Nature communications, 2021-02, Vol.12 (1), p.1088-1088, Article 1088
2021
Details
- Autor(en) / Beteiligte
- Titel
- Inference and analysis of cell-cell communication using CellChat
- Ist Teil von
- Nature communications, 2021-02, Vol.12 (1), p.1088-1088, Article 1088
- Ort / Verlag
- England: Nature Publishing Group
- Erscheinungsjahr
- 2021
- Link zum Volltext
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2041-1723eISSN: 2041-1723DOI: 10.1038/s41467-021-21246-9Titel-ID: cdi_doaj_primary_oai_doaj_org_article_eac776c8aeed43009b282c2dd2c6db3b
- Format
- –
- Schlagworte
- Algorithms, Animals, Cell Communication - genetics, Cell interactions, Cell signaling, Cofactors, Communication, Communication networks, Communications networks, Computational Biology - methods, Datasets, Gene Expression Profiling - methods, Gene sequencing, Humans, Internet, Learning algorithms, Machine learning, Manifolds (mathematics), Mice, Models, Theoretical, Network analysis, Pathways, Pattern analysis, Pattern recognition, Ribonucleic acid, RNA, Sequence Analysis, RNA - methods, Signal classification, Signal transduction, Signal Transduction - genetics, Signaling, Single-Cell Analysis - methods, Skin - cytology, Skin - embryology, Skin - metabolism, Software, System effectiveness