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.

Install the latest version of this package by entering the following in R:
install.packages("BCSub")
AuthorJiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut]
Date of publication2017-03-16 23:41:02 UTC
MaintainerJiehuan Sun <jiehuan.sun@yale.edu>
LicenseGPL-2
Version0.5

View on CRAN

Files

inst
inst/doc
inst/doc/BCSub.pdf
inst/doc/BCSub.Rmd
inst/doc/BCSub.R
src
src/Makevars
src/factor_DP_fun.cpp
src/Makevars.win
src/init.c
src/RcppExports.cpp
NAMESPACE
R
R/RcppExports.R R/factor_DP_fun.R
vignettes
vignettes/BCSub.Rmd
vignettes/BCSub.bib
MD5
build
build/vignette.rds
DESCRIPTION
man
man/samSige.Rd man/BCSub.Rd man/polyurncpp.Rd man/samLamV3Cpp.Rd man/samRho2.Rd man/samEta.Rd man/myfind.Rd man/mvrnormArma.Rd man/calSim.Rd man/samSig.Rd man/dmvnrm_arma.Rd man/samMu.Rd

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