## 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 ## Description 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. ## Installation ### Bioconductor Version The `coMethDMR::` package has been accepted to the [Bioconductor](https://bioconductor.org/) repository of R packages. It will be included in version 3.15 (April 2022 release). To install [this version](https://www.bioconductor.org/packages/devel/bioc/html/coMethDMR.html), 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") ``` ### Development Version The development version of `coMethDMR::` can also be installed from this GitHub repository by ```{r eval=FALSE, message=FALSE, warning=FALSE, results='hide'} library(devtools) install_github("TransBioInfoLab/coMethDMR") ``` Please note that using compiled code from GitHub may require your computer to have additional software ([Rtools](https://cran.r-project.org/bin/windows/Rtools/rtools40.html) for Windows or [Xcode](https://developer.apple.com/xcode/) for Mac). Also note that installing this development version may result in some errors. We have outlined potential troubleshooting steps below. #### Install Errors: Cache 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>. #### Install Errors: `.onLoad()` Failure 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. ### Loading the Package After installation, the coMethDMR package can be loaded into R using: ```{r eval=TRUE, message=FALSE, warning=FALSE, results='hide'} library(coMethDMR) ``` ## Manual 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](https://github.com/TransBioInfoLab/coMethDMR_old/blob/master/docs/coMethDMR_0.0.0.9001.pdf). Two vignettes are available in the same directory: [1_Introduction_coMethDMR_10-9-2019.pdf](https://github.com/TransBioInfoLab/coMethDMR_old/blob/master/docs/1_Introduction_coMethDMR_10-9-2019.pdf) and [2_BiocParallel_for_coMethDMR_geneBasedPipeline.pdf](https://github.com/TransBioInfoLab/coMethDMR_old/blob/master/docs/2_BiocParallel_for_coMethDMR_geneBasedPipeline.pdf). ## Frequently Asked Questions 1. 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. ## Development History Our development history is at https://github.com/TransBioInfoLab/coMethDMR_old