Publication
Title
Simple data-reduction method for high-resolution LCMS data in metabolomics
Author
Abstract
Background: Metabolomics LCMS experiments yield large numbers of peaks, few of which can be identified by database matching. Many of the remaining peaks correspond to derivatives of identified peaks (e.g., isotope peaks, adducts, fragments and multiply charged molecules). In this article, we present a data-reduction approach that automatically identifies these derivative peaks. Results: Using data-driven clustering based on chromatographic peak shape correlation and intensity patterns across biological replicates, derivative peaks can be reliably identified. Using a test data set obtained from Leishmania donovani extracts, we achieved a 60% reduction of the number of peaks. After quality control filtering, almost 80% of the peaks could putatively be identified by database matching. Conclusion: Automated peak filtering substantially speeds up the data-interpretation process.
Language
Dutch
Source (journal)
Bioanalysis
Publication
2009
Volume/pages
1:9(2009), p. 1551-1557
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 23.02.2011
Last edited 04.09.2013
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