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Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues

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 t...

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Autores principales: Zhvansky, Evgeny, Sorokin, Anatoly, Shurkhay, Vsevolod, Zavorotnyuk, Denis, Bormotov, Denis, Pekov, Stanislav, Potapov, Alexander, Nikolaev, Evgeny, Popov, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Mass Spectrometry Society of Japan 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953827/
https://www.ncbi.nlm.nih.gov/pubmed/33747696
http://dx.doi.org/10.5702/massspectrometry.A0094
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author Zhvansky, Evgeny
Sorokin, Anatoly
Shurkhay, Vsevolod
Zavorotnyuk, Denis
Bormotov, Denis
Pekov, Stanislav
Potapov, Alexander
Nikolaev, Evgeny
Popov, Igor
author_facet Zhvansky, Evgeny
Sorokin, Anatoly
Shurkhay, Vsevolod
Zavorotnyuk, Denis
Bormotov, Denis
Pekov, Stanislav
Potapov, Alexander
Nikolaev, Evgeny
Popov, Igor
author_sort Zhvansky, Evgeny
collection PubMed
description 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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spelling pubmed-79538272021-03-18 Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues Zhvansky, Evgeny Sorokin, Anatoly Shurkhay, Vsevolod Zavorotnyuk, Denis Bormotov, Denis Pekov, Stanislav Potapov, Alexander Nikolaev, Evgeny Popov, Igor Mass Spectrom (Tokyo) Original Article 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. The Mass Spectrometry Society of Japan 2021 2021-03-13 /pmc/articles/PMC7953827/ /pubmed/33747696 http://dx.doi.org/10.5702/massspectrometry.A0094 Text en Copyright © 2021 Evgeny Zhvansky, Anatoly Sorokin, Vsevolod Shurkhay, Denis Zavorotnyuk, Denis Bormotov, Stanislav Pekov, Alexander Potapov, Evgeny Nikolaev, and Igor Popov. http://creativecommons.org/licenses/by/2.5/ 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.
spellingShingle Original Article
Zhvansky, Evgeny
Sorokin, Anatoly
Shurkhay, Vsevolod
Zavorotnyuk, Denis
Bormotov, Denis
Pekov, Stanislav
Potapov, Alexander
Nikolaev, Evgeny
Popov, Igor
Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
title Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
title_full Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
title_fullStr Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
title_full_unstemmed Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
title_short Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues
title_sort comparison of dimensionality reduction methods in mass spectra of astrocytoma and glioblastoma tissues
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953827/
https://www.ncbi.nlm.nih.gov/pubmed/33747696
http://dx.doi.org/10.5702/massspectrometry.A0094
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