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<title>Select cell cycle genes</title>

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<h1 class="title toc-ignore">Select cell cycle genes</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>

</div>


<!-- The file analysis/chunks.R contains chunks that define default settings
shared across the workflowr files. -->
<!-- Update knitr chunk options -->
<!-- Insert the date the file was last updated -->
<p><strong>Last updated:</strong> 2018-02-28</p>
<!-- Insert the code version (Git commit SHA1) if Git repository exists and R
 package git2r is installed -->
<p><strong>Code version:</strong> 4131ce7</p>
<hr />
<div id="overview" class="section level2">
<h2>Overview</h2>
<ol style="list-style-type: decimal">
<li><p>Select genes annotated to cell cycle in the GO annotation (<a href="GO:0007049" class="uri">GO:0007049</a>).</p></li>
<li><p>Select genes documented in CycleBase - previously found to relate to cell cycle in microarray studies.</p></li>
<li><p>Select genes whose expression variability is above certain threshold.</p></li>
</ol>
<p>This document generated several RDS for later use:</p>
<p>*<code>genes.go.rds</code>: 677 human cell cycle genes associated with <a href="GO:0007049" class="uri">GO:0007049</a></p>
<p>*<code>genes.detect.rds</code>: 6313 genes in the current study with detection rate &gt; 50%</p>
<p>*<code>genes.go.detect.rds</code>: 450 genes in the current study with detection rate &gt; 50% and associated with <a href="GO:0007049" class="uri">GO:0007049</a></p>
<p>*<code>genes.variable.rds</code>: 359 genes in the current study with detection rate &gt; 50% and dispersion z-score &gt; 1.5 across expression mean bins</p>
<p>*<code>genes.go.variable.rds</code>: 24 genes in the current study with detection rate &gt; 50% and dispersion z-score &gt; 1.5 across expression mean bins and associated with <a href="GO:0007049" class="uri">GO:0007049</a></p>
<hr />
</div>
<div id="data-and-packages" class="section level2">
<h2>Data and packages</h2>
<p>Packages</p>
<pre class="r"><code>library(biomaRt)
library(Biobase)</code></pre>
<p>Load data</p>
<pre class="r"><code>df &lt;- readRDS(file=&quot;../data/eset-filtered.rds&quot;)
pdata &lt;- pData(df)
fdata &lt;- fData(df)

# select endogeneous genes
counts &lt;- exprs(df)[grep(&quot;ENSG&quot;, rownames(df)), ]</code></pre>
</div>
<div id="go-cell-cycle" class="section level2">
<h2>GO cell cycle</h2>
<pre class="r"><code>ensembl = useMart(&quot;ensembl&quot;,dataset=&quot;hsapiens_gene_ensembl&quot;) 
#gets gene symbol, transcript_id and go_id for all genes annotated with GO:0007507
genes.go &lt;- getBM(attributes=c(&#39;hgnc_symbol&#39;, &#39;ensembl_gene_id&#39;, &#39;go_id&#39;),
                   filters = &#39;go&#39;, values = &#39;GO:0007049&#39;, mart = ensembl)
genes.go &lt;- unique(genes.go$ensembl_gene_id)</code></pre>
</div>
<div id="variable-genes" class="section level2">
<h2>Variable genes</h2>
<p>Across individuals, I computed gene mean and coefficient of variation including cells that are detected as expressed for each gene.</p>
<pre class="r"><code>gene.means &lt;- apply(counts, 1, function(x) {
  ii_pos &lt;- x &gt;0
  mean(x[ii_pos])
})


gene.cvs &lt;- apply(counts, 1, function(x) {
  ii_pos &lt;- x &gt;0
  mn &lt;- mean(x[ii_pos])
  sd &lt;- sd(x[ii_pos])
  sd/mn
})

