Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimensionality, sparsity, and complex biological variations. Existing generative models often struggle to capture these unique characteristics and ensure robustness to structural noise in cellular networks. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model for robust and high-fidelity scRNA-seq generation. LapDDPM uniquely integrates graph-based representations with a score-based diffusion model, enhanced by a novel spectral adversarial perturbation mechanism on graph edge weights. Our contributions are threefold: we leverage Laplacian Positional Encodings (LPEs) to enrich the latent space with crucial cellular relationship information; we develop a conditional score-based diffusion model for effective learning and generation from complex scRNA-seq distributions; and we employ a unique spectral adversarial training scheme on graph edge weights, boosting robustness against structural variations. Extensive experiments on diverse scRNA-seq datasets demonstrate LapDDPM's superior performance, achieving high fidelity and generating biologically-plausible, cell-type-specific samples. LapDDPM sets a new benchmark for conditional scRNA-seq data generation, offering a robust tool for various downstream biological applications.

翻译：生成高保真且生物学上可信的合成单细胞RNA测序数据，尤其是在条件控制下，由于其高维度、稀疏性和复杂的生物学变异而具有挑战性。现有的生成模型往往难以捕捉这些独特特征，并确保对细胞网络结构噪声的鲁棒性。我们提出了LapDDPM，一种新颖的、用于鲁棒且高保真scRNA-seq生成的条件图扩散概率模型。LapDDPM独特地将基于图的表示与基于分数的扩散模型相结合，并通过一种新颖的图边权重谱对抗扰动机制进行增强。我们的贡献有三方面：我们利用拉普拉斯位置编码来丰富潜在空间，使其包含关键的细胞关系信息；我们开发了一种条件分数扩散模型，用于从复杂的scRNA-seq分布中进行有效的学习和生成；并且我们采用了一种独特的图边权重谱对抗训练方案，增强了对结构变异的鲁棒性。在多种scRNA-seq数据集上进行的大量实验证明了LapDDPM的卓越性能，实现了高保真度并生成了生物学上可信的、细胞类型特异性的样本。LapDDPM为条件性scRNA-seq数据生成设立了新的基准，为各种下游生物学应用提供了一个鲁棒的工具。