scDEFT：一个用于药物效应预测与反事实推理的深度学习框架
scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning
- Causal Data AI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
scDEFT通过将药物作为条件算子，利用特征级线性调制和双头聚合，在单细胞图谱上预测药物效应并识别驱动基因，支持靶点提名和患者分层。
AI中文摘要：
纵向单细胞图谱现在能够捕获应答者和无应答者在治疗前和治疗后的匹配状态，这为从机制上解释为什么两位接受相同药物治疗的患者会出现不同结果提供了机会。我们提出了scDEFT（单细胞药物效应转导器），它将药物视为对细胞表征的条件算子，从而实现预测和解释。在scDEFT中，特征级线性调制产生药物条件下的细胞潜在表示，这些潜在表示在丰富的单细胞监督下学习，然后被冻结。两个独立的头在共享的转录邻域上聚合这些潜在表示，以预测药物诱导的状态变化和应答者状态。一个后向阶段根据潜在维度在区分应答者和无应答者方面的强度对其进行排序，并在细胞组成控制下将其映射到基因。在一个包含116万个细胞、三个队列和两类药物的统一炎症性肠病图谱上，scDEFT在基线到可重复性上限的间隙中预测状态变化达到45%的水平，并在治疗前以AUROC 0.70对应答者进行分层，而标准预测器仍处于随机水平。这些预测及其背后的驱动因素支持靶点和联合靶点提名、患者分层以及对未见药物队列效应的反事实预测。
英文摘要：
Longitudinal single cell atlases now capture matched pre treatment and post treatment states from responders and non responders, presenting an opportunity to mechanistically explain why two patients on the same drug diverge. We introduce scDEFT (single cell Drug EFfect Transducer), which treats a drug as a conditioning operator on cell representations, enabling prediction and explanation. In scDEFT, feature wise linear modulation produces drug conditioned cell latents, learned under abundant per cell supervision and then frozen. Two independent heads aggregate those latents over shared transcriptional neighborhoods to predict drug induced state change and responder status. A backward stage ranks the latent dimensions by how strongly they separate responders from non responders and maps them to genes under a cell composition control. On a harmonized inflammatory bowel disease atlas of 1.16 million cells, three cohorts and two drug classes, scDEFT predicts state change at 45% of the baseline to reproducibility ceiling headroom and stratifies responders before treatment at AUROC 0.70, where standard predictors remain at chance. These predictions and the drivers behind them support target and co target nomination, patient stratification, and counterfactual prediction of unseen drug cohort effects.
