Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.
Alexander A. Aksenov,
Sophie LF Doran,
Louis Felix Nothias,
Katherine N. Maloney,
Biswapriya B. Misra,
Alexey V. Melnik,
Kenneth L. Jones,
Justin J. J. van der Hooft,
Andrea Albarracín Orio,
Andrea M. Smania,
Sneha P. Couvillion,
Meagan C. Burnet,
Carrie D. Nicora,
Thomas O. Metz,
Michael M. Meijler,
Pauline Le Boulch,
Bruno Le Bizec,
Noga Sikron Persi,
Jose U. Scher,
Roman S. Borisov,
Larisa N. Kulikova,
George B. Hanna,
Pieter C. Dorrestein,
Posted 14 Jan 2020
bioRxiv DOI: 10.1101/2020.01.13.905091
Posted 14 Jan 2020
Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.
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