A Bayesian approach to modelling uncertainty in gene expression clusters
The use of clustering methods has rapidly become one of the standard computational approaches to understanding microarray gene expression data [3, 1, 7]. In clustering, the patterns of expression of different genes across time, treatments, and tissues are grouped into distinct clusters (perhaps organized hierarchically) in which genes in the same cluster are assumed to be potentially functionally related or to be influenced by a common upstream factor. Such cluster structure can be used to aid in the elucidation of regulatory networks. For example, a compendium of gene expression profiles corresponding to mutants and chemical treatments can be used as a systematic tool to identify gene functions because mutants or drug targets that display similar profiles are likely to share cellular functions [5].