ArticleBiostatistics (Oxford, England)2020
Empirical Bayes shrinkage and false discovery rate estimation, allowing for unwanted variation.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Simultaneous inference for generalized linear models with unmeasured confounders.Journal of the American Statistical Association · 2025Article
- SEMbap: Bow-free covariance search and data de-correlation.PLoS computational biology · 2024Article
- The Bayesian lens and Bayesian blinkers.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING.Annals of statistics · 2022Article
- Estimating and accounting for unobserved covariates in high-dimensional correlated data.Journal of the American Statistical Association · 2022Article
- UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.Statistica Sinica · 2021Article
- Data-based RNA-seq simulations by binomial thinning.BMC bioinformatics · 2020Article
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2 authors.
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Abstract
We combine two important ideas in the analysis of large-scale genomics experiments (e.g. experiments that aim to identify genes that are differentially expressed between two conditions). The first is use of Empirical Bayes (EB) methods to handle the large number of potentially-sparse effects, and estimate false discovery rates and related quantities. The second is use of factor analysis methods to deal with sources of unwanted variation such as batch effects and unmeasured confounders. We describe a simple modular fitting procedure that combines key ideas from both these lines of research. This yields new, powerful EB methods for analyzing genomics experiments that account for both sparse effects and unwanted variation. In realistic simulations, these new methods provide significant gains in power and calibration over competing methods. In real data analysis, we find that different methods, while often conceptually similar, can vary widely in their assessments of statistical significance. This highlights the need for care in both choice of methods and interpretation of results.
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