{"type":"ScholarlyArticle","title":"scSiameseClu: A Siamese Clustering Framework for Interpreting Single-cell RNA Sequencing Data","authors":[{"name":"Ping Xu","slug":"ping-xu","given":"Ping","family":"Xu","affiliation":""},{"name":"Zhiyuan Ning","slug":"zhiyuan-ning","given":"Zhiyuan","family":"Ning","affiliation":""},{"name":"Pengjiang Li","slug":"pengjiang-li","given":"Pengjiang","family":"Li","affiliation":""},{"name":"Wenhao Liu","slug":"wenhao-liu","given":"Wenhao","family":"Liu","affiliation":""},{"name":"Pengyang Wang","slug":"pengyang-wang","given":"Pengyang","family":"Wang","affiliation":""},{"name":"Jiaxu Cui","slug":"jiaxu-cui","given":"Jiaxu","family":"Cui","affiliation":""},{"name":"Yuanchun Zhou","slug":"yuanchun-zhou","given":"Yuanchun","family":"Zhou","affiliation":""},{"name":"Pengfei Wang","slug":"pengfei-wang","given":"Pengfei","family":"Wang","affiliation":""}],"abstract":"Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially graph neural networks (GNNs)-based methods, have significantly improved clustering performance. However, the analysis of scRNA-seq data remains challenging due to noise, sparsity, and high dimensionality. Compounding these challenges, GNNs often suffer from over-smoothing, limiting their ability to capture complex biological information. In response, we propose scSiameseClu, a novel Siamese Clustering framework for interpreting single-cell RNA-seq data, comprising of 3 key steps: (1) Dual Augmentation Module, which applies biologically informed perturbations to the gene expression matrix and cell graph relationships to enhance representation robustness; (2) Siamese Fusion Module, which combines cross-correlation refinement and adaptive information fusion to capture complex cellular relationships while mitigating over-smoothing; and (3) Optimal Transport Clustering, which utilizes Sinkhorn distance to efficiently align cluster assignments with predefined proportions while maintaining balance. Comprehensive evaluations on seven real-world datasets demonstrate that scSiameseClu outperforms state-of-the-art methods in single-cell clustering, cell type annotation, and cell type classification, providing a powerful tool for scRNA-seq data interpretation.","doi":"10.24963/ijcai.2025/875","url":"https://ijcai.org/proceedings/2025/875","pdf":"https://ijcai.org/proceedings/2025/0875.pdf","markdown":"https://ijcai.org/proceedings/2025/875.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/875","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":875,"pages":{"first":7867,"last":7875},"keywords":["Machine Learning: ML: Applications","Data Mining: DM: Applications","Multidisciplinary Topics and Applications: MTA: Bioinformatics"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}