Dataset and Code of SpaMGCL Project
Year: 2025
Type: article
Abstract: The rapid development of spatial transcriptomics (ST) has enabled researchers to simultaneously capture gene expression profiles and spatial localization, offering unprecedented insights into tissue structure and function. However, spatial domain identification remains a critical challenge in ST data analysis, as current methods often struggle to fully capture the complex interplay between gene expression and spatial topology. To address this challenge, we propose SpaMGCL (Spatial Masked Graph Contrastive Learning), a novel self-supervised learning framework that integrates a masking mechanism with a contrastive learning strategy to jointly optimize gene expression and spatial information for more discriminative latent representation learning.
Author: Zhao, Jinjie
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6949860376
FWCI:
Cites:
Cited by:
Related to: