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Bioinformatics Advance Access published online on October 17, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl527
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 29, 2006
Revised October 9, 2006
Accepted October 10, 2006

Article

Estimation of false discovery proportion under general dependence

Yudi Pawitan 1 *, Stefano Calza 2, and Alexander Ploner 1

1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Biomedical Sciences and Biotechnology, Brescia, Italy

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


  Abstract

Motivation: Wide-scale correlations between genes are commonly observed in gene expression data, due to both biological and technical reasons. These correlations increase the variability of the standard estimate of the false discovery rate (FDR). We highlight the false discovery proportion (FDP, instead of the FDR) as the suitable quantity for assessing differential expression in microarray data, demonstrate the deleterious effects of correlation on FDP estimation and propose an improved estimation method that accounts for the correlations.

Methods: We analyse the variation pattern of the distribution of test statistics under permutation using the singular value decomposition. The results suggest a latent FDR model that accounts for the effects of correlation, and is statistically closer to the FDP. We develop a procedure for estimating the latent FDR (ELF) based on a Poisson regression model.

Results: For simulated data based on the correlation structure of real datasets, we find that ELF performs substantially better than the standard FDR approach in estimating the FDP. We illustrate the use of ELF in the analysis of breast cancer and lymphoma data.

Availability: R code to perform ELF is available in http://www.meb.ki.se/~yudpaw.

Associate Editor: John Quackenbus
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