scDECA: integrating gene-cell interactions with global priors and local structures in single-cell transcriptomics

Single-cell RNA sequencing enables high-resolution characterization of cellular transcriptomes, but learning robust representations remains challenging because of sparsity, dropout, and technical noise. Methods that rely directly on raw gene expression are highly sensitive to these artifacts, even though they can capture dataset-specific transcriptional patterns in individual datasets. In contrast, large-scale pretrained single-cell models provide global biological priors through pretrained gene representations, but often fail to capture context-dependent signals. As a result, methods that effectively integrate pretrained biological priors with dataset-specific expression patterns remain relatively limited. We present scDECA, a dual-encoder cross-attention framework that integrates raw gene expression, pretrained gene priors, and local structure derived from protein–protein interaction networks and expression-based cell similarity graphs. The gene encoder incorporates raw expression, pretrained gene embeddings, and protein–protein interaction structure, while the cell encoder models context-specific cellular relationships using a similarity graph constructed from observed expression profiles. Cross-attention aligns the gene and cell domains to produce unified representations that capture both global biological organization and dataset-specific transcriptional heterogeneity. Across multiple disease datasets, scDECA improves clustering performance, recovers functional gene programs more consistently, and produces biologically interpretable representations. These results demonstrate the benefit of jointly modeling global biological priors and local structure for robust and biologically meaningful representation learning in single-cell transcriptomics. scDECA provides an effective framework for integrating pretrained knowledge with dataset-specific transcriptional structure, improving interpretability and downstream analysis performance.