DMCFB: Differentially Methylated Cytosines via a Bayesian Functional Approach

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.

Package details

AuthorFarhad Shokoohi [aut, cre] (<>)
Bioconductor views Bayesian Coverage DifferentialMethylation Regression Sequencing
MaintainerFarhad Shokoohi <>
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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DMCFB documentation built on Nov. 8, 2020, 8:03 p.m.