ConReg-R: Extrapolative recalibration of the empirical distribution of p-values to improve false discovery rate estimates.

ABSTRACT:

Background

False discovery rate (FDR) control is commonly accepted as the most appropriate error control in multiple hypothesis testing problems. The accuracy of FDR estimation depends on the accuracy of the estimation of p-values from each test and validity of the underlying assumptions of the distribution. However, in many practical testing problems such as in genomics, the p-values could be under-estimated or over-estimated for many known or unknown reasons. Consequently, FDR estimation would then be influenced and lose its veracity.

Results

We propose a new extrapolative method called Constrained Regression Recalibration (ConReg-R) to recalibrate the empirical p-values by modeling their distribution to improve the FDR estimates. Our ConReg-R method is based on the observ

SUBMITTER: Li J 

PROVIDER: S-EPMC3130718 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature