StAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics.

ABSTRACT: With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution throug

SUBMITTER: Fang Z 

PROVIDER: S-EPMC10772985 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature