Gomez L, Odom GJ, Young JI, Martin ER, Liu L, Chen X, Griswold AJ, Gao Z, Zhang L, Wang L (2019) Nucleic Acids Research, gkz590, https://doi.org/10.1093/nar/gkz590
coMethDMR is an R package that identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects comethylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously.
The latest version can be installed by
library(devtools)
install_github("TransBioInfoLab/coMethDMR")
After installation, the coMethDMR package can be loaded into R using:
The reference manual for coMethDMR can be downloaded from /docs/coMethDMR_0.0.0.9001.pdf. Two vignettes are available in the same directory: "1_Introduction_coMethDMR_10-9-2019.pdf" and "2_BiocParallel_for_coMethDMR_geneBasedPipeline.pdf"
Frequently Asked QuestionsAnswer: In step (1), using M values and beta values produce similar results. See Supplementary Table 2 Comparison of using beta values or M-values for identifying co-methylated regions in first step of coMethDMR pipeline at optimal rdrop parameter value of the coMethDMR paper.
In step (2), M-values should be used because it has better statistical properties. See Du et al. (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis.
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