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InterCellar enables interactive analysis and exploration of cell-cell communication in single-cell transcriptomic data
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DOI：10.1038/s42003-021-02986-2.png)
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Abstract
En 中文
Marta Interlandi et al. develop InterCellar, a user-friendly tool for analyzing ligand-receptor interactions from scRNA-seq data, and establishing a link between these interactions and well-known molecular pathways. 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.
Keywords:
PATHWAY
ONTOLOGY
COVID-19
REVEALS
CANCER
GENE
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