stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning

作者：Xin Lü, Murong Zhou, Bo Gao, Fang Wang, Shuilin Jin, Qiaoming Liu, Guohua Wang · 发表于：BMC Biology · 年份：2025 · DOI：10.1186/s12915-025-02290-z · 被引用次数：11 · 研究领域：Single-cell and spatial transcriptomics、Ferroptosis and cancer prognosis、Gene expression and cancer classification

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. RESULTS: Through benchmark experiments on various spatial transcriptomics datasets, stGRL demonstrated a superior ability to identify spatial features compared to current mainstream methods. In-depth analyses reveal that the denoised data generated by stGRL not only preserves the spatial hierarchy of tissues but also accurately identifies differentially expressed genes. When applied to breast cancer datasets, stGRL effectively analyzed the differences between cancerous regions and carcinoma in situ areas, uncovering that carcinoma in situ regions are pre...