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

IF 10.4 1区 生物学 Q1 GENETICS & HEREDITY
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
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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 .

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。
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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.
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