<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Joyce Hsiao" />
<title>Find genes with cyclical patterns</title>
<script src="site_libs/jquery-1.11.3/jquery.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/textmate.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<link href="site_libs/font-awesome-4.5.0/css/font-awesome.min.css" rel="stylesheet" />
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css">
pre:not([class]) {
background-color: white;
}
</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type="text/css">
h1 {
font-size: 34px;
}
h1.title {
font-size: 38px;
}
h2 {
font-size: 30px;
}
h3 {
font-size: 24px;
}
h4 {
font-size: 18px;
}
h5 {
font-size: 16px;
}
h6 {
font-size: 12px;
}
.table th:not([align]) {
text-align: left;
}
</style>
</head>
<body>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
code {
color: inherit;
background-color: rgba(0, 0, 0, 0.04);
}
img {
max-width:100%;
height: auto;
}
.tabbed-pane {
padding-top: 12px;
}
button.code-folding-btn:focus {
outline: none;
}
</style>
<style type="text/css">
/* padding for bootstrap navbar */
body {
padding-top: 51px;
padding-bottom: 40px;
}
/* offset scroll position for anchor links (for fixed navbar) */
.section h1 {
padding-top: 56px;
margin-top: -56px;
}
.section h2 {
padding-top: 56px;
margin-top: -56px;
}
.section h3 {
padding-top: 56px;
margin-top: -56px;
}
.section h4 {
padding-top: 56px;
margin-top: -56px;
}
.section h5 {
padding-top: 56px;
margin-top: -56px;
}
.section h6 {
padding-top: 56px;
margin-top: -56px;
}
</style>
<script>
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark it active
menuAnchor.parent().addClass('active');
// if it's got a parent navbar menu mark it active as well
menuAnchor.closest('li.dropdown').addClass('active');
});
</script>
<div class="container-fluid main-container">
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
</script>
<!-- code folding -->
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">fucci-seq</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="index.html">Home</a>
</li>
<li>
<a href="about.html">About</a>
</li>
<li>
<a href="license.html">License</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/jdblischak/fucci-seq">
<span class="fa fa-github"></span>
</a>
</li>
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<!-- Add a small amount of space between sections. -->
<style type="text/css">
div.section {
padding-top: 12px;
}
</style>
<div class="fluid-row" id="header">
<h1 class="title toc-ignore">Find genes with cyclical patterns</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#data-and-packages">Data and packages</a></li>
<li><a href="#projected-normal-on-pcs-of-redgreen">Projected normal on PCs of Red/Green</a></li>
<li><a href="#compare-different-methods">Compare different methods</a></li>
<li><a href="#results-using-mad">Results using MAD</a></li>
<li><a href="#results-using-pve-for-kernel-based-method">Results using PVE for kernel-based method</a></li>
</ul>
</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> 2018-05-07</p>
<!-- Insert the code version (Git commit SHA1) if Git repository exists and R
package git2r is installed -->
<p><strong>Code version:</strong> a2240aa</p>
<hr />
<div id="data-and-packages" class="section level2">
<h2>Data and packages</h2>
<p>Packages</p>
<pre class="r"><code>library(circular)
library(conicfit)
library(Biobase)
library(dplyr)
library(matrixStats)
library(NPCirc)
library(smashr)</code></pre>
<p>Load data</p>
<pre class="r"><code>df <- readRDS(file="../data/eset-filtered.rds")
pdata <- pData(df)
fdata <- fData(df)
# select endogeneous genes
counts <- exprs(df)[grep("ENSG", rownames(df)), ]
log2cpm.all <- t(log2(1+(10^6)*(t(counts)/pdata$molecules)))
