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Differentially Methylated Cytosines via a Bayesian Functional Approach

Bioconductor version: Release (3.19)

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.

Author: Farhad Shokoohi [aut, cre]

Maintainer: Farhad Shokoohi <shokoohi at>

Citation (from within R, enter citation("DMCFB")):


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Identifying DMCs using Bayesian functional regressions in BS-Seq data HTML R Script
Reference Manual PDF


biocViews Bayesian, Coverage, DifferentialMethylation, Regression, Sequencing, Software
Version 1.18.0
In Bioconductor since BioC 3.10 (R-3.6) (4.5 years)
License GPL-3
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
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Source Package DMCFB_1.18.0.tar.gz
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macOS Binary (x86_64) DMCFB_1.18.0.tgz
macOS Binary (arm64) DMCFB_1.18.0.tgz
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