  Gene regulatory networks are powerful tools for modeling interactions among
genes to regulate their expression for homeostasis and differentiation.
Single-cell sequencing offers a unique opportunity to build these networks with
high-resolution data. There are many proposed computational methods to build
these networks using single-cell data and different approaches are followed to
benchmark these methods. In this review, we lay the basic terminology in the
field and define the success metrics. Next, we present an overview of
approaches for benchmarking computational gene regulatory network approaches
for building gene regulatory networks and point out gaps and future directions
in this regard.
