ArticleBMC bioinformatics2019
Filtering procedures for untargeted LC-MS metabolomics data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Metabolome analyses of the rumen, feces and blood reveal separate and simultaneous impacts of increased dietary concentrate and hindgut starch flow in Holstein cows.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Retrospective Metabolomics Profiling of Clinical Urine Drug Screen Samples Reveals Features Associated with Opiate Exposure.Metabolites · 2026Article
- Differential metabolomic effects of progestagen-based estrus synchronisation in South African Dohne Merino ewes.Tropical animal health and production · 2026Article
- Phthalates measured at birth and risk of testicular cancer in adolescents and young adults.JNCI cancer spectrum · 2026Article
- Seasonal patterns of DOM molecules are linked to microbial functions in the oligotrophic ocean.mSystems · 2026Article
- Development of a novel humanized gut-brain axis model as a tool toward personalized nutrition.Communications biology · 2026Article
- Olive oil-derived monounsaturated fat influences metabolic signatures in serotonergic regions of the brain in broiler chicken.Frontiers in physiology · 2026Article
- Data Analysis in Extreme Resolution Mass Spectrometry Untargeted Metabolomics.Advances in experimental medicine and biology · 2026Review
- Distinct metabolic architectures differentiate HLB-tolerant lemon and susceptible sweet orange.Frontiers in plant science · 2026Article
- Article
- Metabolomic biomarkers predict long-term physical function in survivors of acute respiratory failure.Respiratory research · 2025Article
- Bioactive compound identification without fractionation: an Ocimum spp. case study.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Molecular Reactivity in Maternal Pregnancy Blood and Neonatal Dried Blood Spots Is Associated with the Risk of Pediatric Acute Lymphoblastic Leukemia.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Metabolomic Biomarkers Predict Long-term Physical Function in Survivors of Acute Respiratory Failure.Research square · 2025Article
- Effective data visualization strategies in untargeted metabolomics.Natural product reports · 2025Review
- Application of Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics to Reveal Metabolites Related to Antioxidant Activity in Buckwheat Honey.Molecules (Basel, Switzerland) · 2025Article
- Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer.Nature methods · 2025Article
- Lipids as key biomarkers in unravelling the pathophysiology of obesity-related metabolic dysregulation.Heliyon · 2025Article
- Development of an optimised method for the analysis of human blood plasma samples by atmospheric solids analysis probe mass spectrometry.International journal of mass spectrometry · 2025Article
- Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.Analytical and bioanalytical chemistry · 2025Article
6 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
Abstract
backgroundUntargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.
resultsUsing metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering .
conclusionsOur proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.
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