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However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell\u2013cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.<\/jats:p>","DOI":"10.1093\/bib\/bbaa283","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T19:11:32Z","timestamp":1601061092000},"source":"Crossref","is-referenced-by-count":7,"title":["FunRes: resolving tissue-specific functional cell states based on a cell\u2013cell communication network model"],"prefix":"10.1093","volume":"22","author":[{"given":"Sascha","family":"Jung","sequence":"first","affiliation":[{"name":"Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Derio, Bizkaia, 48160, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kartikeya","family":"Singh","sequence":"additional","affiliation":[{"name":"Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), 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