Package: coMethDMR 1.17.0

Fernanda Veitzman

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

coMethDMR 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 co-methylated 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.

Authors:Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva [aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang [aut], Gabriel Odom [aut]

coMethDMR_1.17.0.tar.gz
coMethDMR_1.17.0.zip(r-4.7)coMethDMR_1.17.0.zip(r-4.6)coMethDMR_1.17.0.zip(r-4.5)
coMethDMR_1.17.0.tgz(r-4.6-any)coMethDMR_1.17.0.tgz(r-4.5-any)
coMethDMR_1.17.0.tar.gz(r-4.7-any)coMethDMR_1.17.0.tar.gz(r-4.6-any)
coMethDMR_1.17.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
coMethDMR/json (API)
NEWS

# Install 'coMethDMR' in R:
install.packages('coMethDMR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/transbioinfolab/comethdmr/issues

Datasets:
  • betaMatrix_ex1 - Alzheimer's Prefrontal Cortex (PFC) Methylation Data
  • betaMatrix_ex2 - Alzheimer's Prefrontal Cortex (PFC) Methylation Data
  • betaMatrix_ex3 - Alzheimer's Prefrontal Cortex (PFC) Methylation Data
  • betaMatrix_ex4 - Alzheimer's Prefrontal Cortex (PFC) Methylation Data
  • betasChr22_df - Prefrontal Cortex (PFC) Methylation Data from Alzheimer's Disease subjects
  • pheno_df - Example phenotype data file from Prefrontal Cortex (PFC) Methylation Data of Alzheimer's Disease subjects

On BioConductor:coMethDMR-1.17.0(bioc 3.24)coMethDMR-1.16.0(bioc 3.23)

dnamethylationepigeneticsmethylationarraydifferentialmethylationgenomewideassociation

6.51 score 7 stars 46 scripts 338 downloads 22 exports 118 dependencies

Last updated from:908a58882d. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING380
linux-devel-x86_64NOTE730
source / vignettesOK678
linux-release-x86_64NOTE775
macos-release-arm64NOTE547
macos-oldrel-arm64NOTE701
windows-develNOTE1736
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windows-oldrelNOTE1713
wasm-releaseOK368

Exports:AnnotateResultsCloseBySingleRegionCoMethAllRegionsCoMethSingleRegionCpGsInfoAllRegionsCpGsInfoOneRegionCreateOutputDFCreateParallelWorkersCreateRdropFindComethylatedRegionsGetCpGsInRegionGetResidualsImportSesameDatalmmTestlmmTestAllRegionsMarkComethylatedCpGsMarkMissingNameRegionOrderCpGsByLocationRegionsToRangesSplitCpGDFbyRegionWriteCloseByAllRegions

Dependencies:abindAnnotationDbiAnnotationHubaskpassBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocIOBiocManagerBiocParallelBiocVersionBiostringsbitbit64bitopsblobbootbumphuntercachemcigarilloclicodetoolscpp11crayoncurlDBIdbplyrDelayedArraydigestdoRNGdplyrExperimentHubfarverfastmapfilelockforeachformatRfutile.loggerfutile.optionsgenericsGenomicAlignmentsGenomicFeaturesGenomicRangesggplot2gluegtablehttrhttr2IRangesisobanditeratorsjsonliteKEGGRESTlabelinglambda.rlatticelifecyclelimmalme4lmerTestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemimeminqanlmenloptrnumDerivopensslpillarpkgconfigpngpurrrR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRCurlRdpackreformulasrestfulrRhtslibrjsonrlangrngtoolsRsamtoolsRSQLitertracklayerS4ArraysS4VectorsS7scalesSeqinfosnowSparseArraystatmodstringistringrSummarizedExperimentsystibbletidyrtidyselectutf8vctrsviridisLitewithrXMLXVectoryaml

Genome-wide methylation analysis using coMethDMR via parallel computing

Gabriel J. Odom, Lissette Gomez, Tiago Chedraoui Silva, Lanyu Zhang and Lily Wang

Rendered fromvin2_BiocParallel_for_coMethDMR_geneBasedPipeline.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2022-01-26
Started: 2021-04-21

Introduction to coMethDMR

Lissette Gomez, Gabriel Odom, Tiago Chedraoui Silva, Lanyu Zhang, and Lily Wang

Rendered fromvin1_Introduction_to_coMethDMR_geneBasedPipeline.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2022-03-28
Started: 2021-04-21

Readme and manuals

Help Manual

Help pageTopics
Annotate 'coMethDMR' Pipeline ResultsAnnotateResults
Alzheimer's Prefrontal Cortex (PFC) Methylation DatabetaMatrix_ex1
Alzheimer's Prefrontal Cortex (PFC) Methylation DatabetaMatrix_ex2
Alzheimer's Prefrontal Cortex (PFC) Methylation DatabetaMatrix_ex3
Alzheimer's Prefrontal Cortex (PFC) Methylation DatabetaMatrix_ex4
Prefrontal Cortex (PFC) Methylation Data from Alzheimer's Disease subjectsbetasChr22_df
Extract clusters of CpGs located closely in a genomic region.CloseBySingleRegion
Extract contiguous co-methylated genomic regions from a list of pre-defined genomic regionsCoMethAllRegions
Wrapper function to find contiguous and comethyalted sub-regions within a pre-defined genomic regionCoMethSingleRegion
Test Associations Between Regions and PhenotypeCpGsInfoAllRegions
Test Associations Between a Region and PhenotypeCpGsInfoOneRegion
Create a Parallel Computing ClusterCreateParallelWorkers
Computes leave-one-out correlations (rDrop) for each CpGCreateRdrop
Find Contiguous Co-Methylated RegionsFindComethylatedRegions
Extract probe IDs for CpGs located in a genomic regionGetCpGsInRegion
Get Linear Model ResidualsGetResiduals
Import Illumina manifests (sesameData versions)ImportSesameData
Fit mixed model to methylation values in one genomic regionlmmTest
Linear Mixed Models by RegionlmmTestAllRegions
Mark CpGs in contiguous and co-methylated regionMarkComethylatedCpGs
Return Column and Row Names of Samples and Probes under the Missingness ThesholdMarkMissing
Name a region with several CpGs based on its genomic locationNameRegion
Order CpGs by genomic locationOrderCpGsByLocation
Example phenotype data file from Prefrontal Cortex (PFC) Methylation Data of Alzheimer's Disease subjectspheno_df
Convert genomic regions in a data frame to GRanges formatRegionsToRanges
Extract clusters of CpG probes located closelyWriteCloseByAllRegions