Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network
Abstract Recent developments in spatially resolved transcriptomics (SRT) enable the characterization of spatial structures for different tissues. Many decomposition methods have been proposed to depict the cellular distribution within tissues. However, existing computational methods struggle to bala...
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| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-06003-1 |
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| author | Zhongning Jiang Wei Huang Raymond H. W. Lam Wei Zhang |
| author_facet | Zhongning Jiang Wei Huang Raymond H. W. Lam Wei Zhang |
| author_sort | Zhongning Jiang |
| collection | DOAJ |
| description | Abstract Recent developments in spatially resolved transcriptomics (SRT) enable the characterization of spatial structures for different tissues. Many decomposition methods have been proposed to depict the cellular distribution within tissues. However, existing computational methods struggle to balance spatial continuity in cell distribution with the preservation of cell-specific characteristics. To address this, we propose Spall, a novel decomposition network that integrates scRNA-seq data with SRT data to accurately infer cell type proportions. Spall introduced the GATv2 module, featuring a flexible dynamic attention mechanism to capture relationships between spots. This improves the identification of cellular distribution patterns in spatial analysis. Additionally, Spall incorporates skip connections to address the loss of cell-specific information, thereby enhancing the prediction capability for rare cell types. Experimental results show that Spall outperforms the state-of-the-art methods in reconstructing cell distribution patterns on multiple datasets. Notably, Spall reveals tumor heterogeneity in human pancreatic ductal adenocarcinoma samples and delineates complex tissue structures, such as the laminar organization of the mouse cerebral cortex and the mouse cerebellum. These findings highlight the ability of Spall to provide reliable low-dimensional embeddings for downstream analyses, offering new opportunities for deciphering tissue structures. |
| format | Article |
| id | doaj-art-9894c841b2b847d0bb4b3244e7b4576f |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-9894c841b2b847d0bb4b3244e7b4576f2024-12-22T12:51:14ZengBMCBMC Bioinformatics1471-21052024-12-0125111910.1186/s12859-024-06003-1Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition networkZhongning Jiang0Wei Huang1Raymond H. W. Lam2Wei Zhang3Department of Biomedical Engineering, City University of Hong KongDepartment of Biomedical Engineering, City University of Hong KongDepartment of Biomedical Engineering, City University of Hong KongCenter of Intelligent Medicine, School of Control Science and Engineering, Shandong UniversityAbstract Recent developments in spatially resolved transcriptomics (SRT) enable the characterization of spatial structures for different tissues. Many decomposition methods have been proposed to depict the cellular distribution within tissues. However, existing computational methods struggle to balance spatial continuity in cell distribution with the preservation of cell-specific characteristics. To address this, we propose Spall, a novel decomposition network that integrates scRNA-seq data with SRT data to accurately infer cell type proportions. Spall introduced the GATv2 module, featuring a flexible dynamic attention mechanism to capture relationships between spots. This improves the identification of cellular distribution patterns in spatial analysis. Additionally, Spall incorporates skip connections to address the loss of cell-specific information, thereby enhancing the prediction capability for rare cell types. Experimental results show that Spall outperforms the state-of-the-art methods in reconstructing cell distribution patterns on multiple datasets. Notably, Spall reveals tumor heterogeneity in human pancreatic ductal adenocarcinoma samples and delineates complex tissue structures, such as the laminar organization of the mouse cerebral cortex and the mouse cerebellum. These findings highlight the ability of Spall to provide reliable low-dimensional embeddings for downstream analyses, offering new opportunities for deciphering tissue structures.https://doi.org/10.1186/s12859-024-06003-1Spatially resolved transcriptomicsDecompositionCell type proportionTissue structureGraph neural network |
| spellingShingle | Zhongning Jiang Wei Huang Raymond H. W. Lam Wei Zhang Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network BMC Bioinformatics Spatially resolved transcriptomics Decomposition Cell type proportion Tissue structure Graph neural network |
| title | Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network |
| title_full | Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network |
| title_fullStr | Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network |
| title_full_unstemmed | Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network |
| title_short | Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network |
| title_sort | spall accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network |
| topic | Spatially resolved transcriptomics Decomposition Cell type proportion Tissue structure Graph neural network |
| url | https://doi.org/10.1186/s12859-024-06003-1 |
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