{"type":"ScholarlyArticle","title":"scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering","authors":[{"name":"Jinke Wu","slug":"jinke-wu","given":"Jinke","family":"Wu","affiliation":""},{"name":"Yifan Wang","slug":"yifan-wang","given":"Yifan","family":"Wang","affiliation":""},{"name":"Siyu Yi","slug":"siyu-yi","given":"Siyu","family":"Yi","affiliation":""},{"name":"Caiyang Yu","slug":"caiyang-yu","given":"Caiyang","family":"Yu","affiliation":""},{"name":"Ziyue Qiao","slug":"ziyue-qiao","given":"Ziyue","family":"Qiao","affiliation":""},{"name":"Nan Yin","slug":"nan-yin","given":"Nan","family":"Yin","affiliation":""},{"name":"Jiancheng Lv","slug":"jiancheng-lv","given":"Jiancheng","family":"Lv","affiliation":""},{"name":"Wei Ju","slug":"wei-ju","given":"Wei","family":"Ju","affiliation":""}],"abstract":"Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-cell RNA-seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self-supervised manner. Extensive experiments on multiple benchmark scRNA-seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.","doi":"10.24963/ijcai.2026/346","url":"https://ijcai.org/proceedings/2026/346","pdf":"https://ijcai.org/proceedings/2026/0346.pdf","markdown":"https://ijcai.org/proceedings/2026/346.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/346","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":"Data Mining","track":"Main Track","number":346,"pages":{"first":3115,"last":3123},"keywords":["Multidisciplinary Topics and Applications: Bioinformatics","Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}