International Journal of Plant Genomics 
Volume 2008 (2008), Article ID 231897, 4 pages
doi:10.1155/2008/231897
Research Article

Bayesian Functional Data Clustering for Temporal Microarray Data

Ping Ma,1 Wenxuan Zhong,1 Yang Feng,1 and Jun S. Liu2

 1Department of Statistics, University of Illinois, Champaign, IL 61820, USA
2Department of Statistics, Harvard University, Cambridge, MA 02138, USA
Received 24 July 2007; Accepted 18 December 2007

Recommended by Nengjun Yi

Abstract

We propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.