ArticleOmics : a journal of integrative biology2013
Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments.
Article in Omics : a journal of integrative biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed, 110 citations in OpenAlex.
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- Dual-Ionization SPME-GC-HRMS Metabolomic Profiling of Broccoli Volatiles for the Construction of a Broccoli Metabolic Database.Molecules (Basel, Switzerland) · 2025Article
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- Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.Nature communications · 2025Article
- Comparative Study of Lipid Profile for Mice Treated with Cyclophosphamide by HPLC-HRMS and Bioinformatics.Metabolites · 2025Article
- Closing the Knowledge Gap of Post-Acquisition Sample Normalization in Untargeted Metabolomics.ACS measurement science au · 2024Article
- LC-HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients.International journal of molecular sciences · 2024Article
- Concept of Normativity in Multi-Omics Analysis of Axon Regeneration.Biomolecules · 2024Article
- MetaboReport: from metabolomics data analysis to comprehensive reporting.Bioinformatics (Oxford, England) · 2024Article
- Associational Effects of Desmodium Intercropping on Maize Resistance and Secondary Metabolism.Journal of chemical ecology · 2024Article
- The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics.Metabolites · 2024Article
- Article
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.BMC medicine · 2023Review
- metabolomicsR: a streamlined workflow to analyze metabolomic data in R.Bioinformatics advances · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments.
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