Clustering analysis of SAGE data using a Poisson approach.
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.
Cai
Li
L
Department of Biostatistics, Harvard School of Public Health, 66 Huntington Avenue, Boston, MA 02115, USA. lcai@research.dfci.harvard.edu
Huang
Haiyan
H
Blackshaw
Seth
S
Liu
Jun S
JS
Cepko
Connie
C
Wong
Wing H
WH
D000818:Animals; D000831:Animals, Newborn / Q000235:genetics; D002874:Chromosome Mapping / Q000706:statistics & numerical data; D016000:Cluster Analysis; D003198:Computer Simulation / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015723:Gene Library; D051379:Mice; D015233:Models, Statistical; D016012:Poisson Distribution; D012160:Retina / Q000737:chemistry / Q000196:embryology / Q000378:metabolism; D012680:Sensitivity and Specificity