QCs: Quality Control Measures

In andreyshabalin/ramwas: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

Description

RaMWAS calculates a number of QC measures for each BAM and saves them in R .rds files.

For full description of the QC measures and the ploting options run
vignette("RW3_BAM_QCs")

Usage

qcmean(x)
## S3 method for class 'NULL'
qcmean(x)
## S3 method for class 'qcChrX'
qcmean(x)
## S3 method for class 'qcChrY'
qcmean(x)
## S3 method for class 'qcCoverageByDensity'
qcmean(x)
## S3 method for class 'qcEditDist'
qcmean(x)
## S3 method for class 'qcEditDistBF'
qcmean(x)
## S3 method for class 'qcFrwrev'
qcmean(x)
## S3 method for class 'qcHistScore'
qcmean(x)
## S3 method for class 'qcHistScoreBF'
qcmean(x)
## S3 method for class 'qcIsoDist'
qcmean(x)
## S3 method for class 'qcLengthMatched'
qcmean(x)
## S3 method for class 'qcLengthMatchedBF'
qcmean(x)
## S3 method for class 'qcNonCpGreads'
qcmean(x)

## S3 method for class 'qcHistScore'
plot(x, samplename="", xstep = 25, ...)
## S3 method for class 'qcHistScoreBF'
plot(x, samplename="", xstep = 25, ...)
## S3 method for class 'qcEditDist'
plot(x, samplename="", xstep = 5, ...)
## S3 method for class 'qcEditDistBF'
plot(x, samplename="", xstep = 5, ...)
## S3 method for class 'qcLengthMatched'
plot(x, samplename="", xstep = 25, ...)
## S3 method for class 'qcLengthMatchedBF'
plot(x, samplename="", xstep = 25, ...)
## S3 method for class 'qcIsoDist'
plot(x, samplename="", xstep = 25, ...)
## S3 method for class 'qcCoverageByDensity'
plot(x, samplename="", ...)

Arguments

x	The QC object. See the examples below.
samplename	Name of the sample for plot title.
xstep	The distance between x axis ticks.
...	Parameters passed to the underlying plot or barplot function.

Value

Function qcmean returns one value summary of most QC measures. Run vignette("RW3_BAM_QCs") for description of values returned by it.

Author(s)

Andrey A Shabalin [e-mail]

See Also

See vignettes: browseVignettes("ramwas").

Examples

# Load QC data from a sample project
filename = system.file("extdata", "bigQC.rds", package = "ramwas")
qc = readRDS(filename)$qc


## The number of BAM files
cat("N BAMs:", qc$nbams)

## Total number of reads in the BAM file(s)
cat("Reads total:", qc$reads.total)

## Number of reads aligned to the reference genome
cat("Reads aligned:", qc$reads.aligned, "\n")
cat("This is ", qc$reads.aligned / qc$reads.total * 100,
    "% of all reads", sep="")

## Number of reads that passed minimum score filter and are recorded
cat("Reads recorded:",qc$reads.recorded,"\n")
cat("This is ", qc$reads.recorded / qc$reads.aligned * 100,
    "% of aligned reads", sep="")

## Number of recorded reads aligned to
## the forward and reverse strands respectively
cat("Reads on forward strand:", qc$frwrev[1],"\n")
cat("Reads on reverse strand:", qc$frwrev[2],"\n")
cat("Fraction of reads on forward strand:", qcmean(qc$frwrev), "\n")

## Distribution of the read scores
cat("Average alignment score:", qcmean(qc$hist.score1), "\n")
cat("Average alignment score, no filter:", qcmean(qc$bf.hist.score1), "\n")
par(mfrow=c(1,2))
plot(qc$hist.score1)
plot(qc$bf.hist.score1)

## Distribution of the length of the aligned part of the reads
cat("Average aligned length:", qcmean(qc$hist.length.matched), "\n")
cat("Average aligned length, no filter:",
    qcmean(qc$bf.hist.length.matched), "\n")
par(mfrow = c(1,2))
plot(qc$hist.length.matched)
plot(qc$bf.hist.length.matched)

## Distribution of edit distance between
## the aligned part of the read and the reference genome
cat("Average edit distance:", qcmean(qc$hist.edit.dist1), "\n")
cat("Average edit distance, no filter:", qcmean(qc$bf.hist.edit.dist1), "\n")
par(mfrow = c(1,2))
plot(qc$hist.edit.dist1)
plot(qc$bf.hist.edit.dist1)

## Number of reads after removal of duplicate reads
cat("Reads without duplicates:", qc$reads.recorded.no.repeats, "\n")
cat("This is ", qc$reads.recorded.no.repeats / qc$reads.recorded * 100,
    "% of aligned reads", "\n", sep="")
cat("Fraction of reads on forward strand (with    duplicates):",
    qcmean(qc$frwrev), "\n")
cat("Fraction of reads on forward strand (without duplicates):",
    qcmean(qc$frwrev.no.repeats), "\n")

## Number of reads away from CpGs
cat("Non-CpG reads:", qc$cnt.nonCpG.reads[1], "\n")
cat("This is ", qcmean(qc$cnt.nonCpG.reads)*100, "% of recorded reads",
    sep="")

## Average coverage of CpGs and non-CpGs
cat("Summed across", qc$nbams, "bams", "\n")
cat("Average     CpG coverage:", qc$avg.cpg.coverage,   "\n")
cat("Average non-CpG coverage:", qc$avg.noncpg.coverage,"\n")
cat("Enrichment ratio:", qc$avg.cpg.coverage / qc$avg.noncpg.coverage)

## Coverage around isolated CpGs
plot(qc$hist.isolated.dist1)

## Fraction of reads from chrX and chrY
cat("ChrX reads: ", qc$chrX.count[1], ", which is ",
    qcmean(qc$chrX.count)*100, "% of total", sep="", "\n")
cat("ChrX reads: ", qc$chrY.count[1], ", which is ",
    qcmean(qc$chrY.count)*100, "% of total", sep="", "\n")

## Coverage vs. CpG density
cat("Highest coverage is observed at CpG density of",
    qcmean(qc$avg.coverage.by.density)^2)
plot(qc$avg.coverage.by.density)

andreyshabalin/ramwas documentation built on Sept. 27, 2021, 7:25 p.m.