Štěpán Kouřil1,2, Julie de Sousa1,3, Jan Václavík1, David Friedecký1,2, Tomáš Adam1,2. 1. Laboratory of Metabolomics, Institute of Molecular and Translational Medicine, Palacký University Olomouc, Olomouc 779 00, Czech Republic. 2. Department of Clinical Biochemistry, University Hospital Olomouc, Olomouc 779 00, Czech Republic. 3. Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University Olomouc, Olomouc 779 00, Czech Republic.
Abstract
SUMMARY: Untargeted liquid chromatography-high-resolution mass spectrometry analysis produces a large number of features which correspond to the potential compounds in the sample that is analyzed. During the data processing, it is necessary to merge features associated with one compound to prevent multiplicities in the data and possible misidentification. The processing tools that are currently employed use complex algorithms to detect abundances, such as adducts or isotopes. However, most of them are not able to deal with unpredictable adducts and in-source fragments. We introduce a simple open-source R-script CROP based on Pearson pairwise correlations and retention time together with a graphical representation of the correlation network to remove these redundant features. AVAILABILITY AND IMPLEMENTATION: The CROP R-script is available online at www.github.com/rendju/CROP under GNU GPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Untargeted liquid chromatography-high-resolution mass spectrometry analysis produces a large number of features which correspond to the potential compounds in the sample that is analyzed. During the data processing, it is necessary to merge features associated with one compound to prevent multiplicities in the data and possible misidentification. The processing tools that are currently employed use complex algorithms to detect abundances, such as adducts or isotopes. However, most of them are not able to deal with unpredictable adducts and in-source fragments. We introduce a simple open-source R-script CROP based on Pearson pairwise correlations and retention time together with a graphical representation of the correlation network to remove these redundant features. AVAILABILITY AND IMPLEMENTATION: The CROP R-script is available online at www.github.com/rendju/CROP under GNU GPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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