Bioinformatics Advance Access published online on August 16, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti626
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1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
* To whom correspondence should be addressed.
Motivation: The false discovery rate (FDR) provides a key statistical assessment for microarray studies. Its value depends on the proportion 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 Results: The analysis shows that a large bias occurs when Availability: An R-package OCplus containing functions to compute
Received June 8, 2005
Revised July 11, 2005
Accepted August 10, 2005
Article
Bias in the estimation of false discovery rate in microarray studies
2 Genome Institute of Singapore, Singapore
3 Unit of Biostatistics and Epidemiology, Institute Gustave Roussy, Villejuif, France
Yudi Pawitan, E-mail: yudi.pawitan{at}meb.ki.se
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Abstract
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
0 and FDR, leading to unnecessary loss of power in the overall analysis.
0. We suggest an improved estimation of
0 based on the mixture model and describe a practical likelihood-based procedure for this purpose.
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
0. Simulation studies indicate mixture-model estimates are less biased than standard estimates. The method is applied to breast cancer and lymphoma data examples.
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.![]()
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