A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer.

ABSTRACT: Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysis of bounded DNA methylation data often requires data transformation, reducing biological interpretability. To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data. The BMMs achieve this through model-based clustering of CpG sites and by employing parameter constraints, facilitating application to different study settings. Inference proceeds via an expectation-maximisation algorithm, with an approximate maximization step providing tra

SUBMITTER: Majumdar K 

PROVIDER: S-EPMC11633993 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature