Improving metabolomics data comparability without long term quality controls using a post-acquisition correction strategy
The proposed post-acquisition correction strategy, which combines batch-wise standardization and mixed modeling, enhances data comparability and scalability for metabolomics studies. By addressing both batch and group effects, this approach minimizes the influence of analytical conditions while preserving biological variability. Therefore, it offers a valuable tool for harmonizing datasets across different studies or cohorts without common long-term quality control samples.
Authors
- Elfried Salanon (ORCID: https://orcid.org/0009-0007-7560-2144)
- Stéphanie Durand (ORCID: https://orcid.org/0000-0003-2434-9429)
- Estelle Pujos‐Guillot (ORCID: https://orcid.org/0000-0002-4693-5712)
- Delphine Centeno (ORCID: https://orcid.org/0000-0002-2747-6290)
- Julien Boccard (ORCID: https://orcid.org/0000-0001-5913-9566)
- Blandine Comte (ORCID: https://orcid.org/0000-0002-4662-6581)
Institutions
- University of Geneva (CH)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- University of Clermont Auvergne (FR)
- Centre de Recherche en Nutrition Humaine d'Auvergne (FR)
- Clermont Université (FR)
Publication Details
- Journal
- Analytica Chimica Acta
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1016/j.aca.2025.344753
- Citations
- 1
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.81