Topological benchmarking of algorithms to infer Gene Regulatory Networks from Single-Cell RNA-seq Data
Marco Stock, Niclas Popp, Jonathan Fiorentino, Antonio Scialdone- Computational Mathematics
- Computational Theory and Mathematics
- Computer Science Applications
- Molecular Biology
- Biochemistry
- Statistics and Probability
Abstract
Motivation
In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms’ ability to capture structural properties of networks, which are fundamental, for example, for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.
Results
To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.
Availability and implementation
STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.
Contact
Direct inquiries should be addressed to the corresponding authors.
Supplementary information
Supplementary Information is available online.