A Decision‐Theory Approach to Interpretable Set Analysis for High‐Dimensional Data

DOI10.1111/biom.12060zbMath1429.62496OpenAlexW2154153600WikidataQ30658910 ScholiaQ30658910MaRDI QIDQ2861947

Simina M. Boca, Héctor Céorrada Bravo, Jeffrey T. Leek, Giovanni Parmigiani, Brian S. Caffo

Publication date: 13 November 2013

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://biostats.bepress.com/cgi/viewcontent.cgi?article=1211&context=jhubiostat

zbMATH Keywords

hypothesis testingatomic false discovery rategene-setsset-level inference

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

• EBarrays
• SAM
• KEGG
• ROAST
• hgu133a.db

Cites Work

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• On testing the significance of sets of genes
• The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
• Simultaneous Testing of Grouped Hypotheses: Finding Needles in Multiple Haystacks
• Penalized maximum likelihood estimation in logistic regression and discrimination
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
• Empirical Bayes Analysis of a Microarray Experiment
• Optimal Sample Size for Multiple Testing
• A Direct Approach to False Discovery Rates
• Operating Characteristics and Extensions of the False Discovery Rate Procedure
• Detecting differential gene expression with a semiparametric hierarchical mixture method

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