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<h3>vcfR documentation</h3>
by
<br>
Brian J. Knaus and Niklaus J. Grünwald
</center>
</p>
<div id="header">
<h1 class="title toc-ignore">Omitting data</h1>
</div>
<div id="TOC">
<ul>
<li><a href="#omitting-samples" id="toc-omitting-samples">Omitting
samples</a></li>
<li><a href="#omitting-variants" id="toc-omitting-variants">Omitting
variants</a></li>
<li><a href="#summary." id="toc-summary.">Summary.</a></li>
</ul>
</div>
<p>In the section on depth we learned how we can visualize variant
depth, or any other numeric value provided in the gt portion of VCF
data. In the section on censoring data we learned how to rescore
variants that were outside our acceptance thershold as missing. And in
the section on missing data we learned how to quantify and visualize
missingness in our dataset. Here we put all of these skills together in
order to omit samples and variants that have been set as missing
(NA).</p>
<pre class="r"><code>library(vcfR)
vcf <- read.vcfR('TASSEL_GBS0077.vcf.gz')
dp <- extract.gt(vcf, element = "DP", as.numeric=TRUE)</code></pre>
<pre class="r"><code>vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 61 samples
## 7171 CHROMs
## 69,296 variants
## Object size: 47.7 Mb
## 37.62 percent missing data
## ***** ***** *****</code></pre>
<p>Because part of this exercise involves setting cells in our data
matrix as NA we should begin by reminding ourselves of how abundant they
are. By using the show method we see that we have over 35 percent
missing data. We can now use what we learned previously to set variants
that are outside our per sample inclusion threshold as NA.</p>
<pre class="r"><code>quants <- apply(dp, MARGIN=2, quantile, probs=c(0.1, 0.8), na.rm=TRUE)
dp2 <- sweep(dp, MARGIN=2, FUN = "-", quants[1,])
dp[dp2 < 0] <- NA
dp2 <- sweep(dp, MARGIN=2, FUN = "-", quants[2,])
dp[dp2 > 0] <- NA
dp[dp < 4] <- NA
vcf@gt[,-1][ is.na(dp) == TRUE ] <- NA</code></pre>
<pre class="r"><code>vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 61 samples
## 7171 CHROMs
## 69,296 variants
## Object size: 46.3 Mb
## 66.63 percent missing data
## ***** ***** *****</code></pre>
<p>We see that this censoring has increased the degree of missingness in
our matrix to over 60 percent. Ideally we should visualize the results
of this action. For brevity, we will not here. But you can return to the
section on depth and reuse the code presented there to visualize how
this change has affected the distribution of the data.</p>
<pre class="r"><code>heatmap.bp(dp[1:1000,], rlabels = FALSE)</code></pre>
<p><img src="omitting_data_files/figure-html/unnamed-chunk-5-1.png" width="1152" style="display: block; margin: auto;" /></p>
<p>In this heatmap we see that we have samples in columns and variants
in rows. The color ramp on the right represents the depth of coverage
for each variant where yellow is high coverage and purple is low
coverage. White is not part of our color ramp but it is a part of the
heatmap. This is because white represents cells in our matrix that
having missing data (NA). The marginal barplots sum the information
accross columns and rows to tell us which samples (columns) or variants
(rows) include a high or low amount of sequence coverage. In general we
would like to see our sequence coverage uniformly distributed among our
samples and variants.</p>
<div id="omitting-samples" class="section level2">
<h2>Omitting samples</h2>
<p>We can see that some samples have a high degree of missingness. By
omiting these samples we may reduce the overall missingness in the data
set.</p>
<pre class="r"><code>myMiss <- apply(dp, MARGIN = 2, function(x){ sum( is.na(x) ) } )
myMiss <- myMiss / nrow(dp)
vcf@gt <- vcf@gt[, c(TRUE, myMiss < 0.7)]
vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 41 samples
## 7171 CHROMs
## 69,296 variants
## Object size: 35.7 Mb
## 53.33 percent missing data
## ***** ***** *****</code></pre>
<pre class="r"><code>dp <- extract.gt(vcf, element = "DP", as.numeric=TRUE)
heatmap.bp(dp[1:1000,], rlabels = FALSE)</code></pre>
<p><img src="omitting_data_files/figure-html/unnamed-chunk-7-1.png" width="1152" style="display: block; margin: auto;" /></p>
</div>
<div id="omitting-variants" class="section level2">
<h2>Omitting variants</h2>
<p>Previously we have seen how to quantify and visualize missingness for
variants in our dataset. We can use this information to omit variants
that have a high degree of missingness.</p>
<pre class="r"><code>myMiss <- apply(dp, MARGIN = 1, function(x){ sum( is.na(x) ) } )
myMiss <- myMiss / ncol(dp)
vcf <- vcf[myMiss < 0.2, ]
vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 41 samples
## 4563 CHROMs
## 18,782 variants
## Object size: 10.2 Mb
## 9.421 percent missing data
## ***** ***** *****</code></pre>
<pre class="r"><code>dp <- extract.gt(vcf, element = "DP", as.numeric=TRUE)
heatmap.bp(dp[1:1000,], rlabels = FALSE)</code></pre>
<p><img src="omitting_data_files/figure-html/unnamed-chunk-9-1.png" width="1152" style="display: block; margin: auto;" /></p>
</div>
<div id="summary." class="section level2">
<h2>Summary.</h2>
<p>Through omitting samples and variants with a high degree of
missingness we have taken a dataset that was over 35 percent missing
data to a dataset that is now below ten percent missing data. We’ve also
reduced tha sample size from 61 samples to 41. And we’ve reduced the
number of variants from over 60 thousand to just below 20 thousand. How
important any particular sample or variant is will have to be determined
base on the specifics of any particular project. These actions have
greatly improved the ratio of data to missing or low quality data in our
dataset. Through exploring thresholds that are different from those
implemented here one may be able to improve on this more. We can now
proceed to downstream analyses of this dataset with greater confidence
that the variants we are analyzing are of high quality.</p>
</div>
<center>
<hr class="style1">
<p>Copyright © 2017, 2018 Brian J. Knaus. All rights reserved.</p>
<p>USDA Agricultural Research Service, Horticultural Crops Research Lab.</p>
</center>
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