  We propose and develop a Bayesian plaid model for biclustering that accounts
for the prior dependency between genes (and/or conditions) through a stochastic
relational graph. This work is motivated by the need for improved understanding
of the molecular mechanisms of human diseases for which effective drugs are
lacking, and based on the extensive raw data available through gene expression
profiling. We model the prior dependency information from biological knowledge
gathered from gene ontologies. Our model, the Gibbs-plaid model, assumes that
the relational graph is governed by a Gibbs random field. To estimate the
posterior distribution of the bicluster membership labels, we develop a
stochastic algorithm that is partly based on the Wang-Landau flat-histogram
algorithm. We apply our method to a gene expression database created from the
study of retinal detachment, with the aim of confirming known or finding novel
subnetworks of proteins associated with this disorder.
