spatialvista 0.1.0

3D ST Visualization in Jupyter

SpatialVista - Interactive 3D Spatial Transcriptomics Visualization in Jupyter

Overview

SpatialVista is an interactive 3D spatial transcriptomics visualization tool designed specifically for Jupyter Notebook/Lab. It seamlessly integrates into your data analysis workflow, providing efficient and intuitive exploration of spatial omics data.

✨ Key Features

• 🚀 High-Performance Rendering - WebGL-based 3D rendering supporting millions of cells
• 📊 Multi-Dimensional Data Display - Support for categorical annotations, continuous values, gene expression, and more
• 🎨 Interactive Controls - Real-time adjustment of colors, transparency, point size, and other parameters
• 🔬 2D/3D View Switching - Flexible switching between 3D point cloud and 2D slice views
• 🧬 Gene Expression Query - Quick visualization of spatial expression patterns for any gene
• 📐 Multiple Layout Modes - Support for original coordinates, 2D Treemap, histogram, and more
• 🎯 Precise Filtering - Filter data points by category, numerical range, and other conditions
• 💾 One-Click Screenshots - Easily save current views for publications and reports

🎯 Use Cases

SpatialVista is particularly suitable for:

• Spatial Transcriptomics Data Exploration - Visium, MERFISH, seqFISH, STARmap, and other technologies
• Single-Cell Spatial Data Analysis - Visualize spatial distribution of cell types
• Tissue Architecture Studies - Explore molecular features of tissue regions
• Gene Expression Pattern Analysis - View spatial expression of specific genes
• Data Quality Control - Quickly check data integrity and outliers

🚀 Quick Start

Installation

pip install spatialvista

Basic Usage

import spatialvista as spv
import scanpy as sc

# Load spatial transcriptomics data
adata = sc.read_h5ad("spatial_data.h5ad")

# Create interactive visualization
widget = spv.vis(
    adata,
    position="spatial",  # obsm key containing spatial coordinates
    color="celltype",    # Default annotation for coloring
    height=600               # Widget height in pixels
)

# Display widget
widget

That's it! 🎉

📚 Core Features

1. Categorical Annotation Visualization

# Color by cell type
widget = spv.vis(
    adata,
    position="spatial",
    color="celltype",
    annotations=["leiden", "tissue_region"]  # Additional annotations to load
)

2. Continuous Value Visualization

# Visualize continuous values (e.g., QC metrics)
widget = spv.vis(
    adata,
    position="spatial",
    color="celltype",
    continuous=["total_counts", "n_genes"]  # Continuous value fields
)

3. Gene Expression Visualization

# View expression patterns of specific genes
widget = spv.vis(
    adata,
    position="spatial",
    color="celltype",
    genes=["Pecam1", "Cd3e", "Epcam"],  # Gene list
    layer="normalized"  # Optional: use specific layer if available
)

4. 2D/3D View Switching

# If data has section information, switch to 2D view in UI
widget = spv.vis(
    adata,
    position="spatial",
    color="celltype",
    section="slice_id",  # Section identifier field for section browser
)

🎨 Interactive Controls

Once displayed, the widget provides rich interactive controls for exploring your data:

• Navigate in 3D space (rotate, pan, zoom)
• Switch between annotations and customize colors
• Query continuous values and gene expression
• Filter by thresholds and hide specific categories
• Adjust visualization parameters (size, opacity, layout)
• Export screenshots

🤝 Contributing & Support

Issues and Pull Requests are welcome!

• GitHub: https://github.com/JianYang-Lab/spatial-vista-py
• Documentation: https://spatial-vista-py.readthedocs.io

📄 License

SpatialVista is open-sourced under the MIT License.

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Built with ❤️ by WenjieWei@YangLab