Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The impact of short-term ambient air pollution on CPAP therapy outcomes in obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung·2026

Same author

Cord-blood black carbon burden is associated with coordinated inflammatory and heme-metabolic transcriptional programs at birth in Bradford, United Kingdom.

The Science of the total environment·2026

Same author

Exposure to currently used pesticides in Belgian children: Urinary biomonitoring and risk assessment of frequently detected chlorpyrifos and pyrethroid metabolites.

Environmental research·2026

Same author

The East Flanders Prospective Twin Survey: The Methodology of a Population-Based Registry With Biobank.

Twin research and human genetics : the official journal of the International Society for Twin Studies·2026

Same author

Challenges and Good Practices in Preprocessing and Normalization of Untargeted DNA Adductomics Data in Exposomics Research.

Analytical chemistry·2026

Same author

Black carbon is detectable in association with small extracellular vesicles in fetal circulation.

Environment international·2026

Same journal

Biodegradable Self-Powered Electrotherapy Patch for Integrated Smart Wound Management.

Analytical chemistry·2026

Same journal

Metabolite Fraction Libraries for Quantitative NMR Metabolomics.

Analytical chemistry·2026

Same journal

Self-Contained Lateral-Flow Microfluidic Bead-Based Assay for Rapid Quantification of Early-Stage Kidney Biomarkers.

Analytical chemistry·2026

Same journal

Overcoming the Debye Shielding Effect with Concave-Convex Structures for Sensitivity-Enhanced Thin-Film Transistors.

Analytical chemistry·2026

Same journal

Mode-Phase-Difference Photothermal Spectroscopy Assisted by a Bent Biconically Tapered Microfiber for Gas Sensing.

Analytical chemistry·2026

Same journal

Negative-Pressure-Actuated Microfluidics: A Dual-Mode Point-of-Care Sensor for Allergen-Specific IgE in Interstitial Fluid.

Analytical chemistry·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 12, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Robust Metabolomics Data Normalization across Scales and Experimental Designs.

Matthijs Vynck¹, Pablo Vangeenderhuysen¹, Ellen De Paepe¹

¹Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Salisburylaan 133, Merelbeke 9820, Belgium.

Analytical Chemistry

|June 11, 2026

Summary

This summary is machine-generated.

New robust normalization methods, rLOESS, rGAM, and tGAM, reduce technical variance in metabolomics studies. These methods improve data quality and downstream analysis by mitigating outliers and batch effects.

More Related Videos

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Related Experiment Videos

Last Updated: Jun 12, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Area of Science:

Analytical Chemistry
Bioinformatics
Systems Biology

Background:

Metabolomics studies using liquid chromatography-mass spectrometry (LC-MS) are susceptible to signal drift and batch effects.
These technical variations introduce noise, hindering the discovery of true biological insights.
Current quality control (QC) sample-based normalization methods are often compromised by outliers, limiting their effectiveness.

Purpose of the Study:

To develop and evaluate robust normalization methods for LC-MS metabolomics data.
To enhance the accuracy and reliability of biological discoveries by minimizing technical variance.
To provide a versatile R package, Metanorm, for implementing these advanced normalization strategies.

Main Methods:

Introduction of three robust normalization methods: rLOESS, rGAM, and tGAM, designed to resist outliers.
Utilization of additive models in rGAM and tGAM for flexible nonlinear modeling and differential sample weighting.
Development of the Metanorm R package for integrated normalization, visualization, and parallel processing.

Main Results:

Robust methods demonstrated improved replicate concordance and reduced drift and batch effects compared to existing strategies in both in silico and experimental datasets.
Simulations showed enhanced recovery of underlying signals with robust methods.
Distinct differential abundance results were observed, underscoring the impact of normalization on statistical inference.

Conclusions:

The proposed robust normalization methods, particularly tGAM, offer superior performance in metabolomics data analysis.
Metanorm package provides a versatile and efficient tool for robust normalization across various metabolomics study scales and setups.
These advancements are crucial for improving the reliability and reproducibility of metabolomics research.