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Algorithm of Combining Chromatography–Mass Spectrometry Untargeted Profiling and Multivariate Analysis for Identification of Marker Substances in Samples of Complex Composition

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Abstract

A viral development of statistical data processing, computing capabilities, chromatography–mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, and metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results and at the same time hinder consolidation and analysis of data gained in large-scale multiday experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography–mass spectrometry. The markers were selected using methods of multivariate analysis, including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

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Funding

This work was supported by the Russian Foundation for Basic Research (grant no.: Aspiranty 19-33-90071).

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Authors and Affiliations

  1. Department of Chemistry, Moscow State University, 119991, Moscow, Russia

    I. V. Plyushchenko & I. A. Rodin

  2. State Scientific Center of Coloproctology, 123423, Moscow, Russia

    D. G. Shakhmatov

Authors
  1. I. V. Plyushchenko
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  2. D. G. Shakhmatov
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  3. I. A. Rodin
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Correspondence to I. V. Plyushchenko.

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Plyushchenko, I.V., Shakhmatov, D.G. & Rodin, I.A. Algorithm of Combining Chromatography–Mass Spectrometry Untargeted Profiling and Multivariate Analysis for Identification of Marker Substances in Samples of Complex Composition. Inorg Mater 57, 1397–1403 (2021). https://doi.org/10.1134/S0020168521140089

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  • DOI: https://doi.org/10.1134/S0020168521140089

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