AI 中文总结
FlowGRN整合条件流匹配、得分匹配与dynGENIE3，采用抗失活细胞相似性度量，在BEELINE基准上实现GRN推断的先进性能，可准确建模动态调控关系。
AI 中文摘要
从单细胞RNA测序（scRNA-seq）数据推断基因调控网络（GRN）可为细胞行为提供见解，但因缺乏时间信息和失活噪声的普遍存在而变得复杂。为解决这些挑战，我们提出FlowGRN，一种整合条件流匹配与得分匹配以进行鲁棒轨迹重建，并结合dynGENIE3实现可扩展GRN推断的方法。FlowGRN采用了一种新型细胞相似性度量，该度量对高维scRNA-seq数据中的失活效应具有抵御能力。在BEELINE基准上的评估表明，FlowGRN在合成数据集和实验数据集上均达到了最先进的性能。消融研究验证了抗失活相似性度量和轨迹重建步骤的重要性，凸显了FlowGRN准确建模动态调控关系的能力。
英文摘要
Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data offers insights into cellular behavior, but is complicated by the lack of temporal information and the prevalence of dropout noise. To address these challenges, we present FlowGRN, a method that integrates conditional flow matching and score matching for robust trajectory reconstruction with dynGENIE3 for scalable GRN inference. FlowGRN incorporates a novel cell similarity measure that is resilient to dropout effects in high-dimensional scRNA-seq data. Evaluation on the BEELINE benchmark demonstrates that FlowGRN achieves state-of-the-art performance on both synthetic and experimental datasets. Ablation studies validate the importance of both the dropout-robust similarity measure and the trajectory reconstruction step, highlighting FlowGRN's ability to accurately model dynamic regulatory relationships.
Comments26 pages, 9 figures, an extension of our publication at the ACM BCB 2025 conference
