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Bioinformatics Advance Access originally published online on June 2, 2005
Bioinformatics 2005 21(15):3264-3272; doi:10.1093/bioinformatics/bti519
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Practical FDR-based sample size calculations in microarray experiments

Jianhua Hu 1,*, Fei Zou 2 and Fred A. Wright 2

1Department of Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center TX 77030-4009, USA
2Department of Biostatistics, University of North Carolina at Chapel Hill NC 27599-3260, USA

*To whom correspondence should be addressed.

Motivation: Owing to the experimental cost and difficulty in obtaining biological materials, it is essential to consider appropriate sample sizes in microarray studies. With the growing use of the False Discovery Rate (FDR) in microarray analysis, an FDR-based sample size calculation is essential.

Method: We describe an approach to explicitly connect the sample size to the FDR and the number of differentially expressed genes to be detected. The method fits parametric models for degree of differential expression using the Expectation–Maximization algorithm.

Results: The applicability of the method is illustrated with simulations and studies of a lung microarray dataset. We propose to use a small training set or published data from relevant biological settings to calculate the sample size of an experiment.

Availability: Code to implement the method in the statistical package R is available from the authors.

Contact: jhu{at}mdanderson.org

Received on January 3, 2005; revised on May 22, 2005; accepted on May 25, 2005

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