scCIF: contribution-informed fusion for single-cell multi-omics clustering

Single-cell multi-omics technologies provide complementary molecular information for characterizing cellular heterogeneity. However, sparsity, noise, cross-omics heterogeneity, and imbalanced omics information make accurate cell clustering challenging. We propose scCIF, an end-to-end contribution-informed fusion framework for single-cell multi-omics clustering. scCIF employs contribution-aware fusion to adaptively construct a consensus semantic space, and further introduces hard-aware assignment calibration to align omics-specific assignments with the fused assignment distribution. Experiments on eight public single-cell multi-omics datasets show that scCIF consistently outperforms competing methods across ARI, NMI, and AMI. Ablation studies verify the effectiveness of each key component. UMAP visualization, batch effect analysis, trajectory inference, and marker gene analysis further demonstrate that scCIF preserves biologically meaningful cellular structures. scCIF improves single-cell multi-omics clustering by jointly modeling contribution imbalance and cross-omics semantic drift, providing a robust framework for learning discriminative and biologically interpretable cell representations.