Skip Navigation



Bioinformatics Advance Access published online on August 16, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti626
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/20/3865    most recent
bti626v1
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 Pawitan, Y.
Right arrow Articles by Ploner, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Pawitan, Y.
Right arrow Articles by Ploner, A.
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 June 8, 2005
Revised July 11, 2005
Accepted August 10, 2005

Article

Bias in the estimation of false discovery rate in microarray studies

Yudi Pawitan 1*, Karuturi R. Krishna Murthy 2, Stefan Michiels 3, and Alexander Ploner 1

1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
2 Genome Institute of Singapore, Singapore
3 Unit of Biostatistics and Epidemiology, Institute Gustave Roussy, Villejuif, France

* To whom correspondence should be addressed.
Yudi Pawitan, E-mail: yudi.pawitan{at}meb.ki.se


  Abstract

Motivation: The false discovery rate (FDR) provides a key statistical assessment for microarray studies. Its value depends on the proportion {pi}0 of non-differentially expressed (nonDE) genes. In most microarray studies, many genes have small effects not easily separable from nonDE genes. As a result, current methods often overestimate {pi}0 and FDR, leading to unnecessary loss of power in the overall analysis.

Methods: For the common two-sample comparison we derive a natural mixture model of the test statistic and an explicit bias formula in the standard estimation of {pi}0. We suggest an improved estimation of {pi}0 based on the mixture model and describe a practical likelihood-based procedure for this purpose.

Results: The analysis shows that a large bias occurs when {pi}0 is far from 1 and when the non-centrality parameters of the distribution of nonDE genes are near zero. The theoretical result also explains substantial discrepancies between nonparametric and model-based estimates of {pi}0. Simulation studies indicate mixture-model estimates are less biased than standard estimates. The method is applied to breast cancer and lymphoma data examples.

Availability: An R-package OCplus containing functions to compute {pi}0 based on the mixture model, the resulting FDR, and other operating characteristics of microarray data, is freely available at http://www.meb.ki.se/~yudpaw.


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
 BioinformaticsHome page
M. Zhang, C. Yao, Z. Guo, J. Zou, L. Zhang, H. Xiao, D. Wang, D. Yang, X. Gong, J. Zhu, et al.
Apparently low reproducibility of true differential expression discoveries in microarray studies
Bioinformatics, September 15, 2008; 24(18): 2057 - 2063.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Pawitan, S. Calza, and A. Ploner
Estimation of false discovery proportion under general dependence
Bioinformatics, December 15, 2006; 22(24): 3025 - 3031.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
T. S. Mehta, S. O. Zakharkin, G. L. Gadbury, and D. B. Allison
Epistemological issues in omics and high-dimensional biology: give the people what they want
Physiol Genomics, December 13, 2006; 28(1): 24 - 32.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Ploner, S. Calza, A. Gusnanto, and Y. Pawitan
Multidimensional local false discovery rate for microarray studies
Bioinformatics, March 1, 2006; 22(5): 556 - 565.
[Abstract] [Full Text] [PDF]

Home page
 J. Bacteriol.Home page
L. Wolfram, E. Haas, and P. Bauerfeind
Nickel Represses the Synthesis of the Nickel Permease NixA of Helicobacter pylori
J. Bacteriol., February 15, 2006; 188(4): 1245 - 1250.
[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.