Motivation: Identifying genes with bimodal expression patterns from large-scale expression profiling data is an important analytical task. Model-based clustering is popular for this purpose. That technique commonly uses the Bayesian information criterion (BIC) for model selection. In practice, however, BIC appears to be overly sensitive and may lead to the identification of bimodally expressed genes that are unreliable or not clinically useful. We propose using a novel criterion, the bimodality index, not only to identify but also to rank meaningful and reliable bimodal patterns. The bimodality index can be computed using either a mixture model-based algorithm or Markov chain Monte Carlo techniques.
Results: We carried out simulation studies and applied the method to real data from a cancer gene expression profiling study. Our findings suggest that BIC behaves like a lax cutoff based on the bimodality index, and that the bimodality index provides an objective measure to identify and rank meaningful and reliable bimodal patterns from large-scale gene expression datasets. R code to compute the bimodality index is included in the ClassDiscovery package of the Object-Oriented Microarray and Proteomic Analysis (OOMPA) suite available at the web site http;//bioinformatics.mdanderson.org/Software/OOMPA.