ChatSpatial Tutorials

These tutorials demonstrate how to analyze spatial transcriptomics data through natural conversation by interacting with ChatSpatial in Claude Desktop.

🚀 Getting Started

Choose Your Learning Path

New to spatial transcriptomics? → Beginner Learning Path - Complete guided journey (4-6 hours)

Comfortable with basics, want advanced methods? → Intermediate Learning Path - Method mastery and integration (6-8 hours)

Ready for cutting-edge research? → Advanced Learning Path - ML, trajectory analysis, and multi-modal integration (8-12 hours)

Your First Spatial Analysis

Perfect for beginners. Learn how to load data, preprocess, identify spatial domains, and create visualizations through natural conversation.

What you will learn:

  • How to talk to ChatSpatial about your data
  • Loading and exploring spatial transcriptomics datasets
  • Discovering tissue organization and spatial domains
  • Creating beautiful visualizations automatically

🧬 Core Analysis Tutorials

Identifying Cell Types

Discover what types of cells are in your tissue and where they are located.

What you will learn:

  • “What cell types are in my tissue?”
  • Understanding marker gene expression
  • Validating cell type assignments
  • Visualizing cell type distributions spatially

Cell Communication Analysis

Uncover how different cells talk to each other in your tissue.

What you will learn:

  • “How do my cells communicate?”
  • Identifying ligand-receptor interactions
  • Exploring spatial communication patterns
  • Understanding biological significance

Advanced Spatial Statistics

Unlock hidden spatial patterns using sophisticated statistical methods through natural conversation.

What you will learn:

  • “Are these genes spatially correlated?” - Bivariate Moran’s I analysis
  • “Where are the hotspots in my tissue?” - Local Indicators of Spatial Association (LISA)
  • “How do cell types organize spatially?” - Neighborhood enrichment analysis
  • “Are cells randomly distributed?” - Ripley’s K function analysis
  • “Which regions are network hubs?” - Spatial centrality analysis
  • “Do similar states cluster together?” - Join count analysis

Spatial Gene Set Enrichment Analysis

Discover biological pathways and processes that are spatially organized in your tissue using EnrichMap.

What you will learn:

  • “Which biological processes are active in different regions?”
  • Loading gene sets from MSigDB and other databases
  • Performing spatially-aware enrichment analysis
  • Creating pathway territory maps and interpreting results
  • Validating spatial enrichment patterns biologically

RNA Velocity & Trajectory Analysis

Uncover cellular dynamics, developmental trajectories, and temporal processes in your tissue.

What you will learn:

  • “How are my cells changing over time?”
  • Computing RNA velocity to see directional changes
  • Inferring developmental trajectories and cell fate transitions
  • Exploring spatial-temporal patterns and biological significance

Creating Beautiful Visualizations

Make publication-ready figures through simple conversation.

What you will learn:

  • “Can you create a nice plot of my data?”
  • Customizing colors, styles, and layouts
  • Publication-ready figure generation
  • Interactive and animated visualizations

💡 Tutorial Philosophy

These tutorials are designed around natural conversation, not code. You will learn to:

  • Ask questions instead of writing commands
  • Describe what you want instead of remembering syntax
  • Focus on biology instead of technical details
  • Get instant results instead of debugging code

🎯 How to Use These Tutorials

For Structured Learning

  1. Choose your learning path based on experience level (see above)
  2. Follow the guided progression with time estimates and checkpoints
  3. Complete practice exercises to reinforce concepts
  4. Track your progress through learning objectives

For Targeted Learning

  1. Browse individual tutorials for specific techniques
  2. Follow the conversation examples - they show real interactions
  3. Try the suggested questions with your own data
  4. Adapt the examples to your specific research questions

🔄 Tutorial Progression

Basic Analysis → Cell Types → Communication → Statistics → Trajectories → Visualization
     ↓              ↓            ↓             ↓             ↓             ↓
  Load data    Identify cells  Find signals   Spatial stats Track dynamics Make figures
  Find domains  Validate types  Explore space  LISA/Moran's RNA velocity   Create plots  
  Preprocess   Spatial maps    Pathways       Hotspots      Pseudotime     Customize
                                              Networks
                                              Centrality

📚 Learning Paths Detail

Beginner Path - Complete Foundation

  • Phase 1: Load data → Identify cell types → Create visualizations
  • Phase 2: Explore cell communication → Practice integration
  • Success: Independent analysis through conversation
  • Next: Intermediate methods and statistical approaches

Intermediate Path - Method Mastery

  • Phase 1: Spatial statistics → Gene set enrichment
  • Phase 2: Advanced communication → Multi-method integration
  • Phase 3: Multi-sample analysis → Method comparison
  • Success: Choose optimal methods for research questions
  • Next: Cutting-edge ML and temporal analysis

Advanced Path - Research Leadership

  • Phase 1: Advanced spatial analysis → Neural network approaches
  • Phase 2: RNA velocity → Trajectory analysis → Temporal dynamics
  • Phase 3: Multi-modal integration → Publication workflows
  • Success: Design and execute research-quality studies
  • Next: Method development and community contribution

📚 Additional Resources

Ready to start? Begin with Your First Spatial Analysis and explore your tissue through conversation.

Table of contents