False Discovery Rate Control With Groups

In the context of large-scale multiple hypothesis testing, the hypotheses often possess certain group structures based on additional information such as Gene Ontology in gene expression data and phenotypes in genome-wide association studies. It is hence desirable to incorporate such information when...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2010-09, Vol.105 (491), p.1215-1227
Main Authors: Hu, James X., Zhao, Hongyu, Zhou, Harrison H.
Format: Article
Language:English
Subjects:
Adaptive procedure
Applications
Benjamini, Yoav
Benjamini-Hochberg procedure
Breast cancer
Cancer
Computational methods
Data analysis
Discovery
Estimation methods
Estimators
Exact sciences and technology
Gene expression
General topics
Genes
Genetics
Genomics
Genotype & phenotype
Group structure
Hochberg, Yosef
Hypotheses
Hypothesis testing
Mathematical procedures
Mathematics
Nonparametric inference
Null hypothesis
Ontology
Oracles
P values
Parametric inference
Phenotypes
Positive regression dependence
Probability and statistics
Regression analysis
Sciences and techniques of general use
Simulation
Statistical data
Statistical methods
Statistical power
Statistics
Term weighting
Theory and Methods
Weighting
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Description
Summary:In the context of large-scale multiple hypothesis testing, the hypotheses often possess certain group structures based on additional information such as Gene Ontology in gene expression data and phenotypes in genome-wide association studies. It is hence desirable to incorporate such information when dealing with multiplicity problems to increase statistical power. In this article, we demonstrate the benefit of considering group structure by presenting a p-value weighting procedure which utilizes the relative importance of each group while controlling the false discovery rate under weak conditions. The procedure is easy to implement and shown to be more powerful than the classical Benjamini-Hochberg procedure in both theoretical and simulation studies. By estimating the proportion of true null hypotheses, the data-driven procedure controls the false discovery rate asymptotically. Our analysis on one breast cancer dataset confirms that the procedure performs favorably compared with the classical method.
ISSN:0162-1459
1537-274X
DOI:10.1198/jasa.2010.tm09329