Inferring cluster-based networks from differently stimulated multiple time-course gene expression data

Motivation: Clustering and gene network inference often help to predict the biological functions of gene subsets. Recently, researchers have accumulated a large amount of time-course transcriptome data collected under different treatment conditions to understand the physiological states of cells in response to extracellular stimuli and to identify drug-responsive genes. Although a variety of statistical methods for clustering and inferring gene networks from expression profiles have been proposed, most of these are not tailored to simultaneously treat expression data collected under multiple stimulation conditions. Results: We propose a new statistical method for analyzing temporal profiles under multiple experimental conditions. Our method simultaneously performs clustering of temporal expression profiles and inference of regulatory relationships among gene clusters. We applied this method to MCF7 human breast cancer cells treated with epidermal growth factor and heregulin which induce cellular proliferation and differentiation, respectively. The results showed that the method is useful for extracting biologically relevant information.