Generation of Quality Controls

SeSAMe provides a set of quality control steps. Be sure to pre-cache HM450 annotation data from ExperimentHub. This only needs to be done once per sesame installation.

## [1] TRUE

## [1] TRUE

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

sesameQC(ssets[[1]])

## 
## =======================
## =      Intensities    =
## =======================
## No. probes (num_probes_all)        485577 
## mean (M/U) (mean_intensity):       NaN 
## mean (M+U) (mean_intensity_total): 11059.01 
## 
## -- Infinium II --
## No. probes: (num_probes_II)        350076 (72.095%)
## Mean Intensity (mean_ii):          5160.813 
## 
## -- Infinium I (Red) -- 
## No. probes: (num_probes_IR)        89203 (18.371%)
## No. Probes Consistent Channel:     88799 
## No. Porbes Swapped Channel:        162 
## No. Probes Low Intensity:          242 
## Mean Intensity (in-band):          6527.3 
## Mean Intensity (out-of-band):      928.2117 
## 
## -- Infinium I (Grn) -- 
## No. probes:                     46298 (9.535%)
## No. Probes Consistent Channel:     46000 
## No. Probes Swapped Channel:        254 
## No. Probes Low Intensity:          44 
## Mean Intensity (in-band):          6394.865 
## Mean Intensity (out-of-band):      640.0676 
## 
## =======================
## =    Non-detection    =
## =======================
## No. probes:                        0 
## No. probes w/ NA (num_na):         0 (0.000%)
## No. nondetection (num_nondt):      18174 (3.743%)
## 
## =======================
## =      Beta Values    =
## =======================
## Mean Betas:                        0.5333022 
## Median Betas:                      0.6721648 
## 
## -- cg probes --
## No. Probes:                        482421 
## No. Probes with NA:                0 (0.000%)
## Mean Betas:                        0.5359922 
## Median Betas:                      0.6792458 
## % Unmethylated (Beta < 0.3):       37.788%
## % Methylated (Beta > 0.7):         48.984%
## 
## -- ch probes --
## No. Probes:                        3091 
## No. Probes with NA:                0 (0.000%)
## Mean Betas:                        0.1138859 
## Median Betas:                      0.08069617 
## % Unmethylated (Beta < 0.3):       94.856%
## % Methylated (Beta > 0.7):         0.000%
## 
## -- rs probes --
## No. Probes:                        65 
## No. Probes with NA:                0 (0.000%)
## Mean Betas:                        0.513024 
## Median Betas:                      0.5378352 
## % Unmethylated (Beta < 0.3):       30.769%
## % Methylated (Beta > 0.7):         32.308%
## 
## =======================
## =      Inferences     =
## =======================
## Sex:                            MALE 
## Ethnicity:                      WHITE 
## Age:                            61.43636 
## Bisulfite Conversion (GCT):     1.10858

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
as.data.frame(qualityRank(sset))

Output explicit and Infinium-I-derived SNP to VCF

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

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

## Retrieving SNP annotation from  http://zhouserver.research.chop.edu/InfiniumAnnotation/20200704/EPIC/EPIC.hg19.snp_overlap_b151.rds ... Done.

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

## Retrieving SNP annotation from  http://zhouserver.research.chop.edu/InfiniumAnnotation/20200704/EPIC/EPIC.hg19.typeI_overlap_b151.rds ... Done.

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

One can output to actual VCF file with a header by formatVCF(sset, vcf=path_to_vcf).

Quality Controls

19 May 2021