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<h1 class="title toc-ignore">Differential expression analysis–EdgeR log likelihood ratio tests</h1>
<h4 class="author"><em>Siming Zhao</em></h4>
<h4 class="date"><em>December 2, 2018</em></h4>
</div>
<p><strong>Last updated:</strong> 2018-12-17</p>
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<p><summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/simingz/cropseq/tree/49ecf6e2cf5686b9d11690f78c565c7361d08d6e" target="_blank">49ecf6e</a> </summary></p>
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<hr />
<p>Load data</p>
<pre class="r"><code>source("code/summary_functions.R")
library(dplyr)
load("data/DE_input.Rd")
glocus <- "VPS45"
dim(dm)[1]</code></pre>
<pre><code>NULL</code></pre>
<pre class="r"><code>gcount <- dm[1:(dim(dm)[1]-76), colnames(dm1dfagg)[dm1dfagg[glocus,] >0 & nlocus==1]]
# negative control cells defined as neg gRNA targeted cells
ncount <- dm[1:(dim(dm)[1]-76), colnames(dm1dfagg)[dm1dfagg["neg",] >0 & nlocus==1]]
coldata <- data.frame(row.names = c(colnames(gcount),colnames(ncount)),
condition=c(rep('G',dim(gcount)[2]),rep('N',dim(ncount)[2])))
countall <- cbind(gcount,ncount)
totalcount <- apply(countall,1,sum)
cellpercent <- apply(countall,1,function(x) length(x[x>0])/length(x))</code></pre>
<p>edgeR log likelihood ratio tests function</p>
<pre class="r"><code>library(edgeR)
run_edgeR <- function(y) {
# y is DGElist object
y <- calcNormFactors(y)
group=coldata$condition
design <- model.matrix(~group)
y <- estimateDisp(y,design)
fitlrt <- glmFit(y,design)
lrt <- glmLRT(fitlrt,coef=2)
summ_pvalues(lrt$table$PValue)
out <- topTags(lrt, n=Inf, adjust.method = "BH")
outsig <- subset(out$table,FDR <0.1)
print(paste0("There are ",dim(outsig)[1], " genes passed FDR <0.1 cutoff"))
print(knitr::kable(signif(as.matrix(head(out$table[order(out$table$PValue),])),digit=2)))
return(out)
}</code></pre>
<div id="run-edgerno-filtering" class="section level2">
<h2>Run edgeR–No filtering</h2>
<pre class="r"><code>y <- DGEList(counts= countall,group=coldata$condition)
res <- run_edgeR(y)</code></pre>
<p><img src="figure/EdgeR-LRT.Rmd/edgeRall-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code>[1] "There are 0 genes passed FDR <0.1 cutoff"
logFC logCPM LR PValue FDR
------------------- ------ ------- --- -------- -----
ENSG00000175899.14 -1.50 6.9 20 8.5e-06 0.12
ENSG00000176956.12 -2.60 6.6 19 1.1e-05 0.12
ENSG00000100097.11 -2.20 6.6 19 1.2e-05 0.12
ENSG00000100300.17 -1.50 6.4 14 1.5e-04 0.97
ENSG00000119906.11 1.10 6.4 14 1.9e-04 0.97
ENSG00000185900.9 -0.79 6.3 14 2.2e-04 0.97</code></pre>
</div>
<div id="run-edgerat-least-one-cell-umi-0" class="section level2">
<h2>Run edgeR–at least one cell UMI > 0</h2>
<pre class="r"><code>y <- DGEList(counts= countall[totalcount>0,],group=coldata$condition)
res <- run_edgeR(y)</code></pre>
<p><img src="figure/EdgeR-LRT.Rmd/edgeR%3E0-1.png" width=“672” style=“display: block; margin: auto;” /></p>
<pre><code>[1] "There are 3 genes passed FDR <0.1 cutoff"
logFC logCPM LR PValue FDR
------------------- ------ ------- --- -------- ------
ENSG00000175899.14 -1.50 6.9 20 8.5e-06 0.062
ENSG00000176956.12 -2.60 6.6 19 1.1e-05 0.062
ENSG00000100097.11 -2.20 6.6 19 1.2e-05 0.062
ENSG00000100300.17 -1.50 6.4 14 1.5e-04 0.510
ENSG00000119906.11 1.10 6.4 14 1.9e-04 0.510
ENSG00000185900.9 -0.79 6.3 14 2.2e-04 0.510</code></pre>
