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King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Single-cell multi-omics clustering has emerged as a critical technology for deciphering cellular heterogeneity and functional diversity, enabling the simultaneous measurement of multiple omics layers within individual cells. Nevertheless, the inherent characteristics of single-cell multi-omics data, such as high noise, sparsity, and heterogeneity, continue to pose significant challenges to achieving accurate clustering analyses. Consequently, the effective integration of multi-omics data to enhance clustering performance remains a critical focus in current research. To overcome these challenges, we propose scTGIC, a clustering method based on a transformer graph autoencoder (TGAE) for deep information fusion. The TGAE integrates a multihead attention mechanism with local structural similarity, fusing the normalized adjacency matrix with the attention matrix to directly model multi-hop relationships and higher-order topological features, optimizing inter-node topology and overcoming the limitations of traditional graph convolutional neural networks (GCNs) in capturing global patterns. Furthermore, we introduce structural information in the information fusion mechanism, which combines a collaborative supervised clustering strategy and a dual-level redundant reduction mechanism. The experimental results demonstrate that the scTGIC exhibits strong competitiveness across five single-cell multi-omics datasets, providing more robust and reliable clustering results.<\/jats:p>","DOI":"10.1007\/s44443-025-00193-1","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T08:05:59Z","timestamp":1755504359000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep information fusion based on a transformer graph encoder for single-cell multi-omics clustering"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0235-2096","authenticated-orcid":false,"given":"Qianqian","family":"Ren","sequence":"first","affiliation":[]},{"given":"Shaoyi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Junliang","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Xiyu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"issue":"9","key":"193_CR1","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1038\/s41592-023-01938-4","volume":"20","author":"C Bravo Gonz\u00e1lez-Blas","year":"2023","unstructured":"Bravo Gonz\u00e1lez-Blas C, De Winter S, Hulselmans G, Hecker N, Matetovici I, Christiaens V, Poovathingal S, Wouters J, Aibar S, Aerts S (2023) Scenic+: single-cell multiomic inference of enhancers and gene regulatory networks. 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