Unsupervised Evolutionary Cell Type Matching via Entropy-Minimized Optimal Transport

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. It begins by selecting genes with high Signal-to-Noise Ratio (SNR) to capture the most informative features, from which a cost matrix is constructed using cosine distances between cell-type centroids. Importantly, the MESH procedure iteratively refines the cost matrix, leading to a transport plan with significantly enhanced sparsity and interpretability of the resulting correspondence matrices. 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, symmetric, and interpretable solution for evolutionary cell type mapping, facilitating deeper insights into cellular specialization and conservation across species.