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      Inference and analysis of cell-cell communication using CellChat

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          Abstract

          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.

          Abstract

          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.

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            Fast unfolding of communities in large networks

            Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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              KEGG: new perspectives on genomes, pathways, diseases and drugs

              KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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                Author and article information

                Contributors
                plikus@uci.edu
                qnie@uci.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 February 2021
                17 February 2021
                2021
                : 12
                : 1088
                Affiliations
                [1 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Mathematics, , University of California, Irvine, ; Irvine, CA USA
                [2 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, NSF-Simons Center for Multiscale Cell Fate Research, , University of California, Irvine, ; Irvine, CA USA
                [3 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Developmental and Cell Biology, , University of California, Irvine, ; Irvine, CA USA
                [4 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Sue and Bill Gross Stem Cell Research Center, , University of California, Irvine, ; Irvine, CA USA
                [5 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Biological Chemistry, , University of California, Irvine, ; Irvine, CA USA
                [6 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Research Cyberinfrastructure Center, , University of California, Irvine, ; Irvine, CA USA
                [7 ]GRID grid.19188.39, ISNI 0000 0004 0546 0241, Graduate Institute of Clinical Medicine, College of Medicine, , National Taiwan University, ; Taipei, Taiwan
                [8 ]GRID grid.19188.39, ISNI 0000 0004 0546 0241, Division of Plastic Surgery, Department of Surgery, , National Taiwan University, ; Taipei, Taiwan
                [9 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Dermatology, , Yale University, ; New Haven, CT USA
                [10 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Pathology, , Yale University, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0002-5131-0215
                http://orcid.org/0000-0002-6245-6412
                http://orcid.org/0000-0002-2970-4170
                http://orcid.org/0000-0002-8845-2559
                http://orcid.org/0000-0002-8804-3368
                Article
                21246
                10.1038/s41467-021-21246-9
                7889871
                33597522
                667857d7-fbe8-4cfa-b958-f56acab8be79
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 April 2020
                : 8 January 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: DMS1763272
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cell signalling,cellular signalling networks,regulatory networks
                Uncategorized
                cell signalling, cellular signalling networks, regulatory networks

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