A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions

TL;DR

This paper introduces a Bayesian hidden Markov mixture model that leverages spatial and expression data to accurately identify overexpressed chromosome regions in gene expression studies, aiding cancer research.

Contribution

It presents a novel Bayesian hidden Markov mixture model that incorporates spatial dependencies and uses MCMC for detecting overexpressed chromosome regions.

Findings

Successfully identified overexpressed regions in cancer datasets

Model outperforms traditional methods in accuracy

Applicable to multiple cancer types

Abstract

In this study, we propose a hidden Markov mixture model for the analysis of gene expression measurements mapped to chromosome locations. These expression values represent preprocessed light intensities observed in each probe of Affymetrix oligonucleotide arrays. Here, the algorithm BLAT is used to align thousands of probe sequences to each chromosome. The main goal is to identify genome regions associated with high expression values which define clusters composed by consecutive observations. The proposed model assumes a mixture distribution in which one of the components (the one with the highest expected value) is supposed to accommodate the overexpressed clusters. The model takes advantage of the serial structure of the data and uses the distance information between neighbours to infer about the existence of a Markov dependence. This dependence is crucially important in the detection of overexpressed regions. We propose and discuss a Markov chain Monte Carlo algorithm to fit the model. Finally, the proposed methodology is used to analyse five data sets representing three types of cancer (breast, ovarian and brain).