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nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on potential ligand-target links can then be used to infer active ligand-target links between interacting cells. NicheNet prioritizes ligands according to their activity (i.e., how well they predict observed changes in gene expression in the receiver cell) and looks for affected targets with high potential to be regulated by these prioritized ligands.
Features
- NicheNet strongly differs from most current computational approaches to study intercellular communication
- Assess how well ligands expressed by a sender cell can predict changes in gene expression in the receiver cell prioritizing ligands based on their effect on gene expression
- Infer putative ligand-target links active in the system under study
- Validate prior ligand-target model
- Construction of user-defined prior ligand-target models
- We provide instructions on how to make intuitive visualizations of the main predictions
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