DMCHMM
Differentially Methylated CpG using Hidden Markov Model
Bioconductor version: Release (3.17)
A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks.
Author: Farhad Shokoohi
Maintainer: Farhad Shokoohi <shokoohi at icloud.com>
citation("DMCHMM")):
Installation
To install this package, start R (version "4.3") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DMCHMM")For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DMCHMM")| DMCHMM: Differentially Methylated CpG using Hidden Markov Model | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Coverage, DifferentialMethylation, HiddenMarkovModel, Sequencing, Software |
| Version | 1.22.0 |
| In Bioconductor since | BioC 3.6 (R-3.4) (6 years) |
| License | GPL-3 |
| Depends | R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool |
| Imports | utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics |
| Linking To | |
| Suggests | testthat, knitr, rmarkdown |
| System Requirements | |
| Enhances | |
| URL | |
| Bug Reports | https://github.com/shokoohi/DMCHMM/issues |
See More
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | DMCHMM_1.22.0.tar.gz |
| Windows Binary | DMCHMM_1.22.0.zip |
| macOS Binary (x86_64) | DMCHMM_1.22.0.tgz |
| macOS Binary (arm64) | DMCHMM_1.22.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/DMCHMM |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/DMCHMM |
| Bioc Package Browser | https://code.bioconductor.org/browse/DMCHMM/ |
| Package Short Url | https://bioconductor.org/packages/DMCHMM/ |
| Package Downloads Report | Download Stats |