Subset clustering of binary sequences, with an application to genomic abnormality data.
This article develops a model-based approach to clustering multivariate binary data, in which the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The clustering approach is based on a multivariate Dirichlet process mixture model, which allows for the estimation of the number of clusters, the cluster memberships, and the cluster-specific parameters in a unified way. Such a clustering approach has applications in the analysis of genomic abnormality data, in which the development of different types of tumors may depend on the presence of certain abnormalities at subsets of locations along the genome. Additionally, such a mixture model provides a nonparametric estimation scheme for dependent sequences of binary data.
Hoff
Peter D
PD
Department of Statistics and Biostatistics, University of Washington, Seattle, 98195-4322, USA. hoff@stat.washington.edu
D002292:Carcinoma, Renal Cell / Q000235:genetics; D002869:Chromosome Aberrations; D016000:Cluster Analysis; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method