Year: 2026
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide The increasing size and complexity of mass spectrometry (MS) data sets necessitate advanced computational tools. This study presents Correland, a MATLAB-based software for clustering and visualizing metabolite correlations through weighted correlation networks, which directly represent pairwise associations. Its effectiveness was tested on a data set from nontargeted LC-MS analysis of 14-day-old Arabidopsis thaliana seedlings inoculated with Alternaria alternata and Fusarium oxysporum, demonstrating effective clustering of biosynthetically related metabolites. The ion grouping algorithm resulted in a substantial reduction in network scale (83 nodes/metabolites from approximately 900 features). In addition to network construction, Correland enables pseudomolecular ion identification with a success rate of 86–90% achieved in Arabidopsis extract. Network density is reduced by limiting visible edges, producing interpretable and visually coherent networks generated in a single step using rescaled Pearson correlation coefficients.
Authors: Andrea Kosinova, Jiri Gruz
Institution: Palacký University Olomouc
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funder: Univerzita Palackého v Olomouci
Award: G7887684443
OpenAlex ID: W7155168552