Gan Yanglan, Hu Xin, Zou Guobing, Yan Cairong, Xu Guangwei
School of Computer Science and Technology, Donghua University, Shanghai, China.
School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Front Oncol. 2022 May 26;12:899825. doi: 10.3389/fonc.2022.899825. eCollection 2022.
Accurate inference of gene regulatory rules is critical to understanding cellular processes. Existing computational methods usually decompose the inference of gene regulatory networks (GRNs) into multiple subproblems, rather than detecting potential causal relationships simultaneously, which limits the application to data with a small number of genes. Here, we propose BiRGRN, a novel computational algorithm for inferring GRNs from time-series single-cell RNA-seq (scRNA-seq) data. BiRGRN utilizes a bidirectional recurrent neural network to infer GRNs. The recurrent neural network is a complex deep neural network that can capture complex, non-linear, and dynamic relationships among variables. It maps neurons to genes, and maps the connections between neural network layers to the regulatory relationship between genes, providing an intuitive solution to model GRNs with biological closeness and mathematical flexibility. Based on the deep network, we transform the inference of GRNs into a regression problem, using the gene expression data at previous time points to predict the gene expression data at the later time point. Furthermore, we adopt two strategies to improve the accuracy and stability of the algorithm. Specifically, we utilize a bidirectional structure to integrate the forward and reverse inference results and exploit an incomplete set of prior knowledge to filter out some candidate inferences of low confidence. BiRGRN is applied to four simulated datasets and three real scRNA-seq datasets to verify the proposed method. We perform comprehensive comparisons between our proposed method with other state-of-the-art techniques. These experimental results indicate that BiRGRN is capable of inferring GRN simultaneously from time-series scRNA-seq data. Our method BiRGRN is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://gitee.com/DHUDBLab/bi-rgrn.
准确推断基因调控规则对于理解细胞过程至关重要。现有的计算方法通常将基因调控网络（GRN）的推断分解为多个子问题，而不是同时检测潜在的因果关系，这限制了其在少量基因数据中的应用。在此，我们提出了BiRGRN，一种从时间序列单细胞RNA测序（scRNA-seq）数据推断GRN的新型计算算法。BiRGRN利用双向循环神经网络来推断GRN。循环神经网络是一种复杂的深度神经网络，能够捕捉变量之间复杂、非线性和动态的关系。它将神经元映射到基因，并将神经网络层之间的连接映射到基因之间的调控关系，为以生物学紧密性和数学灵活性对GRN进行建模提供了直观的解决方案。基于深度网络，我们将GRN的推断转化为一个回归问题，利用先前时间点的基因表达数据预测后续时间点的基因表达数据。此外，我们采用两种策略来提高算法的准确性和稳定性。具体而言，我们利用双向结构整合正向和反向推断结果，并利用不完整的先验知识集过滤掉一些低置信度的候选推断。BiRGRN应用于四个模拟数据集和三个真实的scRNA-seq数据集以验证所提出的方法。我们将我们提出的方法与其他现有技术进行了全面比较。这些实验结果表明，BiRGRN能够从时间序列scRNA-seq数据中同时推断GRN。我们的方法BiRGRN使用TensorFlow机器学习库在Python中实现，可在https://gitee.com/DHUDBLab/bi-rgrn上免费获取。
