Clustering Analysis of SAGE Data using a Poisson Approach

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Clustering Analysis of SAGE Data using a Poisson Approach

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Title: Clustering Analysis of SAGE Data using a Poisson Approach
Author: Cai, Li; Huang, Haiyan; Blackshaw, Seth; Liu, Jun; Cepko, Connie; Wong, Wing H.

Note: Order does not necessarily reflect citation order of authors.

Citation: Cai, Li, Haiyan Huang, Seth Blackshaw, Jun S. Liu, Connie Cepko, and Wing H. Wong. 2004. Clustering analysis of SAGE data using a Poisson approach. Genome Biology 5(7): R51.
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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.
Published Version: doi:10.1186/gb-2004-5-7-r51
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  • FAS Scholarly Articles [8255]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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