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Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles

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/f...

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Autores principales: Hauschild, Anne-Christin, Frisch, Tobias, Baumbach, Jörg Ingo, Baumbach, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495376/
https://www.ncbi.nlm.nih.gov/pubmed/26065494
http://dx.doi.org/10.3390/metabo5020344
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author Hauschild, Anne-Christin
Frisch, Tobias
Baumbach, Jörg Ingo
Baumbach, Jan
author_facet Hauschild, Anne-Christin
Frisch, Tobias
Baumbach, Jörg Ingo
Baumbach, Jan
author_sort Hauschild, Anne-Christin
collection PubMed
description 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].
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spelling pubmed-44953762015-07-08 Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles Hauschild, Anne-Christin Frisch, Tobias Baumbach, Jörg Ingo Baumbach, Jan Metabolites Article 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]. MDPI 2015-06-10 /pmc/articles/PMC4495376/ /pubmed/26065494 http://dx.doi.org/10.3390/metabo5020344 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hauschild, Anne-Christin
Frisch, Tobias
Baumbach, Jörg Ingo
Baumbach, Jan
Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
title Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
title_full Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
title_fullStr Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
title_full_unstemmed Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
title_short Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
title_sort carotta: revealing hidden confounder markers in metabolic breath profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495376/
https://www.ncbi.nlm.nih.gov/pubmed/26065494
http://dx.doi.org/10.3390/metabo5020344
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