A semiparametric mixture method for local false discovery rate estimation from multiple studies

DOI10.1214/20-AOAS1341zbMath1470.62158OpenAlexW3088814962MaRDI QIDQ2044249

Seok-Oh Jeong, Dongseok Choi, Woncheol Jang

Publication date: 4 August 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1600454864

zbMATH Keywords

mixture modelfalse discovery ratemicroarraylog concavenext generation sequencing data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants ⋮ Maximum likelihood estimation of the log-concave component in a semi-parametric mixture with a standard normal density

Uses Software

• LogConcDEAD
• limma
• EBayesThresh
• logcondens
• logcondens.mode
• locfdr

Cites Work

• Unnamed Item
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