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Cao L, Yang C, Hu L, Jiang W, Ren Y, Xia T, Xu M, Ji Y, Li M, Xu X, Li Y, Zhang Y, Fang S. Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder. Gigascience 2024; 13:giae003. [PMID: 38373745 PMCID: PMC10939418 DOI: 10.1093/gigascience/giae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/26/2023] [Accepted: 01/10/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. RESULTS To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. CONCLUSIONS Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.
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Affiliation(s)
- Lei Cao
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Chao Yang
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Luni Hu
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Wenjian Jiang
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Yating Ren
- School of Software, Beihang University, Beijing 100191, China
| | - Tianyi Xia
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Mengyang Xu
- BGI Research, Shenzhen 518083, China
- BGI Research, Qingdao 266555, China
| | | | - Mei Li
- BGI Research, Shenzhen 518083, China
| | - Xun Xu
- BGI Research, Wuhan 430074, China
| | - Yuxiang Li
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| | - Yong Zhang
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| | - Shuangsang Fang
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
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