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

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...

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Veröffentlicht in:Nature communications Jg. 16; H. 1; S. 8714 - 19
Hauptverfasser: Idkowiak, Jakub, Dehairs, Jonas, Schwarzerová, Jana, Olešová, Dominika, Truong, Jacob X. M., Kvasnička, Aleš, Eftychiou, Marios, Cools, Ruben, Spotbeen, Xander, Jirásko, Robert, Veseli, Vullnet, Giampà, Marco, de Laat, Vincent, Butler, Lisa M., Weckwerth, Wolfram, Friedecký, David, Demeulemeester, Jonas, Hron, Karel, Swinnen, Johannes V., Holčapek, Michal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 30.09.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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Accessibility
Best practice
Biological variation
Chromatography
Complexity
Data analysis
Datasets
Humanities and Social Sciences
Humans
Lipidomics - methods
Lipids
Mass Spectrometry
Mass spectroscopy
Metabolites
Metabolomics
Metabolomics - methods
multidisciplinary
Parameter identification
Programming Languages
Python
Quality standards
Review Article
Science
Science (multidisciplinary)
Scientific imaging
Skills
Software
Statistical analysis
Statistical methods
Statistics
Variables
Visualization
ISSN:2041-1723, 2041-1723
Online-Zugang:Volltext
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Zusammenfassung: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. Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-63751-1