The gibbs-plaid biclustering model

Thierry Chekouo, Alejandro Murua, Wolfgang Raffelsberger

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1643-1670
Number of pages28
JournalAnnals of Applied Statistics
Issue number3
StatePublished - Sep 2015

Bibliographical note

Publisher Copyright:
© Institute of Mathematical Statistics, 2015.


  • Autologistic model
  • Clustering
  • Gene expression
  • Gene ontology
  • Plaid model
  • Relational graph
  • Retinal detachment
  • Wang–Landau algorithm


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