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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) data is prone to dropout events and technical noise.
• These artifacts obscure true gene expression and compromise downstream analysis reliability.
• Current imputation and denoising methods often over-smooth data and fail to capture cellular heterogeneity.

Purpose of the Study:

• To introduce scSTD, a novel framework for scRNA-seq data imputation and denoising.
• To address limitations of existing methods in accurately recovering gene expression and preserving biological variation.
• To improve the reliability of scRNA-seq data analysis.

Main Methods:

• scSTD integrates the Swin Transformer (SwinT) architecture with a latent diffusion model.
• A deep autoencoder encodes cells into latent embeddings.
• A SwinT-based latent diffusion process models the scRNA-seq data distribution for imputation and denoising.

Main Results:

• scSTD accurately recovers gene expression profiles while preserving subtle biological variation.
• The framework achieves high-fidelity imputation and denoising by synthesizing realistic latent neighbors.
• Evaluations show scSTD outperforms existing methods in gene expression recovery and maintaining cellular landscape topology.

Conclusions:

• scSTD offers a robust solution for scRNA-seq data imputation and denoising.
• The novel framework enhances the accuracy and reliability of single-cell data analysis.
• scSTD effectively addresses technical noise and dropout events, preserving crucial biological insights.