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

摘要

Microarray time course (MTC) gene expression data are commonly collected tostudy the dynamic nature of biological processes. One important problem is toidentify genes that show different expression profiles over time and pathwaysthat are perturbed during a given biological process. While methods areavailable to identify the genes with differential expression levels over time,there is a lack of methods that can incorporate the pathway information inidentifying 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 tobiological processes, where the dependency of the differential expressionpatterns of genes on the networks are modeled over time and over the network ofpathways. Simulation studies indicated that the method is quite effective inidentifying genes and modified subnetworks and has higher sensitivity than thecommonly used procedures that do not use the pathway structure or timedependency information, with similar false discovery rates. Application to amicroarray gene expression study of systemic inflammation in humans identifieda core set of genes on the KEGG pathways that show clear differentialexpression patterns over time. In addition, the method confirmed that theTOLL-like signaling pathway plays an important role in immune response toendotoxins.