{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-evolutionary-cell-type-matching","title":"Unsupervised Evolutionary Cell Type Matching via Entropy-Minimized Optimal Transport","arxiv_id":"2505.24759","date":"2025-05-30","proceeding":null,"authors":["Mu Qiao"],"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.","url_abs":"https://arxiv.org/abs/2505.24759v1","url_pdf":"https://arxiv.org/pdf/2505.24759v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-evolutionary-cell-type-matching","repo_url":"https://github.com/muqiao0626/evo-cell-type-ot-mesh","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}