Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics.

ABSTRACT: Spatial transcriptomics is an emerging technology that enables the profiling of gene expression in tissues while preserving spatial location information. This innovative approach is anticipated to provide a comprehensive understanding of the spatial distribution of different cells within tissues and facilitate in-depth analysis of tissue structure. To accurately recognize spatial domains from spatial transcriptomics, we have introduced a generalized deep learning method called GAAEST (Graph Attention-based Autoencoder for Spatial Transcriptomics). Our proposed approach effectively integrates both spatial location information and gene expression data from spatial transcriptomics. Specifically, it leverages spatial location details to construct a neighborhood graph and employs a graph attent

SUBMITTER: Wang T 

PROVIDER: S-EPMC11489439 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature