Skip Navigation



Bioinformatics Advance Access published online on April 21, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti456
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
21/14/3097    most recent
bti456v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
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 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 arrowRequest Permissions
Google Scholar
Right arrow Articles by Jung, S.-H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jung, S.-H.
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@oupjournals.org
Received December 8, 2004
Revised March 30, 2005
Accepted April 6, 2005

Article

Sample size for FDR-control in microarray data analysis

Sin-Ho Jung 1*

1 Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA

* To whom correspondence should be addressed.
Sin-Ho Jung, E-mail: jung0005{at}mc.duke.edu


  Abstract

We consider identifying differentially expressing genes between two patient groups using microarray experiment. We propose a sample size calculation method for a specified number of true rejections while controlling the false discovery rate at a desired level. Input parameters for the sample size calculation include the allocation proportion in each group, the number of genes in each array, the number of differentially expressing genes, and the effect sizes among the differentially expressing genes. We have a closed-form sample size formula if the projected effect sizes are equal among differentially expressing genes. Otherwise, our method requires a numerical method to solve an equation. Simulation studies are conducted to show that the calculated sample sizes are accurate in practical settings. The proposed method is demonstrated with a real study.


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
 BiostatisticsHome page
T. Oura, S. Matsui, and K. Kawakami
Sample size calculations for controlling the distribution of false discovery proportion in microarray experiments
Biostat., October 1, 2009; 10(4): 694 - 705.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
P. de Valpine, H.-M. Bitter, M. P. S. Brown, and J. Heller
A simulation-approximation approach to sample size planning for high-dimensional classification studies
Biostat., July 1, 2009; 10(3): 424 - 435.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
Y. Lai
A moment-based method for estimating the proportion of true null hypotheses and its application to microarray gene expression data
Biostat., October 1, 2007; 8(4): 744 - 755.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
W. Zhang, A. Carriquiry, D. Nettleton, and J. C.M. Dekkers
Pooling mRNA in microarray experiments and its effect on power
Bioinformatics, May 15, 2007; 23(10): 1217 - 1224.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
P. Liu and J. T. G. Hwang
Quick calculation for sample size while controlling false discovery rate with application to microarray analysis
Bioinformatics, March 15, 2007; 23(6): 739 - 746.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
G. J. M. Rosa, N. de Leon, and A. J. M. Rosa
Review of microarray experimental design strategies for genetical genomics studies
Physiol Genomics, December 13, 2006; 28(1): 15 - 23.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
S.-H. Jung and W. Jang
How accurately can we control the FDR in analyzing microarray data?
Bioinformatics, July 15, 2006; 22(14): 1730 - 1736.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
S. B. Pounds
Estimation and control of multiple testing error rates for microarray studies
Brief Bioinform, March 1, 2006; 7(1): 25 - 36.


Home page
 BioinformaticsHome page
S. Pounds and C. Cheng
Sample size determination for the false discovery rate
Bioinformatics, December 1, 2005; 21(23): 4263 - 4271.
[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.