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Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell\u2013cell communication can be inferred through ligand\u2013receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand\u2013receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell\u2013cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell\u2013cell communication estimation tools for tumor-targeted therapy.<\/jats:p>","DOI":"10.1093\/bib\/bbac234","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T23:44:31Z","timestamp":1656287071000},"source":"Crossref","is-referenced-by-count":92,"title":["Cell\u2013cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies"],"prefix":"10.1093","volume":"23","author":[{"given":"Lihong","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"},{"name":"College of Life Sciences and Chemistry, Hunan University of Technology , 412007, Hunan, China"}]},{"given":"Feixiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"}]},{"given":"Zhao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"}]},{"given":"Jingwei","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[{"name":"Academy of Arts and Design, Tsinghua University , 10084, Beijing, China"},{"name":"The Future Laboratory, Tsinghua University , 10084, Beijing, China"}]},{"given":"Xiongfei","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"}]},{"given":"Guangyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"}]},{"given":"Liqian","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , 412007, Hunan, China"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"issue":"10","key":"2022071906100254800_ref1","doi-asserted-by":"crossref","first-page":"e55","DOI":"10.1093\/nar\/gkaa183","article-title":"Singlecellsignalr: inference of intercellular networks from single-cell transcriptomics","volume":"48","author":"Cabello-Aguilar","year":"2020","journal-title":"Nucleic Acids Res"},{"issue":"15","key":"2022071906100254800_ref2","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1093\/bioinformatics\/btaa482","article-title":"Comunet: a tool to explore and visualize intercellular 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