A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data
High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time. Consequently, temporal and spatial dynamics must be inferred from independently sampled, unaligned cell populations, making it challenging to reconstruct developmental trajectories. Optimal transport (OT) offers a geometric framework for aligning cell populations and inferring developmental trajectories, but many existing approaches focus on modeling the evolution of distributions of cells in gene expression space rather than the relational structure encoded by gene e...
| Authors | Mary Chriselda Antony Oliver, Kaitlyn Hohmeier, Tuyen Tran et al. |
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| Primary category | stat.ML |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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