# log2cpm.all <- readRDS("../output/seqdata-batch-correction.Rmd/log2cpm.rds")
# log2cpm.adjust <- readRDS("../output/seqdata-batch-correction.Rmd/log2cpm.adjust.rds")
# log2cpm <- log2cpm.all[grep("ENSG", rownames(log2cpm.all)), ]
# import corrected intensities
pdata.adj <- readRDS("../output/images-normalize-anova.Rmd/pdata.adj.rds")
macosko <- readRDS("../data/cellcycle-genes-previous-studies/rds/macosko-2015.rds")
# log2cpm.high <- log2cpm.detected[order(rowMeans(log2cpm.detected))[1:100], ]
source("../code/utility.R")
summary(colSums(counts))</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
22207 44586 52597 53445 61790 103255 </code></pre>
<pre class="r"><code>summary(rowMeans(log2cpm.all>0))</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
0.08884 0.48104 0.74756 0.69915 0.94691 1.00000 </code></pre>
<pre class="r"><code>log2cpm.detected <- log2cpm.all[rowMeans(log2cpm.all>0)>.8,]</code></pre>
<hr />
</div>
<div id="projected-normal-on-pcs-of-redgreen" class="section level2">
<h2>Projected normal on PCs of Red/Green</h2>
<pre class="r"><code>pc.fucci <- prcomp(subset(pdata.adj,
select=c("rfp.median.log10sum.adjust",
"gfp.median.log10sum.adjust")),
center = T, scale. = T)
Theta.cart <- pc.fucci$x
library(circular)
Theta.fucci <- coord2rad(Theta.cart)
df <- data.frame(theta=as.numeric(Theta.fucci))
rownames(df) <- rownames(pdata.adj)
Theta.fucci <- 2*pi - Theta.fucci
#saveRDS(as.numeric(Theta.fucci), file = "../output/npreg.Rmd/theta.rds")</code></pre>
<p>–</p>
</div>
<div id="compare-different-methods" class="section level2">
<h2>Compare different methods</h2>
<ul>
<li><p>Analysis was done in a batch job. See <code>/code/npreg.Rmd</code> for codes for submitting the batch job.</p></li>
<li><p>Three methods are compared: smash, kernel regression using NW-estimate, and kernel regression using LL-estimate.</p></li>
<li><p>In running smash, I imputed the zero-valued expression with mean of expression for each gene. In running kernel methods, I omitted the zero-valued cells</p></li>
<li><p>For comparison involving smash, we take 512 randomly selected cells and analyze these for all three methods. MAD is employed to evaluate the fit. Specifically, MAD(data-fitted mean)/MAD(data-data.mean).</p></li>
<li><p>For comparison of the two kernel regression methods, I included all of the ~900 cells. PVE (proportion of variance explained) is computed to evalulate the fit. Specifically, 1-var(data-fitted mean)/var(data).</p></li>
<li><p><code>smashr</code> package was used to run smash, and <code>NPCirc</code> package was used to run the two kernel regression methods.</p></li>
</ul>
<hr />
</div>
<div id="results-using-mad" class="section level2">
<h2>Results using MAD</h2>
<p>Compare MAD ratio of data from the fitted mean versus the grand mean (not fitted). Briefly,</p>
<p><span class="math display">\[
mad.ratio = MAD(expression_g - fitted.g)/MAD(expression_g - mean.g)
\]</span></p>
<pre class="r"><code>out.methods <- readRDS("../output/npreg-methods.Rmd/out.methods.rds")
mad.ratio <- data.frame(smash.mad.ratio=sapply(out.methods, "[[", "smash.mad.ratio"),
smash.pois.mad.ratio=sapply(out.methods, "[[", "smash.pois.mad.ratio"),
npll.mad.ratio=sapply(out.methods, "[[", "npll.mad.ratio"),
npnw.mad.ratio=sapply(out.methods, "[[", "npnw.mad.ratio"))
pve <- data.frame(npll.pve=sapply(out.methods, "[[", "npll.pve"),
npnw.pve=sapply(out.methods, "[[", "npnw.pve"))
boxplot(mad.ratio)</code></pre>
<p><img src="figure/npreg-methods.Rmd/unnamed-chunk-4-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>boxplot(pve)</code></pre>
<p><img src="figure/npreg-methods.Rmd/unnamed-chunk-4-2.png" width="672" style="display: block; margin: auto;" /></p>
<p>enrichment function choose top ranked genes</p>
<pre class="r"><code>enrich.order <- function(cutoffs, metrics, cyclegenes, allgenes) {
