Clustering analysis of SAGE data using a Poisson approach

被引：0
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作者：
Li Cai
Haiyan Huang
Seth Blackshaw
Jun S Liu
Connie Cepko
Wing H Wong
机构：
[1] Dana-Farber Cancer Institute,Department of Research Computing
[2] Harvard School of Public Health,Department of Biostatistics
[3] Harvard Medical School,Department of Genetics
[4] Harvard University,Department of Statistics
[5] Science Center,Department of Statistics
[6] University of California,Department of Neuroscience
[7] Johns Hopkins University School of Medicine,undefined
来源：
Genome Biology | / 5卷
关键词：
Additional Data File; Massive Parallel Signature Sequencing; Joint Likelihood; Unknown Biological Function; RIKEN cDNAs;
D O I：
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中图分类号：
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摘要：
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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