Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1021/acs.analchem.0c00899
Volltext Autorenversion
Titel (primär) Symbolic aggregate approximation improves gap filling in high-resolution mass spectrometry data processing
Autor Müller, E.; Huber, C.E.; Brack, W.; Krauss, M. ORCID logo ; Schulze, T. ORCID logo
Quelle Analytical Chemistry
Erscheinungsjahr 2020
Department WANA
Band/Volume 92
Heft 15
Seite von 10425
Seite bis 10432
Sprache englisch
Supplements https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.0c00899/suppl_file/ac0c00899_si_001.pdf
https://pubs.acs.org/doi/10.1021/acs.analchem.0c00899?goto=supporting-info
Abstract Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23416
Müller, E., Huber, C.E., Brack, W., Krauss, M., Schulze, T. (2020):
Symbolic aggregate approximation improves gap filling in high-resolution mass spectrometry data processing
Anal. Chem. 92 (15), 10425 - 10432 10.1021/acs.analchem.0c00899