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Related Experiment Video

Updated: May 8, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Batch profiling calibration for robust NMR metabonomic data analysis.

Anne Fages1, Clément Pontoizeau, Elodie Jobard

  • 1Institut des Sciences Analytiques, Centre de RMN à très hauts champs, CNRS/ENS Lyon/UCB Lyon-1, Université de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France.

Analytical and Bioanalytical Chemistry
|August 27, 2013
PubMed
Summary

This study introduces a Grouped-Batch Profile (GBP) calibration strategy to correct systematic variations in nuclear magnetic resonance (NMR) metabolomic data. GBP improves the accuracy of identifying biological variability in large-scale studies and cell analyses.

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Area of Science:

  • Metabolomics
  • Nuclear Magnetic Resonance (NMR) Spectroscopy
  • Bioinformatics

Background:

  • Metabonomic studies analyze metabolic fingerprints across numerous samples.
  • Batch effects from sample preparation and data acquisition can introduce systematic variation.
  • Removing batch effects is crucial for accurately identifying biological variability.

Purpose of the Study:

  • To introduce a Grouped-Batch Profile (GBP) calibration strategy.
  • To adjust nuclear magnetic resonance (NMR) metabolomic datasets for batch effects.
  • To enhance the analysis of biological variability in metabonomic studies.

Main Methods:

  • Developed a Grouped-Batch Profile (GBP) calibration strategy.
  • Applied GBP to adjust NMR metabolomic data for batch variations.
  • Utilized quality control samples in a large-scale NMR epidemiological study.

Main Results:

  • GBP effectively calibrates NMR metabolomic datasets for batch effects.
  • The method was successfully applied to NMR-based cell extract investigations.
  • GBP significantly improved the predictive power of statistical models for discriminant analysis.

Conclusions:

  • GBP is an effective strategy for correcting batch effects in NMR metabolomics.
  • The method enhances the focus on relevant biological variability.
  • GBP is broadly applicable to NMR metabolomic cohort studies.