FDR made easy in differential feature discovery and correlation analyses.
CONCLUSIONS
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.


BACKGROUND
(http://translationalmedicine.stanford.edu/Mass-Conductor/FDR.html).
Ling
Xuefeng Bruce
XB
Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA. xuefeng_ling@yahoo.com
Cohen
Harvey
H
Jin
Joseph
J
Lau
Irwin
I
Schilling
James
J
D019295:Computational Biology / Q000379:methods*; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods; D012984:Software