Data Transformation for Clustering Utilization for Feature
Detection in Mass Spectrometry

D 2022

Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

BARTOŇ, Vojtěch a Helena SKUTKOVA

Základní údaje

Originální název

Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

Autoři

BARTOŇ, Vojtěch (203 Česká republika, garant, domácí) a Helena SKUTKOVA

Vydání

Cham, Bioinformatics and Biomedical Engineering (IWBBIO 2022) : Lecture Notes in Computer Science, vol 13347, od s. 288-299, 12 s. 2022

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14310/22:00128788

Organizační jednotka

Přírodovědecká fakulta

ISBN

978-3-031-07801-9

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-07802-6_24

UT WoS

000871766000024

Klíčová slova anglicky

Mass spectrometry; Clustering; Feature identification

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 1. 3. 2023 10:11, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

Feature detection and peak detection are one of the first steps of mass spectrometry data processing. This data comes in large volumes; thus, the processing needs to be optimized, not overloaded. State-of-the-art clustering algorithms can not perform feature detection for several reasons. First issue is the volume of the data, second is the disparity of the sampling frequency in the MZ and RT axis. Here we show the data transformation to utilize the clustering algorithms without the need to redefine its kernel. Data are first pre-clustered to obtain regions that can be processed independently. Then we transform the data so that the numerical differences between consecutive points should be the same in both space axes. We applied a set of clustering algorithms for each region to find the features, and we compared the result with the Gridmass peak detector. These findings may facilitate better utilization of the 2D clustering method as feature detectors for mass spectra.

Návaznosti

EF17_043/0009632, projekt VaV

Název: CETOCOEN Excellence

LM2018121, projekt VaV

Název: Výzkumná infrastruktura RECETOX (Akronym: RECETOX RI)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, RECETOX RI

Citovat

BARTOŇ, Vojtěch a Helena SKUTKOVA. Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry. In Rojas I., Valenzuela O., Rojas F., Herrera L.J., Ortuño F. Bioinformatics and Biomedical Engineering (IWBBIO 2022) : Lecture Notes in Computer Science, vol 13347. Cham: Springer, 2022, s. 288-299. ISBN 978-3-031-07801-9. Dostupné z: https://dx.doi.org/10.1007/978-3-031-07802-6_24.

@inproceedings{2262797,
   author = {Bartoň, Vojtěch and Skutkova, Helena},
   address = {Cham},
   booktitle = {Bioinformatics and Biomedical Engineering (IWBBIO 2022) : Lecture Notes in Computer Science, vol 13347},
   doi = {http://dx.doi.org/10.1007/978-3-031-07802-6_24},
   editor = {Rojas I., Valenzuela O., Rojas F., Herrera L.J., Ortuño F.},
   keywords = {Mass spectrometry; Clustering; Feature identification},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-07801-9},
   pages = {288-299},
   publisher = {Springer},
   title = {Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry},
   url = {https://doi.org/10.1007/978-3-031-07802-6_24},
   year = {2022}
}

TY  - JOUR
ID  - 2262797
AU  - Bartoň, Vojtěch - Skutkova, Helena
PY  - 2022
TI  - Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry
PB  - Springer
CY  - Cham
SN  - 9783031078019
KW  - Mass spectrometry
KW  - Clustering
KW  - Feature identification
UR  - https://doi.org/10.1007/978-3-031-07802-6_24
N2  - Feature detection and peak detection are one of the first steps of mass spectrometry data processing. This data comes in large volumes; thus, the processing needs to be optimized, not overloaded. State-of-the-art clustering algorithms can not perform feature detection for several reasons. First issue is the volume of the data, second is the disparity of the sampling frequency in the MZ and RT axis. Here we show the data transformation to utilize the clustering algorithms without the need to redefine its kernel. Data are first pre-clustered to obtain regions that can be processed independently. Then we transform the data so that the numerical differences between consecutive points should be the same in both space axes. We applied a set of clustering algorithms for each region to find the features, and we compared the result with the Gridmass peak detector. These findings may facilitate better utilization of the 2D clustering method as feature detectors for mass spectra.
ER  -

BARTOŇ, Vojtěch a Helena SKUTKOVA. Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry. In Rojas I., Valenzuela O., Rojas F., Herrera L.J., Ortuño F. \textit{Bioinformatics and Biomedical Engineering (IWBBIO 2022) : Lecture Notes in Computer Science, vol 13347}. Cham: Springer, 2022, s.~288-299. ISBN~978-3-031-07801-9. Dostupné z: https://dx.doi.org/10.1007/978-3-031-07802-6\_{}24.

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