Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis

PLoS Comput Biol. 2021 May 6;17(5):e1008962. doi: 10.1371/journal.pcbi.1008962. eCollection 2021 May.

Abstract

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.

Grant support

This paper was supported by the National Natural Science Foundation of China (62002329 (WFG), 61873202 (SWZ), 61876619 (JL), 61802141 (QQS),61803360 (XY) and 11871456 (TZ)) , and Key scientific and technological projects of Henan Province (212102310083(WFG)), and Henan postdoctoral foundation (202002021(WFG)), and Research start-up funds for top doctors in Zhengzhou University (32211739 (WFG)), and Strategic Priority Research Program of the Chinese Academy of Sciences (XDB38050200(TZ)), and the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01(TZ)), and the Fundamental Research Funds for the Central Universities (2662017QD043(QQS)).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.