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However, most of spatial clustering methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering. First, STMSGAL constructs ctaSNN, a cell type\u2013aware shared nearest neighbor graph, using Louvian clustering exclusively based on gene expression profiles. Subsequently, it integrates expression profiles and ctaSNN to generate spot latent representations using a graph attention autoencoder and multiscale deep subspace clustering. Lastly, STMSGAL implements spatial clustering, differential expression analysis, and trajectory inference, providing comprehensive capabilities for thorough data exploration and interpretation. STMSGAL was evaluated against 7 methods, including SCANPY, SEDR, CCST, DeepST, GraphST, STAGATE, and SiGra, using four 10x Genomics Visium datasets, 1 mouse visual cortex STARmap dataset, and 2 Stereo-seq mouse embryo datasets. The comparison showcased STMSGAL\u2019s remarkable performance across Davies\u2013Bouldin, Calinski\u2013Harabasz, S_Dbw, and ARI values. STMSGAL significantly enhanced the identification of layer structures across ST data with different spatial resolutions and accurately delineated spatial domains in 2 breast cancer tissues, adult mouse brain (FFPE), and mouse embryos.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>STMSGAL can serve as an essential tool for bridging the analysis of cellular spatial organization and disease pathology, offering valuable insights for researchers in the field.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/gigascience\/giae103","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T16:03:50Z","timestamp":1736784230000},"source":"Crossref","is-referenced-by-count":16,"title":["Unveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multiscale deep subspace clustering network"],"prefix":"10.1093","volume":"14","author":[{"given":"Liqian","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , Zhuzhou 412007, Hunan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhuai","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , Zhuzhou 412007, Hunan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan Institute of Technology , Hengyang 421002, Hunan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianzhi","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hunan University of Technology , Zhuzhou 412007, Hunan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5752-4436","authenticated-orcid":false,"given":"Geng","family":"Tian","sequence":"additional","affiliation":[{"name":"Geneis (Beijing) Co. 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