ArticleMetabolites2020
Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Emerging Quantitative Targeted Metabolomics Approaches for Future Inborn Errors of Metabolism Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Characterising the urinary metabolome in HIV/TB coinfection and the impact of antiretroviral treatment.BMC infectious diseases · 2026Article
- Multiplatform untargeted metabolomics.Magnetic resonance in chemistry : MRC · 2023Review
- Uveal Melanoma Patients Have a Distinct Metabolic Phenotype in Peripheral Blood.International journal of molecular sciences · 2023Article
- Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data.Metabolites · 2023Article
- A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.Translational pediatrics · 2022Review
- Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders.Journal of inherited metabolic disease · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop
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Registered trials
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