Data Transformation for Clustering Utilization for Feature Detection in Mass Spectrometry

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Publikace nespadá pod Fakultu sportovních studií, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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BARTOŇ Vojtěch SKUTKOVA Helena

Rok publikování 2022
Druh Článek ve sborníku
Konference Bioinformatics and Biomedical Engineering (IWBBIO 2022) : Lecture Notes in Computer Science, vol 13347
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://doi.org/10.1007/978-3-031-07802-6_24
Doi http://dx.doi.org/10.1007/978-3-031-07802-6_24
Klíčová slova Mass spectrometry; Clustering; Feature identification
Popis 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.
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