Xi Xi, Li Jiaqi, Jia Jinmeng, Meng Qiuchen, Li Chen, Wang Xiaowo, Wei Lei, Zhang Xuegong
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST / Department of Automation, Tsinghua University, Beijing, China.
School of Life Sciences, Tsinghua University, Beijing, China.
Nat Commun. 2025 Feb 3;16(1):1284. doi: 10.1038/s41467-025-56475-9.
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory mechanisms, limiting the decoding of regulations behind key cellular events, such as cell state transitions. Here, we present regX, a deep neural network incorporating both gene-level regulation and gene-gene interaction mechanisms, which enables prioritizing potential driver regulators of cell state transitions and providing mechanistic interpretations. Applied to single-cell multi-omics data on type 2 diabetes and hair follicle development, regX reliably prioritizes key transcription factors and candidate cis-regulatory elements that drive cell state transitions. Some regulators reveal potential new therapeutic targets, drug repurposing possibilities, and putative causal single nucleotide polymorphisms. This method to analyze single-cell multi-omics data demonstrates how the interpretable design of neural networks can better decode biological systems.
细胞在多个层面受到调控，从单个基因的调控到多个基因之间的相互作用。最近的一些神经网络模型可以将分子变化与细胞表型联系起来，但其设计缺乏调控机制的建模，限制了对关键细胞事件（如细胞状态转变）背后调控的解码。在这里，我们展示了regX，这是一种结合了基因水平调控和基因-基因相互作用机制的深度神经网络，它能够对细胞状态转变的潜在驱动调节因子进行优先级排序，并提供机制性解释。应用于2型糖尿病和毛囊发育的单细胞多组学数据时，regX能够可靠地对驱动细胞状态转变的关键转录因子和候选顺式调控元件进行优先级排序。一些调节因子揭示了潜在的新治疗靶点、药物重新利用的可能性以及推定的因果单核苷酸多态性。这种分析单细胞多组学数据的方法证明了神经网络的可解释设计如何能够更好地解码生物系统。
