Clustering analysis of SAGE data using a Poisson approach

doi:10.1186/gb-2004-5-7-r51

Genome Biology

Volume 5
Issue 7

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Blackshaw S
Liu JS
Cepko C
Wong WH

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Clustering analysis of SAGE data using a Poisson approach

Li Cai¹^* , Haiyan Huang²^,5^* , Seth Blackshaw³^,6, Jun S Liu⁴, Connie Cepko³ and Wing H Wong²^,4

¹Department of Research Computing, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

²Department of Biostatistics, Harvard School of Public Health, 66 Huntington Avenue, Boston, MA 02115, USA

³Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

⁴Department of Statistics, Harvard University, Science Center, 1 Oxford Street, Cambridge, MA 02138, USA

⁵Current address: Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA

⁶Current address: Department of Neuroscience, Johns Hopkins University School of Medicine, 773 N Broadway Ave, Baltimore, MD 21287, USA

author email corresponding author email* Contributed equally

Genome Biology 2004, 5:R51doi:10.1186/gb-2004-5-7-r51


Published:	29 June 2004

Subject areas: Bioinformatics, Methods, Genome studies, Neurobiology

Abstract

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.