Abstract

Single-cell multi-omics technologies profile multiple molecular layers in individual cells, but existing methods often struggle to integrate transcriptomic, proteomic, and epigenomic measurements into an interpretable representation while preserving relationships among cells. Here, we present the single-cell multi-omics adversarial graph convolutional autoencoder (scMAGCA), which constructs cell graphs and uses adversarial alignment to learn interpretable shared embeddings that capture cellular heterogeneity and regulatory complexity. Across diverse datasets, scMAGCA outperforms existing methods in modality alignment, clustering, and batch correction. In Alzheimer’s disease, scMAGCA resolves neuronal subtypes and regulatory programs that are missed by single-modality analyses. In kidney cancer, it identifies tumor-specific epithelial and endothelial populations and uncovers biomarkers validated by quantitative polymerase chain reaction. These results support scMAGCA as an interpretable framework for resolving complex cell states in disease.