Feng Zhanying, Chen Xi, Duren Zhana, Xin Jingxue, Miao Hao, Yuan Qiuyue, Wang Yong, Wong Wing Hung
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford University, Stanford, CA, 94305, USA.
Genome Biol. 2025 Jul 24;26(1):220. doi: 10.1186/s13059-025-03680-w.
Advances in single-cell technology enable large-scale generation of omics data, promising for clarifying gene regulatory networks governing different cell type/states. Nonetheless, prevailing methods fail to account for universal and reusable regulatory modules in GRNs, which are fundamental underpinnings of cell type landscape. We introduce cRegulon to infer regulatory modules by modeling combinatorial regulation of transcription factors based on diverse GRNs from single-cell multi-omics data. Through benchmarking and applications using simulated datasets and real datasets, cRegulon outperforms existing approaches in identifying TF combinatorial modules as regulatory units and annotating cell types. cRegulon offers new insights and methodology into combinatorial regulation.
单细胞技术的进步使得大规模生成组学数据成为可能，这有望阐明控制不同细胞类型/状态的基因调控网络。尽管如此，目前的方法未能考虑基因调控网络中普遍且可重复使用的调控模块，而这些模块是细胞类型格局的基本支撑。我们引入cRegulon，通过基于单细胞多组学数据中的不同基因调控网络对转录因子的组合调控进行建模来推断调控模块。通过使用模拟数据集和真实数据集进行基准测试和应用，cRegulon在将转录因子组合模块识别为调控单元以及注释细胞类型方面优于现有方法。cRegulon为组合调控提供了新的见解和方法。
