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<p>
<center>
<h3>vcfR documentation</h3>
by
<br>
Brian J. Knaus and Niklaus J. Grünwald
</center>
</p>
<div id="header">
<h1 class="title toc-ignore">Sequence coverage</h1>
</div>
<p>Once data is received from a sequencing center, an initial question
is whether the sequencing was successful? A common attempt to address
this question is by asking how much sequence coverage was attained. For
a diploid organism, one would require at least two reads per variant to
infer a genotype. Ideally, we would like a little more so that we have
redundancy. How much redundancy is necessary may be a question the
researchers need to address themselves. In this vignette we’ll learn how
to read in VCF data, extract sequence depth information from it and how
to visualize this data.</p>
<div id="reading-vcf-data" class="section level2">
<h2>Reading VCF data</h2>
<p>The topic of reading in VCF format data is covered in other
vignettes. We include it here for redundancy, and because every example
involving VCF data needs to start with some data.</p>
<pre class="r"><code>library(vcfR)
vcf_file <- system.file("extdata", "pinf_sc50.vcf.gz", package = "pinfsc50")
vcf <- read.vcfR(vcf_file, verbose = FALSE)</code></pre>
<p>Calling the object by name, with no options (the show method),
provides a summary of the data.</p>
<pre class="r"><code>vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 18 samples
## 1 CHROMs
## 22,031 variants
## Object size: 22.4 Mb
## 7.929 percent missing data
## ***** ***** *****</code></pre>
</div>
<div id="querying-vcf-information" class="section level2">
<h2>Querying VCF information</h2>
<p>Some, but not all, variant callers will provide per variant and per
sample sequence depth information. Some variant callers may provide this
information only as an option (see relevant documentation). We can use
the ‘head’ method to explore what sort of information exists in our
data.</p>
<pre class="r"><code>head(vcf)</code></pre>
<pre><code>## [1] "***** Object of class 'vcfR' *****"
