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.


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0.5 by Jiehuan Sun, 5 years ago

Browse source code at https://github.com/cran/BCSub

Authors: Jiehuan Sun [aut, cre] , Joshua L. Warren [aut] , and Hongyu Zhao [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp

Depends on MASS, mcclust, nFactors

Suggests knitr

Linking to Rcpp, RcppArmadillo

See at CRAN