AI 中文总结
该研究提出生成驱动推断（GPI）框架，用于利用辅助生成模型改进分布值参数的推断，经模拟和K562细胞的Perturb-seq研究验证，其效率更高且在模型误设下表现稳健。
AI 中文摘要
现代生成模型越来越多地生成分布值输出，例如单细胞基因组学中对遗传扰动的预测细胞响应。尽管这些模型提供了有价值的辅助信息，但它们本身并不完美，因此需要统计方法来利用其预测结果，而不依赖于其正确性。我们提出了生成驱动推断（Generation-Powered Inference, GPI），这是一种利用辅助生成模型改进分布值参数推断的通用框架。聚焦于Wasserstein重心及相关分布泛函，我们引入了一种函数值桥表示，将非线性Wasserstein空间中的推断转换为Hilbert空间中的均值函数估计，从而实现类似于预测驱动推断的增强估计框架。我们开发了具有最优信息利用的GPI估计量家族，建立了其一致性、渐近正态性和同时置信带，并推导了线性泛函和Wasserstein距离的有效推断。模拟研究表明，与仅使用标记数据的方法相比，该方法能提升效率，且在生成模型误设下表现稳健。我们通过一项针对K562细胞的Perturb-seq研究说明了所提出的框架，其中由State基础模型生成的合成扰动响应被用于改进与40S核糖体模块扰动相关的通路水平共识基因表达分布的推断。
英文摘要
Modern generative models increasingly produce distribution-valued outputs, such as predicted cellular responses to genetic perturbations in single-cell genomics. While these models provide valuable auxiliary information, they are inherently imperfect, creating a need for statistical methods that leverage their predictions without relying on their correctness. We propose generation-powered inference (GPI), a general framework for improving inference on distribution-valued parameters using auxiliary generative models. Focusing on Wasserstein barycenters and related distributional functionals, we introduce a function-valued bridge representation that transforms inference in the nonlinear Wasserstein space into estimation of a mean function in a Hilbert space, enabling an augmented estimation framework analogous to prediction-powered inference. We develop a family of GPI estimators with optimal information borrowing, establish consistency, asymptotic normality, and simultaneous confidence bands, and derive valid inference for linear functionals and Wasserstein distances. Simulation studies demonstrate efficiency gains over labeled-data-only methods and robust performance under generative model misspecification. We illustrate the proposed framework using a Perturb-seq study of K562 cells, where synthetic perturbation responses generated by the State foundation model are used to improve inference for pathway-level consensus gene expression distributions associated with perturbations of the 40S ribosome module.
Comments36 pages, 5 figures
