School of Computer Science and Technology, Donghua University, Shanghai, China.
School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Comput Biol Chem. 2021 Aug;93:107512. doi: 10.1016/j.compbiolchem.2021.107512. Epub 2021 May 19.
Gene regulatory network models the interactions between transcription factors and target genes. Reconstructing gene regulation network is critically important to understand gene function in a particular cellular context, providing key insights into complex biological systems. We develop a new computational method, named iMPRN, which integrates multiple prior networks to infer regulatory network. Based on the network component analysis model, iMPRN adopts linear regression, graph embedding, and elastic networks to optimize each prior network in line with specific biological context. For each rewired prior networks, iMPRN evaluate the confidence of the regulatory edges in each network based on B scores and finally integrated these optimized networks. We validate the effectiveness of iMPRN by comparing it with four widely-used gene regulatory network reconstruction algorithms on a simulation data set. The results show that iMPRN can infer the gene regulatory network more accurately. Further, on a real scRNA-seq dataset, iMPRN is respectively applied to reconstruct gene regulatory networks for malignant and nonmalignant head and neck tumor cells, demonstrating distinctive differences in their corresponding regulatory networks.
基因调控网络模型描述了转录因子和靶基因之间的相互作用。重建基因调控网络对于理解特定细胞环境中的基因功能至关重要，为复杂的生物系统提供了关键的见解。我们开发了一种新的计算方法，名为 iMPRN，它整合了多个先验网络来推断调控网络。基于网络组件分析模型，iMPRN 采用线性回归、图嵌入和弹性网络来优化每个先验网络，以适应特定的生物学背景。对于每个重新连接的先验网络，iMPRN 根据 B 分数评估每个网络中调控边缘的置信度，最后整合这些优化后的网络。我们通过将 iMPRN 与四种广泛使用的基因调控网络重建算法在模拟数据集上进行比较，验证了 iMPRN 的有效性。结果表明，iMPRN 可以更准确地推断基因调控网络。此外，在真实的 scRNA-seq 数据集上，iMPRN 分别应用于重建恶性和非恶性头颈部肿瘤细胞的基因调控网络，展示了它们相应调控网络的显著差异。
