ArticleJournal of proteome research2019
MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment.
Article in Journal of proteome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Arginine catabolism metabolites and atrial fibrillation or heart failure risk: 2 case-control studies within the Prevención con Dieta Mediterránea (PREDIMED) trial.The American journal of clinical nutrition · 2022Article
- Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.Analytical chemistry · 2021Review
- A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics.Metabolites · 2019Article
- Lysine pathway metabolites and the risk of type 2 diabetes and cardiovascular disease in the PREDIMED study: results from two case-cohort studies.Cardiovascular diabetology · 2019Article
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
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Authors and funding
18 authors.
Funding
Abstract
High-throughput metabolomics using liquid chromatography and mass spectrometry (LC/MS) provides a useful method to identify biomarkers of disease and explore biological systems. However, the majority of metabolic features detected from untargeted metabolomics experiments have unknown ion signatures, making it critical that data should be thoroughly quality controlled to avoid analyzing false signals. Here, we present a postalignment method relying on intermittent pooled study samples to separate genuine metabolic features from potential measurement artifacts. We apply the method to lipid metabolite data from the PREDIMED (PREvención con DIeta MEDi-terránea) study to demonstrate clear removal of measurement artifacts. The method is publicly available as the R package MetProc, available on CRAN under the GPL-v2 license.
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Registered trials
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