Cluster-based network model for time-course gene expression data.
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.
Inoue
Lurdes Y T
LY
Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, WA 98195, USA. linoue@u.washington.edu
Neira
Mauricio
M
Nelson
Colleen
C
Gleave
Martin
M
Etzioni
Ruth
R
D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D016014:Linear Models; D008297:Male; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D005810:Multigene Family; D011471:Prostatic Neoplasms / Q000235:genetics; D013997:Time Factors