# Cellchat Pkg Local Complete

> CellChat 包源码随附的本地教程 + Rd 转换的函数文档 + R 源码 HTML - 完整覆盖173个文件

- Skill: `ketomihine/cellchat-pkg-local-complete` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add ketomihine/cellchat-pkg-local-complete`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ketomihine/cellchat-pkg-local-complete/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: Ketomihine (https://skillmd.com/u/ketomihine)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/ketomihine/cellchat-pkg-local-complete

---


# CellChat-Pkg-Local-Complete Skill

Comprehensive assistance with CellChat package for cell-cell communication analysis, generated from official documentation and source code.

## When to Use This Skill

This skill should be triggered when:

**Data Preparation & Object Creation:**
- Converting single-cell data from Seurat, SingleCellExperiment, or AnnData objects to CellChat
- Preparing normalized expression matrices and cell metadata for CellChat analysis
- Setting up CellChat objects from different data formats (count matrices, spatial data)
- Handling data input issues from Python/Scanpy or other single-cell tools

**Cell-Cell Communication Analysis:**
- Inferring ligand-receptor interactions and signaling pathways
- Computing communication probabilities and network analysis
- Analyzing spatially resolved cell-cell communication
- Performing comparative analysis across conditions or time points

**Network Analysis & Visualization:**
- Visualizing communication networks using hierarchy, circle, or chord diagrams
- Computing network centrality measures to identify key signaling players
- Creating spatial plots of communication on tissue sections
- Extracting and filtering specific communications of interest

**Advanced Applications:**
- Integrating custom ligand-receptor databases
- Analyzing contact-dependent signaling
- Projecting expression data onto protein-protein interaction networks
- Batch correction and multi-sample analysis

**Troubleshooting & Optimization:**
- Debugging issues with object creation or data conversion
- Optimizing parameters for communication inference
- Handling spatial transcriptomics data from different platforms
- Understanding and customizing analysis workflows

## Quick Reference

### Core Examples

**Example 1: Create CellChat object from Seurat**
```r
library(CellChat)
cellchat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "RNA")
```

**Example 2: Create from expression matrix**
```r
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
```

**Example 3: Set up database and preprocess**
```r
cellchat@DB <- CellChatDB.human
cellchat <- subsetData(cellchat)
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
```

**Example 4: Infer communication network**
```r
cellchat <- computeCommunProb(cellchat)
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)
```

**Example 5: Extract communications**
```r
# All communications
df.net <- subsetCommunication(cellchat)

# Specific pathways
df.net <- subsetCommunication(cellchat, signaling = c("WNT", "TGFb"))

# Specific cell groups
df.net <- subsetCommunication(cellchat, sources.use = c(1,2), targets.use = c(4,5))
```

**Example 6: Visualize signaling network**
```r
netVisual(cellchat, signaling = "TGFb", layout = "hierarchy")
```

**Example 7: Network centrality analysis**
```r
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")
netAnalysis_signalingRole_network(cellchat, signaling = "TGFb")
```

**Example 8: Spatial data setup**
```r
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels",
                          coordinates = coordinates, spatial.factors = spatial.factors)
```

**Example 9: Data conversion from AnnData**
```r
library(anndata)
ad <- read_h5ad("scanpy_object.h5ad")
counts <- t(as.matrix(ad$X))
library.size <- Matrix::colSums(counts)
data.input <- as(log1p(Matrix::t(Matrix::t(counts)/library.size) * 10000), "dgCMatrix")
meta <- ad$obs
```

**Example 10: Rank signaling pathways**
```r
rankNet(cellchat, mode = "single", measure = "weight")
```

## Key Concepts

**Core CellChat Objects:**
- `cellchat@data`: Normalized expression data
- `cellchat@meta`: Cell metadata and group information
- `cellchat@net`: Ligand-receptor level communication network
- `cellchat@netP`: Pathway level communication network
- `cellchat@idents`: Cell group identities

**Data Requirements:**
- **Expression Matrix**: Genes in rows, cells in columns, normalized data required
- **Cell Metadata**: Dataframe with cell labels and sample information
- **Spatial Data**: Coordinates and distance factors for spatial analysis

**Communication Inference:**
- **Probability Calculation**: Based on mass action law integrating expression and prior knowledge
- **Permutation Testing**: Statistical significance assessment
- **Pathway Aggregation**: Summarizing multiple L-R pairs per signaling pathway

**Network Analysis Types:**
- **Functional Networks**: Based on inferred communication probabilities
- **Structural Networks**: Based on ligand-receptor interaction structure
- **Centrality Measures**: Identifying key senders, receivers, mediators, influencers

## Reference Files

This skill includes comprehensive documentation in `references/`:

