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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) (KAR id:31583)

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http://dx.doi.org/10.1111/j.1467-9876.2010.00716.x

Abstract

The paper proposes a Bayesian method for biclustering with applications to gene

We begin by embedding bicluster analysis into the framework of a plaid model with random

The resulting posterior, which is asymptotically equivalent to a penalized likelihood, can attenuate

algorithm for sampling posteriors, in which we estimate the cluster memberships of all genes

makes the estimation of the Bayesian plaid model computationally feasible and efficient.

expression data sets. The numerical results show that our proposal substantially outperforms

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
DOI/Identification number: 10.1111/j.1467-9876.2010.00716.x
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: 13 Feb 2020 04:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31583 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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