Interactive analysis and exploration of cell-cell communication in single-cell transcriptomics with InterCellar

Abstract 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 expertise. 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, several visualization options and the possibility to link biological pathways to ligand-receptor interactions. 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.