Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

被引:3
|
作者
Stock, Marco [1 ,2 ,3 ,4 ]
Popp, Niclas [1 ,2 ,3 ]
Fiorentino, Jonathan [1 ,2 ,3 ,5 ]
Scialdone, Antonio [1 ,2 ,3 ]
机构
[1] German Res Ctr Environm Hlth, Helmholtz Zentrum Munchen, Inst Epigenet & Stem Cells, D-81377 Neuherberg, Germany
[2] Helmholtz Zentrum Munchen, Inst Funct Epigenet, German Res Ctr Environm Hlth, D-85764 Munich, Germany
[3] German Res Ctr Environm Hlth, Helmholtz Zentrum Munchen, Inst Computat Biol, D-85764 Munich, Germany
[4] Tech Univ Munich, TUM Sch Life Sci Weihenstephan, D-85354 Munich, Germany
[5] Fdn Ist Italiano Tecnol, Ctr Life Nano& Neurosci, Viale Regina Elena 291, I-00161 Rome, Italy
关键词
SMALL-WORLD; CENTRALITY; INTEGRATION; CHALLENGES; ROBUSTNESS; BIOLOGY;
D O I
10.1093/bioinformatics/btae267
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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, e.g., 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.
引用
收藏
页数:15
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