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DMCFB

Differentially Methylated Cytosines via a Bayesian Functional Approach

Bioconductor version: 3.24 · Package version: 1.27.0

DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("DMCFB")

Details

MaintainerFarhad Shokoohi <shokoohi@icloud.com>
AuthorFarhad Shokoohi [aut, cre] (ORCID: <https://orcid.org/0000-0002-6224-2609>)
LicenseGPL-3
Bug Reportshttps://github.com/shokoohi/DMCFB/issues
Downloads rank468
Source branchdevel
biocViewsBayesian, Coverage, DifferentialMethylation, Regression, Sequencing, Software

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageDMCFB_1.27.0.tar.gz
Windows binary (x86_64)DMCFB_1.27.0.zip
macOS binary (arm64)DMCFB_1.27.0.tgz
macOS binary (x86_64)DMCFB_1.27.0.tgz
Dependencies

Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges

Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics

Suggests: testthat, knitr, rmarkdown, BiocStyle