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Metabolites 2015, 5(2), 344-363; doi:10.3390/metabo5020344

Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles

1
Computational Systems Biology Group, Max Planck Institute for Informatics, Saarbrücken 66123, Germany
2
Computational Biology Group, Department of Mathematics and Computer Science, University of Southern Denmark, Odense 5230, Denmark
3
Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany
4
Faculty of Applied Chemistry, Reutlingen University, Reutlingen 72762, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Christoph Kaleta
Received: 13 March 2015 / Revised: 20 May 2015 / Accepted: 25 May 2015 / Published: 10 June 2015
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Abstract

Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1]. View Full-Text
Keywords: breathomics; multicapillary column/ion mobility spectrometry; clustering; breath analysis breathomics; multicapillary column/ion mobility spectrometry; clustering; breath analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hauschild, A.-C.; Frisch, T.; Baumbach, J.I.; Baumbach, J. Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles. Metabolites 2015, 5, 344-363.

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