{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:17:26Z","timestamp":1776878246933,"version":"3.51.2"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/875","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"7867-7875","source":"Crossref","is-referenced-by-count":5,"title":["scSiameseClu: A Siamese Clustering Framework for Interpreting Single-cell RNA Sequencing Data"],"prefix":"10.24963","author":[{"given":"Ping","family":"Xu","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing\uff1b"},{"name":"University of Chinese Academy of Sciences, Beijing\uff1b"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Ning","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing\uff1b"},{"name":"University of Chinese Academy of Sciences, Beijing\uff1b"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengjiang","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing\uff1b"},{"name":"University of Chinese Academy of Sciences, Beijing\uff1b"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Life Science, Northeast Agricultural University, Harbin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"SKL-IOTSC and Department of CIS, University of Macau, Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxu","family":"Cui","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing\uff1b"},{"name":"University of Chinese Academy of Sciences, Beijing\uff1b"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing\uff1b"},{"name":"University of Chinese Academy of Sciences, Beijing\uff1b"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:22Z","timestamp":1758627322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/875"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/875","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}