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arXiv · 2610.04839

PuriGen: Purity-Aware Deep Generative Modeling for Predicting Stem Cell Lineage Fate

Yanli Li·Tianying Sheng·Hala Zreiqat·ZuFu Lu·Zhiyong Wang
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Abstract

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.

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Yanli Li, Tianying Sheng, Hala Zreiqat, ZuFu Lu, Zhiyong Wang. 2026-10-04. PuriGen: Purity-Aware Deep Generative Modeling for Predicting Stem Cell Lineage Fate. https://arxiv.org/abs/2610.04839

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