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InterCellar enables interactive analysis and exploration of cell−cell communication in single-cell transcriptomic data
Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752611/ https://www.ncbi.nlm.nih.gov/pubmed/35017628 http://dx.doi.org/10.1038/s42003-021-02986-2 |
Sumario: | Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand−receptor interactions. Despite the existence of various tools capable of inferring cell−cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell−cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand−receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell−cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell−cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell−cell communication and facilitate hypothesis generation in diverse biological systems. |
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