Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data

Jakub Idkowiak, Jonas Dehairs, Jana Schwarzerová, Dominika Olešová, Jacob X. M. Truong, Aleš Kvasnička, Marios Eftychiou, Ruben Cools, Xander Spotbeen, Robert Jirásko, Vullnet Veseli, Marco Giampà, Vincent de Laat, Lisa M. Butler, Wolfram Weckwerth, David Friedecký, Jonas Demeulemeester, Karel Hron, Johannes V. Swinnen and Michal Holčapek ()
Additional contact information
Jakub Idkowiak: University of Pardubice
Jonas Dehairs: KU Leuven
Jana Schwarzerová: Brno University of Technology
Dominika Olešová: Slovak Academy of Sciences
Jacob X. M. Truong: North Terrace
Aleš Kvasnička: Palacký University Olomouc
Marios Eftychiou: VIB-KU Leuven Center for Cancer Biology
Ruben Cools: VIB-KU Leuven Center for Cancer Biology
Xander Spotbeen: KU Leuven
Robert Jirásko: University of Pardubice
Vullnet Veseli: University of Pardubice
Marco Giampà: KU Leuven
Vincent de Laat: KU Leuven
Lisa M. Butler: North Terrace
Wolfram Weckwerth: University of Vienna
David Friedecký: Palacký University Olomouc
Jonas Demeulemeester: VIB-KU Leuven Center for Cancer Biology
Karel Hron: Palacký University Olomouc
Johannes V. Swinnen: KU Leuven
Michal Holčapek: University of Pardubice

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and visualize statistically significant trends and biologically relevant differences. Besides tailored methods developed by individual labs, a solid core of freely accessible tools exists for exploratory data analysis and visualization, which we have compiled here, including preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps and fatty acyl chain plots, unsupervised and supervised dimensionality reduction, dendrograms, and heat maps. This review is intended for those who would like to develop their skills in data analysis and visualization using freely available R or Python solutions. Beginners are guided through a selection of R and Python libraries for producing publication-ready graphics without being overwhelmed by the code complexity. This manuscript, along with associated GitBook code repository containing step-by-step instructions, offers readers a comprehensive guide, encouraging the application of R and Python for robust and reproducible chemometric analysis of omics data.

Date: 2025
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DOI: 10.1038/s41467-025-63751-1

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