Mass Spectrometry
Online ISSN : 2186-5116
Print ISSN : 2187-137X
ISSN-L : 2186-5116
Original Article
Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
Evgeny ZhvanskyAnatoly SorokinVsevolod ShurkhayDenis ZavorotnyukDenis BormotovStanislav PekovAlexander PotapovEvgeny NikolaevIgor Popov
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Supplementary material

2021 Volume 10 Issue 1 Pages A0094


Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. In this paper tumor tissues of astrocytoma and glioblastoma are compared. The vast majority of the data representation methods are hard to use, as the number of features is high and the amount of samples is limited. Furthermore, the ratio of features and samples number restricts the use of many machine learning methods. The number of features could be reduced through feature selection algorithms or dimensionality reduction methods. Different algorithms of dimensionality reduction are considered along with the traditional noise thresholding for the mass spectra. From our analysis, the Isomap algorithm appears to be the most effective dimensionality reduction algorithm for negative mode, whereas the positive mode could be processed with a simple noise reduction by a threshold. Also, negative and positive mode correspond to different sample properties: negative mode is responsible for the inner variability and the details of the sample, whereas positive mode describes measurement in general.

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© 2021 Evgeny Zhvansky, Anatoly Sorokin, Vsevolod Shurkhay, Denis Zavorotnyuk, Denis Bormotov, Stanislav Pekov, Alexander Potapov, Evgeny Nikolaev, and Igor Popov. This is an open-access article distributed under the terms of Creative Commons Attribution Non-Commercial 4.0 International License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

This article is licensed under a Creative Commons [Attribution-NonCommercial 4.0 International] license.
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