CellChat: Inference and analysis of cell-cell communication using CellChat

Investigators
Qing Nie
Contact info (email)
qnie@uci.edu
1. Define context(s)
reveal new biological insights
Current Conformance Level / Target Conformance Level
Extensive
Primary goal of the model/tool/database

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively 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 learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.

 
Biological domain of the model
scRNA-seq data of various tissues
Structure(s) of interest in the model
cell-cell communications in scRNA-seq data
Spatial scales included in the model
tissue
Time scales included in the model
seconds to days
2. Data for building and validating the model
Data for building the model Published? Private? How is credibility checked? Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
in vivo pre-clinical (lower-level organism or small animal) Yes No The model was built in an unsupervised way on unbiased single-cell RNA sequencing data. Extensive
in vivo pre-clinical (large animal)
Human subjects/clinical Yes No The model was built in an unsupervised way on unbiased single-cell RNA sequencing data. Extensive
Other: ________________________
Data for validating the model Published? Private? How is credibility checked? Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
in vivo pre-clinical (lower-level organism or small animal) Yes No By comparing to existing knowledge and conducting new experiments to confirm the computational findings.. Extensive
in vivo pre-clinical (large animal)
Human subjects/clinical Yes No By comparing to existing knowledge and conducting new experiments to confirm the computational findings. Extensive
Other: ________________________
3. Validate within context(s)
Who does it? When does it happen? How is it done? Current Conformance Level / Target Conformance Level
Verification Postdocs/investigators Throughout the project. The convergence of algorithms are checked and statistical tests are developed. Extensive
Validation Postdocs/investigators After the core tool is developed 1) The inferred cell-cell communication agree with prior knowledge. 2) The inferred cell-cell communications are validated by spatial data. 3) The new insights of computational analysis are validated by experiments. Extensive
Uncertainty quantification
Sensitivity analysis
Other:__________
Additional Comments
4. Limitations
Disclaimer statement (explain key limitations) Who needs to know about this disclaimer? How is this disclaimer shared with that audience? Current Conformance Level / Target Conformance Level
This method does not take spatial constraints into account Scientific community who intends to apply this method to systems where spatial constraint of cell-cell communication is important. Throughout the paper and the associated tutorials. Extensive
5. Version control
Current Conformance Level / Target Conformance Level
Extensive
Naming Conventions? Repository? Code Review?
individual modeler Yes Yes Peer
within the lab Yes Yes Peer
collaborators Yes Yes Peer
6. Documentation
Current Conformance Level / Target Conformance Level
Code commented? Adequate
Scope and intended use described? Extensive
User’s guide? Extensive
Developer’s guide? Adequate
7. Dissemination
Current Conformance Level / Target Conformance Level
Extensive
Target Audience(s): “Inner circle” Scientific community Public
Simulations
Models
Software R package: https://github.com/sqjin/CellChat R package: https://github.com/sqjin/CellChat R package: https://github.com/sqjin/CellChat
Results Paper and Interactive web-based explorer: http://www.cellchat.org/ Paper and Interactive web-based explorer: http://www.cellchat.org/ Paper and Interactive web-based explorer: http://www.cellchat.org/
Implications of results
8. Independent reviews
Current Conformance Level / Target Conformance Level
Adequate
Reviewer(s) name & affiliation: N/A
When was review performed? By installing and using the package
How was review performed and outcomes of the review? CellChat has be successfully used by many researchers.
9. Test competing implementations
Current Conformance Level / Target Conformance Level
Extensive
Yes or No (briefly summarize)
Were competing implementations tested? Yes. The method has been compared to several other commonly used methods.
Did this lead to model refinement or improvement? No
10. Conform to standards
Current Conformance Level / Target Conformance Level
Extensive
Yes or No (briefly summarize)
Are there operating procedures, guidelines, or standards for this type of multiscale modeling? Yes. There are several standard procedures for preprocessing scRNA-seq data.
How do your modeling efforts conform? Common data preprocessing procedures are followed.