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Article

Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis

1
Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden
2
Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy
3
Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
4
Chalmers Mass Spectrometry Infrastructure, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Hunter N. B. Moseley
Metabolites 2022, 12(2), 137; https://doi.org/10.3390/metabo12020137
Received: 7 December 2021 / Revised: 21 January 2022 / Accepted: 29 January 2022 / Published: 2 February 2022
(This article belongs to the Special Issue Metabolomics Data Analysis and Quality Assessment)
LC–MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow. View Full-Text
Keywords: metabolomics; untargeted; peak characterization; peak detection; XCMS; false peaks; peak filtering; data processing; algorithm; data quality metabolomics; untargeted; peak characterization; peak detection; XCMS; false peaks; peak filtering; data processing; algorithm; data quality
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MDPI and ACS Style

Pirttilä, K.; Balgoma, D.; Rainer, J.; Pettersson, C.; Hedeland, M.; Brunius, C. Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis. Metabolites 2022, 12, 137. https://doi.org/10.3390/metabo12020137

AMA Style

Pirttilä K, Balgoma D, Rainer J, Pettersson C, Hedeland M, Brunius C. Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis. Metabolites. 2022; 12(2):137. https://doi.org/10.3390/metabo12020137

Chicago/Turabian Style

Pirttilä, Kristian, David Balgoma, Johannes Rainer, Curt Pettersson, Mikael Hedeland, and Carl Brunius. 2022. "Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis" Metabolites 12, no. 2: 137. https://doi.org/10.3390/metabo12020137

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