gene.drops &lt;- rowMeans(counts&gt;0)</code></pre>
<p>Relationship between gene mean and CV is the same as we observed before in the batch paper. The genes with low average expression across cells are those that have high dropout rate.</p>
<pre class="r"><code>par(mfrow=c(1,2))
plot(x=log10(gene.means), y = gene.cvs,
     xlab = &quot;log10 gene mean count&quot;, ylab = &quot;CV&quot;)
abline(v=.25, col = &quot;red&quot;)
plot(x=gene.drops, y = log10(gene.means),
     xlab = &quot;Proportion of samples detected (count 0)&quot;,
     ylab = &quot;log10 gene mean count&quot;)
abline(v=.5, col = &quot;blue&quot;)
abline(h=.25, col = &quot;red&quot;)</code></pre>
<p><img src="figure/seqdata-select-cellcyclegenes.Rmd/unnamed-chunk-5-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>Two steps here for choosing variable genes:</p>
<ol style="list-style-type: decimal">
<li><p>I arbitrary decided on a cutoff for gene mean based on the relationship between CV and log10 gene count. The idea was to filter the genes among which dispersion increases with gene mean (approximately when detection rate 50% or lower).</p></li>
<li><p>Scan across gene expression bins, identify the ones with high variability in each bin.</p></li>
</ol>
<pre class="r"><code>genes.detect &lt;- rownames(counts)[which(gene.drops &gt; .5)]

gene.means.detect &lt;- gene.means[which(gene.drops &gt; .5)]
gene.cvs.detect &lt;- gene.cvs[which(gene.drops &gt; .5)]

bins &lt;- cut(gene.means.detect, 
            breaks = quantile(gene.means.detect, prob = seq(0,1, .05)), 
            include.lowest = TRUE)
bins &lt;- as.numeric(bins)

genes.variable &lt;- do.call(c, lapply(1:length(bins), function(i) {
  ii_bin &lt;- bins == i
  cv.z &lt;- scale(gene.cvs.detect[ii_bin])
  rownames(cv.z)[which(cv.z&gt;1.5)]
}) )

plot(x=log10(gene.means.detect), 
     y=gene.cvs.detect,
     xlab = &quot;log10 gene mean count&quot;,
     ylab = &quot;CV&quot;, main = &quot;Genes detected in &gt; 50% cells&quot;,
     col = 1+as.numeric(names(gene.means.detect) %in% genes.variable))</code></pre>
<p><img src="figure/seqdata-select-cellcyclegenes.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<hr />
</div>
<div id="output" class="section level2">
<h2>Output</h2>
<pre class="r"><code>saveRDS(genes.go,
        file = &quot;../output/seqdata-select-cellcyclegenes.Rmd/genes.go.rds&quot;)
saveRDS(genes.variable,
        file = &quot;../output/seqdata-select-cellcyclegenes.Rmd/genes.variable.rds&quot;)

saveRDS(genes.detect,
        file = &quot;../output/seqdata-select-cellcyclegenes.Rmd/genes.detect.rds&quot;)

genes.go.detect &lt;- genes.detect[which(genes.detect %in% genes.go)]
saveRDS(genes.go.detect,
        file = &quot;../output/seqdata-select-cellcyclegenes.Rmd/genes.go.detect.rds&quot;)

genes.go.variable &lt;- genes.variable[which(genes.variable %in% genes.go)]
saveRDS(genes.go.variable,
        file = &quot;../output/seqdata-select-cellcyclegenes.Rmd/genes.go.variable.rds&quot;)</code></pre>
<hr />
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre><code>R version 3.4.1 (2017-06-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Scientific Linux 7.2 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] Biobase_2.38.0      BiocGenerics_0.24.0 biomaRt_2.34.2     

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.15         AnnotationDbi_1.40.0 knitr_1.20          
 [4] magrittr_1.5         progress_1.1.2       IRanges_2.12.0      
 [7] bit_1.1-12           R6_2.2.2             rlang_0.2.0         
[10] httr_1.3.1           stringr_1.3.0        blob_1.1.0          
[13] tools_3.4.1          DBI_0.7              git2r_0.21.0        
[16] htmltools_0.3.6      assertthat_0.2.0     yaml_2.1.16         
[19] bit64_0.9-7          rprojroot_1.3-2      digest_0.6.15       
[22] tibble_1.4.2         S4Vectors_0.16.0     bitops_1.0-6        
[25] curl_3.1             RCurl_1.95-4.10      memoise_1.1.0       
[28] evaluate_0.10.1      RSQLite_2.0          rmarkdown_1.8       
[31] stringi_1.1.6        pillar_1.1.0         compiler_3.4.1      
[34] prettyunits_1.0.2    backports_1.1.2      stats4_3.4.1        
[37] XML_3.98-1.10       </code></pre>
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