跨域解耦药物扰动表示，提升单细胞转录响应的可迁移性。
Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses
- 通过域分离编码器解耦基础状态与药物扰动特征。
- 在隐空间跨域转移扰动表示，重建效果优于现有方法。
- 适用于药物发现中的跨数据集泛化，适合生物信息研究者。
表型药物发现因其识别生物活性分子的潜力而备受关注。转录组分析能全面反映细胞对外部扰动的表型变化。本文提出XTransferCDR，一种新型生成框架，用于跨域特征解耦与可迁移表示学习。给定一对扰动表达谱，该方法通过域分离编码器将扰动表示从基础状态中解耦，并在隐空间内进行跨域转移，再通过共享解码器重建对应的扰动表达谱。这种跨域转移约束有效促进了可迁移药物扰动表示的学习。我们在多个数据集上进行了广泛评估，包括药物、单基因及组合基因扰动的单细胞转录响应。实验结果表明，XTransferCDR性能优于当前最先进的方法，展现了其在推动表型药物发现方面的潜力。
原文摘要 · Abstract (English)
Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external perturbations. In this paper, we propose XTransferCDR, a novel generative framework designed for feature decoupling and transferable representation learning across domains. Given a pair of perturbed expression profiles, our approach decouples the perturbation representations from basal states through domain separation encoders and then cross-transfers them in the latent space. The transferred representations are then used to reconstruct the corresponding perturbed expression profiles via a shared decoder. This cross-transfer constraint effectively promotes the learning of transferable drug perturbation representations. We conducted extensive evaluations of our model on multiple datasets, including single-cell transcriptional responses to drugs and single- and combinatorial genetic perturbations. The experimental results show that XTransferCDR achieved better performance than current state-of-the-art methods, showcasing its potential to advance phenotypic drug discovery.
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