Abstract
One of the great challenges faced by spatial transcriptomics research is to identify spatial domains that have similarities in gene expression and histology. Most research only depends on gene expression information and is incapable of efficiently utilizing spatial information. Auto-encoder has been proven to be an effective foundation for unsupervised learning. However, traditional auto-encoder cannot utilize explicit relationships in structured data. In order to make better use of embedded feature representation and exploit relationships in graph structured data, an improvement has been made to the graph attention auto-encoder: the auto-encoder is made up of three encoder layers and three decoder layers, and random Gaussian noise is added to the encoder’s working process, thereby generating a graph attention denoising auto-encoder (GADAE). Latent embeddings in low dimensions can be learned by merging spatial information with underlying expression patterns to effectively identify spatial domains. Experimental results show that compared to competitive methods, it can identify spatial domains and locate genes with more abundant spatial expression patterns.
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This work was supported in part by the National Natural Science Foundation of China under Grant No. 62172254.
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Gao, Y., Zhang, DJ., Jiao, CN., Gao, YL., Liu, JX. (2023). Spatial Domain Identification Based on Graph Attention Denoising Auto-encoder. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_31
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