Explore, visualize, and analyze your MOFA+ (Multi-Omics Factor Analysis) results with ease! This Streamlit app provides an interactive interface to load and interrogate multi-omics models in .hdf5 format.
- Upload Models: Analyze MOFA+ models via
.hdf5uploads - Intuitive Metrics: Displays total cells, features, and groups at a glance
- Enrichment Analysis:
- Pathway and GO term enrichment for top features
- Interactive filtering by factors and sources
- Downloadable enrichment results
- Feature Analysis:
- Top feature weights heatmap
- Ranked weight plots
- Statistical Insights:
- Factor correlations (Pearson)
- Variance explained analysis
- Enrichment Plots:
- Factor-specific enrichment visualizations
- Significance bar plots
- Download weights data as CSV
- Export variance explained metrics
- Save enrichment analysis results
- Adjust analysis parameters
- Control feature selection counts
- Filter enrichment results by source
- Customize visualization options
# Clone the repository
git clone https://github.com/kris96tian/mofa-explorer.git
# Install dependencies
pip install -r requirements.txt
# Run the app
streamlit run app.pyAccess the app at MOFA+ Model Explorer.
The app leverages MOFA+ for integrative analysis of multi-modal single-cell data. The enrichment analysis feature helps identify biological pathways and processes associated with MOFA factors. To learn more, see:
Argelaguet, R., Arnol, D., Bredikhin, D., et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biology, 21, 111 (2020). DOI:10.1186/s13059-020-02015-1
- Upload your MOFA+ model (.hdf5 file)
- View basic model metrics and statistics
- Explore feature weights and correlations
- Run enrichment analysis on factors of interest
- Export results for further analysis
Developed by Kristian Alikaj. Connect on: