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<h1 class="title toc-ignore">Gene filtering and sample filtering</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#summary">Summary</a></li>
<li><a href="#import-data">Import data</a></li>
<li><a href="#over-expressed-genes">Over-expressed genes</a></li>
<li><a href="#filter-out-lowly-expressed-genes">Filter out lowly-expressed genes</a></li>
<li><a href="#combine-filters">Combine filters</a></li>
<li><a href="#make-filtered-data">Make filtered data</a></li>
<li><a href="#session-information">Session information</a></li>
</ul>
</div>
<!-- The file analysis/chunks.R contains chunks that define default settings
shared across the workflowr files. -->
<!-- Update knitr chunk options -->
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<p><strong>Last updated:</strong> 2018-04-10</p>
<!-- Insert the code version (Git commit SHA1) if Git repository exists and R
package git2r is installed -->
<p><strong>Code version:</strong> 77d10d4</p>
<hr />
<div id="summary" class="section level2">
<h2>Summary</h2>
<p>I performed gene filtering based on the criterion set forth in our previous paper.</p>
<ol style="list-style-type: decimal">
<li>Remove outlier genes: molecule counts > 4,096 in any sample (x is the theoretical maximum of UMI count for 6-bp UMI)</li>
</ol>
<p><em>Results</em> There’s one, and turns out this over-expressed gene is one of the mitochrondrial genes.</p>
<p><em>Output</em>: gene annotation saved in <code>../output/gene-filtering.Rmd/over-expressed-genes-info.csv</code></p>
<p><span class="math inline">\(~\)</span></p>
<ol start="2" style="list-style-type: decimal">
<li>Remove lowly expressed genes: Lowly-expressed genes := gene mean < 2 CPM.</li>
</ol>
<p><em>Results</em>: * Of 20,421 genes, 7,628 genes are classifed as lowly-expressed. Of these, 34 are ERCC control genes and 7,594 are endogenoeus genes.</p>
<p><em>Output</em>: gene annotation saved in <code>../output/gene-filtering.Rmd/lowly-expressed-genes-info.csv</code></p>
<ol start="3" style="list-style-type: decimal">
<li><p>Include 1025 samples classifed as high quality samples (of all the 1536 samples).</p></li>
<li><p>Finally, create a filtered dataset (<code>eset_filtered</code>) that includes 1025 samples and 12792 genes and save in <code>../output/gene-filtering.Rmd/eset-gene-filtering.rds</code>.</p></li>
</ol>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
<div id="import-data" class="section level2">
<h2>Import data</h2>
<p>Combine <code>eSet</code> objects.</p>
<pre class="r"><code>library(knitr)
library(Biobase)
library(dplyr)
#library(gdata)
library(heatmap3)
library(testit)
library(cowplot)
library(biomaRt)
library(knitr)
library(data.table)
source("../code/utility.R")
eset <- readRDS("../data/eset-raw.rds")</code></pre>
<p>Filter out low-quality single cell samples.</p>
<pre class="r"><code>pdata_filter <- pData(eset)[pData(eset)$filter_all == TRUE,]
count_filter <- exprs(eset[,pData(eset)$filter_all == TRUE])
dim(count_filter)</code></pre>
<pre><code>[1] 20421 1025</code></pre>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
<div id="over-expressed-genes" class="section level2">
<h2>Over-expressed genes</h2>
<p>There’s one, and turns out this over-expressed gene.</p>
<pre class="r"><code>which_over_expressed <- which(apply(count_filter, 1, function(x) any(x>(4^6)) ))
over_expressed_genes <- rownames(count_filter)[which_over_expressed]
over_expressed_genes</code></pre>
<pre><code>[1] "ENSG00000198886"</code></pre>
<p>Get over-expressed gene info via <code>biomaRt</code>.</p>
<pre class="r"><code>over_expressed_genes_info <- getBM(
attributes = c("ensembl_gene_id", "chromosome_name",
"external_gene_name", "transcript_count",
"description"),
filters = "ensembl_gene_id",
values = over_expressed_genes,
