Pheno-GS: Phenoscape-scale Geodesic Sinkhorn
High-throughput single-cell data is now collected across large patient cohorts. Understanding patient-level heterogeneity from cellular-level data motivates phenoscaping: embedding each single-cell distribution as a "datapoint," with distances given by optimal transport (OT). Computing geometry-aware OT at this scale, between all pairs of patient datasets, remains an open challenge, since existing methods either rely on Euclidean ground metrics that distort manifold structure or fail under sparse, unevenly sampled, or large-scale data. We present \textbf{Pheno-GS} (Phenoscape-scale Geodesic Sinkhorn), which computes accurate, scalable geodesic transport distances under noisy, unbalanced, ...
| Authors | Alistair Wilkinson, Christopher J. Tape, Smita Krishnaswamy |
|---|---|
| Primary category | cs.LG |
| Published | 23 September 2026 |
| Last updated at source | 1 October 2026 |
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