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...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 January 2022
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| In: |
Communications biology
Year: 2022, Jahrgang: 5, Pages: 1-13 |
| ISSN: | 2399-3642 |
| DOI: | 10.1038/s42003-021-02986-2 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s42003-021-02986-2 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s42003-021-02986-2 
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| Verfasserangaben: | Marta Interlandi, Kornelius Kerl & Martin Dugas |
MARC
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| 520 | |a 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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