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Journal Article

A stochastic downhill search algorithm for estimating the local false discovery rate


Scheid,  Stefanie
Max Planck Society;


Spang,  Rainer
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Scheid, S., & Spang, R. (2004). A stochastic downhill search algorithm for estimating the local false discovery rate. IEEE ACM Transactions on Computational Biology and Bioinformatics, 1(3), 98-108.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-8818-C
Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a statistical concept for quantifying uncertainty in multiple testing. In this paper, we introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p{\hbox{-}}{\rm values} of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.