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

library(CellChat)
cellchat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "RNA")

Example 2: Create from expression matrix

cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

Example 3: Set up database and preprocess

cellchat@DB <- CellChatDB.human
cellchat <- subsetData(cellchat)
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)

Example 4: Infer communication network

cellchat <- computeCommunProb(cellchat)
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)

Example 5: Extract communications

# 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

netVisual(cellchat, signaling = "TGFb", layout = "hierarchy")

Example 7: Network centrality analysis

cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")
netAnalysis_signalingRole_network(cellchat, signaling = "TGFb")

Example 8: Spatial data setup

cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels",
                          coordinates = coordinates, spatial.factors = spatial.factors)

Example 9: Data conversion from AnnData

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

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

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

For Intermediate Users

Custom Analyses: Explore different type parameters in computeAveExpr
Advanced Visualizations: Try chord diagrams and spatial plots
Network Analysis: Use centrality measures to identify key regulators
Multi-sample Analysis: Learn comparative analysis across conditions

For Advanced Users

Custom Databases: Update CellChatDB with domain-specific interactions
Spatial Applications: Adapt parameters for different spatial technologies
Performance Optimization: Use C++ functions for large datasets
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

# 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

# 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

# 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:

Restart the local CellChat documentation server
Re-run the documentation scraper with the same configuration
The skill will be rebuilt with the latest information and examples

Search AI Tools

cellchat-pkg-local-complete

Install this agent skill to your Project

SKILL.md