stDGAC: A novel identifying spatial domains method via graph attention contrastive network for spatial transcriptomics data

Complex biological tissues are composed of many cells in a highly coordinated manner and play a variety of biological functions. In recent years, many methods for spatial clustering have been developed. However, it remains a major challenge to effectively utilize these high-dimensional and noisy spa...

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Vydáno v:Computers in biology and medicine Ročník 193; s. 110280
Hlavní autoři: Jing, Jing, Gao, Yue, Gao, Ying-Lian, Li, Feng, Wang, Juan, Liu, Jin-Xing
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.07.2025
Témata:
Algorithms
Computational Biology - methods
Contrastive learning
Denoise autoencoder
Gene Expression Profiling - methods
Graph attention network
Humans
Internal Medicine
Other
Spatial domains
Spatial transcriptomics
Transcriptome
Spatial transcriptomics
Graph attention network
Denoise autoencoder
Contrastive learning
Spatial domains
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí:Complex biological tissues are composed of many cells in a highly coordinated manner and play a variety of biological functions. In recent years, many methods for spatial clustering have been developed. However, it remains a major challenge to effectively utilize these high-dimensional and noisy spatial transcriptomics data, with similar gene expression and histological features, to more accurately identify spatial domains. Here, a novel method of spatial domain identification, named stDGAC, was proposed by jointly using a denoising autoencoder and a graph attention contrastive network for subsequent analysis of spatial transcriptomics data. The pre-trained denoising autoencoder performed dimensionality reduction and denoising, and then the low-dimensional latent representation was learned through a graph attention contrastive network to aggregate the neighborhood information of the spatial context and acquire a more robust representation. Finally, the proposed method stDGAC was experimentally demonstrated to outperform other existing methods in downstream analysis, such as identifying spatial domains, trajectory inference, and gene expression data denoising. •Denoising autoencoder can reduce the noise of spatial transcriptomics data.•Graph attention contrastive network enable optimize nodes embeddings.•Contrastive learning mechanism made the learned representation more discriminative.•The experimental results demonstrate the effectiveness of stDGAC in spatial clustering.
Bibliografie:ObjectType-Article-1
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.110280