Detail publikace

Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

BARTOŇ, V. ŠKUTKOVÁ, H.

Originální název

Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Clustering; Feature identification; Mass spectrometry

Autoři

BARTOŇ, V.; ŠKUTKOVÁ, H.

Vydáno

1. 7. 2022

Nakladatel

Springer

ISBN

978-3-031-07801-9

Kniha

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Edice

13347

Číslo edice

2

Strany od

288

Strany do

299

Strany počet

12

URL

https://link.springer.com/chapter/10.1007/978-3-031-07802-6_24

BibTex

@inproceedings{BUT178506,
  author="Vojtěch {Bartoň} and Helena {Vítková}",
  title="Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry",
  booktitle="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  year="2022",
  series="13347",
  number="2",
  pages="288--299",
  publisher="Springer",
  doi="10.1007/978-3-031-07802-6\{_}24",
  isbn="978-3-031-07801-9",
  url="https://link.springer.com/chapter/10.1007/978-3-031-07802-6_24"
}