Dai Bingjie, Li Hanshuang, Wang Peizhuo, Hu Pengwei, Xing Jixiang, Hu Yanan, Xi Qilemuge, Zuo Yongchun
State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010020, China.
School of Life Science and Technology, Xidian University, Shaanxi, Xi'an, 710071, China.
BMC Biol. 2025 Aug 12;23(1):254. doi: 10.1186/s12915-025-02367-9.
Understanding how genes regulate each other in cells is crucial for determining cell identity and development, and single-cell sequencing technologies facilitate such research through gene regulatory networks (GRNs). However, identifying important marker genes within these complex networks remains difficult.
Consequently, we present DualNetM, a deep generative model with a dual-network framework for inferring functional-oriented markers. It employs graph neural networks with adaptive attention mechanisms to construct GRNs from single-cell data. Functional-oriented markers are identified from bidirectional co-regulatory networks through the integration of gene co-expression networks. Benchmark tests highlighted the superior performance of DualNetM in constructing GRNs, along with a stronger association with biological functions in marker inference. In the melanoma dataset, DualNetM successfully inferred novel malignant markers, and survival analysis results showed that multiple novel markers were associated with lethality in malignant melanoma. Additionally, DualNetM identified stage-specific functional markers and clarified their specific roles in mouse embryonic fibroblast reprogramming. DualNetM's marker inference function demonstrated stronger biological relevance during primed reprogramming.
In summary, DualNetM effectively facilitated the inference of functional-oriented markers from complex GRNs.
了解基因在细胞中如何相互调节对于确定细胞身份和发育至关重要，而单细胞测序技术通过基因调控网络（GRN）促进了此类研究。然而，在这些复杂网络中识别重要的标记基因仍然很困难。
因此，我们提出了DualNetM，一种具有双网络框架的深度生成模型，用于推断功能导向型标记。它采用具有自适应注意力机制的图神经网络从单细胞数据构建GRN。通过整合基因共表达网络，从双向共调控网络中识别功能导向型标记。基准测试突出了DualNetM在构建GRN方面的卓越性能，以及在标记推断中与生物学功能更强的关联。在黑色素瘤数据集中，DualNetM成功推断出新型恶性标记，生存分析结果表明多个新型标记与恶性黑色素瘤的致死率相关。此外，DualNetM识别出阶段特异性功能标记，并阐明了它们在小鼠胚胎成纤维细胞重编程中的具体作用。DualNetM的标记推断功能在原始重编程过程中表现出更强的生物学相关性。
总之，DualNetM有效地促进了从复杂GRN中推断功能导向型标记。
