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WiPP: workflow for improved peak picking for gas chromatography-mass spectrometry (GC-MS) data

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Item Type:Article
Title:WiPP: workflow for improved peak picking for gas chromatography-mass spectrometry (GC-MS) data
Creators Name:Borgsmüller, N. and Gloaguen, Y. and Opialla, T. and Blanc, E. and Sicard, E. and Royer, A.L. and Le Bizec, B. and Durand, S. and Migné, C. and Pétéra, M. and Pujos-Guillot, E. and Giacomoni, F. and Guitton, Y. and Beule, D. and Kirwan, J.
Abstract:Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.
Keywords:Gas Chromatography-Mass Spectrometry (GC-MS), Metabolomics, Peak Detection, Peak Classification, Pre-Processing Workflow, Parameter Optimisation, Machine Learning, Support Vector Machine
Source:Metabolites
ISSN:2218-1989
Publisher:MDPI (Switzerland)
Volume:9
Number:9
Page Range:171
Date:21 August 2019
Official Publication:https://doi.org/10.3390/metabo9090171
PubMed:View item in PubMed
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https://edoc.mdc-berlin.de/18362/Preprint version

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