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Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data
Shan, Xu1; Chen, Jinyu2; Dong, Kangning3; Zhou, Wei1; Zhang, Shihua3,4,5,6,7
2022-06-21
Source PublicationJOURNAL OF COMPUTATIONAL BIOLOGY
ISSN1066-5277
Pages14
AbstractSingle-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at a cellular resolution. However, it could not capture the spatial organization of cells in a tissue. The spatially resolved transcriptomics technologies (ST) have been developed to address this issue. However, the emerging STs are still inefficient at single-cell resolution and/or fail to capture the sufficient reads. To this end, we adopted a partial least squares-based method (spatial modular patterns [SpaMOD]) to simultaneously integrate the two data modalities, as well as the networks related to cells and spots, to identify the cell-spot comodules for deciphering the SpaMOD of tissues. We applied SpaMOD to three paired scRNA-seq and ST datasets, derived from the mouse brain, granuloma, and pancreatic ductal adenocarcinoma, respectively. The identified cell-spot comodules provide detailed biological insights into the spatial relationships between cell populations and their spatial locations in the tissue.
Keyworddata integration single-cell transcriptomics spatial modular patterns spatial transcriptomics
DOI10.1089/cmb.2021.0617
Indexed BySCI
Language英语
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000813901800001
PublisherMARY ANN LIEBERT, INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/61248
Collection应用数学研究所
Corresponding AuthorZhang, Shihua
Affiliation1.Yunnan Univ, Department Software Engn, Kunming, Peoples R China
2.Beijing Univ Technol, Fac Sci, College Stat & Data Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, CEMS, RCSDS,NCMIS, Beijing, Peoples R China
4.Univ Chinese Acad Sci, School Math Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Center Excellence Anim Evolut & Genet, Kunming, Peoples R China
6.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Chinese Acad Sci, Key Lab Syst Biol, Hangzhou, Peoples R China
7.Chinese Acad Sci, Acad Math & Syst Sci, CEMS, RCSDS,NCMIS, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shan, Xu,Chen, Jinyu,Dong, Kangning,et al. Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2022:14.
APA Shan, Xu,Chen, Jinyu,Dong, Kangning,Zhou, Wei,&Zhang, Shihua.(2022).Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data.JOURNAL OF COMPUTATIONAL BIOLOGY,14.
MLA Shan, Xu,et al."Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data".JOURNAL OF COMPUTATIONAL BIOLOGY (2022):14.
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