Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study.

Many scientific studies, especially in the biomedical sciences, generate data measured simultaneously over a multitude of units, over a period of time, and under different conditions or combinations of factors. Often, an important question of interest asked relates to which units behave similarly under different conditions, but measuring the variation over time complicates the analysis significantly. In this article we address such a problem arising from a gene expression study relating to bone aging, and develop a Bayesian statistical method that can simultaneously detect and uncover signals on three levels within such data: factorial, longitudinal, and transcriptional. Our model framework considers both cluster and time-point-specific parameters and these parameters uniquely determine the shapes of the temporal gene expression profiles, allowing the discovery and characterization of latent gene clusters based on similar underlying biological mechanisms. Our methodology was successfully applied to discover transcriptional networks in a microarray data set comparing the transcriptomic changes that occurred during bone aging in male and female mice expressing one or both copies of the bromodomain (Brd2) gene, a transcriptional regulator which exhibits an age-dependent sex-linked bone loss phenotype.