s Integrated Pipelines for Inferring Gene Regulatory Networks from Single-Cell Data

Background: Single-cell technologies provide unprecedented opportunities to study heterogeneity of molecular mechanisms. In particular, single-cell RNA-sequence data have been successfully used to infer gene regulatory networks with stochastic expressions. However, there are still substantial challenges in measuring the relationships between genes and selecting the important genetic regulations. Objective: This prospective provides a brief review of effective methods for the inference of gene regulatory networks. Methods: We concentrate on two types of inference methods, namely the model-free methods and mechanistic methods for constructing gene networks. Results: For the model-free methods, we mainly discuss two issues, namely the measures for quantifying gene relationship and criteria for selecting significant connections between genes. The issue for mechanistic methods is different mathematical models to describe genetic regulations accurately. Conclusions: We advocate the development of ensemble methods that combine two or more methods together.