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Updated: Dec 31, 2025
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
Published on: November 10, 2023
CROP: correlation-based reduction of feature multiplicities in untargeted metabolomic data
Štěpán Kouřil1,2, Julie de Sousa1,3, Jan Václavík1
1Laboratory of Metabolomics, Institute of Molecular and Translational Medicine, Palacký University Olomouc, Olomouc 779 00, Czech Republic.
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.
