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.
Highlights
From dynamical system viewpoint, single samples can be modeled as sample-specific networked systems, of which the state transitions are determined by the sample-specific driver variables/nodes [1,2,3,4,5,6]
Performance assessment of sample-specific network control methods (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
The state transition network is a graph in which nodes denote the system variables and edges denote the significant interactions to trigger the state transition of system from one attractor to another attractor
Summary
Single samples can be modeled as sample-specific networked systems, of which the state transitions are determined by the sample-specific driver variables/nodes [1,2,3,4,5,6]. It is a big challenge for identifying sample-specific driver variables/nodes in dynamical biological processes called Sample-Specific network Control (SSC) problem, as the true functional form of the underlying dynamics for sample-specific biological systems are always unavailable. It is time to survey the SSC analysis workflows for the identification of sample-specific driver variables related to heterogeneous biological processes
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