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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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An approach for feature selection with data modelling in LC-MS metabolomics.

Ivan Plyushchenko1, Dmitry Shakhmatov, Timofey Bolotnik

  • 1Lomonosov Moscow State University, Chemistry Department, 119992, GSP-2, Lenin Hills, 1b3, Moscow, Russia. plyushchenko.ivan@gmail.com igorrodin@yandex.ru.

Analytical Methods : Advancing Methods and Applications
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Summary

This study introduces a data processing workflow for metabolomics studies using liquid chromatography-mass spectrometry (LC-MS). It efficiently identifies key features for classification and biomarker discovery in complex datasets.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Data Science

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) generates high-dimensional data.
  • Processing LC-MS data for metabolomics studies presents significant challenges.
  • Existing workflows may require extensive data annotation and complex modeling.

Purpose of the Study:

  • To propose a robust and efficient data processing workflow for LC-MS based metabolomics.
  • To enable effective feature selection and biomarker discovery.
  • To facilitate classification and exploratory analysis in high-dimensional metabolomics.

Main Methods:

  • Signal drift correction
  • Univariate analysis
  • Supervised learning
  • Feature selection
  • Unsupervised modeling
  • Receiver Operating Characteristic (ROC) analysis
  • Cross-validation

Main Results:

  • The workflow requires only an annotation-free peak table.
  • It produces a highly reduced set of the most relevant features.
  • Validation was performed using ROC analysis, cross-validation, and unsupervised projection.
  • The workflow was optimized on an experimental dataset and tested on 36 datasets from 21 public projects.

Conclusions:

  • The proposed workflow is effective for classification in high-dimensional metabolomics.
  • It serves as a valuable first step for exploratory analysis, data projection, biomarker selection, and data integration.