Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants

DOI10.1002/bimj.202000256zbMath1523.62186OpenAlexW3186185370MaRDI QIDQ6068304

Junyong Park, Unnamed Author, Johan Lim, Unnamed Author, Unnamed Author, Khoa Tran, Dohwan Park

Publication date: 15 December 2023

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.202000256

zbMATH Keywords

multiple testingfalse discovery rateempirical approximationmixture of normal

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Unnamed Item
• Estimation of empirical null using a mixture of normals and its use in local false discovery rate
• Empirical null and false discovery rate inference for exponential families
• A semi-parametric approach for mixture models: application to local false discovery rate estimation
• The control of the false discovery rate in multiple testing under dependency.
• A semiparametric mixture method for local false discovery rate estimation from multiple studies
• Empirical null estimation using zero-inflated discrete mixture distributions and its application to protein domain data
• Statistical significance for genomewide studies
• Large-Scale Simultaneous Hypothesis Testing

This page was built for publication: Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants