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.
Package details 


Author  Jiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut] 
Date of publication  20170316 23:41:02 UTC 
Maintainer  Jiehuan Sun <jiehuan.sun@yale.edu> 
License  GPL2 
Version  0.5 
Package repository  View on CRAN 
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