- This software takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values. A point-and-click interface is now available!
- The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant.
- A short tutorial on q-values and false discovery rates is provided with the manual.
- Various plots are automatically generated, allowing one to make sensible significance cut-offs.
- Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software.
- The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.
- Estimation Methodology: Storey JD. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64: 479-498. [PDF]
- Genomics: Storey JD and Tibshirani R. (2003) Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences, 100: 9440-9445. [PDF] [Supplementary Information]
- Bayesian Connections: Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035. [PDF]
- Theory: Storey JD, Taylor JE, and Siegmund D. (2004) Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. Journal of the Royal Statistical Society, Series B, 66: 187-205. [PDF]
QVALUE was written by Alan Dabney and John Storey.
Copyright 2002-2008 by John D. Storey. All rights reserved.
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