Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics.

ABSTRACT: Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constrai

SUBMITTER: Hu Y 

PROVIDER: S-EPMC10849194 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature