Pheno-GS: Phenoscape-scale Geodesic Sinkhorn

2026-09-23

AI Summary

为解决大规模单细胞数据中患者水平异质性理解问题，Pheno-GS通过图连通性正则化、非平衡OT公式和批量矩阵算法，提供准确可扩展的测地运输距离计算方法。

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.

Problem

• high-throughput single-cell data
• patient-level heterogeneity
• optimal transport
• geodesic distances
• large-scale data

Innovation

• graph connectivity regularization
• unbalanced OT
• batched matrix algorithm