Your browser version may not work well with NCBI's Web applications. More information here...
1: Genome Biol. 2004;5(7):R51. Epub 2004 Jun 29.Click here to read Click here to read Links

Clustering analysis of SAGE data using a Poisson approach.

Cai L, Huang H, Blackshaw S, Liu JS, Cepko C, Wong WH.

Department of Biostatistics, Harvard School of Public Health, 66 Huntington Avenue, Boston, MA 02115, USA. lcai@research.dfci.harvard.edu

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.

PMID: 15239836 [PubMed - indexed for MEDLINE]

PMCID: PMC463327