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 mixt

SUBMITTER: Lin L 

PROVIDER: S-EPMC4679067 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature