Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets

Abstract : 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 will be available in Workflow4Metabolomics (http://workflow4metabolomics.org). Source code is freely available for download under CeCILL 2.1 license at https://services.pfem.clermont.inra.fr/gitlab/grandpa /tool-acf and implement in Perl.
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Stéphanie Monnerie, Mélanie Pétéra, Bernard Lyan, Pierrette Gaudreau, Blandine Comte, et al.. Analytic correlation filtration: A new tool to reduce analytical complexity of metabolomic datasets. Metabolites, MDPI, 2019, 9 (11), ⟨10.3390/metabo9110250⟩. ⟨hal-02352488⟩

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