DRIFT：用于单细胞扰动预测的解耦响应-不变流传输

中文摘要 AI 辅助

预测细胞对扰动的反应是细胞生物学中的一个核心问题，在系统生物学和药物发现中具有广泛的应用。这项任务具有挑战性，因为细胞反应可能复杂且依赖于细胞状态，细胞间的内在变异性可能与扰动效应相混淆，并且破坏性的单细胞RNA测序无法对同一细胞在治疗前后进行配对测量。流匹配灵活地将对照细胞传输到扰动状态，但作用于完整细胞状态可能会将扰动效应与预先存在的细胞间变异性混淆。解耦方法将响应成分与不变成分分离，但通过规定的机制（如潜在偏移或图编辑）对扰动进行建模，限制了其灵活性。我们在一个统一框架中解决了这两个局限性。一个变分编码器通过条件先验和信息论不变性约束，将每个细胞解耦为不变块（捕获不受扰动影响的状态）和响应块（捕获其变化的状态）。条件流匹配仅传输响应块，以扰动和不变状态为条件，产生灵活的、数据驱动的扰动效应模型，而不会混淆预先存在的变异性。在多个基准测试中，我们的方法在涉及组合和未见扰动预测的设置中优于最强已发表方法。

英文摘要

Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery. This task is challenging because cellular responses can be complex and cell-state dependent, intrinsic cell-to-cell variability can be confounded with perturbation effects, and destructive single-cell RNA sequencing precludes paired measurements of the same cell before and after treatment. Flow matching transports control cells to perturbed states flexibly, but acting on the full cell state can confound perturbation effects with pre-existing cell-to-cell variability. Disentangled approaches separate responsive from invariant components, but model perturbations through prescribed mechanisms, such as latent shifts or graph edits, limiting their flexibility. We address both limitations in a unified framework. A variational encoder disentangles each cell into an invariant block, capturing state unaffected by the perturbation, and a responsive block, capturing state it changes, through conditional priors and an information-theoretic invariance constraint. Conditional flow matching transports only the responsive block, conditioned on the perturbation and invariant state, yielding a flexible, data-driven model of perturbation effects without confounding pre-existing variability. Across several benchmarks, our method outperforms the strongest published method in settings involving combinatorial and unseen perturbation prediction.

发表机构

• EURECOM

机构由 AI 辅助整理，请以论文原文为准。