A Bayesian model for biclustering with applications

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.