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Identifying differentially expressed genes from microarray experiments via statistic synthesis
Yee Hwa Yang, University of California, San Francisco
Yuanyuan Xiao, University of California, San Francisco
Mark R. Segal, University of California, San Francisco
The approach is implemented in an R package called DEDS, which is available for download from http://www.biostat.ucsf.edu/jean/DEDS.htm
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ABSTRACT: Motivation: A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice.
Results: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in data sets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.
SUGGESTED CITATION:
Yee Hwa Yang, Yuanyuan Xiao, and Mark R. Segal,
"Identifying differentially expressed genes from microarray experiments via statistic synthesis"
(July 2, 2004).
Center for Bioinformatics & Molecular Biostatistics.
Paper deds.
http://repositories.cdlib.org/cbmb/deds
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