1. Quality Controls
In sesame: Tools For Analyzing Illumina Infinium DNA Methylation Arrays

Generation of Quality Controls

SeSAMe provides a set of quality control steps.

library(sesame)
library(FlowSorted.Blood.450k)
options(rmarkdown.html_vignette.check_title = FALSE)

ssets <- RGChannelSetToSigSets(FlowSorted.Blood.450k[,1:10])

The SeSAMe QC function returns an sesameQC object which can be directly printed onto the screen.

sesameQC(ssets[[1]])

The sesameQC object can be coerced into data.frame and linked using the following code

qc10 <- do.call(rbind, lapply(ssets, function(x)
    as.data.frame(sesameQC(x))))
qc10$sample_name <- names(ssets)

qc10[,c('mean_beta_cg','frac_meth_cg','frac_unmeth_cg','sex','age')]

Background

The background level is given by mean_oob_grn and mean_oob_red

library(ggplot2)
ggplot(qc10,
    aes(x = mean_oob_grn, y= mean_oob_red, label = sample_name)) +
    geom_point() + geom_text(hjust = -0.1, vjust = 0.1) +
    geom_abline(intercept = 0, slope = 1, linetype = 'dotted') +
    xlab('Green Background') + ylab('Red Background') +
    xlim(c(500,1200)) + ylim(c(500,1200))

Mean Intensity

The mean {M,U} intensity can be reached by mean_intensity. Similarly, the mean M+U intensity can be reached by mean_intensity_total. Low intensities are symptomatic of low input or poor hybridization.

library(wheatmap)
p1 <- ggplot(qc10) +
    geom_bar(aes(sample_name, mean_intensity), stat='identity') +
    xlab('Sample Name') + ylab('Mean Intensity') +
    ylim(0,18000) +
    theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))
p2 <- ggplot(qc10) +
    geom_bar(aes(sample_name, mean_intensity_total), stat='identity') +
    xlab('Sample Name') + ylab('Mean M+U Intensity') +
    ylim(0,18000) +
    theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))
WGG(p1) + WGG(p2, RightOf())

Fraction of color channel switch

The fraction of color channel switch can be found in InfI_switch_G2R and InfI_switch_R2G. These numbers are symptomatic of how Infinium I probes are affected by SNP-induced color channel switching.

ggplot(qc10) +
    geom_point(aes(InfI_switch_G2R, InfI_switch_R2G))

Fraction of NA

The fraction of NAs are signs of masking due to variety of reasons including failed detection, high background, putative low quality probes etc. This number can be reached in frac_na_cg and num_na_cg (the cg stands for CpG probes, so we also have num_na_ch and num_na_rs)

p1 <- ggplot(qc10) +
    geom_bar(aes(sample_name, num_na_cg), stat='identity') +
    xlab('Sample Name') + ylab('Number of NAs') +
    theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))
p2 <- ggplot(qc10) +
    geom_bar(aes(sample_name, frac_na_cg), stat='identity') +
    xlab('Sample Name') + ylab('Fraction of NAs (%)') +
    theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))
WGG(p1) + WGG(p2, RightOf())

Quality Ranking

Sesame provide convenient function to compare your sample with public data sets processed with the same pipeline. All you need is a raw SigSet.

sset <- sesameDataGet('EPIC.1.LNCaP')$sset
qualityRank(sset)

Output explicit and Infinium-I-derived SNP to VCF

sset <- sesameDataGet('EPIC.1.LNCaP')$sset

annoS <- sesameDataPullVariantAnno_SNP('EPIC','hg19')
annoI <- sesameDataPullVariantAnno_InfiniumI('EPIC','hg19')

## output to console
head(formatVCF(sset, annoS=annoS, annoI=annoI))