Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/10729
Title: | STModule: Identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes |
Authors: | Wang, Ran Qian, Yan Guo, Xiaojing Song, Fangda Xiong, Zhiqiang Cai, Shirong Bian, Xiuwu Wong, Man Hon Cao, Qin Cheng, Lixin Lu, Gang Prof. LEUNG Kwong Sak |
Issue Date: | 2025 |
Source: | Genome Medicine, 2025, vol. 17, article no. 18. |
Journal: | Genome Medicine |
Abstract: | Here 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. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/10729 |
ISSN: | 1756-994X |
DOI: | 10.1186/s13073-025-01441-9 |
Appears in Collections: | Applied Data Science - Publication |
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