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

Author

Listed:
  • Jakub Idkowiak

    (University of Pardubice
    KU Leuven)

  • Jonas Dehairs

    (KU Leuven)

  • Jana Schwarzerová

    (Brno University of Technology
    University of Vienna
    University Hospital Ostrava)

  • Dominika Olešová

    (Slovak Academy of Sciences
    Slovak Academy of Sciences)

  • Jacob X. M. Truong

    (North Terrace
    North Terrace)

  • Aleš Kvasnička

    (Palacký University Olomouc
    Oslo University Hospital)

  • Marios Eftychiou

    (VIB-KU Leuven Center for Cancer Biology
    VIB Center for AI & Computational Biology
    KU Leuven
    KU Leuven)

  • Ruben Cools

    (VIB-KU Leuven Center for Cancer Biology
    VIB Center for AI & Computational Biology
    KU Leuven)

  • Xander Spotbeen

    (KU Leuven)

  • Robert Jirásko

    (University of Pardubice)

  • Vullnet Veseli

    (University of Pardubice)

  • Marco Giampà

    (KU Leuven
    VIB-KU Leuven Center for Cancer Biology)

  • Vincent de Laat

    (KU Leuven)

  • Lisa M. Butler

    (North Terrace
    North Terrace)

  • Wolfram Weckwerth

    (University of Vienna
    University of Vienna)

  • David Friedecký

    (Palacký University Olomouc)

  • Jonas Demeulemeester

    (VIB-KU Leuven Center for Cancer Biology
    VIB Center for AI & Computational Biology
    KU Leuven)

  • Karel Hron

    (Palacký University Olomouc)

  • Johannes V. Swinnen

    (KU Leuven)

  • Michal Holčapek

    (University of Pardubice)

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.

Suggested Citation

  • 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, 2025. "Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63751-1
    DOI: 10.1038/s41467-025-63751-1
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    References listed on IDEAS

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