Optimal Transport Meets t-SNE: Integrated Visualization without Batch Effects in OT-tSNE

Visual exploration is an integral step in understanding the structure of datasets. However, systematic variation between datasets collected under different conditions, known as batch effects, can obscure shared structure and complicate their joint visualization. Existing methods often rely on cross-batch nearest-neighbor relations to integrate the batches. Yet, these local correspondences can be unreliable for challenging cross-batch geometries. We propose optimal-transport t-SNE (OT-tSNE), which replaces nearest-neighbor-based matching with globally coupled affinities derived from entropic optimal transport. Controlled synthetic experiments demonstrated that nearest-neighbor-based methods can deteriorate while optimal-transport affinities and OT-tSNE embeddings remain robust. Across seven real single-cell datasets, we benchmarked OT-tSNE against a broad range of integration and visualization methods and found that it achieves a Pareto-efficient trade-off between batch mixing and biological conservation, and integrates batches particularly well.