Abstract
Transcriptomic data quantifying gene expression states for single cells or cell populations at a genomic level is now readily available. Changes in transcriptional state during cell development and function are governed by gene regulatory networks, comprising a collection of genes and regulatory interactions between these genes (or gene products). Network inference algorithms aim to infer functional interactions between genes from experimentally observed expression profiles, and identify the structure of the underlying regulatory networks. Here we describe popular classes of network inference algorithms, highlighting their respective strengths and weaknesses, along with some general challenges faced by these methods. Analyzing inferred network structures can provide insight into the genes, transcriptional changes, and regulatory interactions that play key roles in biological and disease-related processes of interest.