ScDiVa：用于单细胞身份和表达联合建模的掩码离散扩散
ScDiVa: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression
AI总结：
ScDiVa通过掩码离散扩散模型，联合建模单细胞身份与表达，实现高效去噪与高精度恢复。
摘要：
单细胞RNA测序图谱是高维、稀疏且无序的，导致自回归生成引入人工顺序偏见并产生误差累积。为此，我们提出了scDiVa，一种掩码离散扩散基础模型，通过在标记空间中定义连续时间前向掩码机制，使生成过程与dropout-like破坏过程对齐。scDiVa具有双向去噪器，联合建模离散基因身份和连续值，利用熵归一化的序列化和潜在锚定标记以最大化信息效率并保持全局细胞身份。该模型通过深度不变时间采样和双去噪目标进行训练，以模拟变化的稀疏水平，同时确保身份和幅度的精确恢复。在5900万细胞上预训练后，scDiVa在主要基准上实现了强大的迁移性能，包括批次整合、细胞类型注释和扰动响应预测。这些结果表明，掩码离散扩散在生物上具有相关性和有效性，是自回归的有效替代方案。
英文摘要：
Single-cell RNA-seq profiles are high-dimensional, sparse, and unordered, causing autoregressive generation to impose an artificial ordering bias and suffer from error accumulation. To address this, we propose scDiVa, a masked discrete diffusion foundation model that aligns generation with the dropout-like corruption process by defining a continuous-time forward masking mechanism in token space. ScDiVa features a bidirectional denoiser that jointly models discrete gene identities and continuous values, utilizing entropy-normalized serialization and a latent anchor token to maximize information efficiency and preserve global cell identity. The model is trained via depth-invariant time sampling and a dual denoising objective to simulate varying sparsity levels while ensuring precise recovery of both identity and magnitude. Pre-trained on 59 million cells, scDiVa achieves strong transfer performance across major benchmarks, including batch integration, cell type annotation, and perturbation response prediction. These results suggest that masked discrete diffusion serves as a biologically coherent and effective alternative to autoregression.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
