无需配对细胞数据，就能精准预测基因扰动后细胞群体变化。
SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction
- 用集合感知编码器和条件DiT模型，直接学习细胞间转移路径。
- 在七项指标上超越现有方法，准确恢复基因表达变化与群体结构。
- 适合生物实验设计，可优先筛选潜在免疫激活因子。
虚拟细胞模型旨在预测细胞群对扰动的响应，但对照组与处理组数据通常为非配对群体，难以学习特异性扰动效应。本文提出SCALE，一种基于条件传输的模型，将细胞视为无序集合，无需逐细胞匹配即可预测处理后的群体状态。通过共享的集合感知编码器与条件DiT主干网络，学习潜在传输路径，使终点监督直接对齐差异（delta-aligned），无需额外的差值目标。在基因、化学、发育及免疫扰动数据上，SCALE成功恢复了基因表达变化、响应方向与群体结构。在具有显著细胞系效应的CRISPR数据中，其在七项指标上均优于对比方法，并保持不同基因靶点表征间的分离性，避免特征坍缩。此外，模型成功识别出能引发不同免疫激活与炎症反应的细胞因子。利用三位供体的匹配PBMC样本验证了预测差异。结果表明，SCALE可从非配对数据中实现特异性扰动预测，并支持实验优先级筛选。
原文摘要 · Abstract (English)
Virtual-cell models aim to predict how cell populations respond to perturbations, but control and treated cells are measured as unpaired populations, complicating the learning of perturbation-specific effects. We present SCALE, a conditional transport model that represents cells as unordered sets and predicts treated populations without cell-level matching. A shared set-aware encoder and conditional DiT backbone learn latent transport, making endpoint supervision directly delta-aligned without an auxiliary delta objective. Across genetic, chemical, developmental and immune perturbations, SCALE recovered gene-expression changes, response directions and population structure. In CRISPR data with dominant cell-line effects, SCALE outperformed competing methods across seven metrics and maintained separation among gene-target representations rather than collapsing them into a shared region. SCALE further prioritized cytokines predicted to produce distinct immune activation and inflammatory responses. Experiments using matched PBMC samples from three donors confirmed these predicted differences. Together, SCALE enables perturbation-specific prediction from unpaired populations and supports experimental prioritization.
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