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VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography - Mass Spectrometry Data

preprint
submitted on 15.02.2019 and posted on 18.02.2019 by Yaser Alkhalifah, Iain Phillips, Andrea Soltoggio, Kareen Darnley, William H. Nailon, Duncan McLaren, Michael Eddleston, Paul Thomas, Dahlia Salman
Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC-MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC-MS breath with similar mass spectra and retention index profiles.

Funding

The authors of this manuscript would like to acknowledge the TOXI-triage project and the clinical research nurse and radiotherapy staff for their help with obtaining the clinical data sets. TOXI-triage received funding from the European Union’s Horizon 2020 Innovation action Programme H2020- EU.3.7. - Secure societies - Protecting freedom and security of Europe and its citizens under grant agreement No 653409.

The authors would also like to acknowledge ministry of education in Saudi Arabia for funding the first author to undertake his PhD at the Loughborough University. .

History

Email Address of Submitting Author

c.l.p.thomas@lboro.ac.uk

Institution

Loughborough University

Country

UK

ORCID For Submitting Author

0000-0003-4631-6417

Declaration of Conflict of Interest

No conflict of interest declared

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