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

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

Článek WoS

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 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.

Lipidomics, Metabolomics, Data analysis, R, Python

Lipidomics, Metabolomics, Data analysis, R, Python

IDKOWIAK, J.; DEHAIRS, J.; SCHWARZEROVÁ, J.; OLEŠOVÁ, D.; TRUONG, J.; KVASNIČKA, A.; EFTYCHIOU, M.; COOLS, R.; SPOTBEEN, X.; JIRÁSKO, R.; VESELI, V.; GIAMPÀ, M.; DE LAAT, V.; BUTLER, L.; WECKWERTH, W.; FRIEDECKÝ, D.; DEMEULEMEESTER, J.; HRON, K.; HOLČAPEK, M.; SWINNEN, J.

30.09.2025

Nature Communications

16

Spojené království Velké Británie a Severního Irska

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https://www.nature.com/articles/s41467-025-63751-1#citeas