Journal Article

FDR made easy in differential feature discovery and correlation analyses

Xuefeng Bruce Ling, Harvey Cohen, Joseph Jin, Irwin Lau and James Schilling

in Bioinformatics

Volume 25, issue 11, pages 1461-1462
Published in print June 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp176
FDR made easy in differential feature discovery and correlation analyses

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Summary: Rapid progress in technology, particularly in high-throughput biology, allows the analysis of thousands of genes or proteins simultaneously, where the multiple comparison problems occurs. Global false discovery rate (gFDR) analysis statistically controls this error, computing the ratio of the number of false positives over the total number of rejections. Local FDR (lFDR) method can associate the corrected significance measure with each hypothesis testing for its feature-by-feature interpretation. Given the large feature number and sample size in any genomics or proteomics analysis, FDR computation, albeit critical, is both beyond the regular biologists' specialty and computationally expensive, easily exceeding the capacity of desktop computers. To overcome this digital divide, a web portal has been developed that provides bench-side biologists easy access to the server-side computing capabilities to analyze for FDR, differential expressed genes or proteins, and for the correlation between molecular data and clinical measurements.

Availability: http://translationalmedicine.stanford.edu/Mass-Conductor/FDR.html

Contacts: xuefeng_ling@yahoo.com; jschill@stanford.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1232 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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