A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data

TL;DR

This paper introduces a hidden spatial-temporal Markov random field model for analyzing time course gene expression data, effectively identifying genes and pathways involved in biological processes by incorporating network and time dependencies.

Contribution

The paper develops a novel hstMRF method that models gene expression dependencies over time and pathway networks, improving detection sensitivity over existing methods.

Findings

Effective in identifying genes and subnetworks related to biological processes

Higher sensitivity than traditional methods without increasing false discoveries

Confirmed the role of TOLL-like signaling pathway in immune response

Abstract

Microarray time course (MTC) gene expression data are commonly collected to study the dynamic nature of biological processes. One important problem is to identify genes that show different expression profiles over time and pathways that are perturbed during a given biological process. While methods are available to identify the genes with differential expression levels over time, there is a lack of methods that can incorporate the pathway information in identifying the pathways being modified/activated during a biological process. In this paper we develop a hidden spatial-temporal Markov random field (hstMRF)-based method for identifying genes and subnetworks that are related to biological processes, where the dependency of the differential expression patterns of genes on the networks are modeled over time and over the network of pathways. Simulation studies indicated that the method is quite effective in identifying genes and modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway structure or time dependency information, with similar false discovery rates. Application to a microarray gene expression study of systemic inflammation in humans identified a core set of genes on the KEGG pathways that show clear differential expression patterns over time. In addition, the method confirmed that the TOLL-like signaling pathway plays an important role in immune response to endotoxins.