Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.

ABSTRACT: The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs comp

SUBMITTER: Crook OM 

PROVIDER: S-EPMC7614016 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature