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.
|Depends:||R (≥ 3.0), MASS (≥ 7.3-45), mcclust (≥ 1.0), nFactors (≥ 2.3.3)|
|Imports:||Rcpp (≥ 0.12.6)|
|Author:||Jiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut]|
|Maintainer:||Jiehuan Sun <jiehuan.sun at yale.edu>|
|CRAN checks:||BCSub results|
|Windows binaries:||r-devel: BCSub_0.5.zip, r-release: BCSub_0.5.zip, r-oldrel: BCSub_0.5.zip|
|OS X Mavericks binaries:||r-release: BCSub_0.5.tgz, r-oldrel: BCSub_0.5.tgz|
|Old sources:||BCSub archive|
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