ArticlePloS one2017
RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Mass Spectrometric Identification of Urinary Biomarkers of Pulmonary Tuberculosis.EBioMedicine · 2018Trial
- Ultra-Processed Foods and the Cardiovascular-Kidney-Metabolic Continuum: Integrating Epidemiological, Multi-Omics, and Translational Evidence.Nutrients · 2026Review
- Multimodal MRI-based radiomics model for predicting short-term efficacy in nasopharyngeal carcinoma.Frontiers in medicine · 2025Article
- Sialic acids Neu5Ac and KDN in adipose tissue samples from individuals following habitual vegetarian or non-vegetarian dietary patterns.Scientific reports · 2023Article
- Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics.Analytical chemistry · 2023Article
- Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples.Journal of proteome research · 2022Article
- A hierarchical approach to removal of unwanted variation for large-scale metabolomics data.Nature communications · 2021Article
- Untargeted metabolomics profiling and hemoglobin normalization for archived newborn dried blood spots from a refrigerated biorepository.Journal of pharmaceutical and biomedical analysis · 2020Article
- Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing.Scientific reports · 2020Article
- ESTIMATION AND INFERENCE IN METABOLOMICS WITH NON-RANDOM MISSING DATA AND LATENT FACTORS.The annals of applied statistics · 2020Article
- Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry.Analytical and bioanalytical chemistry · 2019Article
- Promises and pitfalls of untargeted metabolomics.Journal of inherited metabolic disease · 2018Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of batch effects without the need for internal controls or prior knowledge about the nature and sources of unwanted variation in metabolomics data. We further introduce an algorithm-RRmix-within the family of latent factor models and illustrate its suitability for differential abundance analysis in the presence of strong batch effects. Together this analysis provides a framework for systematically standardizing metabolomics data.
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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.