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Bioinformatics Advance Access originally published online on October 28, 2004
Bioinformatics 2005 21(7):1084-1093; doi:10.1093/bioinformatics/bti108
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Identifying differentially expressed genes from microarray experiments via statistic synthesis

Yee Hwa Yang 1,{dagger}, Yuanyuan Xiao 2,{dagger} and Mark R. Segal 3,*

1Departments of Medicine, Center for Bioinformatics and Molecular Biostatistics, University of California San Francisco, CA 94143, USA
2Department of Biopharmaceutical Sciences, Center for Bioinformatics and Molecular Biostatistics, University of California San Francisco, CA 94143, USA
3Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California San Francisco, CA 94143, USA

*To whom correspondence should be addressed.

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 datasets, 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.

Availability: The approach is implemented in an R package called DEDS, which is available for download from the Bioconductor website (http://www.bioconductor.org/).

Contact: mark{at}biostat.ucsf.edu


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