scMAG: Integrating single-cell multi-omics data via multi-stage deep fusion with manifold-aware gating

Single-cell omics technologies now enable simultaneous profiling of multiple genomic modalities within individual cells. Integration of these multi-omics data necessitates computational frameworks that establish cross-modal associations while preserving biological fidelity. A central challenge lies in balancing two competing objectives: alignment of heterogeneous omics layers and retention of modality-specific distributions. Excessive alignment may lead to semantic loss, while strict distribution preservation may trigger modal separation in potential representations. This fundamental trade-off underscores the need for advanced strategies to harmonize integrative accuracy with biological authenticity. This paper innovatively proposes a multi-stage feature deep fusion formalization method with omics multi-core manifold preservation and guided gating optimization strategy and designs the scMAG algorithm. This is a framework aimed at improving the clustering accuracy and data visualization of single-cell multi-omics, while achieving adaptive alignment of multi-omics latent spaces and optimizing the distribution of omics data in the environmental space, effectively suppressing biological noise and measurement errors. To comprehensively evaluate the scMAG algorithm, we compared the paired datasets scRNA-seq with scATAT-seq and ADT as a benchmark. We consistently observed that scMAG outperformed other algorithms in terms of clustering effect and data visualization clarity. Further multi-task experimental analysis indicates that this enhancement stems from the ability of scMAG to improve the distribution of potential features in the data space and adaptively balance the shared signals and modality-specific signals across multi-omics. scMAG not only significantly improved clustering performance, but also showed excellent performance in feature dimensionality reduction, batch effect removal, multimodal data integration, cell trajectory inference, etc., and also had good biological interpretability. This method provides new theoretical support and practical reference for in-depth analysis of multimodal single-cell data.