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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" 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body {
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min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
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}
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</head>
<body>
<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="tmod-">tmod <a href="https://january3.github.com/tmod/"><img 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" 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<p>Tmod is a suite of gene set enrichment algorithms, visualizations and utilities which comes bundled with a few libraries of gene sets (“modules”). Following features distinguish tmod from other packages:</p>
<ul>
<li>“panel plot” visualizations which allow to compare results of gene set enrichments;</li>
<li>several enrichment algorithms are implemented in tmod, in particular the very efficient, versatile and <a href="https://academic.oup.com/bioinformatics/article/35/24/5146/5511403">reproducible</a> algorithm called “CERNO”;</li>
<li>it includes a library of gene sets derived from clustering of gene expression data from human blood, which is especially useful in functional analysis in infection and immune responses.</li>
</ul>
<h2 id="installation">Installation</h2>
<p>You can install the released version of tmod from <a href="https://CRAN.R-project.org/">CRAN</a> with:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"tmod"</span>)</span></code></pre></div>
<p>And the development version from <a href="https://github.com/">GitHub</a> with:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># install.packages("devtools")</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"january3/tmod"</span>)</span></code></pre></div>
<h2 id="tmod-manual">Tmod manual</h2>
<ul>
<li>Access the documentation using <code>vignette("tmod")</code></li>
<li>Full user manual is found <a href="https://january3.github.io/tmod/">here</a></li>
</ul>
<h2 id="example-usage">Example usage</h2>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tmod)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co">#> For tmod user guide, type `vignette("tmod")`</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(EgambiaResults)</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>tt <span class="ot"><-</span> EgambiaResults</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="do">## gene set enrichment analysis</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>res <span class="ot"><-</span> <span class="fu">tmodCERNOtest</span>(tt<span class="sc">$</span>GENE_SYMBOL)</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(res)</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="co">#> ID Title cerno N1 AUC cES P.Value adj.P.Val</span></span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a><span class="co">#> LI.M37.0 LI.M37.0 immune activation - generic cluster 426.3578 100 0.7462103 2.131789 1.824844e-18 1.105856e-15</span></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a><span class="co">#> DC.M4.2 DC.M4.2 Inflammation 151.1520 20 0.9503953 3.778799 8.040039e-15 2.436132e-12</span></span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a><span class="co">#> DC.M3.4 DC.M3.4 Interferon 129.4727 17 0.8315780 3.808019 4.609405e-13 9.310998e-11</span></span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a><span class="co">#> DC.M1.2 DC.M1.2 Interferon 112.7056 17 0.9004196 3.314869 2.298170e-10 3.481728e-08</span></span>
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a><span class="co">#> DC.M7.29 DC.M7.29 Undetermined 118.6759 20 0.8087599 2.966897 1.002268e-09 1.214749e-07</span></span>
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a><span class="co">#> LI.M11.0 LI.M11.0 enriched in monocytes (II) 113.8086 20 0.7766542 2.845216 5.255069e-09 5.307620e-07</span></span></code></pre></div>
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