VARGG: a deep learning framework advancing precise spatial domain identification and cellular heterogeneity analysis in spatial transcriptomics.

ABSTRACT: Spatial transcriptomics has revolutionized our ability to measure gene expression while preserving spatial information, thus facilitating detailed analysis of tissue structure and function. Identifying spatial domains accurately is key for understanding tissue microenvironments and biological progression. To overcome the challenge of integrating gene expression data with spatial information, we introduce the VARGG deep learning framework. VARGG combines a pretrained Vision Transformer (ViT) with a graph neural network autoencoder, utilizing ViT's self-attention mechanism to capture global contextual information and enhance understanding of spatial relationships. This framework is further enhanced by multi-layer gated residual graph neural networks and Gaussian noise, which improve feature

SUBMITTER: Wang M 

PROVIDER: S-EPMC12640549 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature