Time–course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this paper we propose a method to examine gene network relationships using time course microarray data. We assume that a sample of gene expression profiles is a realization of a process where each profile is modeled as a random functional transformation of a common curve. We propose measures of functional similarity and time order based on estimated time transformation functions. This allows for novel inferences on gene networks, including time–delayed relationships, by taking full account of the timing of the functional features of the gene expression profiles. We discuss the application of our model to simulated data as well as to microarray data in the Shionogi model of progression to androgen independence in prostate cancer.
Keywords: Time–course microarray data, gene networks, time transformation, functional similarity, hierarchical model, Shionogi model, Markov Chain Monte Carlo