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Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles.

Hauschild AC, Frisch T, Baumbach JI, Baumbach J - Metabolites (2015)

Bottom Line: On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking.It does not require much prior knowledge or technical skills to operate.We demonstrate its power and applicability by means of one artificial dataset.

View Article: PubMed Central - PubMed

Affiliation: Computational Systems Biology Group, Max Planck Institute for Informatics, Saarbrücken 66123, Germany. a.hauschild@mpi-inf.mpg.de.

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].

No MeSH data available.


Related in: MedlinePlus

The graphical user interface is split into three basic regions: (A) the data and results area lists available (intermediate and final) results; (B) a “details” panel; (C) the main result visualization panel.
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f3-metabolites-05-00344: The graphical user interface is split into three basic regions: (A) the data and results area lists available (intermediate and final) results; (B) a “details” panel; (C) the main result visualization panel.

Mentions: The graphical user interface (see Figure 3) is split into three basic regions: (1) the data and results area, showing a list of all generated results ordered in a tree-like structure; the categories correspond to the previously described processing steps (data, similarity, clustering results, clustering quality, visualization); (2) a “details” panel, reporting the parameter of the currently presented result; this also includes, for instance, general information on the dataset (such as the minimum and maximum values; (3) the main result visualization panel displays the results of the different intermediate steps, as well as the final results.


Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles.

Hauschild AC, Frisch T, Baumbach JI, Baumbach J - Metabolites (2015)

The graphical user interface is split into three basic regions: (A) the data and results area lists available (intermediate and final) results; (B) a “details” panel; (C) the main result visualization panel.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495376&req=5

f3-metabolites-05-00344: The graphical user interface is split into three basic regions: (A) the data and results area lists available (intermediate and final) results; (B) a “details” panel; (C) the main result visualization panel.
Mentions: The graphical user interface (see Figure 3) is split into three basic regions: (1) the data and results area, showing a list of all generated results ordered in a tree-like structure; the categories correspond to the previously described processing steps (data, similarity, clustering results, clustering quality, visualization); (2) a “details” panel, reporting the parameter of the currently presented result; this also includes, for instance, general information on the dataset (such as the minimum and maximum values; (3) the main result visualization panel displays the results of the different intermediate steps, as well as the final results.

Bottom Line: On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking.It does not require much prior knowledge or technical skills to operate.We demonstrate its power and applicability by means of one artificial dataset.

View Article: PubMed Central - PubMed

Affiliation: Computational Systems Biology Group, Max Planck Institute for Informatics, Saarbrücken 66123, Germany. a.hauschild@mpi-inf.mpg.de.

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].

No MeSH data available.


Related in: MedlinePlus