DMR | R Documentation |
This subroutine uses Euclidean distance to group CpGs and then combine p-values for each segment. The function performs DML test first if cf is NULL. It groups the probe testing results into differential methylated regions in a coefficient table with additional columns designating the segment ID and statistical significance (P-value) testing the segment.
DMR(
betas,
smry,
contrast,
platform = NULL,
probe.coords = NULL,
dist.cutoff = NULL,
seg.per.locus = 0.5
)
betas |
beta values for distance calculation |
smry |
DML |
contrast |
the pair-wise comparison or contrast check colnames(attr(smry, "model.matrix")) if uncertain |
platform |
EPIC, HM450, MM285, ... |
probe.coords |
GRanges object that defines CG coordinates if NULL (default), then the default genome assembly is used. Default genome is given by, e.g., sesameData_check_genome(NULL, "EPIC") For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., probe.coords = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function. |
dist.cutoff |
cutoff of beta value differences for two neighboring CGs to be considered the same DMR (by default it's determined using the quantile function on seg.per.locus) |
seg.per.locus |
number of segments per locus higher value leads to more segments |
coefficient table with segment ID and segment P-value each row is a locus, multiple loci may share a segment ID if they are merged to the same segment. Records are ordered by Seg_Est.
sesameDataCache() # in case not done yet
data <- sesameDataGet('HM450.76.TCGA.matched')
smry <- DML(data$betas[1:1000,], ~type, meta=data$sampleInfo)
colnames(attr(smry, "model.matrix")) # pick a contrast from here
## showing on a small set of 100 CGs
merged_segs <- DMR(data$betas[1:1000,], smry, "typeTumour", platform="HM450")
sesameDataGet_resetEnv()
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