A Nonparametric Bayesian Model for Local Clustering with Application to Proteomics.

ABSTRACT: We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the samples gives meaning to the protein clusters. Any pair of samples might belong to the same cluster for one protein set but to different clusters for another protein set. These local features are different from features obtained by global clustering approaches such as hierarchical clustering, which create only one partition

SUBMITTER: Lee J 

PROVIDER: S-EPMC3821783 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature