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Through three simulations and applications to ST datasets from technologies such as 10x Visium, 10x Xenium, and Spatial Transcriptomics, stIHC outperforms clustering approaches used by popular SVG detection methods, including SPARK, SPARK\u2010X, MERINGUE, and SpatialDE. Gene ontology enrichment analysis confirms that genes within each module share consistent biological functions, supporting the functional relevance of spatial co\u2010expression. Robust across technologies with varying gene numbers and spatial resolution, stIHC provides a powerful tool for decoding the spatial organization of gene expression and the functional structure of complex tissues.<\/jats:p>","DOI":"10.1002\/qub2.70011","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T14:44:34Z","timestamp":1753281874000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene co\u2010expression modules"],"prefix":"10.1002","volume":"13","author":[{"given":"Catherine","family":"Higgins","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics University College Dublin  Dublin Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi Jessica","family":"Li","sequence":"additional","affiliation":[{"name":"Department of 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