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

Jakub Idkowiak1,2, Jonas Dehairs2, Jana Schwarzerová3,4,5

  • 1Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Pardubice, Czechia.

Nature Communications
|September 30, 2025
PubMed
Summary
This summary is machine-generated.

This review compiles freely accessible R and Python tools for exploring and visualizing complex lipidomics and metabolomics data. It guides beginners in using these solutions for robust, reproducible omics data analysis and publication-ready graphics.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Lipidomics and metabolomics generate large datasets requiring advanced data exploration skills.
  • Identifying statistically significant trends and biological differences necessitates specialized visualization techniques.
  • Existing tailored methods are lab-specific, highlighting a need for accessible, standardized tools.

Purpose of the Study:

  • To review and compile freely accessible R and Python tools for exploratory data analysis and visualization of omics data.
  • To guide researchers, particularly beginners, in developing skills for analyzing and visualizing complex biological datasets.
  • To promote the use of R and Python for robust and reproducible chemometric analysis in omics research.

Main Methods:

  • Compilation of existing, freely available R and Python libraries for data analysis and visualization.
  • Inclusion of methods for descriptive statistics, hypothesis testing, and various plot types (e.g., box plots, volcano plots, heat maps).
  • Guidance on unsupervised and supervised dimensionality reduction techniques and hierarchical clustering (dendrograms).

Main Results:

  • A curated selection of R and Python tools for essential omics data exploration tasks is presented.
  • The review covers preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps, and fatty acyl chain plots.
  • Guidance is provided on unsupervised and supervised dimensionality reduction, dendrograms, and heat maps for comprehensive data visualization.

Conclusions:

  • Freely accessible R and Python tools offer robust solutions for analyzing and visualizing omics data.
  • This guide empowers researchers to perform reproducible chemometric analysis and generate publication-ready graphics.
  • The associated GitBook repository provides step-by-step instructions for practical application of these data analysis techniques.