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Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis
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....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878835/ https://www.ncbi.nlm.nih.gov/pubmed/35208212 http://dx.doi.org/10.3390/metabo12020137 |
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author | Pirttilä, Kristian Balgoma, David Rainer, Johannes Pettersson, Curt Hedeland, Mikael Brunius, Carl |
author_facet | Pirttilä, Kristian Balgoma, David Rainer, Johannes Pettersson, Curt Hedeland, Mikael Brunius, Carl |
author_sort | Pirttilä, Kristian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8878835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88788352022-02-26 Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis Pirttilä, Kristian Balgoma, David Rainer, Johannes Pettersson, Curt Hedeland, Mikael Brunius, Carl Metabolites Article 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. MDPI 2022-02-02 /pmc/articles/PMC8878835/ /pubmed/35208212 http://dx.doi.org/10.3390/metabo12020137 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pirttilä, Kristian Balgoma, David Rainer, Johannes Pettersson, Curt Hedeland, Mikael Brunius, Carl Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis |
title | Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis |
title_full | Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis |
title_fullStr | Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis |
title_full_unstemmed | Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis |
title_short | Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis |
title_sort | comprehensive peak characterization (cpc) in untargeted lc–ms analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878835/ https://www.ncbi.nlm.nih.gov/pubmed/35208212 http://dx.doi.org/10.3390/metabo12020137 |
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