In zwdzwd/sesame: SEnsible Step-wise Analysis of DNA MEthylation BeadChips

library(sesame)
sesameDataCache()

Calculate Quality Metrics

The main function to calculate the quality metrics is sesameQC_calcStats. This function takes a SigDF, calculates the QC statistics, and returns a single S4 sesameQC object, which can be printed directly to the console. To calculate QC metrics on a given list of samples or all IDATs in a folder, one can use sesameQC_calcStats within the standard openSesame pipeline. When used with openSesame, a list of sesameQCs will be returned. Note that one should turn off preprocessing using prep="":

## calculate metrics on all IDATs in a specific folder
qcs = openSesame(idat_dir, prep="", func=sesameQC_calcStats)

SeSAMe divides sample quality metrics into multiple groups. These groups are listed below and can be referred to by short keys. For example, "intensity" generates signal intensity-related quality metrics.

library(knitr)
kable(data.frame(
    "Short Key" = c(
        "detection",
        "numProbes",
        "intensity",
        "channel",
        "dyeBias",
        "betas"),
    "Description" = c(
        "Signal Detection",
        "Number of Probes",
        "Signal Intensity",
        "Color Channel",
        "Dye Bias",
        "Beta Value")))

By default, sesameQC_calcStats calculates all QC groups. To save time, one can compute a specific QC group by specifying one or multiple short keys in the funs= argument:

sdfs <- sesameDataGet("EPIC.5.SigDF.normal")[1:2] # get two examples
## only compute signal detection stats
qcs = openSesame(sdfs, prep="", func=sesameQC_calcStats, funs="detection")
qcs[[1]]

We consider signal detection the most important QC metric.

One can retrieve the actual stat numbers from sesameQC using the sesameQC_getStats (the following generates the fraction of probes with detection success):

sesameQC_getStats(qcs[[1]], "frac_dt")

After computing the QCs, one can optionally combine the sesameQC objects into a data frame for easy comparison.

## combine a list of sesameQC into a data frame
head(do.call(rbind, lapply(qcs, as.data.frame)))

Note that when the input is an SigDF object, calling sesameQC_calcStats within openSesame and as a standalone function are equivalent.

sdf <- sesameDataGet('EPIC.1.SigDF')
qc = openSesame(sdf, prep="", func=sesameQC_calcStats, funs=c("detection"))
## equivalent direct call
qc = sesameQC_calcStats(sdf, c("detection"))
qc

Rank Quality Metrics

options(rmarkdown.html_vignette.check_title = FALSE)

SeSAMe features comparison of your sample with public data sets. The sesameQC_rankStats() function ranks the input sesameQC object with sesameQC calculated from public datasets. It shows the rank percentage of the input sample as well as the number of datasets compared.

sdf <- sesameDataGet('EPIC.1.SigDF')
qc <- sesameQC_calcStats(sdf, "intensity")
qc
sesameQC_rankStats(qc, platform="EPIC")

Quality Control Plots

SeSAMe provides functions to create QC plots. Some functions takes sesameQC as input while others directly plot the SigDF objects. Here are some examples:

sesameQC_plotBar() takes a list of sesameQC objects and creates bar plot for each metric calculated.
sesameQC_plotRedGrnQQ() graphs the dye bias between the two color channels.
sesameQC_plotIntensVsBetas() plots the relationship between $\beta$ values and signal intensity and can be used to diagnose artificial readout and influence of signal background.
sesameQC_plotHeatSNPs() plots SNP probes and can be used to detect sample swaps.

More about quality control plots can be found in Supplemental Vignette.