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.

AuthorJiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut]
Date of publication2017-01-20 10:46:47
MaintainerJiehuan Sun <jiehuan.sun@yale.edu>
LicenseGPL-2
Version0.3

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Files in this package

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

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