A Bayesian Semiparametric Factor Analysis Model for Subtype
Identification (Clustering)
Description
Gene expression profiles are commonly utilized to infer disease
subtypes and many clustering methods can be adopted for this task.
However, existing clustering methods may not perform well when
genes are highly correlated and many uninformative genes are included
for clustering. To deal with these challenges, we develop a novel
clustering method in the Bayesian setting. 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.