Bayesian model-based tight clustering for time course data

Yongsung Joo, George Casella, James Hobert

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering method for time course gene expression data, which selects a small number of closely-related genes and constructs tight clusters only with these closely-related genes.

Original languageEnglish
Pages (from-to)17-38
Number of pages22
JournalComputational Statistics
Volume25
Issue number1
DOIs
StatePublished - Mar 2010

Keywords

  • Bayesian cluster analysis
  • Microarray
  • Tight clustering
  • Time course gene expression

Fingerprint

Dive into the research topics of 'Bayesian model-based tight clustering for time course data'. Together they form a unique fingerprint.

Cite this