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Updated: Nov 28, 2025

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

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

Published on: November 29, 2024

Evaluating and minimizing batch effects in metabolomics.

Wei Han¹, Liang Li¹

¹Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada.

Mass Spectrometry Reviews

|November 25, 2020

Summary

This summary is machine-generated.

Batch effects in metabolomics can obscure phenotype-related changes. This review covers batch effect origins, detection, and correction methods to improve data reliability in metabolomic studies.

Keywords:

NMR batch effect mass spectrometry metabolome analysis metabolomics

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Last Updated: Nov 28, 2025

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

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Published on: November 29, 2024

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Area of Science:

Metabolomics
Analytical Chemistry
Bioinformatics

Background:

Accurate metabolomic profiling is crucial for understanding phenotype differences.
Advances in analytical techniques yield more precise metabolite detection.
Experimental artifacts, particularly batch effects, can compromise metabolomic data integrity.

Purpose of the Study:

To review the origins of batch effects in metabolomics.
To discuss methods for detecting inter-batch variations.
To present strategies for correcting batch effects in metabolomic data.

Main Methods:

Literature review of batch effect origins and impacts.
Analysis of current methods for batch effect detection.
Evaluation of data processing techniques for batch effect correction.

Main Results:

Batch effects arise from multiple experimental sources.
Various statistical approaches exist for detecting batch variations.
Correction methods aim to mitigate unwanted variability but pose challenges.

Conclusions:

Minimizing batch effects is an active and challenging research area.
Critical evaluation of existing methods is needed.
Further research is essential for developing improved or novel correction strategies.