Unsupervised Evolutionary Cell Type Matching via Entropy-Minimized Optimal Transport

TL;DR

OT-MESH is an unsupervised optimal transport-based method that accurately and efficiently matches cell types across species, providing interpretable correspondences and uncovering both known and novel evolutionary relationships.

Contribution

The paper introduces OT-MESH, a novel entropy-regularized optimal transport framework with a Minimize Entropy of Sinkhorn technique for scalable, interpretable cross-species cell type matching.

Findings

Achieves near-optimal accuracy with high robustness to noise.

Provides computational speedup over existing OT-based methods.

Successfully recovers known and uncovers novel cell type correspondences.

Abstract

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.