Title

Clustering Time-Course Microarray Data Using Functional Bayesian Infinite Mixture Model

Keywords

Dirichlet processes; MCMC; mixture models; time-course microarray

Abstract

This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data. The problem is to group together genes which have "similar" expression profiles, given the set of noisy measurements of their expression levels over a specific time interval. In order to capture temporal variations of each curve, a non-parametric regression approach is used. Each expression profile is expanded over a set of basis functions and the sets of coefficients of each curve are subsequently modeled through a Bayesian infinite mixture of Gaussian distributions. Therefore, the task of finding clusters of genes with similar expression profiles is then reduced to the problem of grouping together genes whose coefficients are sampled from the same distribution in the mixture. Dirichlet processes prior is naturally employed in such kinds of models, since it allows one to deal automatically with the uncertainty about the number of clusters. The posterior inference is carried out by a split and merge MCMC sampling scheme which integrates out parameters of the component distributions and updates only the latent vector of the cluster membership. The final configuration is obtained via the maximum a posteriori estimator. The performance of the method is studied using synthetic and real microarray data and is compared with the performances of competitive techniques. © 2012 Copyright Taylor and Francis Group, LLC.

Publication Date

1-1-2012

Publication Title

Journal of Applied Statistics

Volume

39

Issue

1

Number of Pages

129-149

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/02664763.2011.578620

Socpus ID

84858177485 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84858177485

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