Bioinformatics Advance Access originally published online on December 20, 2005
Bioinformatics 2006 22(5):556-565; doi:10.1093/bioinformatics/btk013
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Multidimensional local false discovery rate for microarray studies
1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden
2Dipartimento di Scienze Biomediche e Biotecnologie, Università degli Studi di Brescia 11 25123 Brescia, Italy
3MRC Biostatistics Unit, Institute of Public Health Cambridge CB2 2SR, UK
*To whom correspondence should be addressed.
Motivation: The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability.
Methods: We generalize the local fdr as a function of multiple statistics, combining a common test statistic for assessing DE with its standard error information. We use a non-parametric mixture model for DE and non-DE genes to describe the observed multi-dimensional statistics, and estimate the distribution for non-DE genes via the permutation method. We demonstrate this fdr2d approach for simulated and real microarray data.
Results: The fdr2d allows objective assessment of DE as a function of gene variability. We also show that the fdr2d performs better than commonly used modified test statistics.
Availability: An R-package OCplus containing functions for computing fdr2d() and other operating characteristics of microarray data is available at http://www.meb.ki.se/~yudpaw
Contact: alexander.ploner{at}meb.ki.se
Received on August 29, 2005; revised on December 14, 2005; accepted on December 15, 2005
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