CloseBySingleRegion: Extract clusters of CpGs located closely in a genomic region.

In TransBioInfoLab/coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

Extract clusters of CpGs located closely in a genomic region.

Description

Extract clusters of CpGs located closely in a genomic region.

Usage

CloseBySingleRegion(
  CpGs_char,
  genome = c("hg19", "hg38"),
  arrayType = c("450k", "EPIC"),
  manifest_gr = NULL,
  maxGap = 200,
  minCpGs = 3
)

Arguments

CpGs_char	a list of CpG IDs
genome	Human genome of reference hg19 or hg38
arrayType	Type of array, 450k or EPIC
manifest_gr	A GRanges object with the genome manifest (as returned by ExperimentHub or by ImportSesameData). This function by default ignores this argument in favour of the genome and arrayType arguments.
maxGap	an integer, genomic locations within maxGap from each other are placed into the same cluster
minCpGs	an integer, minimum number of CpGs for the resulting CpG cluster

Details

Note that this function depends only on CpG locations, and not on any methylation data. The algorithm is based on the clusterMaker function in the bumphunter R package. Each cluster is essentially a group of CpG locations such that two consecutive locations in the cluster are separated by less than maxGap.

Value

a list, each item in the list is a character vector of CpG IDs located closely (i.e. in the same cluster)

Examples

   CpGs_char <- c(
     "cg02505293", "cg03618257", "cg04421269", "cg17885402", "cg19890033",
     "cg20566587", "cg27505880"
   )

   cluster_ls <- CloseBySingleRegion(
     CpGs_char,
     genome = "hg19",
     arrayType = "450k",
     maxGap = 100,
     minCpGs = 3
   )

TransBioInfoLab/coMethDMR documentation built on Sept. 14, 2022, 7:09 p.m.