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Stock, M. ; Popp, N. ; Fiorentino, J. ; Scialdone, A.

Topological benchmarking of algorithms to infer Gene Regulatory Networks from Single-Cell RNA-seq Data.

Bioinformatics 40:btae267 (2024)

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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.

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