Publication detail

Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

BARTOŇ, V. ŠKUTKOVÁ, H.

Original Title

Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Clustering; Feature identification; Mass spectrometry

Authors

BARTOŇ, V.; ŠKUTKOVÁ, H.

Released

1. 7. 2022

Publisher

Springer

ISBN

978-3-031-07801-9

Book

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

Edition

13347

Edition number

2

Pages from

288

Pages to

299

Pages count

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"
}