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# betaHMM: a hidden Markov model to identify differential methylation sites and regions from beta-valued methylation data Authors: KOYEL MAJUMDAR, ISOBEL CLAIRE GORMLEY, THOMAS BRENDAN MURPHY, ROMINA SILVA, ANTOINETTE SABRINA PERRY, RONALD WILLIAM WATSON, FLORENCE JAFFR ́EZIC, ANDREA RAU A hidden Markov model approach for identifying differentially methylated sites and regions for beta-valued DNA methylation data. The workflow consists of 3 main functions: \itemize{ \item betaHMM: to estimate the hidden Markov model parameters and the hidden methylation states of each CpG sites. \item dmc_identification: uses the output from the above function to identify CpG sites that are mostly differentially methylated between DNA samples collected from \eqn{R} conditions. \item dmr_identification: to identify adjacent DMCs forming DMRs. } A typical call to betaHMM to apply the Baum-Welch algorithm and Viterbi algorithm takes the following form: ``` library(betaHMM) ### read the methylation file and annotation file data(sample_methylation_file) head(sample_methylation_file) data(sample_annotation_file) head(sample_annotation_file) betaHMM_out <- betaHMM(sample_methylation_file, sample_annotation_file, M = 3, N = 4, R = 2, parallel_process = FALSE, seed = 321, treatment_group = c("Benign","Tumour")) ``` where `M` represents the number of methylation state in a DNA sample, `N` represents the number of subjects or DNA replicates, `R` represents the number of conditions from where DNA is extracted. A typical call to dmc_identification takes the following form: ``` dmc_out <- dmc_identification(betaHMM_out) dmc_df <- assay(dmc_out) ``` A typical call to dmr_identification takes the following form: ``` dmr_out <- dmr_identification(dmc_out) dmr_df <- assay(dmr_out) ``` The output of the `betaHMM` function is an S4 object of class `betaHMMResults`, the output of the `dmc_identification` function is an S4 object of class `dmcResults` and the output of the `dmr_identification` function is an S4 object of class `dmrResults` on which standard `plot` and `summary` functions can be directly applied; the former uses functionalities from the [ggplot2]( package. ### Reference ### License The betaHMM package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation. This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details. A copy of the GNU General Public License, version 3, is available at