Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
Nat Commun. 2023 Dec 20;14(1):8459. doi: 10.1038/s41467-023-44103-3.
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
单细胞技术能够实现细胞命运图谱的动态分析。然而，捕捉基因调控关系并确定控制细胞命运决策的驱动因素仍然具有挑战性。我们提出了 CEFCON，这是一个基于网络的框架，首先使用具有注意力机制的图神经网络从单细胞 RNA-seq 数据中推断出细胞谱系特异性基因调控网络（GRN），然后通过网络控制理论来模拟细胞命运动态，以识别驱动调节剂和相关基因模块，揭示它们与细胞状态相关的关键生物学过程。广泛的基准测试一致表明，CEFCON 在 GRN 构建、驱动调节剂识别和基因模块识别方面优于基线方法。当应用于小鼠造血干细胞分化数据时，CEFCON 成功识别了三个发育谱系的驱动调节剂，从网络控制的角度为它们的分化提供了有用的见解。总的来说，CEFCON 为从单细胞 RNA-seq 数据研究细胞命运决策的潜在机制提供了一种有价值的工具。
