A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics.

ABSTRACT: Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting

SUBMITTER: Huang J 

PROVIDER: S-EPMC11393357 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature