{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:54:58Z","timestamp":1754153698189,"version":"3.41.2"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Research Project of the Jilin Provincial Department of Education","award":["JJKH20251051KJ"],"award-info":[{"award-number":["JJKH20251051KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In the last few years, there has been great advancement in the field of single-cell data investigation, particularly in the development of clustering methods. The advanced research is increased for the development of clustering algorithms tailored for single-cell RNA sequencing data. Conventional methods primarily focus on local relationships among cells or genes, while overlooking the global cell-gene interactions. As a result, the high dimensionality, noise, and sparsity of the data continue to pose significant challenges to clustering accuracy. To address the challenges of single-cell clustering analysis, we propose a novel single-cell clustering model, scGGC, which integrates graph autoencoders and generative adversarial network techniques. The innovations of scGGC include two components: (i) construction of an adjacency matrix that incorporates cell\u2013cell and cell-gene relationships to capture complex interactions in a graph structure, enabling nonlinear dimensionality reduction and initial clustering via a graph autoencoder; (ii) enhancement of clustering performance by selecting high-confidence samples from the initial clusters for adversarial neural network training. A comprehensive evaluation on nine publicly available scRNA-seq datasets demonstrates that scGGC outperforms eight comparison methods. For example, on datasets such as MHC3K, the Adjusted Rand Index increases by an average of 10.1%. Furthermore, marker gene identification and cell type annotation further confirm the biological relevance of scGGC, with marker gene overlap rates exceeding 70% across multiple datasets. We conclude that scGGC not only improves the accuracy of single-cell data clustering but also enhances the identification of cell-type-specific marker genes. The scGGC code is available at https:\/\/github.com\/Zhi1002\/scGGC.<\/jats:p>","DOI":"10.1093\/bib\/bbaf368","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T07:37:50Z","timestamp":1751960270000},"source":"Crossref","is-referenced-by-count":0,"title":["scGGC: a two-stage strategy for single-cell clustering through cellular gene pathway construction"],"prefix":"10.1093","volume":"26","author":[{"given":"Zhi","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun Normal University , Changchun 130032 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9769-3242","authenticated-orcid":false,"given":"Qiucheng","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun Normal University , Changchun 130032 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