NicheNet is a computational tool to infer the ligand–receptor interactions that potentially regulate gene expression changes in receiver cell populations, by producing a list of prioritized ligands an...

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YouTube video by nichenet

YouTube video by nichenet

In order to build a comprehensive computation framework to investigate spatial cell-cell interactions, we developed Renoir. As shown in Fig. 1a, Renoir is an end-to-end computational framework for exploring the spatial map of ligand-target activities either by integrating spatial transcriptomics and scRNA-seq datasets from the same tissue or by employing single-cell resolution spatial transcriptomics data. Renoir first curates a set of ligand-target pairs using NATMI’s ConnectomeDB9 and NicheNet12. For a low-resolution spatial transcriptomic dataset (e.g., 10x Visium), using a cell type-annotated scRNA-seq data, Renoir quantifies a neighborhood activity score for each curated ligand-target pair for each spot in the spatial transcriptomic data. For a single-cell-resolution spatial transcriptomic data, Renoir quantifies the neighborhood activity score for each curated ligand-target pair for each cell with a spatial context. The quantification of the neighborhood activity score for a specific ligand-target pair for a particular spot is performed using the cell type proportions and cell type-specific mRNA abundances present in each spot/cell within the defined neighborhood of the spot/cell (see Methods for details). The activity between a ligand-target pair for the given spot and another spot in the neighborhood is scored based on the cell type-specific mRNA abundances of the two genes weighted by...