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.
This primary workflow guides you through each step of the analysis, from
preprocessing raw data to statistical analysis and metabolite annotation.
📄 Full R code: end-to-end-untargeted-metabolomics.qmd
Before diving into the analysis, learn about key aspects to examine in your dataset to ensure smooth processing and avoid troubleshooting later.
Discover how to use a flexible alignment algorithm to integrate new datasets with previously processed ones based on features of interest.
Explore the SpectriPy package for LC-MS/MS data annotation. This tutorial demonstrates how to combining the strengths of Python and R MS libraries for annotation.
We often boast about the scalability of xcms, here we show how to actually deal with a large dataset (>4000 files) processing on an ordinary computer.
For a full list of all available vignettes, visit the Metabonaut website.
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 (3.21), with the
exception of the
SpectriPypackage which will be part of Bioconductor 3.22.
- Metabonaut now works with a stable version of Bioconductor (3.21), with the
exception of the
- Minor updates can be found in the News section.
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.
- Learn Quarto (used for vignettes): Quarto Guide
- Learn RMarkdown: RMarkdown Book
- Intro to R: Learn-R.org
- Interactive R course: Swirl
- Best Practices Cheatsheet: GitHub Repository
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.
Interested in contributing? Please check out the RforMassSpectrometry Contributions Guide.
We follow the RforMassSpectrometry Code of Conduct to maintain an inclusive and respectful community.
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