/cellchat-cell-communication

name: "cellchat-cell-communication"
description: "Infer and visualize intercellular communication from scRNA-seq with CellChat (R). Build CellChat from Seurat/counts → subset CellChatDB ligand-receptor pairs → over-expressed genes per group → communication probabilities → pathway signaling → network centrality (senders/receivers/influencers) → chord/heatmap/bubble plots → cross-condition compare. Human, mouse. Use liana for pure-Python."
license: "MIT"

CellChat — Cell-Cell Communication Analysis

Overview

CellChat is an R package that infers and visualizes intercellular signaling networks from single-cell RNA-seq data. Starting from a normalized expression matrix and cluster labels, CellChat identifies ligand-receptor interactions supported by CellChatDB — a manually curated database of over 2,000 validated ligand-receptor pairs in human and mouse. Communication probability is modeled using the law of mass action, combining expression levels of ligands, receptors, and cofactors. CellChat aggregates pair-level probabilities into pathway-level signaling networks and quantifies each cell group's role as a signal sender, receiver, mediator, or influencer. The result is a rich, interpretable picture of which cell types talk to which, through which signaling pathways, and how these patterns change between conditions.

When to Use

• Characterizing which cell types are the dominant senders or receivers of paracrine and autocrine signals in a tissue atlas or disease sample
• Identifying specific ligand-receptor pairs mediating communication between a cell population of interest (e.g., tumor cells → T cells, fibroblasts → epithelial cells)
• Comparing intercellular signaling networks between two conditions (e.g., healthy vs. diseased, treatment vs. control) to find rewired or lost communication
• Discovering pathway-level signaling programs (e.g., MHC-II, COLLAGEN, VEGF) enriched in a particular cell-cell interaction
• Prioritizing targets for perturbation experiments by ranking signaling pathways by their aggregate communication strength or network centrality
• Use **omics-plotting** SKILL (Python) for generic figures from exported tables; network chord/heatmap/bubble views use CellChat's R `netVisual_*`
• Use **liana** (Python/R) instead when you want a pure-Python workflow or a consensus ranking across multiple ligand-receptor databases (CellChat, CellPhoneDB, Connectome, NicheNet)
• Use **NicheNet** (R) instead when you need ligand-to-target gene regulatory inference — predicting which ligands from sender cells regulate which target genes in receiver cells

Prerequisites

• **R packages**: `CellChat` (>= 2.0), `Seurat` (>= 4.0, for Seurat-based input), `NMF`, `ggplot2`, `ggalluvial`, `igraph`, `dplyr`, `patchwork`, `reticulate` (optional)
• **Data requirements**: Normalized scRNA-seq count matrix (genes × cells) and a cell group identity vector (cluster labels or cell types). Raw counts are acceptable if normalized inside CellChat.
• **Species**: CellChatDB available for human and mouse; other species require custom database construction
• **Memory**: 8 GB RAM minimum for datasets with 10,000–50,000 cells; 32 GB+ recommended for larger datasets

# Install CellChat from GitHub (CRAN version may lag)
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install(c("BiocNeighbors", "ComplexHeatmap"))

install.packages("devtools")
devtools::install_github("jinworks/CellChat")

# Core dependencies
install.packages(c("NMF", "ggplot2", "ggalluvial", "igraph",
                   "dplyr", "patchwork", "circlize", "RColorBrewer"))

Quick Start

library(CellChat)
library(Seurat)

# Assume `seurat_obj` is a processed Seurat object with cell type identities in Idents()
data.input <- GetAssayData(seurat_obj, assay = "RNA", slot = "data")  # normalized counts
meta       <- data.frame(labels = Idents(seurat_obj), row.names = names(Idents(seurat_obj)))

cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
cellchat@DB <- CellChatDB.human  # or CellChatDB.mouse

cellchat <- subsetData(cellchat)
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- computeCommunProb(cellchat, type = "triMean")
cellchat <- filterCommunication(cellchat, min.cells = 10)
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)

# Quick summary
print(cellchat)
# e.g. "An object of class CellChat created from a single dataset
#  with 8 cell groups and 312 inferred ligand-receptor pairs"

Workflow

Step 1: Create CellChat Object

Build a CellChat object from either a Seurat object or a raw count matrix with accompanying metadata.

library(CellChat)
library(Seurat)

# --- Option A: from a Seurat object ---
# seurat_obj must have cell type identities set with Idents() or in meta.data
data.input <- GetAssayData(seurat_obj, assay = "RNA", slot = "data")  # log-normalized
meta <- data.frame(
  labels = Idents(seurat_obj),
  row.names = colnames(seurat_obj)
)
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

# --- Option B: from a count matrix directly ---
# data.input: genes-by-cells normalized matrix (dgCMatrix or dense matrix)
# identity: named factor of cell group labels (length = ncol(data.input))
cellchat <- createCellChat(object = data.input, meta = data.frame(labels = identity),
                           group.by = "labels")

cat("Cell groups:", levels(cellchat@idents), "\n")
cat("Number of cells:", ncol(data.input), "\n")
# Cell groups: B_cell Endothelial Fibroblast Macrophage NK T_cell Tumor
# Number of cells: 12847

Step 2: Set CellChatDB and Subset Interactions

Load the species-appropriate ligand-receptor database and optionally subset to a signaling category of interest.

# Load database for the appropriate species
CellChatDB <- CellChatDB.human   # use CellChatDB.mous