Aligned cross-modal integration and regulatory heterogeneity characterization of single-cell multiomic data with deep contrastive learning

We propose scMDCF, a contrastive learning based framework for single-cell multi-omics integration that harmonizes cross-modality representations while preserving biological heterogeneity. Applications across diverse scMulti-omics datasets demonstrate improved clustering performance, effective batch-effect mitigation, and mechanistic insights into underlying biological processes. Code and reproducible workflows are openly available.