Edited by James O. Berger, Duke University, Durham, NC (received for review November 11, 2004)
We present a method for Bayesian model-based hierarchical coclustering of gene expression data and use it to study the temporal transcription responses of an Anopheles gambiae cell line upon challenge with multiple microbial elicitors. The method fits statistical regression models to the gene expression time series for each experiment and performs coclustering on the genes by optimizing a joint probability model, characterizing gene coregulation between multiple experiments. We compute the model using a two-stage Expectation-Maximization-type algorithm, first fixing the cross-experiment covariance structure and using efficient Bayesian hierarchical clustering to obtain a locally optimal clustering of the gene expression profiles and then, conditional on that clustering, carrying out Bayesian inference on the cross-experiment covariance using Markov chain Monte Carlo simulation to obtain an expectation. For the problem of model choice, we use a cross-validatory approach to decide between individual experiment modeling and varying levels of coclustering. Our method successfully generates tightly coregulated clusters of genes that are implicated in related processes and therefore can be used for analysis of global transcript responses to various stimuli and prediction of gene functions.
↵ † To whom correspondence may be addressed. E-mail: n.heard{at}imperial.ac.uk or gdimopou{at}jhsph.edu.
Author contributions: N.A.H., C.C.H., and D.A.S. designed research and performed research; N.A.H. and G.D. analyzed data; and N.A.H., C.C.H., D.A.S., D.J.H., and G.D. wrote the paper.
Conflict of interest statement: No conflicts declared.
This paper was submitted directly (Track II) to the PNAS office.
Abbreviations: CV, cross-validation; EM, Expectation-Maximization; MC, Monte Carlo; L.m., Listeria monocytogenes; M.l., Micrococcus luteus; S.t., Salmonella typhimurium.
