Detecting Strong Signals in Gene Perturbation Experiments: An Adaptive Approach With Power Guarantee and FDR Control

DOI10.1080/01621459.2019.1635484zbMath1452.62156arXiv1807.06776OpenAlexW2955102996WikidataQ104459539 ScholiaQ104459539MaRDI QIDQ5146027

Wing-Hung Wong, Leying Guan, Xi Chen

Publication date: 22 January 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.06776

zbMATH Keywords

empirical Bayes\( \varepsilon \)-contaminationgene knock-down

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Empirical decision procedures; empirical Bayes procedures (62C12) Paired and multiple comparisons; multiple testing (62J15)

Uses Software

• t-SNE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Two modeling strategies for empirical Bayes estimation
• A general decision theory for Huber's \(\epsilon\)-contamination model
• Microarrays, empirical Bayes and the two-groups model
• Robust Bayes and empirical Bayes analysis with \(\epsilon\)-contaminated priors
• An overview of robust Bayesian analysis. (With discussion)
• Robust Empirical Bayes Analyses of Event Rates
• Optimal Rates of Convergence for Deconvolving a Density
• Empirical Bayes Analysis of a Microarray Experiment
• A Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification
• Correlation and Large-Scale Simultaneous Significance Testing
• Chi-square oracle inequalities
• Making the Cut: Improved Ranking and Selection for Large-Scale Inference
• Robust Estimation of a Location Parameter