title: Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies.
abstract: We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. The novel development of Bayesian classification analysis presented is partly motivated by problems of design and selection of variables in biomolecular studies, particularly involving widely used assays of large-scale single-cell data generated using flow cytometry technology. For such studies and for mixture modeling generally, we define discriminative analysis that overlays fitted mixture models using a natural measure of concordance between mixture component densities, and define an effective and computationally feasible method for assessing and prioritizing subsets of variables according to their roles in discrimination of one or more mixture components. We relate the new discriminative information measures to Bayesian classification probabilities and error rates, and exemplify their use in Bayesian analysis of Dirichlet process mixture models fitted via Markov chain Monte Carlo methods as well as using a novel Bayesian expectation-maximization algorithm. We present a series of theoretical and simulated data examples to fix concepts and exhibit the utility of the approach, and compare with prior approaches. We demonstrate application in the context of automatic classification and discriminative variable selection in high-throughput systems biology using large flow cytometry datasets.
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA llin@fredhutch.org.
authors.forename: Lin
authors.initials: L
authors.lastname: Lin
authors.affiliation: Biostatistics & Bioinformatics, Duke University Medical Center, Durham, NC 27710-2721, and Department of Statistical Science, Duke University, Durham, NC 27708-0251, USA.
authors.forename: Cliburn
authors.initials: C
authors.lastname: Chan
authors.affiliation: Department of Statistical Science, Duke University, Durham, NC 27708-0251, USA.
authors.forename: Mike
authors.initials: M
authors.lastname: West
mesh_terms: D001499:Bayes Theorem; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D050378:T-Lymphocytes, Regulatory
keywords: Bayesian expectation–maximization; Bayesian mixture models; Classification error rates; Concordance of densities; Dirichlet process mixtures; Discriminative information measure; Discriminative threshold probabilities; Flow cytometry data; Non-Gaussian component mixtures; Variable subset selection