</div>
<div id="run-edger3-cells-with-umi-0" class="section level2">
<h2>Run edgeR–3% cells with UMI > 0</h2>
<pre class="r"><code>y <- DGEList(counts= countall[cellpercent > 0.03,],group=coldata$condition)
res <- run_edgeR(y)</code></pre>
<p><img src="figure/EdgeR-LRT.Rmd/edgeR0.03-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code>[1] "There are 3 genes passed FDR <0.1 cutoff"
logFC logCPM LR PValue FDR
------------------- ------ ------- --- -------- ------
ENSG00000176956.12 -2.70 6.6 21 4.1e-06 0.023
ENSG00000100097.11 -2.30 6.6 21 5.3e-06 0.023
ENSG00000175899.14 -1.50 6.9 21 5.9e-06 0.023
ENSG00000100300.17 -1.50 6.4 15 1.1e-04 0.340
ENSG00000219626.8 -1.00 6.5 14 2.0e-04 0.360
ENSG00000185900.9 -0.79 6.3 14 2.1e-04 0.360</code></pre>
</div>
<div id="run-edger10-cells-with-umi-0" class="section level2">
<h2>Run edgeR–10% cells with UMI > 0</h2>
<pre class="r"><code>y <- DGEList(counts= countall[cellpercent > 0.1,],group=coldata$condition)
res <- run_edgeR(y)</code></pre>
<p><img src="figure/EdgeR-LRT.Rmd/edgeR0.1-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code>[1] "There are 2 genes passed FDR <0.1 cutoff"
logFC logCPM LR PValue FDR
------------------- ------ ------- --- -------- -----
ENSG00000175899.14 -1.50 6.9 20 7.0e-06 0.04
ENSG00000100097.11 -2.20 6.6 20 8.4e-06 0.04
ENSG00000100300.17 -1.50 6.4 15 1.3e-04 0.42
ENSG00000119906.11 1.10 6.5 14 1.8e-04 0.44
ENSG00000219626.8 -0.98 6.5 14 2.3e-04 0.44
ENSG00000078061.12 -0.83 6.6 13 3.5e-04 0.46</code></pre>
</div>
<div id="run-edger20-cells-with-umi-0" class="section level2">
<h2>Run edgeR–20% cells with UMI > 0</h2>
<pre class="r"><code>y <- DGEList(counts= countall[cellpercent > 0.2,],group=coldata$condition)
res <- run_edgeR(y)</code></pre>
<p><img src="figure/EdgeR-LRT.Rmd/edgeR0.2-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code>[1] "There are 1 genes passed FDR <0.1 cutoff"
logFC logCPM LR PValue FDR
------------------- ------ ------- --- -------- ------
ENSG00000175899.14 -1.50 6.9 19 1.2e-05 0.093
ENSG00000119906.11 1.10 6.5 14 1.7e-04 0.490
ENSG00000219626.8 -0.99 6.5 14 2.2e-04 0.490
ENSG00000078061.12 -0.82 6.6 13 3.4e-04 0.490
ENSG00000131669.9 0.73 6.9 12 4.5e-04 0.490
ENSG00000141219.15 0.95 6.4 12 4.6e-04 0.490</code></pre>
</div>
<div id="parameters-used" class="section level2">
<h2>Parameters used</h2>
<ul>
<li>We used data processed after QC step <a href="Quality_metrics.html">here</a>.<br />
</li>
<li>targeted locus, choose VPS45.</li>
</ul>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin14.5.0 (64-bit)
Running under: OS X El Capitan 10.11.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gridExtra_2.3 lattice_0.20-35 edgeR_3.22.5 limma_3.36.5
[5] Matrix_1.2-14 dplyr_0.7.6
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 compiler_3.5.1 pillar_1.3.0
[4] git2r_0.23.0 highr_0.7 workflowr_1.1.1
[7] bindr_0.1.1 R.methodsS3_1.7.1 R.utils_2.7.0
[10] tools_3.5.1 digest_0.6.18 evaluate_0.12
[13] tibble_1.4.2 gtable_0.2.0 pkgconfig_2.0.2
[16] rlang_0.3.0.1 yaml_2.2.0 bindrcpp_0.2.2
[19] stringr_1.3.1 knitr_1.20 locfit_1.5-9.1
[22] rprojroot_1.3-2 tidyselect_0.2.4 glue_1.3.0
[25] R6_2.3.0 rmarkdown_1.10 purrr_0.2.5
[28] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[31] htmltools_0.3.6 splines_3.5.1 assertthat_0.2.0
[34] stringi_1.2.4 crayon_1.3.4 R.oo_1.22.0 </code></pre>
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