Clustering time-course microarray data using functional Bayesian infinite mixture model

    Authors

    C. Angelini; D. De Canditiis;M. Pensky

    Comments

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    Abbreviated Journal Title

    J. Appl. Stat.

    Keywords

    mixture models; Dirichlet processes; MCMC; time-course microarray; DIRICHLET PROCESS MIXTURE; GENE-EXPRESSION PROFILES; VARIABLE SELECTION; PATTERNS; Statistics & Probability

    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.

    Journal Title

    Journal of Applied Statistics

    Volume

    39

    Issue/Number

    1

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    129

    Last Page

    149

    WOS Identifier

    WOS:000298925200010

    ISSN

    0266-4763

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