  Disease subtype identification (clustering) is an important problem in
biomedical research. Gene expression profiles are commonly utilized to infer
disease subtypes, which often lead to biologically meaningful insights into
disease. Despite many successes, existing clustering methods may not perform
well when genes are highly correlated and many uninformative genes are included
for clustering due to the high dimensionality. In this article, we introduce a
novel subtype identification method in the Bayesian setting based on gene
expression profiles. This method, called BCSub, adopts an innovative
semiparametric Bayesian factor analysis model to reduce the dimension of the
data to a few factor scores for clustering. Specifically, the factor scores are
assumed to follow the Dirichlet process mixture model in order to induce
clustering. Through extensive simulation studies, we show that BCSub has
improved performance over commonly used clustering methods. When applied to two
gene expression datasets, our model is able to identify subtypes that are
clinically more relevant than those identified from the existing methods.