## [1] "***** Meta section *****"
## [1] "##fileformat=VCFv4.1"
## [1] "##source=\"GATK haplotype Caller, phased with beagle4\""
## [1] "##FILTER=<ID=LowQual,Description=\"Low quality\">"
## [1] "##FORMAT=<ID=AD,Number=.,Type=Integer,Description=\"Allelic depths fo [Truncated]"
## [1] "##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Approximate read [Truncated]"
## [1] "##FORMAT=<ID=GQ,Number=1,Type=Integer,Description=\"Genotype Quality\">"
## [1] "First 6 rows."
## [1]
## [1] "***** Fixed section *****"
## CHROM POS ID REF ALT QUAL FILTER
## [1,] "Supercontig_1.50" "41" NA "AT" "A" "4784.43" NA
## [2,] "Supercontig_1.50" "136" NA "A" "C" "550.27" NA
## [3,] "Supercontig_1.50" "254" NA "T" "G" "774.44" NA
## [4,] "Supercontig_1.50" "275" NA "A" "G" "714.53" NA
## [5,] "Supercontig_1.50" "386" NA "T" "G" "876.55" NA
## [6,] "Supercontig_1.50" "462" NA "T" "G" "1301.07" NA
## [1]
## [1] "***** Genotype section *****"
## FORMAT BL2009P4_us23 DDR7602
## [1,] "GT:AD:DP:GQ:PL" "1|1:0,7:7:21:283,21,0" "1|1:0,6:6:18:243,18,0"
## [2,] "GT:AD:DP:GQ:PL" "0|0:12,0:12:36:0,36,427" "0|0:20,0:20:60:0,60,819"
## [3,] "GT:AD:DP:GQ:PL" "0|0:27,0:27:81:0,81,1117" "0|0:26,0:26:78:0,78,1077"
## [4,] "GT:AD:DP:GQ:PL" "0|0:29,0:29:87:0,87,1243" "0|0:27,0:27:81:0,81,1158"
## [5,] "GT:AD:DP:GQ:PL" "0|0:26,0:26:78:0,78,1034" "0|0:30,0:30:90:0,90,1242"
## [6,] "GT:AD:DP:GQ:PL" "0|0:23,0:23:69:0,69,958" "0|0:36,0:36:99:0,108,1556"
## IN2009T1_us22 LBUS5
## [1,] "1|1:0,8:8:24:324,24,0" "1|1:0,6:6:18:243,18,0"
## [2,] "0|0:16,0:16:48:0,48,650" "0|0:20,0:20:60:0,60,819"
## [3,] "0|0:23,0:23:69:0,69,946" "0|0:26,0:26:78:0,78,1077"
## [4,] "0|0:32,0:32:96:0,96,1299" "0|0:27,0:27:81:0,81,1158"
## [5,] "0|0:41,0:41:99:0,122,1613" "0|0:30,0:30:90:0,90,1242"
## [6,] "0|0:35,0:35:99:0,105,1467" "0|0:36,0:36:99:0,108,1556"
## NL07434
## [1,] "1|1:0,12:12:36:486,36,0"
## [2,] "0|0:28,0:28:84:0,84,948"
## [3,] "0|1:19,20:39:99:565,0,559"
## [4,] "0|1:19,19:38:99:523,0,535"
## [5,] "0|1:22,22:44:99:593,0,651"
## [6,] "0|1:29,25:54:99:723,0,876"
## [1] "First 6 columns only."
## [1]
## [1] "Unique GT formats:"
## [1] "GT:AD:DP:GQ:PL"
## [1]</code></pre>
<p>This provides a summary of the information contained in the VCF data.
In the genotype section, the first column is titled ‘FORMAT’ and
includes fields that describe the genotype data included with each
genotype. According to the VCF definition the first element must be the
genotype. All subsequent data is optional according to the VCF
definition. Each field is defined in the meta region. We can use ‘grep’
to search the meta slot for the definition of these fields.</p>
<pre class="r"><code>#strwrap(grep('DP', vcf@meta, value = TRUE))
queryMETA(vcf, element = 'DP')</code></pre>
<pre><code>## [[1]]
## [1] "FORMAT=ID=DP"
## [2] "Number=1"
## [3] "Type=Integer"
## [4] "Description=Approximate read depth (reads with MQ=255 or with bad mates are filtered)"
##
## [[2]]
## [1] "INFO=ID=DP"
## [2] "Number=1"
## [3] "Type=Integer"
## [4] "Description=Approximate read depth; some reads may have been filtered"</code></pre>
<p>Here ‘DP’ is defined as a ‘FORMAT’ (the genotype region) as well as
in the ‘INFO’ column. We’re interested in the former. This tells us that
data for depth (DP) exists, so we can extract the DP information from
the genotype slot.</p>
<pre class="r"><code>dp <- extract.gt(vcf, element='DP', as.numeric=TRUE)</code></pre>
<p>We can use this function to extract other fields by changing the
value of the ‘element’ parameter. Here, we’ve specified to convert the
data to a numeric. This makes sense because depth should be a number
describing how many times a variant was sequenced. Some fields, such as
the genotype (GT), may not be easily converted to a numeric because they
contain non-numeric characters. For example, the genotype field will
contain a pipe (‘|’) for phased data or a forward slash (‘/’) for
unphased data. If you select a field which is non-numeric, such as the
genotype, and specify as.numeric to be TRUE, you will get a numeric
matrix. However, these numbers may not make much sense. Make sure to
only use as.numeric=TRUE on data that actually are numeric.</p>
</div>
<div id="creating-boxplots-of-sequence-depth" class="section level2">
<h2>Creating boxplots of sequence depth</h2>
<p>Now that we have our data, we will want to visualize it. A good
choice for this type of data is a box and whisker plot.</p>
<pre class="r"><code>par(mar=c(8,4,1,1))
#boxplot(dp, las=3, col=c("#C0C0C0", "#808080"), ylab="Depth", log='y', las=2)
boxplot(dp, las=3, col=c("#C0C0C0", "#808080"), ylab="Depth", las=2)</code></pre>
<pre><code>## Warning in (function (z, notch = FALSE, width = NULL, varwidth = FALSE, :
## Duplicated argument las = 2 is disregarded</code></pre>
<pre class="r"><code>abline(h=seq(0,1e4, by=100), col="#C0C0C088")</code></pre>
<p><img src="sequence_coverage_files/figure-html/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mar=c(5,4,4,2))</code></pre>
<p>This is a reasonable representation of the data. The boxplots contain
50% of the data within each box (the first and third quartiles). The
