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Biostatistics Advance Access originally published online on September 15, 2006
Biostatistics 2007 8(3):507-525; doi:10.1093/biostatistics/kxl026
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Cluster-based network model for time-course gene expression data

Lurdes Y. T. Inoue*

Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, WA 98195, USA and Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, PO Box 19024, Seattle, WA 98109, USA linoue{at}u.washington.edu

Mauricio Neira

The Prostate Centre, Vancouver General Hospital, 2660 Oak Street, Vancouver, British Columbia, Canada

Colleen Nelson and Martin Gleave

The Prostate Centre, Vancouver General Hospital, 2660 Oak Street, Vancouver, British Columbia, Canada and Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada

Ruth Etzioni

Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, PO Box 19024, Seattle, WA 98109, USA

* To whom correspondence should be addressed.

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.

Keywords: Bayesian network; Bioinformatics; Dynamic linear model; Mixture model; Model-based clustering; Prostate cancer; Time-course gene expression

Received December 19, 2005; revised August 4, 2006; revised September 7, 2006; accepted for publication September 7, 2006.


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