CMEnt is an R package for identifying Differentially Methylated Regions (DMRs) from pre-computed seeds, commonly identified Differentially Methylated Positions (DMPs). The package implements a correlation-based approach to expand regions around significant DMPs, considering both statistical significance and biological relevance of methylation changes.
pval/qval are the primary
statistical evidence columns. The optional SVM score is useful as a
complementary measure of sample-level discriminative strength, not as
a replacement for statistical significance and effect size.Install CMEnt from Bioconductor with:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("CMEnt")
You can install the development version of CMEnt from GitHub with:
# install.packages("devtools") # nolint
devtools::install_github("CMG-UA/CMEnt")
Another option is the Docker image on GitHub Container Registry. It contains
CMEnt and common dependencies. At startup, the container installs missing optional
packages for the requested command, inputs, and genome before running CMEnt.
Viewer setup uses the saved analysis metadata. Installation requires network
access and a writable R library when packages are missing; CMEnt package
functions never install software. See INSTALL for external tools and
Windows limitations, and inst/docker/ for the container build and setup scripts.
You can pull the image using:
docker pull ghcr.io/cmg-ua/cment/cment:latest
and run it using:
docker run --rm ghcr.io/cmg-ua/cment/cment:latest --help
To launch the packaged example output in CMEntViewer, publish the
Shiny port from the container and bind the app to 0.0.0.0:
docker run --rm -p 3838:3838 \
ghcr.io/cmg-ua/cment/cment:latest \
launchCMEntViewer \
--output_prefix /CMEnt/inst/extdata/example_outputChr5And11 \
--launch_browser FALSE \
--host 0.0.0.0 \
--port 3838
Then open http://localhost:3838 in your browser.
If you want to view outputs created on your machine, mount them into the container. Keeping the R library and cache on Docker volumes avoids re-installing on-demand packages and cached annotations on every run:
docker volume create cment-r-lib
docker volume create cment-r-cache
docker run --rm -p 3838:3838 \
-v "$PWD:/work" -w /work \
-v cment-r-lib:/usr/local/lib/R/site-library \
-v cment-r-cache:/home/root/.cache/R \
ghcr.io/cmg-ua/cment/cment:latest \
launchCMEntViewer \
--output_prefix /work/path/to/output_prefix \
--launch_browser FALSE \
--host 0.0.0.0 \
--port 3838
Here’s a basic example of how to use CMEnt in a 450K Microarray setting:
suppressPackageStartupMessages(library(CMEnt))
loadExampleInputDataChr5And11("beta", "dmps", "pheno", "array_type")
cat("\nBeta matrix:")
cat(head(beta), "\n")
cat("\nSeeds:")
cat(head(dmps), "\n")
cat("\nPhenotype data:")
cat(head(pheno), "\n")
# Build DMRs using parallel processing
dmrs <- buildDMRs(
beta = beta,
seeds = dmps,
pheno = pheno,
sample_group_col = "Sample_Group",
array = array_type, # Specify array platform
min_sites = 3,
min_seeds = 2,
njobs = 2 # Use parallel processing
)
# View summary of DMR statistics
summary(dmrs)
Here’s the same workflow with a custom BED-like methylation file instead
of array probe IDs. The BED file must have a header, chromosome and
start-position columns, and one column per sample. Sample column names
must match rownames(pheno). Because the beta rows are genomic
coordinates rather than array probes, set array = NULL and provide
genome explicitly. Seeds must also be identified by chr:start
coordinates.
Expected BED format:
chrom start Sample1 Sample2 Sample3
chr1 100000 0.21 0.76 0.73
chr1 100120 0.25 0.79 0.75
chr2 250010 0.64 0.31 0.29
suppressPackageStartupMessages(library(CMEnt))
pheno <- data.frame(
Sample_Group = c("control", "case", "case"),
row.names = c("Sample1", "Sample2", "Sample3")
)
seeds <- data.frame(
site_id = c("chr1:100000", "chr1:100120", "chr2:250010"),
pval = c(1e-5, 2e-4, 8e-4)
)
dmrs <- buildDMRs(
beta = "methylation_values.bed",
seeds = seeds,
seeds_id_col = "site_id",
pheno = pheno,
sample_group_col = "Sample_Group",
array = NULL,
genome = "hg38",
bed_chrom_col = "chrom",
bed_start_col = "start",
min_sites = 2,
min_seeds = 2,
njobs = 2
)
summary(dmrs)
The tool supports also bsseq and tabix-indexed BED files as input, which
can be specified by setting beta to a BSseq object or a path to a
tabix-indexed BED file, respectively. When using tabix-indexed BED
files, the function will automatically fetch only the relevant genomic
regions based on the seed locations, which can significantly reduce
memory usage and improve performance for large datasets.
There are two helper functions that can be used to find DMPs and create
seeds for buildDMRs():
findDMPsArray() takes a beta matrix and phenotype data as input and
identifies DMPs using linear modeling with limma. Please cite limma
if used.findDMPsBSSeq() takes a BSseq object and phenotype data as input
and identifies DMPs using a beta-binomial regression model with DSS.
Please cite DSS
if used.For automated seed cleaning, CMEnt also exposes combinePvalues(),
which implements comb-p-style spatial autocorrelation adjustment. Please
cite comb-p
If you use CMEnt in your research, please cite:
To cite package 'CMEnt' in publications use:
Lemonidis et al (2026). From DMPs to DMRs: CMEnt, Characterization of Methylation using positional Entanglement. R package version 0.99.9.
University of Antwerp, Antwerp, Belgium.
A BibTeX entry for LaTeX users is:
@Manual{
title = {From DMPs to DMRs: CMEnt, Characterization of Methylation using positional Entanglement},
author = {Vasileios Lemonidis, Ken op de Beeck, Joris R Vermeesch, Timon Vandamme, Guy Van Camp, Joe Ibrahim},
year = {2026},
note = {R package version 0.99.9},
url = {https://github.com/CMG-UA/CMEnt},
}
This project is licensed under the GPL-2 License - see the LICENSE.md file for details.