发表机构
The University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对干细胞谱系预测中组成异质性和时间动态建模不足的问题，提出纯度感知深度生成框架PuriGen，通过PuritySCVI和PuritySCANVI模块及正则化约束，在批量间充质干细胞数据上超越现有方法。
AI 中文摘要
预测干细胞谱系命运对于阐明生物材料与干细胞的相互作用以及优化组织再生策略至关重要。尽管现有方法证明了谱系预测的可行性，但它们在建模组成异质性和时间依赖的分化动态方面仍存在局限性。为弥补这一不足，我们提出了PuriGen，一种纯度感知的深度生成框架，用于从批量转录组数据中进行生物材料诱导的谱系预测。PuriGen包含两个关键模块，PuritySCVI和PuritySCANVI，它们通过纳入预训练的GBMPurity头来扩展scVI和scANVI，以提供样本级别的纯度样组成信号。此外，引入了纯度感知正则化和时间约束，以促进信息丰富的纯度预测和生物学一致的潜在表示。在多种生物材料条件和诱导阶段收集的批量间充质干细胞数据集上的实验表明，所提出的框架比现有方法取得了更好的性能。
英文摘要
Predicting stem-cell lineage fate is central to elucidating biomaterial-stem cell interactions and optimizing strategies for tissue regeneration. While existing approaches demonstrate the feasibility of lineage prediction, they remain limited in modelling compositional heterogeneity and time-dependent differentiation dynamics. To mitigate this gap, we propose PuriGen, a purity-aware deep generative framework for biomaterial-induced lineage prediction from bulk transcriptomic data. PuriGen consists of two key modules, PuritySCVI and PuritySCANVI, which extend scVI and scANVI by incorporating a pretrained GBMPurity head to provide a sample-level purity-like compositional signal. In addition, purity-aware regularisation and temporal constraints are introduced to encourage informative purity prediction and biologically consistent latent representations. Experiments on bulk mesenchymal stem-cell datasets collected across multiple biomaterial conditions and induction stages show that the proposed framework achieves better performance than the existing methods.
