基于基因几何的控制锚定残差流匹配虚拟细胞扰动建模
Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling
- East China Normal University（华东师范大学）
- Peking University（北京大学）
- University of Science and Technology of China（中国科学技术大学）
- Nanjing University（南京大学）
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出GeneGeoFlow模型，利用基因本体和控制衍生共表达网络的多尺度谱坐标构建扰动条件基因几何，结合控制锚定残差流与条件最优传输，在虚拟细胞扰动转录响应预测任务中取得优异性能。
AI中文摘要：
虚拟细胞建模的核心任务是预测单细胞转录组对未见过的基因扰动和药物组合的响应，生物网络为基因关系提供了有价值的先验信息。现有基于图的模型通常使用同一网络来构建基因表示并介导基因间交互，从而隐含地将稳定关联视为扰动-响应通路。基因本体（Gene Ontology）和控制衍生的共表达网络编码的是相对稳定的关系，而非干预特定的响应方向或幅度。因此，我们提出GeneGeoFlow，该方法将控制锚定残差流建立在从生物网络衍生的基因层面几何结构上，以学习干预特定的转录响应。GeneGeoFlow从基因本体和控制衍生的共表达网络中推导多尺度谱坐标。扰动条件下的基因门控模块选择相关的结构尺度和网络来源，产生干预特定的基因几何结构。所得几何结构对控制锚定残差流进行条件约束，无需沿图显式传播目标衍生信号。条件最优传输耦合未配对的对照和扰动群体以进行训练，而Delta相关目标使预测和观察到的条件水平表达变化方向对齐。GeneGeoFlow在Norman加性基准上实现了0.8979的Pearson Delta分数，在固定ComboSciPlex测试拆分的5个保留药物组合上实现了0.9088的分数。这些结果表明，扰动条件下的基因几何是干预特定响应预测的有效结构先验，不会将稳定的基因关系与响应传播混为一谈。
英文摘要：
A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and biological networks provide valuable priors on gene relationships. Existing graph-based models commonly use the same network to structure gene representations and mediate intergene interactions, thereby implicitly treating stable associations as perturbation-response pathways. Gene Ontology and control-derived coexpression networks encode relatively stable relationships rather than intervention-specific response directions or magnitudes. We therefore propose GeneGeoFlow, which conditions a control-anchored residual flow on gene-wise geometry derived from biological networks to learn intervention-specific transcriptional responses. GeneGeoFlow derives multi-scale spectral coordinates from Gene Ontology and control-derived coexpression networks. A perturbation-conditioned, gene-wise gating module selects relevant structural scales and network sources, yielding intervention-specific gene geometry. The resulting geometry conditions a control-anchored residual flow without explicitly propagating target-derived signals along the graph. Condition-wise optimal transport couples unpaired control and perturbed populations for training, while a Delta-correlation objective aligns the predicted and observed condition-level expression-shift directions. GeneGeoFlow achieves Pearson Delta scores of 0.8979 on the Norman additive benchmark and 0.9088 on five held-out drug combinations in the fixed ComboSciPlex test split. These results support perturbation-conditioned gene geometry as an effective structural prior for intervention-specific response prediction, without conflating stable gene relationships with response propagation.
