GNPS RDD App

A Streamlit web application for Reference Data-Driven (RDD) analysis of GNPS molecular networking data.

Overview

This application helps researchers analyze GNPS (Global Natural Products Social Molecular Networking) data using a reference-driven approach. It enables ontology-based counting, visualization, and statistical analysis of metabolomics data.

Features

• RDD Count Table Generation - Create reference data-driven counts from GNPS networks
• Interactive Visualizations - Box plots, heatmaps, and bar charts for data exploration
• PCA Analysis - Principal Component Analysis with CLR transformation
• Sankey Diagrams - Flow visualization of metabolite classifications
• GNPS Integration - Direct access via Task ID or file upload

🌐 Live App

Access the app online at: https://gnpsrdd.streamlit.app/

Installation

Requirements

• Python 3.11+
• pip

Local Development

To run the app locally:

# Clone the repository
git clone https://github.com/AlejandroMC28/gnps_rdd_app.git
cd gnps_rdd_app

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run Home.py

The app will open in your default browser at http://localhost:8501

Usage

1. Create RDD Count Table

• Upload GNPS network file or enter GNPS Task ID
• Select sample types (simple/complex/all)
• Choose sample groups
• Generate RDD counts across ontology levels

2. Visualize Data

• Create box plots, heatmaps, and bar charts
• Compare different sample groups
• Explore reference type distributions

3. PCA Analysis

• Perform dimensionality reduction
• Apply CLR transformation
• Visualize sample clustering

4. Sankey Diagrams

• Visualize metabolite classification flows
• Track ontology hierarchies

Testing

# Run all tests
make test

# Or using pytest directly
pytest -v

# Check code quality
make lint

# Format code
make format

Development

Key Dependencies

• Streamlit - Web application framework
• Pandas/NumPy - Data manipulation
• Plotly/Matplotlib - Visualizations
• scikit-learn - PCA analysis
• scikit-bio - CLR transformation
• gnpsdata - GNPS data access

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: make test
5. Format code: make format
6. Submit a pull request

License

Apache license 2.0

Citation

If you use this tool in your research, please cite:

[Citation information to be added]

Contact

For questions or issues, please open a GitHub issue.