Real-time dashboard for spatial single-cell data exploration
An interactive Shiny for Python dashboard that transforms complex spatial single-cell datasets into dynamic, explorable visualizations. Just upload your analyzed data and start discovering biological insights through intuitive point-and-click analysis.
Spatial single-cell datasets are complex and difficult to explore. This interactive dashboard solves that by providing:
- Live exploration - Click, filter, and zoom through your spatial data in real-time
- No coding required - Intuitive interface for biologists
- Multiple view types - Spatial maps, dimensionality reduction plots, heatmaps, and statistics
- Hypothesis testing - Quickly subset data and compare cell populations interactively
- Interactive tissue maps - Color cells by any feature, zoom into regions of interest
- Real-time filtering - Subset cells based on expression, location, or annotations
- Customizable styling - Adjust colors, point sizes, and transparency on-the-fly
- Dynamic plots - UMAP, t-SNE, heatmaps, boxplots that update as you filter data
- Comparative analysis - Side-by-side visualization of different conditions or cell types
- Export ready figures - Generate and download publication-quality plots directly from the interface
No installation needed! Explore SPAC with sample data:
git clone https://github.com/FNLCR-DMAP/SPAC_Shiny.git
cd SPAC_Shiny
make runOpen your browser to http://localhost:8001 to start exploring!
make help # Show all available commands
make logs # View application logs
make stop # Stop the container
make clean # Remove container and imageSupports common spatial single-cell formats:
- AnnData (.h5ad or pickle files) - Standard format with spatial coordinates and features
Perfect for analyzing:
- Multiplex imaging data (IMC, MIBI, MxIF, CyCIF, CODEX)
- Spatial transcriptomics (Visium, Xenium, MERFISH)
- Tumor microenvironments and tissue architecture
- Cell-cell interactions and spatial patterns
- Getting Started Guide - Step-by-step usage instructions
- Technical Details - Full project documentation and benchmarks
- Contributing Guide - Information on contributing
- Issues: GitHub Issues
- Contact: george.zaki@nih.gov
Liu, F., He, R., Sheeley, T., et al. SPAC: A Scalable and Integrated Enterprise
Platform for Single-Cell Spatial Analysis. [under review] (2025)
Developed by Frederick National Laboratory for Cancer Research and Purdue Data Mine