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Poster De Conférence Année : 2019

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

Résumé

Metabolomics generates massive and complex data that need dedicated workflows to extract the meaningful information and to enrich our knowledge of biological systems. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining and statistical methods. 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 correlation coefficient, retention time and mass information from known isotopes, adducts or fragments, the algorithm principle is to group features coming from the same analyte, and to propose one single representative per group. We chose to compare the present tool to one of the most commonly used free package proposing a grouping method: ‘CAMERA’, using its Galaxy version ‘CAMERA.annotate’ available in Workflow4Metabolomics (W4M; http://workflow4metabolomics.org). To illustrate the ‘Analytic correlation filtration tool’ functionalities, a published dataset available on W4M was used as an example: ‘Sacurine’ (Thevenot et al., 2015). Within the 3,120 ions of the urine dataset, 14% of ions are proposed to be filtered because of analytical redundancies. While CAMERA generated more than 20 groups of more than 10 ions, the proposed tool subdivided them into smaller ones corresponding to individual annotated metabolites, thus demonstrating the efficiency and relevance of the present approach. As a key element in metabolomics data analysis, the tool will be available via the web-based galaxy platform W4M with different output files for network vizualisation and for further data analysis within workflows.
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Dates et versions

hal-02154661 , version 1 (02-06-2020)

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  • HAL Id : hal-02154661 , version 1
  • PRODINRA : 473573

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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. 12. Journées Scientifiques du Réseau Francophone de Métabolomique et Fluxomique RFMF, May 2019, Clermont-Ferrand, France. 2019, Livre des résumés. ⟨hal-02154661⟩
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