Li Xinyu, Zhang Wei, Zhang Jianming, Li Guang
State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Zheda Road, 310027, Hangzhou, China.
BMC Bioinformatics. 2021 Mar 24;22(1):153. doi: 10.1186/s12859-021-04074-y.
Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods.
ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms.
As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.
给定表达数据后，基因调控网络（GRN）推理方法试图确定调控关系。然而，当前的推理方法在一定程度上忽略了GRN的固有拓扑特征，导致所构建的结构缺乏清晰的生物学解释。为了增加推断网络的生物物理意义，本研究在网络推理之前进行了数据驱动的模块检测。基因模块通过基于分解的方法进行识别。
基于独立成分分析（ICA）分解的模块检测方法已被用于直接从转录组数据中检测功能模块。关于时间序列表达、整理后的数据集和单细胞RNA测序（scRNA-seq）数据集的实验表明，所提出的模块化增强（ModularBoost）方法相对于现有方法具有优势，尤其是在效率和准确性方面。对于scRNA-seq数据集，ModularBoost方法优于其他候选推理算法。
作为一项复杂的任务，GRN推理可以分解为几个复杂度降低的任务。使用已识别的基因模块作为拓扑约束，初始推理问题可以通过分别推断模块内和模块间的相互作用来完成。实验结果表明，所提出的ModularBoost方法可以通过引入拓扑约束来提高推理算法的准确性和效率。
