Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data
- Author(s)
- 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, Michal Holčapek
- 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.
- Organisation(s)
- Functional and Evolutionary Ecology
- External organisation(s)
- Laboratory of Lipid Metabolism and Cancer, Department of Oncology, Leuven Cancer Institute (LKI) and Leuven Institute for Single Cell Omics (LISCO), KU Leuven, Leuven, Flanders, Belgium., Department of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, Ostrava, Czechia., Slovak Academy of Sciences (SAS), South Australian immunoGENomics Cancer Institute (SAiGENCI) & Freemasons Centre for Male Health and Well-Being, The University of Adelaide Medical School, North Terrace, Adelaide, SA, Australia., Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway., Laboratory of Multi-Omic Integrative Bioinformatics, Department of Human Genetics, KU Leuven, Leuven, Flanders, Belgium., Katholieke Universiteit Leuven, Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czechia., Metabolomics Core Facility, VIB-KU Leuven Center for Cancer Biology, Leuven, Flanders, Belgium., Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria., Laboratory for Inherited Metabolic Disorders, Department of Clinical Biochemistry, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia., Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University Olomouc, Olomouc, Czech Republic., Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czechia. Michal.Holcapek@upce.cz.
- Journal
- Nature Communications
- Volume
- 16
- Pages
- 8714
- ISSN
- 2041-1723
- DOI
- https://doi.org/10.1038/s41467-025-63751-1
- Publication date
- 09-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 106057 Metabolomics
- Keywords
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/d95db428-0def-41d3-99e5-90792609b1f9