Problem
Research questions and friction points this paper is trying to address.
stem cell lineage fate prediction
compositional heterogeneity
differentiation dynamics
bulk transcriptomic data
PuriGen: Purity-Aware Deep Generative Modeling for Predicting Stem Cell Lineage Fate
🤖 AI Summary
This study addresses the challenge of modeling compositional heterogeneity and temporally dependent differentiation dynamics in stem cell lineage prediction by proposing a novel purity-aware deep generative framework. The method extends the scVI/scANVI architecture through the incorporation of a pretrained purity head, combined with regularization and temporal constraints, to effectively integrate sample-level purity signals with biologically consistent latent space representations. Evaluated on mesenchymal stem cell datasets across multiple biomaterial conditions, this transcriptome-based model demonstrates significantly superior predictive performance compared to existing baseline methods. By bridging purity estimation with deep generative modeling, this work establishes a new paradigm for single-cell generative analysis in developmental biology and regenerative medicine.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Purity-aware generative model
Stem cell lineage prediction
Bulk transcriptomics
Temporal constraints
Deep generative framework
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