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

Show full item record

Page view(s)

8
checked on Apr 3, 2025

Google ScholarTM

Impact Indices

Altmetric

PlumX

Metrics


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.