Model-based Bayesian clustering (MBBC)

Yongsung Joo, James G. Booth, Younghwan Namkoong, George Casella

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Motivation: The program MBBC 2.0 clusters time-course microarray data using a Bayesian product partition model. Results: The Bayesian product partition model in Booth et al. (2007) simultaneously searches for the optimal number of clusters, and assigns cluster memberships based on temporal changes of gene expressions. MBBC 2.0 to makes this method easily available for statisticians and scientists, and is built with three free computer language software packages: Ox, R and C++, taking advantage of the strengths of each language. Within MBBC, the search algorithm is implemented with Ox and resulting graphs are drawn with R. A user-friendly graphical interface is built with C++ to run the Ox and R programs internally. Thus, MBBC users are not required to know how to use Ox, R or C++, but they must be pre-installed.

Original languageEnglish
Pages (from-to)874-875
Number of pages2
JournalBioinformatics
Volume24
Issue number6
DOIs
StatePublished - Mar 2008

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