Name Mode Size
AnnotateResults.Rd 100644 2 kb
CloseBySingleRegion.Rd 100644 2 kb
CoMethAllRegions.Rd 100644 4 kb
CoMethSingleRegion.Rd 100644 3 kb
CpGsInfoAllRegions.Rd 100644 2 kb
CpGsInfoOneRegion.Rd 100644 3 kb
CreateOutputDF.Rd 100644 2 kb
CreateParallelWorkers.Rd 100644 1 kb
CreateRdrop.Rd 100644 1 kb
FindComethylatedRegions.Rd 100644 1 kb
GetCpGsInRegion.Rd 100644 1 kb
GetResiduals.Rd 100644 2 kb
ImportSesameData.Rd 100644 1 kb
MarkComethylatedCpGs.Rd 100644 2 kb
MarkMissing.Rd 100644 2 kb
NameRegion.Rd 100644 1 kb
OrderCpGsByLocation.Rd 100644 1 kb
RegionsToRanges.Rd 100644 1 kb
SplitCpGDFbyRegion.Rd 100644 1 kb
WriteCloseByAllRegions.Rd 100644 2 kb
betaMatrix_ex1.Rd 100644 1 kb
betaMatrix_ex2.Rd 100644 1 kb
betaMatrix_ex3.Rd 100644 1 kb
betaMatrix_ex4.Rd 100644 1 kb
betasChr22_df.Rd 100644 1 kb
coMethDMR_setup.Rd 100644 1 kb
lmmTest.Rd 100644 3 kb
lmmTestAllRegions.Rd 100644 5 kb
pheno_df.Rd 100644 1 kb
## 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, ## 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]( 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") ``` ### 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]( 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. #### 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: <>. #### 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::` (<>) 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: <>. 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]( ## 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