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Let’s Explore and Learn to Analyze Untargeted Metabolomics Data

License: CC BY-NC 4.0 DOI

Welcome to Metabonaut! 🚀

Metabonaut presents a series of workflows based on a small LC-MS/MS dataset, utilizing R and Bioconductor packages. These workflows demonstrate how to adapt various algorithms to specific datasets and seamlessly integrate R packages for efficient, reproducible data processing.

Available Vignettes

1. Complete End-to-End LC-MS/MS Metabolomic Data Analysis

This primary workflow guides you through each step of the analysis, from preprocessing raw data to statistical analysis and metabolite annotation.
📄 View Full R Code (Quarto)

2. Dataset Investigation

Before diving into the analysis, learn about key aspects to examine in your dataset to ensure smooth processing and prevent troubleshooting issues later in the pipeline.

3. Seamless Alignment: Merging New Data with an Existing Preprocessed Dataset

Discover how to use a flexible alignment algorithm to integrate new datasets with previously processed ones based on features of interest.

4. Quality Control and Feature Selection Using notame

Explore notame as a robust alternative for normalization and feature selection. This vignette covers essential steps for quality control, normalization, and feature selection to ensure your data is clean and ready for analysis.

5. LC-MS/MS Data Annotation Using R and Python

Explore the SpectriPy package for LC-MS/MS data annotation. This tutorial demonstrates how to combine the strengths of Python and R MS libraries for comprehensive annotation.

6. Large Scale Processing Using xcms

While xcms is known for its scalability, this guide shows you how to practically handle large-scale dataset processing (>4,000 files) on standard hardware.

For a full list of all available vignettes, visit the Metabonaut website.

📌 Reproducibility & Updates

We strive for reproducibility. These workflows are designed to remain stable over time, allowing you to run all vignettes together as one comprehensive super-vignette.

  • Major updates will be documented here.
    • Metabonaut now works with a stable version of Bioconductor (3.22)
  • Minor updates can be found in the News section.

🎓 For R Beginners

The tutorials assume basic knowledge of R and RMarkdown. If you’re new to these, we recommend starting with a short tutorial before running the vignettes.

🛠️ Known Issues

This is just the beginning of our Metabonaut journey, and we’re actively refining the website. If you’re experiencing any issues:

✅ Ensure you have the latest versions of all required packages.
🐛 If the issue persists, report it with a reproducible example on GitHub Issues.

Currently, there are no known issues with the code.

🤝 Contribution

Interested in contributing? Please check out the RforMassSpectrometry Contributions Guide.

📜 Code of Conduct

We follow the RforMassSpectrometry Code of Conduct to maintain an inclusive and respectful community.

🙌 Acknowledgements

EU Logo
EU Logo

This work is funded by the European Union under the HORIZON-MSCA-2021 project 101073062: HUMAN – Harmonising and Unifying Blood Metabolic Analysis Networks.

🔗 Learn more: HUMAN Project Website