Zhou Zhecheng, Wei Jinhang, Liu Mingzhe, Zhuo Linlin, Fu Xiangzheng, Zou Quan
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325027, China.
College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, China.
BMC Biol. 2025 Mar 11;23(1):73. doi: 10.1186/s12915-025-02177-z.
Single-cell RNA sequencing (scRNA-seq) is now essential for cellular-level gene expression studies and deciphering complex gene regulatory mechanisms. Deep learning methods, when combined with scRNA-seq technology, transform gene regulation research into graph link prediction tasks. However, these methods struggle to mitigate the impact of noisy data in gene regulatory networks (GRNs) and address the significant imbalance between positive and negative links.
Consequently, we introduce the AnomalGRN model, focusing on heterogeneity and sparsification to elucidate complex regulatory mechanisms within GRNs. Initially, we consider gene pairs as nodes to construct new networks, thereby converting gene regulation prediction into a node prediction task. Considering the imbalance between positive and negative links in GRNs, we further adapt this issue into a graph anomaly detection (GAD) task, marking the first application of anomaly detection to GRN analysis. Introducing the cosine metric rule enables the AnomalGRN model to differentiate between homogeneity and heterogeneity among nodes in the reconstructed GRNs. The adoption of graph structure sparsification technology reduces noisy data impact and optimizes node representation.
单细胞RNA测序（scRNA-seq）现在对于细胞水平的基因表达研究和解读复杂的基因调控机制至关重要。深度学习方法与scRNA-seq技术相结合，将基因调控研究转化为图链接预测任务。然而，这些方法难以减轻基因调控网络（GRN）中噪声数据的影响，也难以解决正、负链接之间的显著不平衡问题。
因此，我们引入了AnomalGRN模型，专注于异质性和稀疏化，以阐明GRN内的复杂调控机制。最初，我们将基因对视为节点来构建新网络，从而将基因调控预测转化为节点预测任务。考虑到GRN中正、负链接之间的不平衡，我们进一步将此问题转化为图异常检测（GAD）任务，这标志着异常检测首次应用于GRN分析。引入余弦度量规则使AnomalGRN模型能够区分重建GRN中节点之间的同质性和异质性。采用图结构稀疏化技术可减少噪声数据的影响并优化节点表示。
