Dimensionality Reduction and Denoising of Spatial Transcriptomics Data Using Dual-Channel Masked Graph Autoencoder
Recent advances in spatial transcriptomics (ST) technology allow researchers to comprehensively measure gene expression patterns at the level of individual cells or even subcellular compartments while preserving the spatial context of their tissue. Spatial domain identification is a critical task in...
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Cold Spring Harbor Laboratory
02.06.2024
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| Abstract | Recent advances in spatial transcriptomics (ST) technology allow researchers to comprehensively measure gene expression patterns at the level of individual cells or even subcellular compartments while preserving the spatial context of their tissue. Spatial domain identification is a critical task in analyzing the ST data. However, effectively capturing distinctive gene expression features and relationships between genes poses a significant challenge. We develop a graph self-supervised learning method STMask for the analysis and exploration of the ST data. STMask combines the masking mechanism with a graph autoencoder, compelling the gene representation learning channel to acquire more expressive representations. Simultaneously, it combines the masking mechanism with graph self-supervised contrastive learning methods, pulling together the embedding distances between spatially adjacent points and pushing apart the representations of different clusters, allowing the gene relationship learning channel to learn more comprehensive relationships. The applications of STMask to four ST datasets demonstrate that STMask outperforms state-of-the-art methods in various tasks, including spatial clustering and trajectory inference. Source code is available at https://github.com/donghaifang/STMask.
Spatial Transcriptomics (ST) is an emerging transcriptomic sequencing technology aimed at revealing the spatial distribution of gene expression and cell types within tissues. This method enables the acquisition of gene expression profiles at the level of individual cells or spots within the tissue, uncovering the spatial expression patterns of genes. However, accurately identifying spatial domains in ST data remains challenging. In our study, we introduce STMask, a self-supervised learning method that combines a dual-channel masked graph autoencoder with masking and contrastive learning. Our work contributes primarily in two aspects: (1) We propose a novel graph self-supervised learning method (STMask) specifically tailored for the analysis and research of ST data, which enhances the ability to capture the unique features of gene expression and spatial relationships within tissues. (2) Through comprehensive experiments, STMask provides valuable insights into biological processes, particularly in the context of breast cancer. It identifies enrichment of various differentially expressed genes in tumor regions, such as IGHG1, which can serve as effective targets for cancer therapy. |
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| AbstractList | Recent advances in spatial transcriptomics (ST) technology allow researchers to comprehensively measure gene expression patterns at the level of individual cells or even subcellular compartments while preserving the spatial context of their tissue. Spatial domain identification is a critical task in analyzing the ST data. However, effectively capturing distinctive gene expression features and relationships between genes poses a significant challenge. We develop a graph self-supervised learning method STMask for the analysis and exploration of the ST data. STMask combines the masking mechanism with a graph autoencoder, compelling the gene representation learning channel to acquire more expressive representations. Simultaneously, it combines the masking mechanism with graph self-supervised contrastive learning methods, pulling together the embedding distances between spatially adjacent points and pushing apart the representations of different clusters, allowing the gene relationship learning channel to learn more comprehensive relationships. The applications of STMask to four ST datasets demonstrate that STMask outperforms state-of-the-art methods in various tasks, including spatial clustering and trajectory inference. Source code is available at https://github.com/donghaifang/STMask.
Spatial Transcriptomics (ST) is an emerging transcriptomic sequencing technology aimed at revealing the spatial distribution of gene expression and cell types within tissues. This method enables the acquisition of gene expression profiles at the level of individual cells or spots within the tissue, uncovering the spatial expression patterns of genes. However, accurately identifying spatial domains in ST data remains challenging. In our study, we introduce STMask, a self-supervised learning method that combines a dual-channel masked graph autoencoder with masking and contrastive learning. Our work contributes primarily in two aspects: (1) We propose a novel graph self-supervised learning method (STMask) specifically tailored for the analysis and research of ST data, which enhances the ability to capture the unique features of gene expression and spatial relationships within tissues. (2) Through comprehensive experiments, STMask provides valuable insights into biological processes, particularly in the context of breast cancer. It identifies enrichment of various differentially expressed genes in tumor regions, such as IGHG1, which can serve as effective targets for cancer therapy. |
| Author | Fang, Donghai Min, Wenwen Zhang, Shihua Chen, Jinyu |
| Author_xml | – sequence: 1 givenname: Wenwen orcidid: 0000-0002-2558-2911 surname: Min fullname: Min, Wenwen organization: School of Information Science and Engineering, Yunnan University – sequence: 2 givenname: Donghai orcidid: 0009-0000-9050-0700 surname: Fang fullname: Fang, Donghai organization: School of Information Science and Engineering, Yunnan University – sequence: 3 givenname: Jinyu surname: Chen fullname: Chen, Jinyu organization: School of Mathematics, Statistics and Mechanics, Beijing University of Technology – sequence: 4 givenname: Shihua orcidid: 0000-0003-0192-7118 surname: Zhang fullname: Zhang, Shihua organization: Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences |
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| Copyright | 2024, Posted by Cold Spring Harbor Laboratory |
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| DOI | 10.1101/2024.05.30.596562 |
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| Notes | Competing Interest Statement: The authors have declared no competing interest. |
| ORCID | 0009-0000-9050-0700 0000-0002-2558-2911 0000-0003-0192-7118 |
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| Title | Dimensionality Reduction and Denoising of Spatial Transcriptomics Data Using Dual-Channel Masked Graph Autoencoder |
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