Bayesian two-way clustering

Two-way clustering, or biclustering, of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Two-way clustering has more flexibility and can model gene transcription more precisely than traditional clustering. I proposed a Bayesian biclustering model (BBC) and implemented a Gibbs sampling procedure for its statistical inference. I showed that the Bayesian biclustering model can correctly identify multiple clusters of gene expression data. I conducted a comprehensive comparison of biclustering results by four operation characteristics. Using simulated data both from the model and with realistic characters, I demonstrated the BBC algorithm outperforms other methods in both robustness and accuracy. I also showed that the model is stable for two normalization methods, the interquartile range normalization and the smallest quartile range normalization. Applying the BBC algorithm to the yeast expression data, I observed that majority of the biclusters found by BBC are supported by significant biological evidence