AI 中文总结
本研究提出$D^{2}R^{2}$模型，将单细胞扰动预测转化为调控引导的基因渐进式生成，在Norman19数据集上实现5项指标最优，验证了基因生成顺序的有效性与可解释性。
AI 中文摘要
预测单细胞转录组对遗传扰动的响应是功能基因组学和虚拟细胞建模的核心任务。然而，现有方法通常将整个表达谱作为整体预测，未对单个基因响应的生成顺序进行建模。为解决该问题，我们提出$D^{2}R^{2}$（带调控强化的离散扩散模型，Discrete Diffusion with Regulation Reinforcement），将扰动预测重新表述为调控引导的基因渐进式生成过程。掩码离散扩散模型将表达表示为序数标记，逐步重构完全掩码的表达谱，使已生成的基因响应能够对剩余掩码的基因产生条件作用。调控策略模块从对照细胞推断的基因调控网络初始化生成策略，并将其适配到当前扰动和部分生成状态。随后，组相对策略优化仅以最终扰动效应一致性为奖励，优化排序策略。在Norman19和VCC-H1数据集上，$D^{2}R^{2}$在Norman19的全部5项指标上均达到最优性能，在H1数据集上也保持竞争力。保持生成器和生成预算固定的对照消融实验显示，基于生物先验的排序优于随机排序，且比基于不确定性的启发式方法更可靠；而反转生物先验排序会使所有指标下降。生物学分析进一步表明，优化后的策略会优先在早期调控基因，同时促进扰动特异性转录因子和响应基因的表达。这些结果证实，基因生成顺序是单细胞扰动预测中一种有效、可控且具有生物学可解释性的维度。
英文摘要
Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling. Existing approaches, however, typically predict an entire expression profile as a whole, leaving the order in which individual gene responses are generated unmodeled. To address this problem, we introduce \textbf{$D^{2}R^{2}$} (\textbf{D}iscrete \textbf{D}iffusion with \textbf{R}egulation \textbf{R}einforcement), which reformulates perturbation prediction as regulation-guided gene-wise progressive generation. A Masked Discrete Diffusion Model represents expression as ordinal tokens and reconstructs a fully masked profile step by step, allowing generated gene responses to condition those that remain masked. A Regulatory Policy Module initializes the generation policy from a gene regulatory network inferred from control cells and adapts it to the perturbation and current partially generated state. Then, group-relative policy optimization refines only the ordering policy using final perturbation-effect agreement as reward. Across Norman19 and VCC-H1, $D^{2}R^{2}$ achieves the best performance on all five metrics on Norman19 and remains competitive on H1. Controlled ablations holding the generator and generation budget fixed show that biological-prior ordering improves over random ordering and is more reliable than uncertainty-based heuristics, whereas reversing the biological-prior ordering degrades every metric. Biological analyses further show that the refined policy prioritizes regulatory genes early while promoting perturbation-specific transcription factors and responsive genes. These results establish gene generation order as an effective, controllable, and biologically interpretable dimension of single-cell perturbation prediction.
