rmSNPandCH: Filter probes
In DMRcate: Methylation array and sequencing spatial analysis methods

Description Usage Arguments Details Value Author(s) References Examples

Filters a matrix of M-values (or beta values) by distance to SNP/variant. Also (optionally) removes cross-hybridising probes and sex-chromosome probes.

1	rmSNPandCH(object, dist = 2, mafcut = 0.05, and = TRUE, rmcrosshyb = TRUE, rmXY=FALSE)

`object`	A matrix of M-values or beta values, with unique Illumina probe IDs as rownames.
`dist`	Maximum distance (from CpG to SNP/variant) of probes to be filtered out. See details for when Illumina occasionally lists a CpG-to-SNP distance as being < 0.
`mafcut`	Minimum minor allele frequency of probes to be filtered out.
`and`	If `TRUE`, the probe must have at least 1 SNP binding to it that satisfies both requirements in `dist` and `mafcut` for it to be filtered out. If `FALSE`, it will be filtered out if either requirement is satisfied. Default is `TRUE`.
`rmcrosshyb`	If `TRUE`, filters out probes found by Pidsley and Zotenko et al. (2016) for EPIC or Chen et al. (2013) for 450K to be cross-reactive with areas of the genome not at the site of interest. Many of these sites are on the X-chromosome, leading to potential confounding if the sample group is a mix of males and females. There are 63,707 probes in total in this list. Default is `TRUE`.
`rmXY`	If `TRUE`, filters out probe hybridising to sex chromosomes. Or-operator applies when combined with other 2 filters.

Probes in -1:dist will be filtered out for any integer specification of dist. When a probe is listed as being “-1” nucleotides from a SNP (7 in total of the 153,113), that SNP is immediately adjacent to the end of the probe, and is likely to confound the measurement, in addition to those listed as 0, 1 or 2 nucleotides away. See vignette for further details.

A matrix, attenuated from object, with rows corresponding to probes matching user input filtered out.

Tim J. Peters <t.peters@garvan.org.au>

Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, Van Dijk S, Muhlhausler B, Stirzaker C, Clark SJ. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology. 2016 17(1), 208.

Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, Gallinger S, Hudson TJ, Weksberg R. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013 Jan 11;8(2). http://supportres.illumina.com/documents/myillumina/88bab663-307c-444a-848e-0ed6c338ee4d/humanmethylation450_15017482_v.1.2.snpupdate.table.v3.txt

library(ExperimentHub)
library(limma)
eh <- ExperimentHub()
FlowSorted.Blood.EPIC <- eh[["EH1136"]]
tcell <- FlowSorted.Blood.EPIC[,colData(FlowSorted.Blood.EPIC)$CD4T==100 |
                                colData(FlowSorted.Blood.EPIC)$CD8T==100]
detP <- detectionP(tcell)
remove <- apply(detP, 1, function (x) any(x > 0.01))
tcell <- tcell[!remove,]
tcell <- preprocessFunnorm(tcell)
tcellms <- getM(tcell)
tcellms.noSNPs <- rmSNPandCH(tcellms, dist=2, mafcut=0.05)