Unsupervised Evolutionary Cell Type Matching via Entropy-Minimized Optimal Transport
This provides a principled, scalable, and interpretable solution for evolutionary cell type mapping, facilitating insights into cellular specialization and conservation across species, but it is incremental as it builds on existing optimal transport methods.
The paper tackles the problem of identifying evolutionary correspondences between cell types across species by presenting OT-MESH, an unsupervised framework that uses entropy-regularized optimal transport to achieve near-optimal matching accuracy with computational efficiency and robustness to noise, including a speedup over other methods and experimental validation of novel correspondences.
Identifying evolutionary correspondences between cell types across species is a fundamental challenge in comparative genomics and evolutionary biology. Existing approaches often rely on either reference-based matching, which imposes asymmetry by designating one species as the reference, or projection-based matching, which may increase computational complexity and obscure biological interpretability at the cell-type level. Here, we present OT-MESH, an unsupervised computational framework leveraging entropy-regularized optimal transport (OT) to systematically determine cross-species cell type homologies. Our method uniquely integrates the Minimize Entropy of Sinkhorn (MESH) technique to refine the OT plan, transforming diffuse transport matrices into sparse, interpretable correspondences. Through systematic evaluation on synthetic datasets, we demonstrate that OT-MESH achieves near-optimal matching accuracy with computational efficiency, while maintaining remarkable robustness to noise. Compared to other OT-based methods like RefCM, OT-MESH provides speedup while achieving comparable accuracy. Applied to retinal bipolar cells (BCs) and retinal ganglion cells (RGCs) from mouse and macaque, OT-MESH accurately recovers known evolutionary relationships and uncovers novel correspondences, one of which was independently validated experimentally. Thus, our framework offers a principled, scalable, and interpretable solution for evolutionary cell type mapping, facilitating deeper insights into cellular specialization and conservation across species.