BCSub: A Bayesian Semiparametric Factor Analysis Model for Subtype Identification (Clustering)

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.

Version: 0.5
Depends: R (≥ 3.0), MASS (≥ 7.3-45), mcclust (≥ 1.0), nFactors (≥ 2.3.3)
Imports: Rcpp (≥ 0.12.6)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr
Published: 2017-03-16
Author: Jiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut]
Maintainer: Jiehuan Sun <jiehuan.sun at yale.edu>
License: GPL-2
NeedsCompilation: yes
CRAN checks: BCSub results

Downloads:

Reference manual: BCSub.pdf
Vignettes: BCSub
Package source: BCSub_0.5.tar.gz
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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