Skip Navigation


Bioinformatics Advance Access originally published online on August 9, 2005
Bioinformatics 2005 21(19):3771-3777; doi:10.1093/bioinformatics/bti604
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
21/19/3771    most recent
bti604v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (11)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Zehetmayer, S.
Right arrow Articles by Posch, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zehetmayer, S.
Right arrow Articles by Posch, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Two-stage designs for experiments with a large number of hypotheses

Sonja Zehetmayer , Peter Bauer and Martin Posch *

Section of Medical Statistics, Medical University of Vienna Spitalgasse 23, A-1090 Vienna, Austria

*To whom correspondence should be addressed.

Motivation: When a large number of hypotheses are investigated the false discovery rate (FDR) is commonly applied in gene expression analysis or gene association studies. Conventional single-stage designs may lack power due to low sample sizes for the individual hypotheses. We propose two-stage designs where the first stage is used to screen the ‘promising’ hypotheses which are further investigated at the second stage with an increased sample size. A multiple test procedure based on sequential individual P-values is proposed to control the FDR for the case of independent normal distributions with known variance.

Results: The power of optimal two-stage designs is impressively larger than the power of the corresponding single–stage design with equal costs. Extensions to the case of unknown variances and correlated test statistics are investigated by simulations. Moreover, it is shown that the simple multiple test procedure using first stage data for screening purposes and deriving the test decisions only from second stage data is a very powerful option.

Availability: An R-program is available at http://www.meduniwien.ac.at/medstat/research/fdr/application.R

Contact: Martin.Posch{at}meduniwien.ac.at

Supplementary information: Supplementary data for this paper is available at Bioinformatics online.

Received on April 26, 2005; revised on July 1, 2005; accepted on July 28, 2005

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nucleic Acids ResHome page
S. Macgregor, Z. Z. Zhao, A. Henders, M. G. Nicholas, G. W. Montgomery, and P. M. Visscher
Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays
Nucleic Acids Res., April 1, 2008; 36(6): e35 - e35.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
B. Moerkerke and E. Goetghebeur
Optimal screening for promising genes in 2-stage designs
Biostat., March 18, 2008; (2008) kxn002v1.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Reiner-Benaim, D. Yekutieli, N. E. Letwin, G. I. Elmer, N. H. Lee, N. Kafkafi, and Y. Benjamini
Associating quantitative behavioral traits with gene expression in the brain: searching for diamonds in the hay
Bioinformatics, September 1, 2007; 23(17): 2239 - 2246.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Goll and P. Bauer
Two-stage designs applying methods differing in costs
Bioinformatics, June 15, 2007; 23(12): 1519 - 1526.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.