A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation

Single-cell multi-omics technologies offer unprecedented opportunities to decipher complex cellular mechanisms. To overcome experimental limitations in scale, cost, and coverage, powerful computational methods are essential for integrating diverse data modalities and generating high-fidelity in-silico data. In this paper, we present scDiffusion-X, a latent diffusion model for multi-omics data integration, generation, and translation. The core innovation is a Dual-Cross-Attention (DCA) module that adaptively captures intricate, hidden relationships between molecular modalities, offering a more flexible and interpretable approach than existing integration strategies. Extensive benchmarking experiments demonstrate that scDiffusion-X excels at generating realistic multi-omics data, preserving cellular heterogeneity and global data structures with excellent scalability. Beyond simulation, scDiffusion-X uniquely enables accurate modality translation, predicting one molecular modality from another with robust uncertainty quantification. Furthermore, we designed a gradient-based interpretation framework to transform the DCA module into a discovery tool, enabling inference of comprehensive cell-type-specific heterogeneous gene regulatory networks (GRNs). By integrating state-of-the-art generative modeling with biological interpretability, scDiffusion-X serves as a powerful tool for dissecting regulatory relationships, predicting perturbation responses, and accelerating discovery in single-cell multi-omics research.