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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 or from the Bioconductor project at
Local false discovery rates, stochastic search algorithms, microarray analysis, biology and genetics.
Rainer Spang, Stefanie Scheid, "A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 1, no. , pp. 98-108, July-September 2004, doi:10.1109/TCBB.2004.24
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