Integrative Model-based clustering of microarray methylation and expression data

TL;DR

This paper introduces a model-based clustering method for integrating gene expression and DNA methylation data, enhancing the identification of biologically distinct groups in complex biological processes.

Contribution

It develops a novel likelihood-based clustering approach with cluster-specific latent indicators and extends to multiple data types for integrated analysis.

Findings

Method effectively clusters multi-omics data

Improves discrimination of biological groups

Uses EM algorithm for estimation

Abstract

In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.