DMRcate-package: DMR calling from bisulfite sequencing and Illumina array data

In DMRcate: Methylation array and sequencing spatial analysis methods

Description

De novo identification and extraction of differentially methylated regions (DMRs) in the human genome using Illumin array and bisulfite sequencing data. DMRcate extracts and annotates differentially methylated regions (DMRs) using a kernel-smoothed estimate. Functions are provided for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions.

Author(s)

Tim J. Peters <[e-mail]>

References

Peters T.J., Buckley M.J., Statham, A., Pidsley R., Samaras K., Lord R.V., Clark S.J. and Molloy P.L. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin 2015, 8:6, doi:10.1186/1756-8935-8-6

Examples

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)
#Subset to chr2 only
tcell <- tcell[seqnames(tcell) == "chr2",]
tcellms <- getM(tcell)
tcellms.noSNPs <- rmSNPandCH(tcellms, dist=2, mafcut=0.05)
tcell$Replicate[tcell$Replicate==""] <- tcell$Sample_Name[tcell$Replicate==""]
tcellms.noSNPs <- avearrays(tcellms.noSNPs, tcell$Replicate)
tcell <- tcell[,!duplicated(tcell$Replicate)]
tcell <- tcell[rownames(tcellms.noSNPs),]
colnames(tcellms.noSNPs) <- colnames(tcell)
assays(tcell)[["M"]] <- tcellms.noSNPs
assays(tcell)[["Beta"]] <- ilogit2(tcellms.noSNPs)
type <- factor(tcell$CellType)
design <- model.matrix(~type) 
myannotation <- cpg.annotate("array", tcell, arraytype = "EPIC",
                             analysis.type="differential", design=design, coef=2)
dmrcoutput <- dmrcate(myannotation, lambda=1000, C=2)
results.ranges <- extractRanges(dmrcoutput, genome = "hg19")
groups <- c(CD8T="magenta", CD4T="forestgreen")
cols <- groups[as.character(type)]
DMR.plot(ranges=results.ranges, dmr=1, CpGs=getBeta(tcell), what="Beta", 
         arraytype = "EPIC", phen.col=cols, genome="hg19")

DMRcate documentation built on Jan. 17, 2021, 2 a.m.