Inference and analysis of cell-cell communication using CellChat

Abstract Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold…

• NS
  National Science Foundation

  Awards: R01GM123731, DMS1763272
• SF
  Simons Foundation

  Awards: 594598, QN, U01AR073159, 594598
• NI
  National Institutes of Health

  Awards: R01GM123731, 594598, U01AR073159, R01GM123731, and P30AR07504, DMS1763272, U01AR073159