Identifying down and up-regulated chromosome regions using RNA-Seq data

TL;DR

This paper introduces a Bayesian hidden Markov model for identifying chromosome regions with gene regulation changes using RNA-Seq data, demonstrating effectiveness across multiple cancer datasets.

Contribution

It adapts a Gaussian mixture-based HMM approach to RNA-Seq data, offering a flexible, hierarchical model for detecting up- and down-regulated chromosome regions.

Findings

Effective detection of regulation regions in cancer datasets

Robustness to prior specifications

Provides tools for global and local analysis

Abstract

The number of studies dealing with RNA-Seq data analysis has experienced a fast increase in the past years making this type of gene expression a strong competitor to the DNA microarrays. This paper proposes a Bayesian model to detect down and up-regulated chromosome regions using RNA-Seq data. The methodology is based on a recent work developed to detect up-regulated regions in the context of microarray data. A hidden Markov model is developed by considering a mixture of Gaussian distributions with ordered means in a way that first and last mixture components are supposed to accommodate the under and overexpressed genes, respectively. The model is flexible enough to efficiently deal with the highly irregular spaced configuration of the data by assuming a hierarchical Markov dependence structure. The analysis of four cancer data sets (breast, lung, ovarian and uterus) is presented. Results indicate that the proposed model is selective in determining the regulation status, robust with respect to prior specifications and provides tools for a global or local search of under and overexpressed chromosome regions.