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Title: A semiparametric Bayesian model for biclustering Authors:  Alejandro Murua - University of Montreal (Canada) [presenting]
Fernando Quintana - Pontificia Universidad Catolica de Chile (Chile)
Abstract: Motivated by classes of problems frequently encountered in the analysis of gene expression data, we propose a semiparametric Bayesian model to detect biclusters, that is, subsets of individuals sharing similar patterns over a set of conditions. Our approach is based on the well-known plaid model. By assuming a truncated stick-breaking prior we also find the number of biclusters present in the data as part of the inference, thus freeing the traditional plaid model from the restriction of a predefined number of biclusters. The model also introduces a penalty prior that controls the size of biclusters. Evidence from a simulation study shows that the model is capable of correctly detecting biclusters and performs well compared to some competing approaches. The flexibility of the proposed prior is demonstrated with applications to the analysis of gene expression data (continuous responses) and histone modifications data (count responses).