A Family of Beta Mixture Models for Clustering Beta-Valued DNA Methylation Data
AUC and WD function
Fit the K.. model
Fit the KN. model
Fit the K.R Model
The betaclust wrapper function
The DMC identification function
The empirical cumulative distribution function plot
Akaike Information Criterion
Bayesian Information Criterion
Integrated Complete-data Likelihood (ICL) Criterion
Plots for visualizing the betaclust class object
Summarizing the beta mixture model fits
Thresholds under the K.. and the KN. models
A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.