Inference and analysis of cell-cell communication using CellChat

The paper addresses the challenge of understanding global communication among cells, which requires both an accurate representation of cell–cell signaling links and effective systems-level analysis. The authors first construct a database of interactions among ligands, receptors, and their cofactors that accurately represents known heteromeric molecular complexes. They then develop CellChat, a tool that quantitatively infers and analyzes intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data, predicting the major signaling inputs and outputs of cells using network analysis and pattern recognition.

Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying it to mouse and human skin datasets demonstrated its ability to extract complex signaling patterns, and the authors provide the toolkit together with a web-based Explorer to help researchers discover novel intercellular communications and build cell–cell communication atlases across tissues.