Bayesian consensus clustering.

Journal Article (Journal Article)


In biomedical research a growing number of platforms and technologies are used to measure diverse but related information, and the task of clustering a set of objects based on multiple sources of data arises in several applications. Most current approaches to multisource clustering either independently determine a separate clustering for each data source or determine a single 'joint' clustering for all data sources. There is a need for more flexible approaches that simultaneously model the dependence and the heterogeneity of the data sources.


We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings. We demonstrate that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source independently. We present an application to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas.


R code with instructions and examples is available at

Full Text

Duke Authors

Cited Authors

  • Lock, EF; Dunson, DB

Published Date

  • October 2013

Published In

Volume / Issue

  • 29 / 20

Start / End Page

  • 2610 - 2616

PubMed ID

  • 23990412

Pubmed Central ID

  • PMC3789539

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btt425


  • eng