🔬 SpatialScope - User Guide & Tutorial
Demo Server Notice
This website is for demonstration purposes and is hosted on a public server with limited resources. It may not be able to handle large datasets. For analyzing your own data, please use the SpatialScope R package .
Welcome to SpatialScope
SpatialScope is an interactive platform for analyzing and visualizing spatial transcriptomics data. This tool allows you to explore gene expression patterns, identify spatial domains, and perform comprehensive statistical analyses on your Seurat spatial objects.
📖 Quick Start Tutorial
Upload Your Data
Navigate to the Upload panel to load your own Seurat spatial object (.rds file), or use the example dataset to get started immediately.
Select Regions of Interest
Use the freehand drawing tool (✏️ pencil icon) on the map to draw around regions you want to analyze. Click and drag to create custom selection shapes.
Save Groups for Comparison
After selecting spots, click 'Group 1' or 'Group 2' buttons at the bottom of the map to save your selections for comparative analysis.
Explore & Analyze
Use the sidebar tools to visualize gene expression, calculate gene set scores, perform clustering, find differentially expressed genes, and export your results.
🛠️ Available Tools
🏠 Home
Access this documentation and tutorial anytime
📤 Upload & Visualize
Upload Seurat objects and visualize gene expression, metadata, or gene set scores
🧬 Gene Sets
Calculate multi-gene signatures using pre-defined libraries (human/mouse) or custom lists
📊 Clustering
Identify spatial domains using graph-based clustering
📈 DEG Analysis
Find differentially expressed genes between saved groups
⚖️ Compare
Generate plots to compare features with statistics
💡 Tips & Best Practices
- Selection Strategy: Use the freehand tool (✏️) for precise region selection. You can draw multiple regions - they will all be combined into your selection.
- Group Management: The 'Show Groups on Map' checkbox is enabled by default. Save selections to groups before clearing to preserve them.
- Gene Sets: Choose the correct species (Human/Mouse) before loading pre-defined signatures to ensure proper gene symbol matching.
- Clustering: Start with default resolution (0.8) and adjust based on your tissue complexity. Higher resolution = more clusters.
- Export: Download your spot IDs and Seurat subsets for further analysis in R or other tools.
- Clear Everything: Use the 🗑️ Clear Selection button to reset all selections and saved groups.
🚀 Ready to Begin?
Click on the other tools in the sidebar to start your spatial analysis! The map will be visible when you switch to other tools.
Contact: Aodong Qiu (qiuaodon@pitt.edu), Mengyao Lu (my.lu@pitt.edu), Lujia Chen (luc17@pitt.edu)
Data Source
⚠️ Loading new data will replace current analysis
Feature Selection
Color Scheme
📚 Cell Marker Database
Pre-defined signatures are curated from CellMarker 2.0, a manually curated database of 26,915 cell markers across 2,578 cell types and 656 tissues.
Citation: Hu C, Li T, Xu Y, et al. Nucleic Acids Res. 2023;51(D1):D870-D876.
Species Selection
💡 Gene symbols will be updated based on species
Select Signature
💡 Select a pre-defined signature or enter custom genes below
Gene Input
Parameters
💡 Mean: Fastest, simple average
AddModuleScore: Fast, with control features
GSVA: Rank-based enrichment (may take 10-30s)
Color Legend
Spot Selection
💡 Tip: Save spots to Group 1 or Group 2 first using the map buttons
Parameters
Results
Group Information
💡 Tip: Use the group buttons at the bottom of the map to save selections and download spot IDs.
Analysis
⚠️ Clustering must be run first on the selected group