Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning.

ABSTRACT: Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectively understand the underlying biological mechanisms within tissue microenvironments, there is a requisite for methods that can accurately capture external spatial heterogeneity and interpret internal gene regulation from spatial transcriptomics data. However, current methods for region identification often lack the simultaneous characterizing of spatial structure and gene regulation, thereby limiting the ability of spatial dissection and gene interpretation. Here, stDCL is developed, a dual graph contrastive learning method to identify spatial do

SUBMITTER: Yu Z 

PROVIDER: S-EPMC11744562 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature