Clustering analysis of SAGE data using a Poisson approach.

L. Cai, Haiyan Huang, Seth Blackshaw, Jun S. Liu, Connie Cepko, Wing H. Wong

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


Serial analysis of gene expression (SAGE) data have been poorly exploited by clustering analysis owing to the lack of appropriate statistical methods that consider their specific properties. We modeled SAGE data by Poisson statistics and developed two Poisson-based distances. Their application to simulated and experimental mouse retina data show that the Poisson-based distances are more appropriate and reliable for analyzing SAGE data compared to other commonly used distances or similarity measures such as Pearson correlation or Euclidean distance.

Original languageEnglish (US)
Pages (from-to)R51
JournalGenome biology
Issue number7
StatePublished - 2004
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology


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