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How Low Can You Go? Selecting Intensity Thresholds for Untargeted Metabolomics Data Preprocessing.

Joelle Houriet1, Warren S Vidar1, Preston K Manwill1

  • 1Department of Chemistry & Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina 27402, United States.

Analytical Chemistry
|December 14, 2022
PubMed
Summary

Setting low signal thresholds in untargeted mass spectrometry (MS) metabolomics enhances data quality by improving detection limits and feature identification. This approach yields more informative datasets for comprehensive analysis.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Data Science

Background:

  • Untargeted mass spectrometry (MS) metabolomics is crucial for analyzing complex biological mixtures.
  • Data preprocessing significantly impacts the quality and reliability of metabolomics data analysis.
  • Selecting appropriate signal intensity thresholds is a critical, yet challenging, step in metabolomics data processing.

Purpose of the Study:

  • To evaluate the effect of varying signal intensity thresholds on data interpretation in untargeted mass spectrometry metabolomics.
  • To determine the optimal threshold settings for maximizing information content and analytical performance.

Main Methods:

  • Comparison of data interpretation across a range of feature intensity thresholds using an example metabolomics dataset.
  • Analysis of feature detection, isotope patterns, MS-MS fragmentation spectra, and in-source clusters at different thresholds.
  • Application of principal component analysis (PCA) to discriminate samples using datasets processed with low- and high-intensity thresholds.

Main Results:

  • Low signal thresholds improved the limit of detection for metabolites.
  • Increased detection of features with isotope patterns and MS-MS fragmentation spectra was observed at lower thresholds.
  • Lower thresholds also increased the identification of in-source clusters and fragments for known compounds.
  • Principal component analysis (PCA) yielded comparable sample discrimination results regardless of threshold intensity.

Conclusions:

  • Setting low-intensity thresholds in untargeted metabolomics generates the most information-rich datasets.
  • High-intensity thresholds may suffice for qualitative sample comparisons using PCA, simplifying data processing and reducing computational time.