Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation. Results: In this context we present a semi-parametric approach based on kernel estimators which is applied to different high-throughput biological data such as patterns in DNA sequences, genes expression and genome-wide association studies. Conclusion: The proposed method has the practical advantages, over existing approaches, to consider complex heterogeneities in the alternative hypothesis, to take into account prior information (from an expert judgment or previous studies) by allowing a semi-supervised mode, and to deal with truncated distributions such as those obtained in Monte-Carlo simulations. This method has been implemented and is available through the R package kerfdr via the CRAN or at http://stat.genopole.cnrs.fr/software/kerfdr.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Tristan Mary-Huard, Sarmistha Das, Indranil Mukhopadhyay, Stéphane Robin: Querying multiple sets of p-values through composed hypothesis testing (2021) arXiv
- Cao, Xiting; Wu, Baolin; Hertz, Marshall I.: Empirical null distribution-based modeling of multi-class differential gene expression detection (2013)
- Yu, Tianwei; Zhao, Yize; Shen, Shihao: AAPL: assessing association between (P)-value lists (2013)
- Friguet, Chloé; Causeur, David: Estimation of the proportion of true null hypotheses in high-dimensional data under dependence (2011)
- Guedj, Mickaël; Robin, Stéphane; Celisse, Alain; Nuel, Grégory: Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation (2009) ioport