# out <- order(mad.ratio$smash.mad.ratio)
# cutoffs <- c(100, 200, 300)
cycle.rich <- sapply(cutoffs, function(x) {
sig.cycle <- sum(allgenes[metrics[1:x]] %in% cyclegenes)/x
non.cycle <- sum(allgenes[-metrics[1:x]] %in% cyclegenes)/(length(allgenes)-x)
cbind(as.numeric(sum(allgenes[metrics[1:x]] %in% cyclegenes)),
sig.cycle/non.cycle)
})
colnames(cycle.rich) <- cutoffs
rownames(cycle.rich) <- c("nsig.genes.cycle", "fold.sig.vs.nonsig.cycle")
cycle.rich
}</code></pre>
<p>smash gaussian enrichment.</p>
<pre class="r"><code>enrich.order(cutoffs = c(100, 200, 300),
metrics = order(mad.ratio$smash.mad.ratio),
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 100 200 300
nsig.genes.cycle 9.000000 15.000000 19.000000
fold.sig.vs.nonsig.cycle 1.942012 1.622754 1.368612</code></pre>
<p>smash poisson enrichment.</p>
<pre class="r"><code>enrich.order(cutoffs = c(100, 200, 300),
metrics = order(mad.ratio$smash.pois.mad.ratio),
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 100 200 300
nsig.genes.cycle 2.0000000 10.000000 13.0000000
fold.sig.vs.nonsig.cycle 0.4256809 1.071146 0.9252485</code></pre>
<p>kernel LL-estimator enrichment</p>
<pre class="r"><code>enrich.order(cutoffs = c(100, 200, 300),
metrics = order(mad.ratio$npll.mad.ratio),
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 100 200 300
nsig.genes.cycle 5.00000 8.0000000 11.000000
fold.sig.vs.nonsig.cycle 1.07045 0.8535433 0.779802</code></pre>
<p>kernel NW-estimator enrichment</p>
<pre class="r"><code>enrich.order(cutoffs = c(100, 200, 300),
metrics = order(mad.ratio$npnw.mad.ratio),
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.detected))</code></pre>
<pre><code> 100 200 300
nsig.genes.cycle 2.0000000 5.0000000 9.000000
fold.sig.vs.nonsig.cycle 0.2665738 0.3290028 0.390767</code></pre>
<p>Check smash genes.</p>
<pre class="r"><code>ii.check <- order(mad.ratio$smash.mad.ratio)[c(1:16)]
library(mygene)
par(mfrow=c(4,4), mar=c(3,2,2,1))
ensg <- rownames(log2cpm.detected)[ii.check]
symbols <- queryMany(ensg, scopes="ensembl.gene",
fields=c("symbol"), species="human")@listData$symbol</code></pre>
<pre><code>Finished
Pass returnall=TRUE to return lists of duplicate or missing query terms.</code></pre>
<pre class="r"><code>for (i in 1:length(ii.check)) {
ii <- ii.check[i]
with(out.methods[[ii]], {
plot(y=yy.train,
x=xx.train,
xlab = "estimated cell time", ylab = "log2CPM", col = "gray30",
main=paste(symbols[i], ",", round(mad.ratio$smash.mad.ratio[ii],2)))
lines(x=smash.xx,
y=smash.yy, col = "red", pch = 16, cex=.6)
})
}</code></pre>
<p><img src="figure/npreg-methods.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>Check kernel LL genes.</p>
<pre class="r"><code>ii.check <- order(mad.ratio$npll.mad.ratio)[c(1:20)]
library(mygene)
par(mfrow=c(4,5), mar=c(3,2,2,1))
ensg <- rownames(log2cpm.detected)[ii.check]
symbols <- queryMany(ensg, scopes="ensembl.gene",
fields=c("symbol"), species="human")@listData$symbol</code></pre>
<pre><code>Finished
Pass returnall=TRUE to return lists of duplicate or missing query terms.</code></pre>
<pre class="r"><code>for (i in 1:length(ii.check)) {
ii <- ii.check[i]
with(out.methods[[ii]], {
plot(y=yy.train,
x=xx.train,
xlab = "estimated cell time", ylab = "log2CPM", col = "gray30",
main=paste(symbols[i], ",", round(mad.ratio$smash.mad.ratio[ii],2)))
lines(x=smash.xx,
y=smash.yy, col = "red", pch = 16, cex=.6)
})
}</code></pre>
<p><img src="figure/npreg-methods.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="results-using-pve-for-kernel-based-method" class="section level2">
<h2>Results using PVE for kernel-based method</h2>
<pre class="r"><code>pve <- data.frame(npll.pve=sapply(out.methods, "[[", "npll.pve"),
npnw.pve=sapply(out.methods, "[[", "npnw.pve"))
boxplot(pve)</code></pre>
<p><img src="figure/npreg-methods.Rmd/unnamed-chunk-12-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>enrichment function choose top ranked genes</p>
