STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes.

IF 10.4 1区 生物学 Q1 GENETICS & HEREDITY Genome Medicine Pub Date : 2025-03-03 DOI:10.1186/s13073-025-01441-9
Ran Wang, Yan Qian, Xiaojing Guo, Fangda Song, Zhiqiang Xiong, Shirong Cai, Xiuwu Bian, Man Hon Wong, Qin Cao, Lixin Cheng, Gang Lu, Kwong Sak Leung
{"title":"STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes.","authors":"Ran Wang, Yan Qian, Xiaojing Guo, Fangda Song, Zhiqiang Xiong, Shirong Cai, Xiuwu Bian, Man Hon Wong, Qin Cao, Lixin Cheng, Gang Lu, Kwong Sak Leung","doi":"10.1186/s13073-025-01441-9","DOIUrl":null,"url":null,"abstract":"<p><p>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 .</p>","PeriodicalId":12645,"journal":{"name":"Genome Medicine","volume":"17 1","pages":"18"},"PeriodicalIF":10.4000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874447/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Medicine","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13073-025-01441-9","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

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 .

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
STModule:识别组织模块,揭示转录组景观的空间成分和特征。
在这里,我们提出了STModule,一种贝叶斯方法,用于从空间分解转录组学中识别组织模块,揭示组织的空间成分和基本特征。STModule揭示了肿瘤、上皮内瘤变、免疫浸润、结果相关分子特征和各种细胞类型等转录组学景观中的多种表达信号,促进了下游分析,并为肿瘤微环境、疾病机制、治疗发展和组织组织学组织提供了见解。与其他方法相比,STModule捕获更广泛的生物信号,并检测新的空间成分。以基因集为特征的组织模块在不同的活检中表现出更强的稳健性和可转移性。STModule: https://github.com/rwang-z/STModule.git。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Genome Medicine
Genome Medicine GENETICS & HEREDITY-
CiteScore
20.80
自引率
0.80%
发文量
128
审稿时长
6-12 weeks
期刊介绍: Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.
期刊最新文献
DposFinder: an interpretable transformer model for predicting phage-derived polysaccharide depolymerases and their host capsular serotypes. Intermittent myopic visual exposure triggers myopia progression via H3K27me3. Strain-level microbial signatures and inferred functional alterations in infants with food protein-induced allergic proctocolitis. Allele-specific suppression of pathogenic bestrophin-1 transcripts by CRISPR/Cas9-mediated genome editing. Developing a germline prostate cancer risk test incorporating rare and common variants, to inform clinical decisions: a route to precision oncology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1