标题： DiffKnock：基于扩散的神经网络推断敲诈统计量 显示英文标题

标题： DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference

摘要： 我们引入了DiffKnock，一种基于扩散的敲除框架，用于高维特征选择，并具有有限样本错误发现率（FDR）控制。 DiffKnock解决了现有敲除方法的两个关键限制：保留复杂的特征依赖关系和检测非线性关联。 我们的方法训练扩散模型以生成有效的敲除样本，并使用基于神经网络的梯度和过滤器统计量来构建反对称特征重要性度量。 通过模拟，我们证明了DiffKnock在保持目标FDR的同时比基于自编码器的敲除方法具有更高的功效，表明其在涉及复杂非线性架构的场景中表现更优。 应用于LPS刺激的巨噬细胞小鼠单细胞RNA-seq数据，DiffKnock识别出经典的NF-$\kappa$B目标基因（Ccl3, Hmox1）和调节因子（Fosb, Pdgfb）。 这些结果表明，通过结合深度生成模型的灵活性与严格的统计保证，DiffKnock是一种强大且可靠的工具，可用于分析单细胞RNA-seq数据，以及其它领域中的高维和结构化数据。 显示英文摘要

摘要： We introduce DiffKnock, a diffusion-based knockoff framework for high-dimensional feature selection with finite-sample false discovery rate (FDR) control. DiffKnock addresses two key limitations of existing knockoff methods: preserving complex feature dependencies and detecting non-linear associations. Our approach trains diffusion models to generate valid knockoffs and uses neural network--based gradient and filter statistics to construct antisymmetric feature importance measures. Through simulations, we showed that DiffKnock achieved higher power than autoencoder-based knockoffs while maintaining target FDR, indicating its superior performance in scenarios involving complex non-linear architectures. Applied to murine single-cell RNA-seq data of LPS-stimulated macrophages, DiffKnock identifies canonical NF-$\kappa$B target genes (Ccl3, Hmox1) and regulators (Fosb, Pdgfb). These results highlight that, by combining the flexibility of deep generative models with rigorous statistical guarantees, DiffKnock is a powerful and reliable tool for analyzing single-cell RNA-seq data, as well as high-dimensional and structured data in other domains.