Pheno-GS: Phenoscape-scale Geodesic Sinkhorn

Researchers introduced Pheno-GS (Phenoscape-scale Geodesic Sinkhorn), a method that computes geodesic optimal transport distances between all pairs of patient single-cell datasets in a single heat diffusion, running over 200 times faster than Geodesic Sinkhorn for 500 distributions. Pheno-GS combines graph connectivity regularization for well-defined geodesics on sparse or disconnected manifolds, an unbalanced optimal transport formulation using KL marginal penalties, and a batched matrix algorithm, and was validated on synthetic benchmarks and a CyTOF perturbation dataset. The work targets phenoscaping, in which each single-cell distribution is embedded as a datapoint with optimal transport distances, addressing Euclidean ground metrics that distort manifold structure and failures under sparse, unevenly sampled, or large-scale data.

arXiv:2609.27633v1 Announce Type: new Abstract: 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, large-scale settings via three components: $1$ graph connectivity regularization for well-defined geodesics on sparse/disconnected manifolds; $2$ an unbalanced OT formulation via KL marginal penalties; and $3$ a batched matrix algorithm computing all pairwise distances in one heat diffusion over $200 \times$ faster than Geodesic Sinkhorn for $500$ distributions . We validate Pheno-GS on synthetic benchmarks and a CyTOF perturbation dataset.