Bayesian coclustering of Anopheles gene expression time series: study of immune defense response to multiple experimental challenges

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 102; no. 47; pp. 16939 - 16944
• Main Authors: Heard, N.A, Holmes, C.C, Stephens, D.A, Hand, D.J, Dimopoulos, G
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 22.11.2005; National Acad Sciences
• Subjects: Algorithms; Animals; Anopheles - drug effects; Anopheles - genetics; Anopheles - immunology; Anopheles - microbiology; Anopheles gambiae; Bayes Theorem; Bayesian analysis; Bayesian networks; Bayesian theory; Bee clustering; Cell Line; Cell lines; Cluster Analysis; complementary DNA; Covariance matrices; Expectation-Maximization algorithm; Gene expression; Gene Expression - drug effects; Gene Expression - immunology; Gene Expression Profiling; Genes; Grain boundaries; immune response; Immune system; Immunity - genetics; Immunity - physiology; insect pests; Markov chain Monte Carlo simulation; Markov chains; messenger RNA; microarray technology; Modeling; Models, Genetic; Monte Carlo method; Monte Carlo simulation; Physical Sciences; Statistical analysis; Statistical models; Thin films; Time series; Zymosan - pharmacology
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.0408393102

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.