scDCT: a conditional diffusion-based deep learning model for high-fidelity single-cell cross-modality translation.

Abstract

Single-cell multi-omics technologies enable comprehensive molecular profiling, offering insights into cellular heterogeneity and biological mechanisms. However, current cross-modality translation methods struggle with high-dimensional, noisy, and sparse single-cell data. We propose single-cell Diffusion models for Cross-modality Translation (scDCT), a probabilistic framework for bidirectional cross-modality translation in single-cell data, including single-cell RNA sequencing, single-cell assay for transposase-accessible chromatin sequencing, and protein expression. scDCT integrates modality-specific autoencoders with conditional denoising diffusion probabilistic models to map inputs to latent spaces and perform probabilistic translation across modalities. This design captures cell-type heterogeneity, accounts for data sparsity, and models uncertainty during translation. Extensive experiments on eight benchmark datasets demonstrate that scDCT outperforms state-of-the-art methods across paired, unpaired, cross-type, and cross-tissue settings, offering a robust and interpretable solution for single-cell multi-omics integration.