### **api_functions.md** (134 pages)
Complete API documentation for all CellChat functions:
- `subsetCommunication`: Extract specific cell-cell communications
- `computeAveExpr`: Calculate average expression per cell group
- `netVisual_embedding`: 2D visualization of signaling manifolds
- `rankNet`: Rank signaling networks by information flow
- `netAnalysis_computeCentrality`: Network centrality analysis
- `computeExpr_antagonist`: Model antagonist effects

### **cpp_source.md** (2 pages)
C++ source code for performance-critical operations:
- `ComputeSNN`: Shared nearest neighbor computation
- Rcpp bindings for high-performance calculations

### **r_source.md** (10 pages)
R source code for core functionality:
- `visualization`: Network visualization functions and themes
- Color palettes and plotting utilities
- Core analysis algorithms

### **tutorials.md** (27 pages)
Comprehensive tutorials covering:
- **Spatial transcriptomics analysis**: Complete workflow for spatial data
- **Data preparation**: From various single-cell formats
- **Network inference**: Step-by-step communication analysis
- **Visualization**: Multiple plotting options and customization
- **Advanced applications**: Custom databases and comparative analysis

## Working with This Skill

### For Beginners
1. **Start with Data Preparation**: Use the tutorials to understand data input requirements
2. **Basic Workflow**: Follow the spatial transcriptomics tutorial for a complete example
3. **Simple Visualizations**: Begin with circle plots and hierarchy diagrams
4. **Reference Functions**: Use api_functions.md for detailed parameter explanations

### For Intermediate Users
1. **Custom Analyses**: Explore different `type` parameters in `computeAveExpr`
2. **Advanced Visualizations**: Try chord diagrams and spatial plots
3. **Network Analysis**: Use centrality measures to identify key regulators
4. **Multi-sample Analysis**: Learn comparative analysis across conditions

### For Advanced Users
1. **Custom Databases**: Update CellChatDB with domain-specific interactions
2. **Spatial Applications**: Adapt parameters for different spatial technologies
3. **Performance Optimization**: Use C++ functions for large datasets
4. **Integration Workflows**: Combine with other single-cell analysis tools

### Navigation Tips
- **Function Lookup**: Search api_functions.md for specific function names
- **Parameter Details**: Each function entry includes complete parameter descriptions
- **Code Examples**: All tutorials include reproducible R code
- **Troubleshooting**: Check the tutorials section for common issues and solutions

## Resources

### references/
Organized documentation extracted from official sources:
- **Detailed explanations** of all parameters and return values
- **Code examples** with proper language annotations
- **Links to original documentation** for deeper exploration
- **Table of contents** for quick navigation

### scripts/
Add helper scripts here for common automation tasks:
- Data conversion utilities
- Custom visualization functions
- Batch processing workflows

### assets/
Store templates and examples:
- Example datasets in proper format
- Custom ligand-receptor databases
- Configuration files for different analysis types

## Common Workflows

### Basic CellChat Analysis
```r
# 1. Create object
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

# 2. Set database and preprocess
cellchat@DB <- CellChatDB.human
cellchat <- subsetData(cellchat)
cellchat <- normalizeData(cellchat)

# 3. Identify over-expressed genes/interactions
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)

# 4. Infer communication
cellchat <- computeCommunProb(cellchat)
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)

# 5. Visualize
netVisual(cellchat, signaling = "TGFb")
```

### Spatial Transcriptomics Analysis
```r
# 1. Create with spatial information
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels",
                          coordinates = coordinates, spatial.factors = spatial.factors)

# 2. Spatial communication inference
cellchat <- computeCommunProb(cellchat, contact.dependent = FALSE,
                             interaction.range = 250, scale.distance = 1)

# 3. Spatial visualization
netVisual_spatial(cellchat, signaling = "WNT")
```

### Comparative Analysis
```r
# 1. Create separate objects for each condition
cellchat1 <- createCellChat(object = data.input1, meta = meta1, group.by = "labels")
cellchat2 <- createCellChat(object = data.input2, meta = meta2, group.by = "labels")

# 2. Process each object
# ... (preprocessing steps for each)

# 3. Merge for comparison
cellchat <- mergeCellChat(cellchat1, cellchat2, add.names = c("LS", "NL"))

# 4. Compare signaling
rankNet(cellchat, mode = "comparison", comparison = c(1, 2))
```

## Notes

- **Coverage**: This skill covers 173 files including tutorials, API docs, and source code
- **Languages**: Primarily R with some C++ components
- **Data Types**: Supports scRNA-seq, spatial transcriptomics, and bulk data
- **Integration**: Works with Seurat, SingleCellExperiment, AnnData objects
- **Documentation**: Preserves original structure and examples from official sources

## Updating

To refresh this skill with updated documentation:
1. Restart the local CellChat documentation server
2. Re-run the documentation scraper with the same configuration
3. The skill will be rebuilt with the latest information and examples