Approaches for benchmarking single-cell gene regulatory network inference methods

TL;DR

This review discusses the current methods and challenges in benchmarking computational approaches for inferring gene regulatory networks from single-cell sequencing data, highlighting gaps and future directions.

Contribution

It provides a comprehensive overview of benchmarking strategies for single-cell gene regulatory network inference methods and identifies key gaps and future research directions.

Findings

Overview of existing benchmarking approaches

Identification of gaps in current benchmarking practices

Suggestions for future research directions

Abstract

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.