Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST
Abstract Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-35947-w |
| _version_ | 1797558323495567360 |
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| author | Wei Liu Xu Liao Ziye Luo Yi Yang Mai Chan Lau Yuling Jiao Xingjie Shi Weiwei Zhai Hongkai Ji Joe Yeong Jin Liu |
| author_facet | Wei Liu Xu Liao Ziye Luo Yi Yang Mai Chan Lau Yuling Jiao Xingjie Shi Weiwei Zhai Hongkai Ji Joe Yeong Jin Liu |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Abstract Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms. |
| first_indexed | 2024-03-10T17:29:53Z |
| format | Article |
| id | doaj.art-9abd87ac205140bb9a3046b91896c166 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:29:53Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-9abd87ac205140bb9a3046b91896c1662023-11-20T10:04:41ZengNature PortfolioNature Communications2041-17232023-01-0114111810.1038/s41467-023-35947-wProbabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECASTWei Liu0Xu Liao1Ziye Luo2Yi Yang3Mai Chan Lau4Yuling Jiao5Xingjie Shi6Weiwei Zhai7Hongkai Ji8Joe Yeong9Jin Liu10Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical SchoolCentre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical SchoolCentre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical SchoolCentre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical SchoolInstitute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR)School of Mathematics and Statistics, Wuhan UniversityAcademy of Statistics and Interdisciplinary Sciences, East China Normal UniversityKey Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of SciencesDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthInstitute of Molecular and Cell Biology (IMCB), Agency of Science, Technology and Research (A*STAR)Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical SchoolAbstract Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.https://doi.org/10.1038/s41467-023-35947-w |
| spellingShingle | Wei Liu Xu Liao Ziye Luo Yi Yang Mai Chan Lau Yuling Jiao Xingjie Shi Weiwei Zhai Hongkai Ji Joe Yeong Jin Liu Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST Nature Communications |
| title | Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST |
| title_full | Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST |
| title_fullStr | Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST |
| title_full_unstemmed | Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST |
| title_short | Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST |
| title_sort | probabilistic embedding clustering and alignment for integrating spatial transcriptomics data with precast |
| url | https://doi.org/10.1038/s41467-023-35947-w |
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