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Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues

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  • Denis S. Zavorotnyuk

    (The Moscow Institute of Physics and Technology, National Research University, 141701 Dolgoprudny, Russia)

  • Anatoly A. Sorokin

    (The Moscow Institute of Physics and Technology, National Research University, 141701 Dolgoprudny, Russia)

  • Stanislav I. Pekov

    (Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
    Siberian State Medical University, 634050 Tomsk, Russia)

  • Denis S. Bormotov

    (The Moscow Institute of Physics and Technology, National Research University, 141701 Dolgoprudny, Russia)

  • Vasiliy A. Eliferov

    (The Moscow Institute of Physics and Technology, National Research University, 141701 Dolgoprudny, Russia)

  • Konstantin V. Bocharov

    (V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Science, 119334 Moscow, Russia)

  • Eugene N. Nikolaev

    (Skolkovo Institute of Science and Technology, 121205 Moscow, Russia)

  • Igor A. Popov

    (The Moscow Institute of Physics and Technology, National Research University, 141701 Dolgoprudny, Russia)

Abstract

The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models’ accuracy.

Suggested Citation

  • Denis S. Zavorotnyuk & Anatoly A. Sorokin & Stanislav I. Pekov & Denis S. Bormotov & Vasiliy A. Eliferov & Konstantin V. Bocharov & Eugene N. Nikolaev & Igor A. Popov, 2023. "Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues," Data, MDPI, vol. 8(1), pages 1-9, January.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:1:p:21-:d:1037357
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Denis S. Zavorotnyuk & Stanislav I. Pekov & Anatoly A. Sorokin & Denis S. Bormotov & Nikita Levin & Evgeny Zhvansky & Savva Semenov & Polina Strelnikova & Konstantin V. Bocharov & Alexander Vorobiev &, 2021. "Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry," Data, MDPI, vol. 6(12), pages 1-7, December.
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