Title
Simple data-reduction method for high-resolution LCMS data in metabolomics Simple data-reduction method for high-resolution LCMS data in metabolomics
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Subject
Biology
Human medicine
Source (journal)
Bioanalysis
Volume/pages
1(2009) :9 , p. 1551-1557
ISSN
1757-6199
Carrier
E
Target language
Dutch (dut)
Affiliation
University of Antwerp
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.
Handle