Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets.
data filtration
high-resolution mass spectrometry
metabolomics
Journal
Metabolites
ISSN: 2218-1989
Titre abrégé: Metabolites
Pays: Switzerland
ID NLM: 101578790
Informations de publication
Date de publication:
24 Oct 2019
24 Oct 2019
Historique:
received:
03
10
2019
revised:
21
10
2019
accepted:
22
10
2019
entrez:
27
10
2019
pubmed:
28
10
2019
medline:
28
10
2019
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31653057
pii: metabo9110250
doi: 10.3390/metabo9110250
pmc: PMC6918187
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Institut National de la Recherche Agronomique
ID : DID'IT metaprogramme
Organisme : Agence Nationale de la Recherche
ID : ANR-11-INBS-0010
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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