coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies
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 coMethDMR:: package has been accepted to the Bioconductor repository of R packages. It will be included in version 3.15 (April 2022 release). To install this version, please use the following code:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("coMethDMR")
The development version of coMethDMR:: can also be installed from this GitHub repository by
library(devtools)
install_github("TransBioInfoLab/coMethDMR")
Please note that using compiled code from GitHub may require your computer to have additional software (Rtools for Windows or Xcode for Mac). Also note that installing this development version may result in some errors. We have outlined potential troubleshooting steps below.
You may get the following error during installation:
Error: package or namespace load failed for 'coMethDMR':
.onLoad failed in loadNamespace() for 'coMethDMR', details:
call: .updateHubDB(hub_bfc, .class, url, proxy, localHub)
error: Invalid Cache: sqlite file
Hub has not been added to cache
Run again with 'localHub=FALSE'
Error: loading failed
If so, please fix this by running ExperimentHub::ExperimentHub() first (and type yes if you receive a prompt to create a local cache for your data), then re-installing the package. Please see this white paper for more information: https://bioconductor.org/packages/devel/bioc/vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html.
You may also get this error during installation:
Error: package or namespace load failed for 'coMethDMR':
.onLoad failed in loadNamespace() for 'coMethDMR', details:
call: NULL
error: $ operator is invalid for atomic vectors
This error is caused by a version mismatch issue for the sesameData:: (https://bioconductor.org/packages/sesameData/) package. We require sesameData:: version 1.12 or higher. To fix this, you will need Biocdonductor version 3.14 or later. The following code will assist here:
BiocManager::install(version = "3.14")
BiocManager::install("sesameData")
After successfully executing the above installation, you should be able to install coMethDMR:: from GitHub like normal.
After installation, the coMethDMR package can be loaded into R using:
library(coMethDMR)
The reference manual for coMethDMR can be downloaded from old repository: https://github.com/TransBioInfoLab/coMethDMR_old/tree/master/docs/. The reference manual is 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.
- There are two main steps in coMethDMR: (1) identifying comethylatyed clusters (2) testing methylation levels in those comethylated clusters against a phenotype". In step 1 and 2, should we use beta value or M values for CoMethDMR?
Answer: 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.
Our development history is at https://github.com/TransBioInfoLab/coMethDMR_old