mart = ensembl)
fwrite(over_expressed_genes_info,
file = "../output/gene-filtering.Rmd/over-expressed-genes-info.csv")</code></pre>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
<div id="filter-out-lowly-expressed-genes" class="section level2">
<h2>Filter out lowly-expressed genes</h2>
<ul>
<li>Of 20,421 genes, 7,864 genes are classifed as lowly-expressed. Of these, 34 are ERCC control genes and 7,830 are endogenoeus genes.</li>
</ul>
<p>Compute CPM</p>
<pre class="r"><code>cpm <- t(t(count_filter)/colSums(count_filter))*(10^6)</code></pre>
<p>Lowly-expressed genes := gene mean < 2 CPM</p>
<pre class="r"><code>which_lowly_expressed <- which(rowMeans(cpm) < 2)
length(which_lowly_expressed)</code></pre>
<pre><code>[1] 7628</code></pre>
<pre class="r"><code>which_lowly_expressed_genes <- rownames(cpm)[which_lowly_expressed]
length(grep("ERCC", which_lowly_expressed_genes))</code></pre>
<pre><code>[1] 34</code></pre>
<pre class="r"><code>length(grep("ENSG", which_lowly_expressed_genes))</code></pre>
<pre><code>[1] 7594</code></pre>
<p>Get gene info via <code>biomaRt</code>.</p>
<pre class="r"><code>lowly_expressed_genes_info <- getBM(
attributes = c("ensembl_gene_id", "chromosome_name",
"external_gene_name", "transcript_count",
"description"),
filters = "ensembl_gene_id",
values = which_lowly_expressed_genes[grep("ENSG", which_lowly_expressed_genes)],
mart = ensembl)
fwrite(lowly_expressed_genes_info,
file = "../output/gene-filtering.Rmd/lowly-expressed-genes-info.csv")</code></pre>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
<div id="combine-filters" class="section level2">
<h2>Combine filters</h2>
<p>Including 16,460 genes.</p>
<pre class="r"><code>gene_filter <- unique(c(which_lowly_expressed, which_over_expressed))
genes_to_include <- setdiff(1:nrow(count_filter), gene_filter)
length(genes_to_include)</code></pre>
<pre><code>[1] 12792</code></pre>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
<div id="make-filtered-data" class="section level2">
<h2>Make filtered data</h2>
<pre class="r"><code>eset_filtered <- eset[genes_to_include, pData(eset)$filter_all==TRUE]
eset_filtered</code></pre>
<pre><code>ExpressionSet (storageMode: lockedEnvironment)
assayData: 12792 features, 1025 samples
element names: exprs
protocolData: none
phenoData
sampleNames: 20170905-A01 20170905-A02 ... 20170924-H12 (1025
total)
varLabels: experiment well ... filter_all (43 total)
varMetadata: labelDescription
featureData
featureNames: EGFP ENSG00000000003 ... mCherry (12792 total)
fvarLabels: chr start ... source (6 total)
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation: </code></pre>
<pre class="r"><code>saveRDS(eset_filtered,
file = "../output/gene-filtering.Rmd/eset-gene-filtering.rds")</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] data.table_1.10.4-3 biomaRt_2.34.2 cowplot_0.9.2
[4] ggplot2_2.2.1 testit_0.7 heatmap3_1.1.1
[7] dplyr_0.7.4 Biobase_2.38.0 BiocGenerics_0.24.0
[10] knitr_1.20
loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 pillar_1.2.1 compiler_3.4.1
[4] git2r_0.21.0 plyr_1.8.4 bindr_0.1.1
[7] prettyunits_1.0.2 progress_1.1.2 bitops_1.0-6
[10] tools_3.4.1 bit_1.1-12 digest_0.6.15
[13] memoise_1.1.0 RSQLite_2.0 evaluate_0.10.1
[16] tibble_1.4.2 gtable_0.2.0 pkgconfig_2.0.1
[19] rlang_0.2.0 DBI_0.8 yaml_2.1.18
[22] bindrcpp_0.2 httr_1.3.1 stringr_1.3.0
[25] IRanges_2.12.0 S4Vectors_0.16.0 bit64_0.9-7
[28] stats4_3.4.1 rprojroot_1.3-2 grid_3.4.1
[31] glue_1.2.0 R6_2.2.2 AnnotationDbi_1.40.0
[34] XML_3.98-1.10 rmarkdown_1.9 blob_1.1.0
[37] fastcluster_1.1.24 magrittr_1.5 backports_1.1.2
[40] scales_0.5.0 htmltools_0.3.6 assertthat_0.2.0
[43] colorspace_1.3-2 stringi_1.1.7 RCurl_1.95-4.10
[46] lazyeval_0.2.1 munsell_0.4.3 </code></pre>
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