Tutorials

Learn spatial transcriptomics analysis through hands-on tutorials and examples.

Table of contents

  1. Tutorial Categories
    1. 🚀 Core Analysis
    2. 🔬 Analysis Methods
    3. 🎯 Advanced Methods
    4. 📚 Learning Paths
  2. How to Use These Tutorials
    1. Prerequisites
    2. Tutorial Format
    3. Interactive Examples
  3. Getting Started
    1. New to Spatial Transcriptomics?
    2. Have Single-Cell Experience?
    3. Ready for Advanced Methods?
  4. Tutorial Data
    1. Demo Datasets
    2. Loading Demo Data
  5. Common Workflows
    1. Standard Spatial Analysis Pipeline
    2. Advanced Analysis Pipeline
  6. Tips for Success
    1. Best Practices
    2. Common Pitfalls
  7. Getting Help
    1. Within Tutorials
    2. Additional Resources
    3. Reporting Issues

Welcome to the ChatSpatial tutorials! This section provides comprehensive guides for performing spatial transcriptomics analysis using natural language commands.

Tutorial Categories

🚀 Core Analysis

Essential spatial analysis techniques that every user should know:

🔬 Analysis Methods

In-depth guides for specific analysis techniques:

🎯 Advanced Methods

Cutting-edge spatial analysis techniques:

📚 Learning Paths

Structured learning journeys for different skill levels:

How to Use These Tutorials

Prerequisites

Before starting any tutorial, ensure you have:

  • ChatSpatial installed and configured
  • Basic understanding of single-cell RNA-seq concepts
  • Access to Claude Desktop or compatible MCP client

Tutorial Format

Each tutorial follows a consistent format:

  1. Learning Objectives - What you’ll accomplish
  2. Prerequisites - Required knowledge and setup
  3. Step-by-Step Instructions - Detailed commands and explanations
  4. Expected Results - What to expect from each step
  5. Troubleshooting - Common issues and solutions
  6. Next Steps - Where to go from here

Interactive Examples

All tutorials use actual commands you can run in ChatSpatial:

# Example: Load and explore data
Load the mouse brain Visium dataset and show me basic statistics

# Example: Perform analysis
Identify spatial domains using SpaGCN and create visualization plots

Getting Started

New to Spatial Transcriptomics?

Start with the Beginner Learning Path which covers:

  1. Basic concepts and terminology
  2. Data loading and exploration
  3. Quality control and preprocessing
  4. Simple spatial analysis

Have Single-Cell Experience?

Jump to Core Analysis tutorials to learn spatial-specific techniques:

  1. Basic Spatial Analysis
  2. Spatial Statistics
  3. Visualization Tutorial

Ready for Advanced Methods?

Explore Advanced Methods for cutting-edge techniques:

  1. Trajectory Analysis
  2. Spatial Registration

Tutorial Data

Demo Datasets

All tutorials use standardized demo datasets included with ChatSpatial:

  • Visium Mouse Brain - Classic 10X Visium dataset
  • Slide-seq Cerebellum - High-resolution spatial data
  • MERFISH Hypothalamus - Single-cell resolution spatial data
  • SeqFish Embryo - Developmental spatial data

Loading Demo Data

# Load any demo dataset
Load the [dataset_name] demo data

# Examples:
Load the mouse brain Visium demo data
Load the cerebellum Slide-seq demo data
Load the hypothalamus MERFISH demo data

Common Workflows

Standard Spatial Analysis Pipeline

graph LR
    A[Load Data] --> B[Quality Control]
    B --> C[Clustering]
    C --> D[Spatial Domains]
    D --> E[Cell Annotation]
    E --> F[Visualization]

Advanced Analysis Pipeline

graph LR
    A[Load Data] --> B[Preprocessing]
    B --> C[Spatial Domains]
    C --> D[Cell Communication]
    D --> E[Trajectory Analysis]
    E --> F[Integration]

Tips for Success

Best Practices

  1. Start Simple - Begin with basic analysis before moving to advanced methods
  2. Understand Your Data - Always explore data characteristics first
  3. Validate Results - Cross-check findings with biological knowledge
  4. Document Parameters - Keep track of analysis parameters for reproducibility

Common Pitfalls

  1. Skipping Quality Control - Always perform QC before analysis
  2. Over-interpreting Results - Consider statistical significance and biological relevance
  3. Ignoring Spatial Context - Remember that spatial information is key
  4. Using Wrong Parameters - Understand method parameters and their effects

Getting Help

Within Tutorials

  • Each tutorial includes troubleshooting sections
  • Code examples are fully tested and verified
  • Expected outputs are clearly described

Additional Resources

Reporting Issues

Found an error in a tutorial? Please:

  1. Check the troubleshooting section
  2. Search existing issues
  3. Create a new issue with details

Ready to start learning? Choose your path:

Begin with Core Analysis Explore Learning Paths

Table of contents