Yang Le, Chen Runpu, Goodison Steve, Sun Yijun
Department of Microbiology and Immunology, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, New York, NY 14203, United States.
Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, United States.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae692.
Network-based methods utilize protein-protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels. Our analysis revealed insights into algorithmic performance that were previously unattainable. Based on the results of the benchmark study, we presented a practical guide for users on how to select appropriate detection methods and protein-protein interaction networks for cancer pathway identification, and provided suggestions for future algorithm development.
基于网络的方法利用蛋白质-蛋白质相互作用信息来识别癌症中显著扰动的子网，并提出关键分子途径。已经开发了许多方法，但迄今为止，缺乏对现有方法性能进行严格比较分析的基准测试。在本文中，我们提出了一种使用合成数据的新型基准测试框架，并进行了全面分析，以研究现有方法检测目标基因和子网以及控制假阳性的能力，以及它们在基因和子网水平存在拓扑偏差的情况下的表现。我们的分析揭示了以前无法获得的算法性能见解。基于基准研究的结果，我们为用户提供了一份实用指南，指导他们如何选择合适的检测方法和蛋白质-蛋白质相互作用网络用于癌症途径识别，并为未来的算法开发提供了建议。
