A Scalable and Unified Hierarchical Coarsening Hypergraph Framework for Single-Cell Multi-Omics Data Analysis

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Nowadays, technological advancements enable the simultaneous profiling of the epigenome, transcriptome, and proteome at the single-cell level, and an urgent need for tools to integrate such complex tri-modal data is demanded. In this paper, we propose a novel method named scHCHF, a scalable and unified Hierarchical Coarsening Hypergraph Framework. scHCHF first employs a hierarchical coarsening process to groups cells into hypervertices. A unified multiomics hypergraph is then constructed upon these hypervertices, which substantially reduces computational complexity and memory footprint. Subsequently, a hypergraph attention network captures high-order relationships and learns discriminative cell representations, guided by a weakly supervised prototypical contrastive loss. Building upon the pre-trained network, scHCHF further employs a fine-tuning paradigm to achieve accurate cell type annotation using a minimal number of labeled cells. Experiments on real tri-modal datasets demonstrate that scHCHF outperforms other state-of-the-art methods in both cell clustering and cell type annotation tasks.

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