{"ID":2830272,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10640","arxiv_id":"2512.10640","title":"Refinement Contrastive Learning of Cell-Gene Associations for Unsupervised Cell Type Identification","abstract":"Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single-cell RNA-seq and spatial transcriptomics benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene-expression signatures, further validating the biological relevance of our approach. The code is available at https://github.com/THPengL/scRCL.","short_abstract":"Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limit...","url_abs":"https://arxiv.org/abs/2512.10640","url_pdf":"https://arxiv.org/pdf/2512.10640v1","authors":"[\"Liang Peng\",\"Haopeng Liu\",\"Yixuan Ye\",\"Cheng Liu\",\"Wenjun Shen\",\"Si Wu\",\"Hau-San Wong\"]","published":"2025-12-11T13:45:31Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":606016,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2830272,"paper_url":"https://arxiv.org/abs/2512.10640","paper_title":"Refinement Contrastive Learning of Cell-Gene Associations for Unsupervised Cell Type Identification","repo_url":"https://github.com/THPengL/scRCL","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}