majority of data beyond the first and third quartiles are contained in
the whiskers. Outlying data is represented by characters, here open
circles. A shortcoming of this plot is that there is a great amount of
large values. This compresses the plot so that the boxes are small and
the plot is dominated by outliers.</p>
</div>
<div id="creating-violin-plots-of-sequence-depth"
class="section level2">
<h2>Creating violin plots of sequence depth</h2>
<p>For quality control purposes, I’m frequently happy with using
boxplots. They are quick and easy to produce. If I want something a
little more fancy, say for publication, I may use violin plots. This
will require us to recast the data from a matrix to a data.frame, and
then we can plot it with ggplot2.</p>
<pre class="r"><code>if( require(reshape2) & require(ggplot2) ){
dpf <- melt(dp, varnames=c('Index', 'Sample'), value.name = 'Depth', na.rm=TRUE)
dpf <- dpf[ dpf$Depth > 0,]
p <- ggplot(dpf, aes(x=Sample, y=Depth)) + geom_violin(fill="#C0C0C0", adjust=1.0,
scale = "count", trim=TRUE)
p <- p + theme_bw()
p <- p + theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 60, hjust = 1, size=12))
# p <- p + stat_summary(fun.data=mean_sdl, mult=1, geom="pointrange", color="black")
p <- p + scale_y_continuous(trans=scales::log2_trans(),
breaks=c(1, 10, 100, 800),
minor_breaks=c(1:10, 2:10*10, 2:8*100))
p <- p + theme(axis.title.y = element_text(size=12))
p <- p + theme( panel.grid.major.y=element_line(color = "#A9A9A9", size=0.6) )
p <- p + theme( panel.grid.minor.y=element_line(color = "#C0C0C0", size=0.2) )
p <- p + stat_summary(fun.y=median, geom="point", shape=23, size=2)
p
} else {
message("The packages reshape2 and ggplot2 are required for this example but do not appear
to be installed. Please use install.packages(c('reshape2', 'ggplot2', 'scales')) if you would
like to install them.")
}</code></pre>
<pre><code>## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<p><img src="sequence_coverage_files/figure-html/unnamed-chunk-7-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>The violin plot presents the distribution of the data throughout its
range. They also provide an easy way to apply a logarithmic
transformation (here base 2) that effectively compresses large values
and stretches out small values. Here we observe a large density of per
variant sequence depths that are greater than ten (for the most part).
This may be interpreted as having attained sufficient sequence depth for
a diploid organism.</p>
</div>
<div id="filtering-on-sequence-depth." class="section level2">
<h2>Filtering on sequence depth.</h2>
<p>In order to sequence a diploid genotype, we need at least two
sequence reads. Ideally, we would like some redundancy to build more
confidence in our genotypes. This means we may want a lower bound on
coverage for whether we call a genotype. You can also see from the plots
that we have some samples that have exceptionally high sequence coverage
(greater than 100). These may be variants from repetitive portions of
the genome, and therefore may be desireable to exclude. Here we pull out
summary statistics for each sample and use this information to filter
our data. The quantile function is used to build a 90% confidence
interval for each sample. We then use this information to set variants
which are outside this region to missing data (NA). Lastly, we apply a
minimum threshold by setting variants with coverage less than four as
missing.</p>
<pre class="r"><code>sums <- apply(dp, MARGIN=2, quantile, probs=c(0.05, 0.95), na.rm=TRUE)
dp2 <- sweep(dp, MARGIN=2, FUN = "-", sums[1,])
dp[dp2 < 0] <- NA
dp2 <- sweep(dp, MARGIN=2, FUN = "-", sums[2,])
dp[dp2 > 0] <- NA
dp[dp < 4] <- NA</code></pre>
<p>Now that we’ve accomplished these manipulations, we can visualize the
data using boxplots.</p>
<pre class="r"><code>par(mar=c(8,4,1,1))
boxplot(dp, las=3, col=c("#C0C0C0", "#808080"), ylab="Depth")
abline(h=seq(0,200, by=20), col="#C0C0C088")</code></pre>
<p><img src="sequence_coverage_files/figure-html/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mar=c(5,4,4,2))</code></pre>
<p>We can see that we’ve removed many of our outlier variants (circles
beyond the whiskers). And our data appear much better behaved. We can
now use this information to update our vcfR object.</p>
<pre class="r"><code>vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 18 samples
## 1 CHROMs
## 22,031 variants
## Object size: 22.4 Mb
## 7.929 percent missing data
## ***** ***** *****</code></pre>
<pre class="r"><code>is.na( vcf@gt[,-1][ is.na(dp) ] ) <- TRUE
vcf</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 18 samples
## 1 CHROMs
## 22,031 variants
## Object size: 21.3 Mb
## 21.06 percent missing data
## ***** ***** *****</code></pre>
<p>We can see that we’ve increased the amount of missing data in our
vcfR object. This validated the success of our manipulation. However, we
may want to use this information to further mitigate the data set.</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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