Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917747458310144 |
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| author | Fang, Donghai Zhu, Fangfang Xie, Dongting Min, Wenwen |
| author_facet | Fang, Donghai Zhu, Fangfang Xie, Dongting Min, Wenwen |
| contents | With the rapid advancement of Spatial Resolved Transcriptomics (SRT) technology, it is now possible to comprehensively measure gene transcription while preserving the spatial context of tissues. Spatial domain identification and gene denoising are key objectives in SRT data analysis. We propose a Contrastively Augmented Masked Graph Autoencoder (STMGAC) to learn low-dimensional latent representations for domain identification. In the latent space, persistent signals for representations are obtained through self-distillation to guide self-supervised matching. At the same time, positive and negative anchor pairs are constructed using triplet learning to augment the discriminative ability. We evaluated the performance of STMGAC on five datasets, achieving results superior to those of existing baseline methods. All code and public datasets used in this paper are available at https://github.com/wenwenmin/STMGAC and https://zenodo.org/records/13253801. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06377 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data Fang, Donghai Zhu, Fangfang Xie, Dongting Min, Wenwen Genomics Artificial Intelligence Machine Learning With the rapid advancement of Spatial Resolved Transcriptomics (SRT) technology, it is now possible to comprehensively measure gene transcription while preserving the spatial context of tissues. Spatial domain identification and gene denoising are key objectives in SRT data analysis. We propose a Contrastively Augmented Masked Graph Autoencoder (STMGAC) to learn low-dimensional latent representations for domain identification. In the latent space, persistent signals for representations are obtained through self-distillation to guide self-supervised matching. At the same time, positive and negative anchor pairs are constructed using triplet learning to augment the discriminative ability. We evaluated the performance of STMGAC on five datasets, achieving results superior to those of existing baseline methods. All code and public datasets used in this paper are available at https://github.com/wenwenmin/STMGAC and https://zenodo.org/records/13253801. |
| title | Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data |
| topic | Genomics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.06377 |