TWILIGHT

A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate 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{-}}{ m 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.


References in zbMATH (referenced in 9 articles , 1 standard article )

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  1. Qu, Long; Nettleton, Dan; Dekkers, Jack C. M.: Improved estimation of the noncentrality parameter distribution from a large number of (t)-statistics, with applications to false discovery rate estimation in microarray data analysis (2012)
  2. Celisse, Alain; Robin, Stéphane: A cross-validation based estimation of the proportion of true null hypotheses (2010)
  3. Yanofsky, Corey M.; Bickel, David R.: Validation of differential gene expression algorithms: application comparing fold-change estimation to hypothesis testing (2010) ioport
  4. Yang, Xinan; Sun, Xiao; Xie, Jianming; Lu, Zuhong: Comparability of gene expression in human blood, immune and carcinoma cells (2008)
  5. Dalmasso, Cyril; Bar-Hen, Avner; Broët, Philippe: A constrained polynomial regression procedure for estimating the local false discovery rate (2007) ioport
  6. Scheid, Stefanie; Spang, Rainer: Permutation filtering: a novel concept for significance analysis of large-scale genomic data (2006)
  7. Jaeger, Jochen; Weichenhan, Dieter; Ivandic, Boris; Spang, Rainer: Early diagnostic marker panel determination for microarray based clinical studies (2005)
  8. Schäfer, Juliane Stephanie: Small-sample analysis and inference of networked dependency structures from complex genomic data. (2005)
  9. Scheid, Stefanie; Spang, Rainer: A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate (2004) ioport