Inference and analysis of cell-cell communication using CellChat

Inference and analysis of cell-cell communication using CellChat

Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easil...

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Main Authors: Suoqin Jin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen-Hsiang Kuan, Peggy Myung, Maksim V. Plikus, Qing Nie
Format: Article
Language:English
Published: Nature Publishing Group 2021-02-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-21246-9
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spelling doaj-eac776c8aeed43009b282c2dd2c6db3b2021-02-21T12:11:51ZengNature Publishing GroupNature Communications2041-17232021-02-0112112010.1038/s41467-021-21246-9Inference and analysis of cell-cell communication using CellChatSuoqin Jin0Christian F. Guerrero-Juarez1Lihua Zhang2Ivan Chang3Raul Ramos4Chen-Hsiang Kuan5Peggy Myung6Maksim V. Plikus7Qing Nie8Department of Mathematics, University of California, IrvineDepartment of Mathematics, University of California, IrvineDepartment of Mathematics, University of California, IrvineDepartment of Biological Chemistry, University of California, IrvineNSF-Simons Center for Multiscale Cell Fate Research, University of California, IrvineDepartment of Developmental and Cell Biology, University of California, IrvineDepartment of Dermatology, Yale UniversityNSF-Simons Center for Multiscale Cell Fate Research, University of California, IrvineDepartment of Mathematics, University of California, IrvineSingle-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.https://doi.org/10.1038/s41467-021-21246-9
collection DOAJ
language English
format Article
sources DOAJ
author Suoqin Jin
Christian F. Guerrero-Juarez
Lihua Zhang
Ivan Chang
Raul Ramos
Chen-Hsiang Kuan
Peggy Myung
Maksim V. Plikus
Qing Nie
spellingShingle Suoqin Jin
Christian F. Guerrero-Juarez
Lihua Zhang
Ivan Chang
Raul Ramos
Chen-Hsiang Kuan
Peggy Myung
Maksim V. Plikus
Qing Nie
Inference and analysis of cell-cell communication using CellChat
Nature Communications
author_facet Suoqin Jin
Christian F. Guerrero-Juarez
Lihua Zhang
Ivan Chang
Raul Ramos
Chen-Hsiang Kuan
Peggy Myung
Maksim V. Plikus
Qing Nie
author_sort Suoqin Jin
title Inference and analysis of cell-cell communication using CellChat
title_short Inference and analysis of cell-cell communication using CellChat
title_full Inference and analysis of cell-cell communication using CellChat
title_fullStr Inference and analysis of cell-cell communication using CellChat
title_full_unstemmed Inference and analysis of cell-cell communication using CellChat
title_sort inference and analysis of cell-cell communication using cellchat
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-02-01
description Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.
url https://doi.org/10.1038/s41467-021-21246-9
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AT lihuazhang inferenceandanalysisofcellcellcommunicationusingcellchat
AT ivanchang inferenceandanalysisofcellcellcommunicationusingcellchat
AT raulramos inferenceandanalysisofcellcellcommunicationusingcellchat
AT chenhsiangkuan inferenceandanalysisofcellcellcommunicationusingcellchat
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AT maksimvplikus inferenceandanalysisofcellcellcommunicationusingcellchat
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