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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Visualizing metabolomics data with R.

Yannick Berker1,2,3, Isabella H Muti4, Leo L Cheng4

  • 1Hopp Children's Cancer Center Heidelberg (KiTZ), Germany.

NMR in Biomedicine
|November 14, 2022
PubMed
Summary
This summary is machine-generated.

This study demonstrates using R code to create reproducible data visualizations for metabolomics research. The methods ensure clear communication of complex results from NMR spectroscopy and mass spectrometry experiments.

Keywords:
Rdata visualizationmetabolomics

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Scientific Visualization

Background:

  • Effective communication of complex scientific data, particularly in metabolomics, is crucial.
  • High-dimensional metabolomics data requires careful visualization for unambiguous interpretation.
  • Open science principles emphasize the need for traceability and reproducibility in research.

Purpose of the Study:

  • To present the application of the R programming language for visualizing published metabolomics data.
  • To highlight the importance of reproducibility in scientific data visualization.
  • To provide practical examples and R code for reusing visualizations.

Main Methods:

  • Utilizing R programming language for data visualization.
  • Applying various plot types including bar plots, swarm plots, violin plots, volcano plots, heatmaps, Euler diagrams, and Kaplan-Meier survival plots.
  • Incorporating annotations such as groupings, intragroup line connections, significance brackets, and text annotations.

Main Results:

  • Demonstration of code-generated plots for metabolomics data from ex vivo NMR spectroscopy and mass spectrometry.
  • Provision of example figures and associated R code for enhanced reusability and reproducibility.
  • Discussion of the advantages of code-generated plots and advanced visualization techniques.

Conclusions:

  • R programming offers a powerful and reproducible approach to visualizing complex metabolomics data.
  • Code-based visualization enhances traceability and facilitates the reuse of scientific findings.
  • Adoption of these methods can improve the clarity and reliability of published metabolomics results.