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<div id="rediscovertedata" class="section level1">
<h1>REdiscoverTEdata</h1>
<p>Author: <em>Haiyin Chen</em></p>
<p>Date: <em>2019-09</em></p>
</div>
<div id="overview" class="section level1">
<h1>Overview</h1>
<p>This R package enables reproducibility of main figures from the paper TRANSPOSABLE ELEMENT EXPRESSION IN TUMORS IS ASSOCIATED WITH IMMUNE INFILTRATION AND INCREASED ANTIGENICITY by <code>Kong Y, ..., Chen-Harris H (2019)</code>.</p>
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<div id="contents" class="section level1">
<h1>Contents</h1>
<p><code>REdiscoverTEdata</code> is an <code>R</code> package that can be installed locally. It contains source R Markdown (<code>.Rmd</code>) files for five main figures from the paper. Each <code>Rmd</code> file can generate one set of figures, either in HTML format or PDF format.</p>
</div>
<div id="installation-process" class="section level1">
<h1>Installation Process</h1>
<ol style="list-style-type: decimal">
<li>Install <code>R</code> as described at <a href="https://www.r-project.org/">https://www.r-project.org/</a>. <code>REdiscoverTEdata</code> has been tested with <code>R 3.4.3</code>, <code>R 3.5.1</code>.</li>
<li>Install <code>RStudio</code>, which we recommend using to process .Rmd files into final figures. <code>RStudio</code> can be downloaded from <a href="https://www.rstudio.com/">https://www.rstudio.com/</a>.</li>
<li>Install the <code>REdiscoverTEdata</code> package (library) locally with the following:
<ul>
<li><code>cd /your/DOWNLOADS/directory/</code></li>
<li><code>tar -xvf REdiscoverTEdata.tar.gz</code> (uncompress the downloaded archive)</li>
<li><code>R CMD INSTALL REdiscoverTEdata</code></li>
</ul></li>
</ol>
<ul>
<li>That command should automatically install the prerequisite libraries. You will find the <code>.Rmd</code> files for each figure in the <code>REdiscoverTEdata/inst/</code> subdirectory.</li>
</ul>
</div>
<div id="list-of-r-modules-used-by-rediscoverte" class="section level1">
<h1>List of R modules used by REdiscoverTE</h1>
<ul>
<li>Biobase</li>
<li>ComplexHeatmap</li>
<li>circlize</li>
<li>dplyr</li>
<li>DT</li>
<li>GenomicRanges</li>
<li>ggplot2</li>
<li>ggrepel</li>
<li>grid</li>
<li>gridExtra</li>
<li>knitr</li>
<li>multiGSEA (not a CRAN package, see below)</li>
<li>plyr</li>
<li>RColorBrewer</li>
<li>rmarkdown</li>
<li>stats</li>
</ul>
<p><code>multiGSEA</code> is available on github at <a href="https://github.com/lianos/multiGSEA" class="uri">https://github.com/lianos/multiGSEA</a> (you may need to install this manually depending on the version of R and bioconductor you are using)</p>
</div>
<div id="reproducing-figures-from-the-paper" class="section level1">
<h1>Reproducing figures from the paper</h1>
<ul>
<li>Once <code>REdiscoverTEdata</code> is installed to your <code>R</code> library directory, you can launch <code>RStudio</code> and, from within <code>RStudio</code>, open any of the following <code>.Rmd</code> (R Markdown) files.
<ul>
<li><code>REdiscoverTEdata/inst/Figure_1.Rmd</code></li>
<li><code>REdiscoverTEdata/inst/Figure_2.Rmd</code></li>
<li><code>REdiscoverTEdata/inst/Figure_3.Rmd</code></li>
<li><code>REdiscoverTEdata/inst/Figure_4.Rmd</code></li>
<li><code>REdiscoverTEdata/inst/Figure_5.Rmd</code></li>
</ul></li>
<li>Once an <code>.Rmd</code> file is open in <code>RStudio</code>, you can <code>knit</code> it (convert it to a figure) as follows:
<ul>
<li><code>File Menu</code> -&gt; <code>Knit Document</code></li>
<li>alternatively, you can click on the “Knit” submenu and select <code>Knit to HTML</code> or <code>Knit to PDF</code>.</li>
</ul></li>
<li>The output from the <code>knit</code> operation will be an HTML (or PDF) file with the same name as the <code>.Rmd</code>, in the same directory.</li>
</ul>
</div>
<div id="tcga-te-expression-matrix" class="section level1">
<h1>TCGA TE expression matrix</h1>
<ul>
<li>TCGA TE expression matrix for 7000+ samples and 1000+ TE subfamilies can be found in the subdirectory <code>REdiscoverTEdata/inst/Fig4_data/</code> under the filename <code>eset_TCGA_TE_intergenic_logCPM.RDS</code>. The matrix is stored in the form of ExpressionSet, a data structure class defined in the <code>Biobase</code> package.</li>
</ul>
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