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 propos

SUBMITTER: Monnerie S 

PROVIDER: S-EPMC6918187 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature