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However, many existing computational approaches often rely on labeled ST data and overlook the rich spatial information, resulting in limited representations and suboptimal clustering. In this paper, we propose ST-GCP, a self-supervised graph representation learning framework for ST data, which incorporates a structure-feature perturbation mechanism. First, ST-GCP applies feature-level random permutation of the gene expression matrix and random edge dropout in the spatial neighbor network, creating two complementary augmented graph views of ST data. ST-GCP then employs a two-layer graph convolutional network (GCN) encoder-decoder to extract spatial representations and reconstruct gene expression. Finally, a cosine-similarity-based contrastive objective aligns the view-specific representations, and the overall loss jointly optimizes reconstruction fidelity and contrastive consistency, thereby coupling graph topology with transcriptomic profiles in a shared low-dimensional space. Experimental results on multiple ST datasets demonstrate that ST-GCP can uncover biologically meaningful patterns, such as tumor heterogeneity, brain developmental architecture, and cellular developmental trajectories.<\/jats:p>","DOI":"10.1093\/bib\/bbaf643","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:46:07Z","timestamp":1763469967000},"source":"Crossref","is-referenced-by-count":1,"title":["ST-GCP: a graph convolutional network model with contrastive consistency and permutation for spatial transcriptomics"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-1158","authenticated-orcid":false,"given":"Yajie","family":"Meng","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University , No. 1 Sunshine Avenue, Jiangxia District, Wuhan, Hubei 430200 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Baptist University , 7 Baptist University Road, Kowloon Tong, Hong Kong, SAR 999077 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , No. 2006 Xiyuan Avenue, High-Tech Zone, Chengdu, Sichuan 610054 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University; Guangdong Provincial Key Laboratory of Intellectual Property & Big Data , No. 293 Zhongshan Avenue West, Tianhe District, Guangzhou, Guangdong 510665 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