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

• DOI: 10.1073/pnas.0408393102
• Publication Date: 2005-11-16T03:08:43Z
• Authors: Nicholas A. Heard; Christopher C. Ho...; David A. Stephens; David J. Hand; George Dimopoulos
• Keywords: Anopheles gambiae; Hierarchical clustering

We present a method for Bayesian model-based hierarchical coclustering of gene expression data and use it to study the temporal transcription responses an Anopheles gambiae cell line upon challenge with multiple microbial elicitors. The fits statistical regression models time series each experiment performs on genes by optimizing joint probability model, characterizing coregulation between experiments. compute model using two-stage Expectation-Maximization-type algorithm, first fixing cross-experiment covariance structure efficient clustering obtain locally optimal profiles then, conditional that clustering, carrying out inference Markov chain Monte Carlo simulation expectation. For problem choice, we cross-validatory approach decide individual modeling varying levels coclustering. Our successfully generates tightly coregulated clusters are implicated in related processes therefore can be used analysis global transcript various stimuli prediction functions.