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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Zixuan Zhang1, Huaxu Yu1, Ethan Wong-Ma1

  • 1Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, BC, Canada.

Analytical Chemistry
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

A new tool, AVIR, uses machine learning to identify and correct computational variation in metabolomics data. This improves the accuracy of quantitative results in untargeted metabolomics analysis.

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Area of Science:

  • Metabolomics
  • Computational Biology
  • Data Analysis

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) based metabolomics data processing can introduce quantitative uncertainty.
  • This uncertainty, termed computational variation, can affect the reliability of results.

Purpose of the Study:

  • To develop a computational solution for automatically recognizing metabolic features with computational variation.
  • To improve quantitative certainty in untargeted metabolomics analysis.

Main Methods:

  • Developed AVIR (Accurate eValuation of alIgnment and integRation), a support vector machine-based machine learning tool.
  • Trained AVIR on 696 manually curated metabolic features, achieving 94% accuracy in cross-validation.
  • Validated AVIR on external datasets, showing 84%-97% accuracy.

Main Results:

  • AVIR successfully identified features with computational variation in a large-scale metabolomics study.
  • Manual correction of identified features reduced relative intensity differences by over 20% in 75.3% of samples.
  • Demonstrated AVIR's effectiveness in reducing computational variation.

Conclusions:

  • AVIR is a valuable tool for improving quantitative certainty in untargeted metabolomics.
  • Automated identification and correction of computational variation enhance data reliability.
  • AVIR facilitates more accurate downstream biological interpretations from metabolomics data.