Skip Navigation


Bioinformatics Advance Access originally published online on March 29, 2005
Bioinformatics 2005 21(11):2684-2690; doi:10.1093/bioinformatics/bti407
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/11/2684    most recent
bti407v1
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 (16)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Grant, G. R.
Right arrow Articles by Stoeckert, C. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Grant, G. R.
Right arrow Articles by Stoeckert, C. J., Jr.
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}oupjournals.org

A practical false discovery rate approach to identifying patterns of differential expression in microarray data

Gregory R. Grant *, Junmin Liu and Christian J. Stoeckert, Jr.

Center for Bioinformatics, University of Pennsylvania 1429 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA

*To whom correspondence should be addressed.

Summary: Searching for differentially expressed genes is one of the most common applications for microarrays, yet statistically there are difficult hurdles to achieving adequate rigor and practicality. False discovery rate (FDR) approaches have become relatively standard; however, how to define and control the FDR has been hotly debated. Permutation estimation approaches such as SAM and PaGE can be effective; however, they leave much room for improvement. We pursue the permutation estimation method and describe a convenient definition for the FDR that can be estimated in a straightforward manner. We then discuss issues regarding the choice of statistic and data transformation. It is impossible to optimize the power of any statistic for thousands of genes simultaneously, and we look at the practical consequences of this. For example, the log transform can both help and hurt at the same time, depending on the gene. We examine issues surrounding the SAM ‘fudge factor’ parameter, and how to handle these issues by optimizing with respect to power.

Availability: Java and Perl implementations are available at www.cbil.upenn.edu/PaGE

Contact: ggrant{at}pcbi.upenn.edu


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
 DiabetesHome page
P. White, C. Lee May, R. N. Lamounier, J. E. Brestelli, and K. H. Kaestner
Defining Pancreatic Endocrine Precursors and Their Descendants
Diabetes, March 1, 2008; 57(3): 654 - 668.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
M. Kidd, B. Nadler, S. Mane, G. Eick, M. Malfertheiner, M. Champaneria, R. Pfragner, and I. Modlin
GeneChip, geNorm, and gastrointestinal tumors: novel reference genes for real-time PCR
Physiol Genomics, August 20, 2007; 30(3): 363 - 370.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
D. Sahoo, D. L. Dill, R. Tibshirani, and S. K. Plevritis
Extracting binary signals from microarray time-course data
Nucleic Acids Res., June 28, 2007; 35(11): 3705 - 3712.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
S.-D. Zhang and T. W. Gant
Effect of pooling samples on the efficiency of comparative studies using microarrays
Bioinformatics, December 15, 2005; 21(24): 4378 - 4383.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Pawitan, K. R. K. Murthy, S. Michiels, and A. Ploner
Bias in the estimation of false discovery rate in microarray studies
Bioinformatics, October 15, 2005; 21(20): 3865 - 3872.
[Abstract] [Full Text] [PDF]

Home page
 Plant CellHome page
M. A. Mazzella, M. V. Arana, R. J. Staneloni, S. Perelman, M. J. Rodriguez Batiller, J. Muschietti, P. D. Cerdan, K. Chen, R. A. Sanchez, T. Zhu, et al.
Phytochrome Control of the Arabidopsis Transcriptome Anticipates Seedling Exposure to Light
PLANT CELL, September 1, 2005; 17(9): 2507 - 2516.
[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.