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Biostatistics Advance Access published online on September 15, 2006
Biostatistics, 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
Received December 19, 2005
Revised September 7, 2006
Accepted September 7, 2006

Article

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

Lurdes Y. T. Inoue 1 *, Mauricio Neira 2, Colleen Nelson 3, Martin Gleave 3, and Ruth Etzioni 4

1 (Corresponding Author) University of Washington, Department of Biostatistics, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, WA, 98195; Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, P.O. Box 19024, Seattle, WA, 98109
2 The Prostate Centre, Vancouver General Hospital, 2660 Oak St, Vancouver, BC, Canada
3 The Prostate Centre, Vancouver General Hospital, 2660 Oak St, Vancouver, BC, Canada; Department of Surgery, University of British Columbia, Vancouver, BC, Canada
4 Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, P.O. Box 19024, Seattle, WA, 98109

* To whom correspondence should be addressed.
Lurdes Y. T. Inoue, E-mail: linoue{at}u.washington.edu


  Abstract

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: Model-based clustering; Bayesian network; dynamic linear model; mixture model; time course gene expression; prostate cancer; bioinformatics.
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