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 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