scSTD: A Swin Transformer-Based Diffusion Model for Recovering scRNA-Seq Data

Dropout events and technical noise are pervasive challenges in single-cell RNA sequencing (scRNA-seq) data, often obscuring true gene expression profiles and undermining the reliability of downstream analyses. Existing imputation and denoising methods offer partial relief but frequently struggle with over-smoothing and fail to fully capture the complex heterogeneity of cellular states. To address these limitations, we introduce scSTD, a novel imputation and denoising framework that uniquely combines the Swin Transformer (SwinT) architecture with a latent diffusion model. In scSTD, a deep autoencoder first encodes each cell into a compact latent embedding, which is then modeled via a SwinT-based latent diffusion process designed to learn the rich, multimodal distribution of scRNA-seq data. This integration enables scSTD to accurately recover gene expression profiles while preserving subtle biological variation. By synthesizing realistic latent neighbors for each cell and aggregating their decoded outputs, scSTD achieves high-fidelity imputation and denoising. Comprehensive evaluations on both synthetic and real scRNA-seq datasets demonstrate that scSTD significantly outperforms existing methods in recovering true gene expression profiles and maintaining the topological integrity of cellular landscapes.