Empirical Bayes methods and false discovery rates for microarrays
Abstract
In a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian modeling, and the frequentist method of "false discovery rates" proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences.
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The approach is sound and the local posterior corresponds to the local positive predictive value for a computational tool, an equivalent of the local false discovery rate.
“This approach is sound and the local posterior corresponds to the local positive predictive value for a computational tool, an equivalent of the local false discovery rate.36,37”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [37]
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- Authors
- Efron, B., & Tibshirani, R.
- Affiliations
- Stanford University (US)
- Corresponding author
- Efron, Bradley
- ORCID
- Tibshirani, Robert
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2002-06
- Volume
- 23
- Issue
- 1
- Pages
- 70-86
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- OpenAlex
- W2091841926
- Cited by
- 684
- References
- 10
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 19 · 2023: 23 · 2022: 28 · 2021: 23 · 2020: 19 · 2019: 20 · 2018: 22 · 2017: 18 · 2016: 29 · 2015: 32 · 2014: 35 · 2013: 49 · 2012: 32
- FWCI
- 11.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- empirical Bayes, false discovery rate, microarray data, multiple comparisons, simultaneous inference, Wilcoxon test, gene expression, Benjamini-Hochberg procedure, two-sample problem
- MeSH terms
- Bayes Theorem, Breast Neoplasms, Female, Humans, Reproducibility of Results, Genes, BRCA1, Oligonucleotide Array Sequence Analysis, Genes, BRCA2
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