Motivated by classes of problems frequently found 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 Lazzeroni and Owen (2002). By assuming a truncated stick-breaking prior we also find the number of biclusters present in the data as part of the inference. 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).
AM was partially funded by a Discovery grant 2019-05444 from the Natural Sciences and Engineering Research Council of Canada, and by a Fundamental Research Projects grant PRF-2017-20 from the Institute for Data Valorization (IVADO) of Quebec, Canada. FQ was partially supported by grant FONDECYT 1180034 and by ANID - Millennium Science Initiative Program - NCN17_059.
"Biclustering via Semiparametric Bayesian Inference." Bayesian Anal. 17 (3) 969 - 995, September 2022. https://doi.org/10.1214/21-BA1284