Bayesian Functional Data Clustering for Temporal Microarray Data. (30th December 2007)

• Record Type: Journal Article
• Authors: Ma, Ping; Zhong, Wenxuan; Feng, Yang; Liu, Jun S.
• Other Names: Yi Nengjun Academic Editor.
• Is Part Of: International journal of plant genomics. Volume 2008(2008)
• Publication Date: 2007-12-30
• Subjects: Plant genetics -- Periodicals; Plant genomes -- Periodicals; Génétique végétale; Plantes -- Génomes; Plant genetics; Plant genomes; Periodicals; Electronic journals; 572.8605
• DOI: 10.1155/2008/231897
• Languages: English
• ISSNs: 1687-5370

Abstract

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.