betaHMM - A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data

A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

Last updated 4 months ago

dnamethylationdifferentialmethylationimmunooncologybiomedicalinformaticsmethylationarraysoftwaremultiplecomparisonsequencingspatialcoveragegenetargethiddenmarkovmodelmicroarray

4.18 score 140 downloads

betaclust - A Family of Beta Mixture Models for Clustering Beta-Valued DNA Methylation Data

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.

Last updated 6 months ago

4.00 score 2 stars 4 scripts 282 downloads

idiffomix - Integrated Differential Analysis of Multi-Omics Data using a Joint Mixture Model

A joint mixture model is proposed that integrates information from gene expression data and methylation data at the modelling stage to capture their inherent dependency structure, enabling simultaneous identification of DMCs and DEGs. The model leverages a joint likelihood function that accounts for the nested structure in the data, with parameter estimation performed using an expectation-maximisation algorithm.

Last updated 4 months ago

3.30 score 180 downloads