ArticleBMC bioinformatics2018
Alternative empirical Bayes models for adjusting for batch effects in genomic studies.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 4 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy.Nature cell biology · 2026Article
- Epithelial remodeling and microbial dysbiosis in the lower respiratory tract of vitamin A-deficient mouse lungs.Scientific reports · 2026Article
- A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch.Frontiers in microbiology · 2026Article
- Species-specific phylloplane responses to changes in external pH.Journal of experimental botany · 2025Article
- Commentary: a review of technical considerations for planning an RNA-Sequencing experiment.BMC genomics · 2025Review
- The circulating cell-free DNA landscape in sepsis is dominated by impaired liver clearance.Cell genomics · 2025Article
- Aging-related signature stratifies LUAD prognosis and uncovers senescence-induced chemoresistance via NF-κB/RELA activation.Respiratory research · 2025Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundCombining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.
resultsHere we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios.
conclusionsWe demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.