SPAC Interactive Visualization

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.

Why Use This Dashboard?

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 Features

�️ Spatial Visualization

• 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

📊 Statistical Exploration

• 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

Quick Start

🚀 Try the Live Demo

No installation needed! Explore SPAC with sample data:

👉 Launch Interactive Demo

💻 Run Locally with Docker

git clone https://github.com/FNLCR-DMAP/SPAC_Shiny.git
cd SPAC_Shiny
make run

Open your browser to http://localhost:8001 to start exploring!

Other Useful Commands

make help      # Show all available commands
make logs      # View application logs
make stop      # Stop the container
make clean     # Remove container and image

Load Your Data

Supports common spatial single-cell formats:

• AnnData (.h5ad or pickle files) - Standard format with spatial coordinates and features

What You Can Explore

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

Documentation & Support

• Getting Started Guide - Step-by-step usage instructions
• Technical Details - Full project documentation and benchmarks
• Contributing Guide - Information on contributing
• Issues: GitHub Issues
• Contact: [email protected]

Citation

Liu, F., He, R., Sheeley, T., et al. SPAC: A Scalable and Integrated Enterprise
Platform for Single-Cell Spatial Analysis. [under review] (2025)

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Developed by Frederick National Laboratory for Cancer Research and Purdue Data Mine