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<h1 class="title toc-ignore">Overview of 4,026 pre-processed gene sets</h1>
<h4 class="author"><em>Xiang Zhu</em></h4>
<h4 class="date"><em>2018-09-16</em></h4>
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<p><strong>Last updated:</strong> 2018-10-05</p>
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<p>All 4,026 gene sets used in <a href="https://doi.org/10.1101/160770">Zhu and Stephens (2017)</a> are freely available at <a href="https://github.com/xiangzhu/rss-gsea/tree/master/data"><code>xiangzhu/rss-gsea</code></a>, where the folder <a href="https://github.com/xiangzhu/rss-gsea/tree/master/data/biological_pathway"><code>biological_pathway</code></a> contains 3,913 biological pathways, and the folder <a href="https://github.com/xiangzhu/rss-gsea/tree/master/data/tissue_set"><code>tissue_set</code></a> contains 113 GTEx tissue-based gene sets. These gene sets can be referenced in a journal’s “Data availability” section as <a href="https://zenodo.org/badge/latestdoi/55633948"><img src="https://zenodo.org/badge/55633948.svg" alt="DOI" /></a>.</p>
<pre><code>data/
├── README.md
├── biological_pathway
│ ├── gene_37.3.mat
│ └── pathway.mat
└── tissue_set
├── de_genes
├── he_genes
└── se_genes
5 directories, 3 files</code></pre>
<div id="biological-pathways" class="section level2">
<h2>Biological pathways</h2>
<p>The 3,913 GTEx biological pathway used in <a href="https://doi.org/10.1101/160770">Zhu and Stephens (2017)</a> are available in the folder <a href="https://github.com/xiangzhu/rss-gsea/tree/master/data/biological_pathway"><code>biological_pathway</code></a>, which are represented by two files <code>gene_37.3.mat</code> and <code>pathway.mat</code>.</p>
<p>The file <code>gene_37.3.mat</code> contains basic information of genes.</p>
<pre><code>>> load gene_37.3.mat
>> gene
gene =
struct with fields:
id: [18732x1 double]
symbol: {18732x1 cell}
chr: [18732x1 double]
desc: {18732x1 cell}
start: [18732x1 double]
stop: [18732x1 double]
>> [gene.id(10) gene.chr(10) gene.start(10) gene.stop(10)]
ans =
18 16 8768444 8878432
>> gene.symbol(10)
ans =
1x1 cell array
{'ABAT'}
>> gene.desc(10)
ans =
1x1 cell array
{'4-aminobutyrate aminotransferase'}</code></pre>
<p>Note that only 18,313 genes mapped to reference sequence were used in our analyses.</p>
<pre><code>>> [min(gene.start) min(gene.stop)]
ans =
-1 -1
>> inref_genes = ~(gene.start == -1 | gene.stop == -1);
>> sum(inref_genes)
ans =
18313</code></pre>
<p>The file <code>pathway.mat</code> contains basic information of pathways.</p>
<pre><code>>> load pathway.mat
>> pathway
pathway =
struct with fields:
label: {4076x1 cell}
database: {4076x1 cell}
source: {4076x1 cell}
genes: [18732x4076 double]
synonyms: {4076x1 cell}
>> pathway.label(100)
ans =
1x1 cell array
{'Activation of NOXA and translocation to mitochondria'}
>> pathway.database(100)
ans =
1x1 cell array
{'PC'}
>> pathway.source(100)
ans =
1x1 cell array
{'reactome'}</code></pre>
<p>The gene-pathway information is represented as a sparse zero-one matrix <code>pathway.genes</code>, where <code>genes(i,j)==1</code> if gene <code>i</code> is a member of pathway <code>j</code> and <code>genes(i,j)==0</code> otherwise.</p>
<pre><code>>> genes = pathway.genes;
>> whos genes
Name Size Bytes Class Attributes
genes 18732x4076 3257512 double sparse
>> genes(:,100)
ans =
(1243,1) 1
(3410,1) 1
(4567,1) 1
(4668,1) 1 </code></pre>
<p>Finally, our analyses only used 3,913 of 4,076 pathways that</p>
<ul>
<li>include 2-499 RefSeq-mapped genes;</li>
<li>have clear <code>database</code> and <code>source</code> definitions;</li>
<li>exclude one pathway <code>Viral RNP Complexes in the Host Cell Nucleus (PC, reactome)</code> (because no HapMap3 SNP was mapped to this pathway).</li>
</ul>
<pre><code>>> numgenes = pathway.genes' * inref_genes;
>> size(numgenes)
ans =
4076 1
>> paths = find(numgenes > 1 & numgenes < 500);
>> size(paths)
ans =
3916 1
>> database = pathway.database;
>> source = pathway.source;
>> database_na = find(not(cellfun('isempty', strfind(database, 'NA'))));
>> source_na = find(not(cellfun('isempty', strfind(source, 'NA'))));
>> length(union(database_na, source_na))
ans =
2
>> label = pathway.label;
>> pathway_exclude = 'Viral RNP Complexes in the Host Cell Nucleus';
>> label_include = find(cellfun('isempty', strfind(label, pathway_exclude)));
