434 articles – 313 references  [version française]
Short view
A simple procedure for estimating the false discovery rate.
Dalmasso C., Broët P., Moreau T.
Bioinformatics 21 (2005) 660-8 - http://www.hal.inserm.fr/inserm-00086296
(15479710)
A simple procedure for estimating the false discovery rate.
Cyril Dalmasso1, Philippe Broët1, Thierry Moreau1
1:  Epidémiologie et Biostatistique
http://ifr69.vjf.inserm.fr
INSERM : IFR69
Hôpital Paul Brousse 16 av Paul Vaillant Couturier 94807 VILLEJUIF CEDEX
France
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 pi0, which is the probability for a gene to be unmodified. In this paper, we propose a novel family of estimators for pi0 that allows the calculation of FDR. RESULTS: The very simple method for estimating pi0 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.fr.
Life Sciences/Bioinformatics and Systemic Biology
English
1367-4803

Article in peer-reviewed journal
10.1093/bioinformatics/bti063
Bioinformatics (Bioinformatics)
Publisher Oxford University Press (OUP): Policy B - Oxford Open Option B
ISSN 1367-4803 (eISSN : 1460-2059)
2005
21
660-8

Algorithms – Breast Neoplasms – Comparative Study – Computer Simulation – Data Interpretation – Statistical – Diagnosis – Computer-Assisted – False Positive Reactions – Gene Expression Profiling – Humans – Leukemia – Models – Genetic – Neoplasm Pro