From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
arXiv:2605.22340v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over time. In practice, time-resolved scRNA-seq is collected at only a few discrete time points as unpaired snapshot populations, leaving substantial temporal gaps. This motivates trajectory inference at unmeasured time points. Existing methods mainly follow two directions, optimal-transport (OT) alignment provides distribution-level matching between observed snapshots, while continuous-time generative m
The increasing sophistication of AI models and the growth of single-cell RNA sequencing data make this an opportune time for advanced computational methods to infer biological dynamics.
This research provides a more granular understanding of cellular processes, which is foundational for drug discovery, disease modeling, and personalized medicine, impacting the biotechnology and pharmaceutical sectors.
The ability to reconstruct gene expression trajectories from limited snapshots significantly enhances the resolution of cellular dynamic studies, moving beyond static analyses to more complete temporal views.
- · Biomedical Researchers
- · Pharmaceutical Companies
- · AI/ML Bio-focused Startups
- · Synthetic Biology
- · Traditional wet-lab only research
- · Methods with sparse temporal data
Improved understanding of disease progression and therapeutic response at a cellular level.
Accelerated development of novel gene therapies and cell-based treatments through more precise targeting.
Potential for designing advanced synthetic biological systems with predictable dynamic behaviors.
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Read at arXiv cs.LG