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Updated: Jul 5, 2026
07:34
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
Published on: March 14, 2013
Instrumental and experimental effects in LC-MS-based metabolomics
Lyle Burton1, Gordana Ivosev, Stephen Tate
1Applied Biosystems/MDS Sciex, 71 Four Valley Drive, Concord, Ontario, Canada. lyle.burton@sciex.com
Summary
Metabolomics studies face experimental complexity causing variance that can distort data. This research identifies artifact sources and demonstrates detection methods using visualization and statistical tools for accurate analysis.
Area of Science:
- Analytical Chemistry
- Bioinformatics
- Systems Biology
Background:
- Metabolomics studies are susceptible to experimental complexity, introducing uncontrolled variance.
- This variance, unrelated to biological effects, can obscure or distort data analysis.
- Some sources of variance are controllable, while others are inherent to analytical techniques.
Purpose of the Study:
- To identify and describe sources of experimental artifacts in metabolomics.
- To illustrate how these artifacts appear in metabolomics data.
- To present methods for detecting and treating artifacts before multivariate analysis.
Main Methods:
- Review of common sources of experimental variance in metabolomics.
- Demonstration of artifact detection using visualization techniques.
- Application of statistical tools for artifact identification.
Main Results:
- Identification of key artifact sources inherent to metabolomics experiments.
- Examples of artifact visualization and statistical detection.
- Validation of artifact treatment strategies prior to multivariate analysis.
Conclusions:
- Experimental artifacts are a significant challenge in metabolomics data analysis.
- Visualization and statistical tools are effective for detecting these artifacts.
- Proper artifact treatment is crucial for reliable multivariate analysis in metabolomics.
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