<pre class="r"><code>enrich.pve <- function(cutoffs, metrics, cyclegenes, allgenes) {
# out <- order(mad.ratio$smash.mad.ratio)
# cutoffs <- c(100, 200, 300)
cycle.rich <- sapply(cutoffs, function(x) {
sig.cycle <- sum(allgenes[metrics>x] %in% cyclegenes)/length(allgenes[metrics>x])
non.cycle <- sum(allgenes[metrics<x] %in% cyclegenes)/length(allgenes[metrics<x])
cbind(as.numeric(sum(allgenes[metrics>x] %in% cyclegenes)),
sig.cycle/non.cycle)
})
colnames(cycle.rich) <- cutoffs
rownames(cycle.rich) <- c("nsig.genes.cycle", "fold.sig.vs.nonsig.cycle")
cycle.rich
}</code></pre>
<p>kernel LL-estimator enrichment</p>
<pre class="r"><code>enrich.pve(cutoffs = c(.05, .1, .2, .3),
metrics = pve$npll.pve,
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 0.05 0.1 0.2 0.3
nsig.genes.cycle 114.000000 48.000000 18.000000 11.000000
fold.sig.vs.nonsig.cycle 1.133994 1.164018 1.204267 1.711535</code></pre>
<pre class="r"><code>enrich.order(cutoffs = c(100, 200, 300),
metrics = order(pve$npll.pve, decreasing=T),
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 100 200 300
nsig.genes.cycle 7.000000 14.000000 17.000000
fold.sig.vs.nonsig.cycle 1.504519 1.511554 1.219639</code></pre>
<p>kernel NW-estimator enrichment</p>
<pre class="r"><code>enrich.pve(cutoffs = c(.05, .1, .2, .3),
metrics = pve$npnw.pve,
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 0.05 0.1 0.2 0.3
nsig.genes.cycle 107.000000 41.000000 10.0000000 6.000000
fold.sig.vs.nonsig.cycle 1.095707 1.056067 0.8478942 1.268477</code></pre>
<pre class="r"><code>enrich.order(cutoffs = c(100, 200, 300),
metrics = order(pve$npnw.pve, decreasing=T),
cyclegenes = macosko$ensembl,
allgenes = rownames(log2cpm.all))</code></pre>
<pre><code> 100 200 300
nsig.genes.cycle 5.00000 9.0000000 10.0000000
fold.sig.vs.nonsig.cycle 1.07045 0.9621302 0.7075099</code></pre>
<p>Print top 500 genes.</p>
<pre class="r"><code>write.table(rownames(log2cpm.all)[order(pve$npll.pve, decreasing=T)[1:5000]],
file = "../output/npreg-methods.Rmd/npll.genes.txt",
quote=F, col.names=F, row.names=F)
write.table(rownames(log2cpm.all)[order(pve$npnw.pve, decreasing=T)[1:5000]],
file = "../output/npreg-methods.Rmd/npnw.genes.txt",
quote=F, col.names=F, row.names=F)
write.table(rownames(log2cpm.all),
file = "../output/npreg-methods.Rmd/allgenes.txt",
quote=F, col.names=F, row.names=F)</code></pre>
</div>
<!-- Adjust MathJax settings so that all math formulae are shown using
TeX fonts only; see
http://docs.mathjax.org/en/latest/configuration.html. This will make
the presentation more consistent at the cost of the webpage sometimes
taking slightly longer to load. Note that this only works because the
footer is added to webpages before the MathJax javascript. -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
"HTML-CSS": { availableFonts: ["TeX"] }
});
</script>
<hr>
<p>
This <a href="http://rmarkdown.rstudio.com">R Markdown</a> site was created with <a href="https://github.com/jdblischak/workflowr">workflowr</a>
</p>
<hr>
<!-- To enable disqus, uncomment the section below and provide your disqus_shortname -->
<!-- disqus
<div id="disqus_thread"></div>
<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
var disqus_shortname = 'rmarkdown'; // required: replace example with your forum shortname
/* * * DON'T EDIT BELOW THIS LINE * * */
(function() {
var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';
(document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
})();
</script>
<noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>
<a href="http://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>
-->
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.header').parent('thead').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>