A simple procedure for estimating the false discovery rate
- 12 October 2004
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 21 (5) , 660-668
- https://doi.org/10.1093/bioinformatics/bti063
Abstract
Motivation: The most used criterion in microarray data analysis is nowadays the false discovery rate (FDR). In the framework of estimating procedures based on the marginal distribution of the P-values without any assumption on gene expression changes, estimators of the FDR are necessarily conservatively biased. Indeed, only an upper bound estimate can be obtained for the key quantity π0, which is the probability for a gene to be unmodified. In this paper, we propose a novel family of estimators for π0 that allows the calculation of FDR. Results: The very simple method for estimating π0 called LBE (Location Based Estimator) is presented together with results on its variability. Simulation results indicate that the proposed estimator performs well in finite sample and has the best mean square error in most of the cases as compared with the procedures QVALUE, BUM and SPLOSH. The different procedures are then applied to real datasets. Availability: The R function LBE is available at http://ifr69.vjf.inserm.fr/lbe Contact: broet@vjf.inserm.frKeywords
This publication has 14 references indexed in Scilit:
- A mixture model-based strategy for selecting sets of genes in multiclass response microarray experimentsBioinformatics, 2004
- Improving false discovery rate estimationBioinformatics, 2004
- The positive false discovery rate: a Bayesian interpretation and the q-valueThe Annals of Statistics, 2003
- Statistical significance for genomewide studiesProceedings of the National Academy of Sciences, 2003
- Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-valuesBioinformatics, 2003
- A Direct Approach to False Discovery RatesJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression DataJournal of the American Statistical Association, 2002
- Empirical Bayes Analysis of a Microarray ExperimentJournal of the American Statistical Association, 2001
- Microarray Expression Profiling Identifies Genes with Altered Expression in HDL-Deficient MiceGenome Research, 2000
- Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression MonitoringScience, 1999