kerfdr swMATH ID: 35302 Software Authors: Guedj, M.; Célisse, A.; Robin, S.; Nuel, G. Description: 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. Homepage: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-84 Source Code: https://github.com/cran/kerfdr Dependencies: R Related Software: R; qch; fdrtool; ks; UpSetR; MetaPCA; Pomelo II; AAPL; OrderedList; Bioconductor; FAMT; VarMixt Cited in: 3 Documents all top 5 Cited by 8 Authors 1 Cao, Xiting 1 Causeur, David J. 1 Friguet, Chloé 1 Hertz, Marshall I. 1 Shen, Shihao 1 Wu, Baolin 1 Yu, Tianwei 1 Zhao, Yize Cited in 3 Serials 1 Computational Statistics and Data Analysis 1 Journal of Applied Statistics 1 Statistical Analysis and Data Mining Cited in 2 Fields 3 Statistics (62-XX) 1 Computer science (68-XX) Citations by Year