SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics.

ABSTRACT: Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains. SGCAST adopts a symmetric graph convolutional auto-encoder to learn aggregated latent embeddings via integrating the gene expression similarity and the proximity of the spatial spots. This framework in SGCAST enables a mini-batch training strategy, which makes SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with a large num

SUBMITTER: Li J 

PROVIDER: S-EPMC10782917 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature