Bayesian Clustering of Gene Expression Dynamics

Abstract

This chapter presents a Bayesian method for model-based clustering of gene expression dynamics and a program implementing it. The method represents gene expression dynamics as autoregressive equations and uses an agglomerative procedure to search for the most probable set of clusters, given the available data. The main contributions of this approach are the ability to take into account the dynamic nature of gene expression time series during clustering and an automated, principled way to decide when two series are different enough to belong to different clusters. The reliance of this method on an explicit statistical representation of gene expression dynamics makes it possible to use standard statistical techniques to assess the goodness of fit of the resulting model and validate the underlying assumptions. A set of gene expression time series, collected to study the response of human fibroblasts to serum, is used to illustrate the properties of the method and the functionality of the program.