CellChat — Cell-Cell Communication Analysis

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"))

Pre-flight Interview