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

Recently structural network control methods which exploit structural controllability of complex networks have become a powerful analysis tool for identifying driver nodes (e.g., driver genes or drug targets) in biological networked systems. In fact, the key point to apply structural network control on biological networked systems is how to characterize the state transition network (or interaction network) of these biological systems. The state transition network is a graph in which nodes denote the system variable and edges denote the significant interactions to trigger the state transition of system from one attractor (e.g., healthy state of individual patients) to another attractor (e.g., disease state of individual patients). The state transition network characterizes the state transition of networked system between any two attractors. Coincidentally, recently single sample network construction methods have been proposed to construct sample specific interaction network for characterizing the state transition of sample-specific biological systems. Therefore it is time to conduct a study to assess the performance of these state-of-the-art methods on the real-world biological systems, including sample-specific network construction methods and structural network control methods.

Researchers from Zhengzhou University comprehensively provided a performance evaluation of 16 analysis workflows based on the combination of four sample-specific network reconstruction methods and four representative structural network control methods on a variety of real-world biological datasets. With this study, the researchers have shed light on the relative behavior and performance of different analysis frameworks for evaluating and recommending these methods in particular biological application scenarios. In addition, the authors would like to point out a few challenges for current methods and suggest several development directions of new SSC analysis workflows when more complex practice conditions of control principles are faced.

Overview of the Sample-Specific Control problem (SSC)

(A) The flowchart of SSC analysis. The process of SCC analysis consists of two steps. The first step is to construct the sample-specific state transition network from the sample datasets. For constructing the sample-specific state transition network, several sample-specific network construction techniques have been proposed, including the Single Pearson Correlation Coefficient (SPCC), Linear Interpolation to Obtain Network Estimates for Single Samples (LIONESS), Single-Sample Network (SSN), and Cell-Specific Network construction (CSN) methods. Among them, the SSN method has the requirement for reference samples for constructing the single-sample differential co-expression network. Note that to filter the noise of sample-specific network reconstructions, the directed protein interaction information networks can be used for keeping the edge direction in the sample-specific state transition network. The second step is to design the network control principles; several structural network control methods have been proposed for finding a minimum set of driver nodes to control the whole network state dependent on adequate knowledge of the network structure, including the directed-network-based methods (MMS and DFVS) and the undirected-network-based methods (MDS and NCUA). (B) Representative biological meaning of “network driver nodes” in the structural network control principles. Assuming that biological samples can be represented as the sample-specific interaction network, the sample-specific network driver nodes can provide an efficient resource of personalized driver genes and cell-specific markers that can be useful for understanding the tumor or cell heterogeneity. 

Guo W-F, Yu X, Shi Q-Q, Liang J, Zhang S-W, Zeng T (2021) Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis. PLoS Comput Biol 17(5): e1008962. [article]

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