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A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data. (English) Zbl 1137.62081

Summary: 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.
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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
92D10 Genetics and epigenetics
62F40 Bootstrap, jackknife and other resampling methods
62F30 Parametric inference under constraints
65C60 Computational problems in statistics (MSC2010)

Software:

KEGG; DAVID

References:

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