stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning.
Journal:
BMC biology
Published Date:
Jul 1, 2025
Abstract
BACKGROUND: Spatial transcriptomics now enables sequencing while preserving the spatial location of cells. This significantly enhances researchers' understanding of cellular and tissue functions in their spatial context. However, due to current technical limitations, spatial transcriptomics data often exhibit high dropout rates and noise, posing challenges for downstream analysis, like spot clustering, differential gene analysis, and spatial domain identification. To address those challenges, we propose stGRL, a novel deep multi-task graph neural network model tailored for spatial transcriptomics. stGRL employs an encoder-decoder architecture with a zero-inflated negative binomial (ZINB) distribution to reconstruct input data while effectively addressing dropout events. Additionally, it integrates graph contrastive representation learning to enhance the consistency of node embeddings, thereby improving clustering performance.
