scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics

Abstract

Paired single-cell multi-omics technologies provide direct within-cell correspondence across molecular layers and offer a powerful route to dissecting cellular heterogeneity and regulatory relationships. However, effective integration requires more than modality mixing: a useful model must accurately align paired cells while preserving modality-specific topological structure and biologically meaningful variation. Existing methods often struggle with topology mismatch across modalities, underuse cross-modal complementarity within paired cells, or improve alignment at the cost of biological fidelity. To address these challenges, we present scGSI, a graph-guided self-supervised framework for paired single-cell multi-omics integration. scGSI combines heterogeneous graph encoders to preserve modality-specific neighborhood structure, a pull-in projection module to stabilize pre-alignment, and a cross-fusion mechanism with contrastive refinement to exploit complementary signals between paired modalities. Across five paired single-cell multi-omics datasets collected from four platforms, scGSI improves paired cell-state alignment while maintaining a favorable balance between modality mixing and biological variation preservation. The learned embeddings also better support downstream analyses, including cell-type discrimination and developmental trajectory inference, showing that accurate alignment need not erase biologically meaningful structure.