Clustering analysis of SAGE data using a Poisson approach

Genome Biology
Li CaiWing H Wong

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.

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Citations

Jul 23, 2008·Molecular & Cellular Proteomics : MCP·Hyungwon ChoiAlexey I Nesvizhskii
Nov 4, 2010·Genome Research·Zhenhua Jeremy WuX Shirley Liu
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Software Mentioned

TightCluster
PoissonL
PoissonC
MPSS
SAGEmap
SAGE

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