dmrFinder | R Documentation |
Finds differentially methylated regions for whole genome bisulfite sequencing data. Essentially identifies regions of the genome where all methylation loci have an associated t-statistic that is beyond a (low, high) cutoff.
dmrFinder(bstat, cutoff = NULL, qcutoff = c(0.025, 0.975),
maxGap=300, stat = "tstat.corrected", verbose = TRUE)
bstat |
An object of class |
cutoff |
The cutoff of the t-statistics. This should be a vector
of length two giving the (low, high) cutoff. If |
qcutoff |
In case |
maxGap |
If two methylation loci are separated by this distance, break a possible DMR. This guarantees that the return DMRs have CpGs that are this distance from each other. |
stat |
Which statistic should be used? |
verbose |
Should the function be verbose? |
The workhorse function is BSmooth.tstat
which sets up a
t-statistic for a comparison between two groups.
Note that post-processing of these DMRs are likely to be necessary, filtering for example for length (or number of loci).
A data.frame
with columns
start , end , width , chr |
genomic locations and width. |
n |
The number of methylation loci. |
invdensity |
Average length per loci. |
group1.mean |
The mean methylation level across samples and loci in 'group1'. |
group2.mean |
The mean methylation level across samples and loci in 'group2'. |
meanDiff |
The mean difference in methylation level; the
difference between |
idxStart , idxEnd , cluster |
Internal use. |
areaStat |
The 'area' of the t-statistic; equal to the sum of the t-statistics for the individual methylation loci. |
direction |
either ‘hyper’ or ‘hypo’. |
areaStat.corrected |
Only present if |
Kasper Daniel Hansen khansen@jhsph.edu.
KD Hansen, B Langmead, and RA Irizarry. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology (2012) 13:R83. doi:10.1186/gb-2012-13-10-r83.
BSmooth.tstat
for the function constructing the input
object, and BSseqTstat
for its class. In the
example below, we use BS.cancer.ex.tstat
as
the actual input object. Also see the package vignette(s) for a
detailed example.
if(require(bsseqData)) {
dmrs0 <- dmrFinder(BS.cancer.ex.tstat, cutoff = c(-4.6, 4.6), verbose = TRUE)
dmrs <- subset(dmrs0, abs(meanDiff) > 0.1 & n >= 3)
}
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