  Ligand-receptor interactions constitute a fundamental mechanism of cell-cell
communication and signaling. NicheNet is a well-established computational tool
that infers ligand-receptor interactions that potentially regulate gene
expression changes in receiver cell populations. Whereas the original
publication delves into the algorithm and validation, this paper describes a
best practices workflow cultivated over four years of experience and user
feedback. Starting from the input single-cell expression matrix, we describe a
"sender-agnostic" approach which considers ligands from the entire
microenvironment, and a "sender-focused" approach which only considers ligands
from cell populations of interest. As output, users will obtain a list of
prioritized ligands and their potential target genes, along with multiple
visualizations. In NicheNet v2, we have updated the data sources and
implemented a downstream procedure for prioritizing cell-type-specific
ligand-receptor pairs. Although a standard NicheNet analysis takes less than 10
minutes to run, users often invest additional time in making decisions about
the approach and parameters that best suit their biological question. This
paper serves to aid in this decision-making process by describing the most
appropriate workflow for common experimental designs like case-control and cell
differentiation studies. Finally, in addition to the step-by-step description
of the code, we also provide wrapper functions that enable the analysis to be
run in one line of code, thus tailoring the workflow to users at all levels of
computational proficiency.
