ScGraphTrans: Pathway-Guided Graph Learning and Domain Adaptation for Cell Type Annotation in Single-Cell RNA-seq.
TL;DR
ScGraphTrans, a graph neural network framework that integrates functional state pseudo-labels, graph structure learning, and graph domain adaptation to improve both cell type annotation and communication inference, is proposed, offering insights into biomarker discovery and therapeutic strategies.
Abstract
The tumor microenvironment (TME) is a complex ecosystem in which intercellular communication regulates tumor progression and therapeutic response. Yet inferring cell-cell interactions from non-spatial scRNA-seq remains challenging due to incomplete ligand-receptor databases and inaccurate cell type annotations. Here, we propose scGraphTrans, a graph neural network framework that integrates functional state pseudo-labels, graph structure learning, and graph domain adaptation to improve both cell type annotation and communication inference. Pathway activity scores across 14 cancer-relevant processes (e.g., angiogenesis, apoptosis, cell cycle) are used as pseudo-labels to refine cell-cell graphs, capturing functional proximity beyond geometric similarity. A domain adaptation module further aligns embeddings across patients, enhancing cross-individual generalization. Evaluated on 38,667 cells from 15 individuals across three cancers, scGraphTrans achieved an average accuracy of 84.28%, surpassing state-of-the-art baselines while maintaining robustness across heterogeneous datasets. Statistical validation demonstrated recovery of disease-specific gene interactions (e.g., LGALS1-SUSD2 in breast invasive carcinoma and BIRC5-CASP6 in colorectal cancer) without prior ligand-receptor supervision. The source code and data used in this paper can be found in https://github.com/LiYuechao1998/scGraphTrans. Our framework thus provides an interpretable and generalizable solution for TME analysis, offering insights into biomarker discovery and therapeutic strategies.