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Bioinformatics Advance Access published online on March 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti407
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 15, 2004
Revised March 16, 2005
Accepted March 22, 2005

Article

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

Gregory R. Grant 1*, Junmin Liu 1, and Christian J. Stoeckert Jr.1

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

* To whom correspondence should be addressed.
Gregory R. Grant, E-mail: ggrant{at}pcbi.upenn.edu


  Abstract

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 (Tusher et al. (2001)) and PaGE (Manduchi et al. (1999)) can be effective, however 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 "fudge factor" parameter introduced by Tusher et al. (2001), and how to handle these issues by optimizing with respect to power. Java and Perl implementations are available at www.cbil.upenn.edu/PaGE.


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