qcplot: Produce a visual summary of QC measures

In andrewparkermorgan/argyle: Basic interface and QC for genotypes from Illumina Infinium arrays

Description

Produce a visual summary of QC measures

Usage

qcplot(gty, draw = TRUE, ...)

Arguments

gty	a genotypes object with intensity data attached
draw	actually show the plot, in addition to returning it
...	ignored

Details

This function will plot any existing QC result attached to gty; if none is present, run.sample.qc(gty) will be run to generate it.

The QC plot has two panels. The upper panel displays the distribution of A, B, H and N (missing) calls for each sample. The lower panel displays a the "sum intensity" quantiles of each sample. Samples are sorted from left to right in increasing order of median intensity, and the sort order is matched between panels. Samples for which the quality filter is set are marked with an open dot in the upper panel.

Although somewhat crude, the count of H and N calls relative to expectations is anecdotally a robust measure of genotyping quality (see Didion et al. (2014)). (But the expectations are important: an outbred sample and an inbred sample should have very different numbers of Hs, but probably similar number of Ns.) Higher variance in the hybridization intensities, as indicated by wider spread of the intensity quantiles in the lower plot, suggests poor input DNA quality. Contamination of one sample with another, if both had good quality DNA, will increase the proportion of no-calls but probably will not shift the intensity quantiles much.

Samples which are diverged from the reference genome used in the array design are expected to have a higher proportion of no-calls due to off-target variation in or near the probe sequence. See Didion et al. (2012) for a fuller discussion.

For discussion of the Illumina Infinium chemistry, see Steemers et al. (2006); for more on intensity QC, see Staaf et al. (2008).

Value

a gtable (ineriting from grid::grob) containg the composite QC plot (see Details)

References

Steemers FJ et al. (2006) Whole-genome genotyping with the single-base extension assay. Nat Methods 3:31-33. doi:10.1038/nmeth842.

Staaf J et al. (2008) BMC Bioinformatics. doi:10.1186/1471-2105-9-409.

Didion JP et al. (2012) Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC Genomics 13:34. doi:10.1186/1471-2164-13-34.

Didion JP et al. (2014) SNP array profiling of mouse cell lines identifies their strains of origin and reveals cross-contamination and widespread aneuploidy. BMC Genomics 15:847. doi:10.1186/1471-2164-15-847.

andrewparkermorgan/argyle documentation built on May 10, 2019, 11:08 a.m.