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Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets

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Université Clermont Auvergne, INRA, UNH, Mapping, F-63000 Clermont Ferrand, France
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Université Clermont Auvergne, INRA, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, F-63000 Clermont-Ferrand, France
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Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, QC H2X 3E4, Canada
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Département de Médecine, Université de Montréal, Montréal, QC H3T 1J4, Canada
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Authors to whom correspondence should be addressed.
Metabolites 2019, 9(11), 250; https://doi.org/10.3390/metabo9110250
Received: 3 October 2019 / Revised: 21 October 2019 / Accepted: 22 October 2019 / Published: 24 October 2019
Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, the algorithm principle is used to group features coming from the same analyte, and to propose one single representative per group. To illustrate the functionalities and added-value of this tool, it was applied to published datasets and compared to one of the most commonly used free packages proposing a grouping method for metabolomics data: ‘CAMERA’. This tool was developed to be included in Galaxy and is available in Workflow4Metabolomics. View Full-Text
Keywords: metabolomics; data filtration; high-resolution mass spectrometry metabolomics; data filtration; high-resolution mass spectrometry
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MDPI and ACS Style

Monnerie, S.; Petera, M.; Lyan, B.; Gaudreau, P.; Comte, B.; Pujos-Guillot, E. Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets. Metabolites 2019, 9, 250.

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