ArticleNature communications2021
A hierarchical approach to removal of unwanted variation for large-scale metabolomics data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Apparent Saturation of Branched-Chain Amino Acid Catabolism After High Dietary Milk Protein Intake in Healthy Adults.The Journal of clinical endocrinology and metabolism · 2025Trial
- Rapid benefits in older age from transition to whole food diet regardless of protein source or fat to carbohydrate ratio: Arandomised control trial.Aging cell · 2024Trial
- Machine-learning-assisted screening of key flavor compounds in pumpkins.Journal of the science of food and agriculture · 2026Article
- Donor-Independent Metabolomics Enables Bloodstain Age Determination at Crime Scenes.Journal of proteome research · 2026Article
- Robust Metabolomics Data Normalization across Scales and Experimental Designs.Analytical chemistry · 2026Article
- Biomolecular profiling for noninvasive health monitoring.Nature biotechnology · 2026Review
- Metabolomics and Human Health: Progress, Insights, Challenges and the Concept of a Healthy Metabolome.Biomolecules · 2026Review
- Comment on "A prognostic microRNA-based signature for localized clear cell renal cell carcinoma: the Bio-miR study".British journal of cancer · 2026Article
- Challenges and Good Practices in Preprocessing and Normalization of Untargeted DNA Adductomics Data in Exposomics Research.Analytical chemistry · 2026Article
- Ultra-Processed Foods and the Cardiovascular-Kidney-Metabolic Continuum: Integrating Epidemiological, Multi-Omics, and Translational Evidence.Nutrients · 2026Review
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Differential Metabolic Dysregulations in Hepatocellular Carcinoma and Cirrhosis: Insights into Lipidomic Signatures.Biomolecules · 2025Article
- Plasma Metabolic Profile with Machine Learning Reveals Distinct Diagnostic and Biological Signatures for Pathologic Myopia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Untargeted metabolomics analysis of serum metabolic signatures as novel biomarkers for gastric carcinoma.World journal of clinical oncology · 2025Article
- Mechanical unloading is accompanied by reverse metabolic remodelling in the failing heart: Identification of a novel citraconate-mediated pathway.European journal of heart failure · 2025Article
- Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling.BMC pregnancy and childbirth · 2024Article
- Unlocking metabolic insights with mouse genetic diversity.The EMBO journal · 2024Article
- An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.Nature communications · 2024Article
- Cardiac Substrate Utilization and Relationship to Invasive Exercise Hemodynamic Parameters in HFpEF.JACC. Basic to translational science · 2024Article
- Microbiome metabolite quantification methods enabling insights into human health and disease.Methods (San Diego, Calif.) · 2024Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.
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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.