We build a user-friendly web-based “CellChat Explorer” that contains two major components:
- Ligand-Receptor Interaction Explorer that allows easy exploration of our novel ligand-receptor interaction database, a comprehensive recapitulation of known molecular compositions including multimeric complexes and co-factors. Our database CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in both human and mouse.
- Cell-Cell Communication Atlas Explorer that allows easy exploration of the cell-cell communication for any given scRNA-seq dataset that has been processed by our R toolkit CellChat.
In addition to infer the intercellular communication from any given scRNA-seq data, CellChat provides functionality for further data exploration, analysis, and visualization.
- It is able to analyze cell-cell communication for continuous states along cellular development trajectories.
- It can quantitatively characterize and compare the inferred cell-cell communication networks using an integrated 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 provides several visualization outputs to facilitate intuitive user-guided data interpretation.
Check out our preprint on bioRxiv for the detailed methods and applications, and a short video for introducing the key features of CellChat.
CellChat R package can be easily installed from Github using devtools:
devtools::install_github("sqjin/CellChat")
CellChat can be installed on a normal computer within few mins.
- Install NMF using
devtools::install_github("sqjin/NMF"). Please check here for other solutions if you encounter any issue. - Install ComplexHeatmap using
devtools::install_github("jokergoo/ComplexHeatmap")if you encounter any issue. - Install UMAP python pacakge for dimension reduction:
pip install umap-learn. Please check here if you encounter any issue.
Some users might have issues when installing CellChat pacakge due to different operating systems and new R version. Please check the following solutions:
- Installation on Mac OX with R > 3.6: Please re-install Xquartz.
- Installation on Windows, Linux and Centos: Please check the solution here.
Please check the vignettes directory of the repo.
- Basic commands tutorial
- Comparison analysis of multiple datasets
- Walkthrough - CellChat analysis of cell-cell communication in mouse skin wounds
- Example streamline for quick analysis and exploration
- Interface with other single-cell analysis toolkits (e.g., Seurat, Scanpy)
CellChat package requires only a standard computer with enough RAM to support the in-memory operations.
This package is supported for macOS, Windows and Linux. The package has been tested on macOS: Mojave (10.14.5) and Windows 10.
Dependencies of CellChat package are indicated in the Description file, and can be automatically installed when installing CellChat pacakge.
If you have any problems, comments or suggestions, please contact us at Suoqin Jin ([email protected]).
Suoqin Jin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Peggy Myung, Maksim V. Plikus, Qing Nie. Inference and analysis of cell-cell communication using CellChat. bioRxiv 2020.07.21.214387; doi: https://doi.org/10.1101/2020.07.21.214387