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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.

Original publication




Journal article


Proceedings of the National Academy of Sciences of the United States of America

Publication Date





16939 - 16944


Department of Mathematics, Imperial College London, Huxley Building, 180 Queens Gate, London SW7 2AZ, United Kingdom.


Cell Line, Animals, Anopheles gambiae, Zymosan, Cluster Analysis, Bayes Theorem, Gene Expression Profiling, Immunity, Gene Expression, Algorithms, Models, Genetic