A practical guide to using the FeatureMAP R package and Shiny application.
Read the tutorial online · View the R package · View the Shiny app
FeatureMAP turns feature-level metabolomics signals into interpretable metabolic modules. This tutorial covers the complete workflow through two complementary interfaces:
- R package: a reproducible, code-based workflow for analysis and custom pipelines.
- Shiny app: a guided, point-and-click workflow for interactive analysis and result exploration.
Both tracks follow the same analysis logic:
- Prepare the feature and optional MS2 annotation tables.
- Annotate metabolite features.
- Extract a network-supported metabolic subnetwork.
- Identify and evaluate functional modules.
- Interpret significant modules and export the results.
| Track | Topics |
|---|---|
| R package | Installation, data preparation, feature annotation, PCSF subnetwork extraction, module identification, and functional annotation |
| Shiny app | Installation, data upload, interactive execution of each analysis step, result inspection, and export |
The functional annotation chapter also introduces optional PubMed retrieval, LLM-assisted interpretation, advanced multi-agent debate, and cross-module synthesis. These services are optional and are not required for the earlier analysis steps.
FeatureMAP is developed by Yuting Wang and Xiaotao Shen (corresponding author).
For questions, documentation corrections, or feature requests, please open an issue.