A1012
Title: Symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics
Authors: Zhixiang Lin - The Chinese University of Hong Kong (Hong Kong) [presenting]
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. The focus is on SGCAST, an efficient auto-encoder framework for identifying 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, making SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with many spots. SGCAST improves the overall accuracy of spatial domain identification on benchmarking data. The performance of SGCAST is validated on ST datasets at various scales across multiple platforms. The superior capacity of SGCAST is illustrated on spatial transcriptomic data analysis.
