zhengweihuaynu/cellchat

R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data

CAUTION

We have updated CellChat to v2 and migrated CellChat to a new repository. This repository will be NOT updated and maintained any more. Please check the new repository jinworks/CellChat for the new updates, and the CellChat v2 paper for a comprehensive protocol of CellChat.

About CellChat and CellChatDB

CellChat is an R package designed for inference, analysis, and visualization of cell-cell communication from single-cell data. CellChat aims to enable users to identify and interpret cell-cell communication within an easily interpretable framework, with the emphasis of clear, attractive, and interpretable visualizations.

CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in mutiple species, leading to a comprehensive recapitulation of known molecular interaction mechanisms including multi-subunit structure of ligand-receptor complexes and co-factors.

If you use CellChat in your research, please considering citing our papers:

• Suoqin Jin et al., CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics, bioRxiv 2023 [CellChat v2]
• Suoqin Jin et al., Inference and analysis of cell-cell communication using CellChat, Nature Communications 2021 [CellChat v1]

Capabilities

In addition to infer the intercellular communication from any given single-cell data, CellChat provides functionality for further data exploration, analysis, and visualization.

• It can quantitatively characterize and compare the inferred cell-cell communication networks using a systems approach by combining social network analysis, pattern recognition, and manifold learning approaches.
• It provides an easy-to-use tool for extracting and visualizing high-order information of the inferred networks. For example, it allows ready prediction of major signaling inputs and outputs for all cell populations and how these populations and signals coordinate together for functions.
• It enables comparative analysis of cell-cell communication across different conditions and identification of altered signaling and cell populations.
• It provides several visualization outputs to facilitate intuitive user-guided data interpretation.