twilight

In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package twilight contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Bickel, David R.: Confidence distributions and empirical Bayes posterior distributions unified as distributions of evidential support (2022)
  2. 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)
  3. Celisse, Alain; Robin, Stéphane: A cross-validation based estimation of the proportion of true null hypotheses (2010)
  4. Hall, Peter; Wang, Qiying: Strong approximations of level exceedences related to multiple hypothesis testing (2010)
  5. Yanofsky, Corey M.; Bickel, David R.: Validation of differential gene expression algorithms: application comparing fold-change estimation to hypothesis testing (2010) ioport
  6. Yang, Xinan; Sun, Xiao; Xie, Jianming; Lu, Zuhong: Comparability of gene expression in human blood, immune and carcinoma cells (2008)
  7. Dalmasso, Cyril; Bar-Hen, Avner; Broët, Philippe: A constrained polynomial regression procedure for estimating the local false discovery rate (2007) ioport
  8. Scheid, Stefanie; Spang, Rainer: Permutation filtering: a novel concept for significance analysis of large-scale genomic data (2006)
  9. Schäfer, Juliane Stephanie: Small-sample analysis and inference of networked dependency structures from complex genomic data. (2005)
  10. Scheid, Stefanie; Spang, Rainer: A Stochastic Downhill Search Algorithm for Estimating the Local False Discovery Rate (2004) ioport