dmrcate: DMR identification

In timpeters82/DMRcate-devel: Methylation array and sequencing spatial analysis methods

DMR identification

Description

The main function of this package. Computes a kernel estimate against a null comparison to identify significantly differentially (or variable) methylated regions.

Usage

dmrcate(object, 
           lambda = 1000,
           C=NULL,
           pcutoff = "fdr", 
           consec = FALSE, 
           conseclambda = 10, 
           betacutoff = NULL,
           min.cpgs = 2
           ) 

Arguments

object	A CpGannotated-class, created from cpg.annotate or sequencing.annotate.
lambda	Gaussian kernel bandwidth for smoothed-function estimation. Also informs DMR bookend definition; gaps >= lambda between significant CpG sites will be in separate DMRs. Support is truncated at 5*lambda. Default is 1000 nucleotides. See details for further info.
C	Scaling factor for bandwidth. Gaussian kernel is calculated where lambda/C = sigma. Empirical testing shows for both Illumina and bisulfite sequencing data that, when lambda=1000, near-optimal prediction of sequencing-derived DMRs is obtained when C is approximately 2, i.e. 1 standard deviation of Gaussian kernel = 500 base pairs. Cannot be < 0.2.
pcutoff	Threshold to determine DMRs. Default implies indexing at the rate of individually significant CpGs and can be set on the CpGannotated-class object using cpg.annotate, sequencing.annotate or changeFDR. Default highly recommended unless you are comfortable with the risk of Type I error. If manually specified, this value will be set on the highly permissive kernel-smoothed FDR values.
consec	Use DMRcate in consecutive mode. Treats CpG sites as equally spaced.
conseclambda	Bandwidth in CpGs (rather than nucleotides) to use when consec=TRUE. When specified the variable lambda simply becomes the minumum distance separating DMRs.
betacutoff	Optional filter; removes any region from the results where the absolute mean beta shift is less than the given value. Only available for Illumina array data and results produced from DSS::DMLtest().
min.cpgs	Minimum number of consecutive CpGs constituting a DMR.

Details

The values of lambda and C should be chosen with care. For array data, we currently recommend that half a kilobase represent 1 standard deviation of support (lambda=1000 and C=2). If lambda is too small or C too large then the kernel estimator will not have enough support to significantly differentiate the weighted estimate from the null distribution. If lambda is too large then dmrcate will report very long DMRs spanning multiple gene loci, and the large amount of support will likely give Type I errors. If you are concerned about Type I errors we highly recommend using the default value of pcutoff, although this will return no DMRs if no DM CpGs are returned by limma/DSS either.

Value

A DMResults object.

Author(s)

Tim J. Peters <[e-mail]>, Mike J. Buckley <[e-mail]>, Tim Triche Jr. <[e-mail]>

References

Peters, T. J., Buckley, M.J., Chen, Y., Smyth, G.K., Goodnow, C. C. and Clark, S. J. (2021). Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate. Nucleic Acids Research, 49(19), e109.

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

Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall.

Duong T. (2013) Local significant differences from nonparametric two-sample tests. Journal of Nonparametric Statistics. 2013 25(3), 635-645.

Examples

library(AnnotationHub)
library(GenomicRanges)
ah <- AnnotationHub()
EPICv2manifest <- ah[["AH116484"]]
chr21probes <- rownames(EPICv2manifest)[EPICv2manifest$CHR=="chr21"]
coords <- EPICv2manifest[chr21probes, "MAPINFO"]
stats <- rt(length(chr21probes), 2)
pvals <- pt(-abs(stats), 100)
fdrs <- p.adjust(2*pvals, "BH")
annotated <- GRanges(rep("chr21", length(stats)), IRanges(coords, coords), stat = stats,
                     diff = 0, rawpval = pvals, ind.fdr = fdrs, is.sig = fdrs < 0.05)
names(annotated) <- chr21probes
myannotation <- new("CpGannotated", ranges=annotated)
dmrcoutput <- dmrcate(myannotation, lambda=1000, C=2)

timpeters82/DMRcate-devel documentation built on Oct. 2, 2024, 10:33 a.m.