>> label_exclude = setdiff(1:4076, label_include);
>> label(label_exclude)
ans =
1x1 cell array
{'Viral RNP Complexes in the Host Cell Nucleus'}
>> database(label_exclude)
ans =
1x1 cell array
{'PC'}
>> source(label_exclude)
ans =
1x1 cell array
{'reactome'}</code></pre>
</div>
<div id="tissue-based-gene-sets" class="section level2">
<h2>Tissue-based gene sets</h2>
<p>The 113 GTEx tissue-based gene sets used in <a href="https://doi.org/10.1101/160770">Zhu and Stephens (2017)</a> are available in the folder <a href="https://github.com/xiangzhu/rss-gsea/tree/master/data/tissue_set"><code>tissue_set</code></a>. There are 44 “highly expressed” (HE) gene sets, 49 “selectively expressed” (SE) gene sets and 20 “distinctively expressed” (DE) gene sets. The creation of SE sets uses a method described in <a href="https://doi.org/10.1101/311563">Yang et al (2018)</a>. The creation of DE sets uses a method described in <a href="https://www.ncbi.nlm.nih.gov/pubmed/28333934">Dey et al (2017)</a>.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode bash"><code class="sourceCode bash"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="fu">ls</span> data/tissue_set/he_genes <span class="kw">|</span> <span class="fu">wc</span> -l</a></code></pre></div>
<pre><code> 44</code></pre>
<div class="sourceCode" id="cb9"><pre class="sourceCode bash"><code class="sourceCode bash"><a class="sourceLine" id="cb9-1" data-line-number="1"><span class="fu">ls</span> data/tissue_set/se_genes <span class="kw">|</span> <span class="fu">wc</span> -l</a></code></pre></div>
<pre><code> 49</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode bash"><code class="sourceCode bash"><a class="sourceLine" id="cb11-1" data-line-number="1"><span class="fu">ls</span> data/tissue_set/de_genes <span class="kw">|</span> <span class="fu">wc</span> -l</a></code></pre></div>
<pre><code> 20</code></pre>
<p>Each of the tissue-based gene sets has the following format.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode bash"><code class="sourceCode bash"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="fu">head</span> -n 5 data/tissue_set/he_genes/gene_names_expr_LIVER.txt</a></code></pre></div>
<pre><code>ensembl_gene_id chromosome_name start_position end_position
ENSG00000002933 7 150497491 150502208
ENSG00000072778 17 7120444 7128592
ENSG00000075624 7 5566782 5603415
ENSG00000087086 19 49468558 49470135</code></pre>
<p>Note that the gene information of tissue-based sets was provided by GTEx, which may not be the same as <code>gene_37.3.mat</code> above.</p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1">devtools<span class="op">::</span><span class="kw">session_info</span>()</a></code></pre></div>
<pre><code>Session info -------------------------------------------------------------</code></pre>
<pre><code> setting value
version R version 3.5.1 (2018-07-02)
system x86_64, darwin15.6.0
ui X11
language (EN)
collate en_US.UTF-8
tz America/Los_Angeles
date 2018-10-05 </code></pre>
<pre><code>Packages -----------------------------------------------------------------</code></pre>
<pre><code> package * version date source
backports 1.1.2 2017-12-13 CRAN (R 3.5.0)
base * 3.5.1 2018-07-05 local
compiler 3.5.1 2018-07-05 local
datasets * 3.5.1 2018-07-05 local
devtools 1.13.6 2018-06-27 CRAN (R 3.5.0)
digest 0.6.17 2018-09-12 CRAN (R 3.5.0)
evaluate 0.11 2018-07-17 CRAN (R 3.5.0)
git2r 0.23.0 2018-07-17 CRAN (R 3.5.0)
graphics * 3.5.1 2018-07-05 local
grDevices * 3.5.1 2018-07-05 local
htmltools 0.3.6 2017-04-28 CRAN (R 3.5.0)
knitr 1.20 2018-02-20 CRAN (R 3.5.0)
magrittr 1.5 2014-11-22 CRAN (R 3.5.0)
memoise 1.1.0 2017-04-21 CRAN (R 3.5.0)
methods * 3.5.1 2018-07-05 local
R.methodsS3 1.7.1 2016-02-16 CRAN (R 3.5.0)
R.oo 1.22.0 2018-04-22 CRAN (R 3.5.0)
R.utils 2.7.0 2018-08-27 CRAN (R 3.5.0)
Rcpp 0.12.19 2018-10-01 CRAN (R 3.5.0)
rmarkdown 1.10 2018-06-11 CRAN (R 3.5.0)
rprojroot 1.3-2 2018-01-03 CRAN (R 3.5.0)
stats * 3.5.1 2018-07-05 local
stringi 1.2.4 2018-07-20 CRAN (R 3.5.0)
stringr 1.3.1 2018-05-10 CRAN (R 3.5.0)
tools 3.5.1 2018-07-05 local
utils * 3.5.1 2018-07-05 local
whisker 0.3-2 2013-04-28 CRAN (R 3.5.0)
withr 2.1.2 2018-03-15 CRAN (R 3.5.0)
workflowr 1.1.1 2018-07-06 CRAN (R 3.5.0)
yaml 2.2.0 2018-07-25 CRAN (R 3.5.0)</code></pre>
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