scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis

Multi-omics profiling-spanning proteomics, transcriptomics, and additional omics data types-is rapidly advancing, providing increasingly detailed maps of cellular identity and function. Yet, identifying rare cell populations while maintaining computational tractability remains a major challenge in large-scale multi-omics clustering. Here, we introduce the supercell paradigm, in which expression-coherent cells are grouped into intermediate units that preserve weak but biologically meaningful local structure across omics layers, thereby improving sensitivity to rare populations that are often masked at the conventional cluster level. Supercells are constructed using angle-aware similarity metrics and second-order co-occurrence neighbors, with impurity cells pruned by degree centrality. Building on this idea, we develop scHG, a high-order graph learning framework with an omics-weighted optimizer that adaptively balances contributions from gene expression, surface proteins, and chromatin accessibility while remaining scalable on large datasets through sparse matrix optimization and iterative graph refinement. Across six benchmark datasets (up to 30672 cells), scHG consistently outperforms state-of-the-art methods, improving mean ARI and NMI by 3.97% and 3.54%, respectively, while reducing runtime by 26.40%. Beyond overall clustering accuracy, scHG resolves fine-grained heterogeneity within conventionally defined T-cell populations and, importantly, uncovers rare populations-including dendritic-cell populations and NK-like B cells-that remain hidden under standard clustering pipelines. These results demonstrate that supercells provide not only an efficient intermediate representation for large-scale multi-omics integration, but also a practical mechanism for rare-cell detection.