A Bayesian model for biclustering with applications

Zhang, Jian (2010) A Bayesian model for biclustering with applications. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59 (4). pp. 635-656. ISSN 0035-9254. (doi:10.1111/j.1467-9876.2010.00716.x) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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The paper proposes a Bayesian method for biclustering with applications to gene microarray studies, where we want to cluster genes and experimental conditions simultaneously. We begin by embedding bicluster analysis into the framework of a plaid model with random effects.The corresponding likelihood is then regularized by the hierarchical priors in each layer. The resulting posterior, which is asymptotically equivalent to a penalized likelihood, can attenuate the effect of high dimensionality on cluster predictions. We provide an empirical Bayes algorithm for sampling posteriors, in which we estimate the cluster memberships of all genes and samples by maximizing an explicit marginal posterior of these memberships.The new algorithm makes the estimation of the Bayesian plaid model computationally feasible and efficient. The performance of our procedure is evaluated on both simulated and real microarray gene expression data sets. The numerical results show that our proposal substantially outperforms the original plaid model in terms of misclassification rates across a range of scenarios. Applying our method to two yeast gene expression data sets, we identify several new biclusters which show the enrichment of known annotations of yeast genes.

Item Type: Article
Uncontrolled keywords: Biclustering; Empirical Bayes methods; Hierarchical Bayesian models; Plaid models
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 11 Oct 2012 17:08 UTC
Last Modified: 16 Jan 2017 17:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31583 (The current URI for this page, for reference purposes)
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