{"type":"ScholarlyArticle","title":"Spot-Adaptive Structural Rectification for Spatially Resolved Transcriptomics Data Clustering","authors":[{"name":"Huanjia Zhao","slug":"huanjia-zhao","given":"Huanjia","family":"Zhao","affiliation":""},{"name":"Shanghui Deng","slug":"shanghui-deng","given":"Shanghui","family":"Deng","affiliation":""},{"name":"Shunfan Li","slug":"shunfan-li","given":"Shunfan","family":"Li","affiliation":""},{"name":"Kun Sun","slug":"kun-sun","given":"Kun","family":"Sun","affiliation":""},{"name":"Weiqing Yan","slug":"weiqing-yan","given":"Weiqing","family":"Yan","affiliation":""},{"name":"Chang Tang","slug":"chang-tang","given":"Chang","family":"Tang","affiliation":""}],"abstract":"Spatially resolved transcriptomics integrates gene expression with spatial coordinates to decode tissue microenvironments. Existing methods predominantly utilize graph structures to model relationships between spots. However, their performance is bottlenecked by the reliability of gene feature graph, facing the following hurdles: (1) ubiquitous housekeeping genes cause high-expression spots to densely connect with heterogeneous spots, leading to a skewed graph structure; (2) information reduction during Highly Variable Gene (HVG) selection results in the loss of intrinsic local structures. To address these challenges, we propose a Spot-Adaptive Structural Rectification method, called SASR. Specifically, SASR employs a hyperspherical expansion constraint that projects gene expression profiles onto a unit hypersphere to maximize angular distances, effectively separating spots falsely clustered by high total counts. Simultaneously, a topological consistency constraint repairs structural fractures caused by HVG selection via aligning latent embeddings with the local structures of the raw full-gene space. The complementary synergy balances angular discriminability with topological fidelity for accurate clustering. Experiments demonstrate that SASR effectively corrects structural biases and surpasses state-of-the-art methods in spatial clustering.","doi":"10.24963/ijcai.2026/598","url":"https://ijcai.org/proceedings/2026/598","pdf":"https://ijcai.org/proceedings/2026/0598.pdf","markdown":"https://ijcai.org/proceedings/2026/598.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/598","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":598,"pages":{"first":5369,"last":5377},"keywords":["Machine Learning: Clustering","Computer Vision: Representation learning","Machine Learning: Feature extraction, selection and dimensionality reduction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}