Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10729
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dc.contributor.authorWang, Ranen_US
dc.contributor.authorQian, Yanen_US
dc.contributor.authorGuo, Xiaojingen_US
dc.contributor.authorSong, Fangdaen_US
dc.contributor.authorXiong, Zhiqiangen_US
dc.contributor.authorCai, Shirongen_US
dc.contributor.authorBian, Xiuwuen_US
dc.contributor.authorWong, Man Honen_US
dc.contributor.authorCao, Qinen_US
dc.contributor.authorCheng, Lixinen_US
dc.contributor.authorLu, Gangen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.date.accessioned2025-03-19T01:31:08Z-
dc.date.available2025-03-19T01:31:08Z-
dc.date.issued2025-
dc.identifier.citationGenome Medicine, 2025, vol. 17, article no. 18.en_US
dc.identifier.issn1756-994X-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/10729-
dc.description.abstractHere we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features and various cell types, which facilitate downstream analysis and provide insights into tumor microenvironments, disease mechanisms, treatment development, and histological organization of tissues. STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components. The tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git.en_US
dc.language.isoenen_US
dc.relation.ispartofGenome Medicineen_US
dc.titleSTModule: Identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapesen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1186/s13073-025-01441-9-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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