>de_genes <span class="ot"><-</span> filtered_de_results <span class="sc">%>%</span></span>
<span id="cb11-2"><a href="#cb11-2"></a> <span class="fu">pull</span>(entrezgene_id)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p><br></p>
<div class="cell">
<div class="cell-output cell-output-stdout">
<pre><code>[1] 55707 6504 57823 2213 7431 5552</code></pre>
</div>
</div>
</section>
<section id="gene-ontology-analysis-1" class="slide level2 smaller">
<h2>Gene Ontology Analysis</h2>
<p>Over representation analysis</p>
<div class="cell">