ArticleBriefings in bioinformatics2023
PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of gut microbiome reveals patterns of dysbiosis in colorectal cancer patients.Journal of medical microbiology · 2025Pooled it
- Microbiome-based therapeutics forGut microbes · 2026Review
- Microbiota and Mycobiota Dysbiosis in Sinonasal Fungus Ball: A Paired Lesional and Contralateral Analysis.Laryngoscope investigative otolaryngology · 2026Article
- mBatchNet: an interactive web server for diagnosis, correction, and benchmarking of batch effects in microbiome data.Bioinformatics (Oxford, England) · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Meta-analytic microbiome target discovery for immune checkpoint inhibitor response in advanced melanoma.Communications medicine · 2026Article
- Machine learning-driven clinical decision support for liver cirrhosis: a gut microbiome-based web prediction model with explainable AI integration.BMC gastroenterology · 2026Article
- Diversity and random forest models of oral microbiomes in periodontal health using publicly available data.Journal of periodontology · 2026Article
- Demography-dependent variability in the human tumor mycobiome.Microbiology spectrum · 2026Article
- ABaCo: addressing heterogeneity challenges in metagenomic data integration with adversarial generative models.Nucleic acids research · 2026Article
- Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.The Journal of infectious diseases · 2026Review
- Design, processing, and modeling for longitudinal multiomics microbiome data.Frontiers in cellular and infection microbiology · 2026Review
- From forest to farm: the impact of a broad spectrum of lifestyles on the porcine gut microbiota.Current research in microbial sciences · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- LimROTS: a hybrid method integrating empirical Bayes and reproducibility-optimized statistics for robust differential expression analysis.Bioinformatics (Oxford, England) · 2025Article
- The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes.Nature methods · 2025Review
- Microbiome data integration via shared dictionary learning.Nature communications · 2025Article
- A cautionary tale of batch corrections on confounded microbiome community profiles.iMetaOmics · 2025Article
- A systematic benchmark of integrative strategies for microbiome-metabolome data.Communications biology · 2025Article
- Microbiota-centered interventions to boost immune checkpoint blockade therapies.The Journal of experimental medicine · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.
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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.