dmrcate | R Documentation |
The main function of this package. Computes a kernel estimate against a null comparison to identify significantly differentially (or variable) methylated regions.
dmrcate(object,
lambda = 1000,
C=NULL,
pcutoff = "fdr",
consec = FALSE,
conseclambda = 10,
betacutoff = NULL,
min.cpgs = 2
)
object |
A |
lambda |
Gaussian kernel bandwidth for smoothed-function estimation. Also informs DMR
bookend definition; gaps >= |
C |
Scaling factor for bandwidth. Gaussian kernel is calculated where
|
pcutoff |
Threshold to determine DMRs. Default implies indexing at the rate of individually significant CpGs and can be set on the |
consec |
Use |
conseclambda |
Bandwidth in CpGs (rather than nucleotides) to use when
|
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. |
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.
A DMResults object.
Tim J. Peters <t.peters@garvan.org.au>, Mike J. Buckley <Mike.Buckley@csiro.au>, Tim Triche Jr. <tim.triche@usc.edu>
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.
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)
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