用分布匹配方法提升单细胞扰动预测的鲁棒性。
scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction
- 基于条件流匹配建模扰动后细胞的完整分布
- 在组合扰动下误差降低19.6%，优于最强基线
- 适合处理稀疏噪声数据，尤其擅长全局变化预测
系统生物学与药物发现的核心目标是预测细胞对扰动的转录响应。该任务因单细胞测量的噪声大、数据稀疏，且扰动常引起群体水平变化而非单个细胞改变而极具挑战。现有深度学习方法通常假设细胞间一一对应，难以捕捉此类全局效应。我们提出scDFM，一种基于条件流匹配的生成框架，可建模以对照状态为条件的扰动细胞完整分布。通过引入最大均值差异（MMD）目标，方法在细胞层面对应之外实现扰动与对照群体的对齐。为增强对稀疏性和噪声的鲁棒性，我们设计了扰动感知差分变压器（PAD-Transformer），利用基因互作图和差分注意力捕捉上下文相关的表达变化。在多个基因与药物扰动基准上，scDFM持续优于已有方法，在未见及组合扰动设置中表现优异；组合场景下，均方误差相对最强基线降低19.6%。结果凸显分布级生成建模在鲁棒体外扰动预测中的重要性。代码已开源：https://github.com/AI4Science-WestlakeU/scDFM。
原文摘要 · Abstract (English)
A central goal in systems biology and drug discovery is to predict the transcriptional response of cells to perturbations. This task is challenging due to the noisy and sparse nature of single-cell measurements, as well as the fact that perturbations often induce population-level shifts rather than changes in individual cells. Existing deep learning methods typically assume cell-level correspondences, limiting their ability to capture such global effects. We present scDFM, a generative framework based on conditional flow matching that models the full distribution of perturbed cells conditioned on control states. By incorporating a maximum mean discrepancy (MMD) objective, our method aligns perturbed and control populations beyond cell-level correspondences. To further improve robustness to sparsity and noise, we introduce the Perturbation-Aware Differential Transformer (PAD-Transformer), a backbone architecture that leverages gene interaction graphs and differential attention to capture context-specific expression changes. Across multiple genetic and drug perturbation benchmarks, scDFM consistently outperforms prior methods, demonstrating strong generalization in both unseen and combinatorial settings. In the combinatorial setting, it reduces mean squared error by 19.6% relative to the strongest baseline. These results highlight the importance of distribution-level generative modeling for robust in silico perturbation prediction. The code is available at https://github.com/AI4Science-WestlakeU/scDFM
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