MarkComethylatedCpGs: Mark CpGs in contiguous and co-methylated region

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

Description

Mark CpGs in contiguous and co-methylated region

Usage

MarkComethylatedCpGs(
  betaCluster_mat,
  betaToM = TRUE,
  rDropThresh_num = 0.4,
  method = c("pearson", "spearman")
)

Arguments

betaCluster_mat	matrix of beta values, with rownames = sample ids, column names = CpG ids. Note that the CpGs need to be ordered by their genomic positions, this can be accomplished by the OrderCpGbyLocation function.
betaToM	indicates if converting to mvalues before computing correlations
rDropThresh_num	thershold for min correlation between a cpg with sum of the rest of the CpGs
method	correlation method, can be pearson or spearman

Details

An outlier CpG in a genomic region will typically have low correlation with the rest of the CpGs in a genomic region. On the other hand, in a cluster of co-methylated CpGs, we expect each CpG to have high correlation with the rest of the CpGs. The r.drop statistic is used to identify these co-methylated CpGs here.

Value

A data frame with the following columns:

• CpG : CpG ID

• keep : The CpGs with keep = 1 belong to the contiguous and co-methylated region

• ind : Index for the CpGs

• r_drop : The correlation between each CpG with the sum of the rest of the CpGs

Examples

   data(betaMatrix_ex1)
   MarkComethylatedCpGs(betaCluster_mat = betaMatrix_ex1, betaToM = FALSE, method = "pearson")

   data(betaMatrix_ex2)
   MarkComethylatedCpGs(betaCluster_mat = betaMatrix_ex2, method = "pearson")

   data(betaMatrix_ex3)
   MarkComethylatedCpGs(betaCluster_mat = betaMatrix_ex3, method = "pearson")

   data(betaMatrix_ex4)
   MarkComethylatedCpGs(betaCluster_mat = betaMatrix_ex4, rDropThresh_num = 0.6, method = "pearson")

lissettegomez/coMethDMR documentation built on April 25, 2021, 1:10 p.m.