GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into cellular heterogeneity and development.
• Insufficient scRNA-seq data due to technical limitations, cost, or scarcity hinders robust computational modeling and analysis.
• A lack of sufficient training data is a common bottleneck for developing accurate predictive models in scRNA-seq studies.

Purpose of the Study:

• To introduce GDSim, a novel deep generative network designed for simulating scRNA-seq data.
• To address the challenge of data scarcity in scRNA-seq analysis.
• To provide a tool that generates realistic scRNA-seq data for improving downstream computational models and biological applications.

Main Methods:

• GDSim utilizes a label-guided diffusion-based model architecture.
• The model captures intricate gene expression dependencies within scRNA-seq datasets.
• The approach focuses on generating simulated data that accurately reflects the distribution of real scRNA-seq data.

Main Results:

• GDSim demonstrates superior performance in recovering original data distribution characteristics compared to existing methods.
• Simulated data generated by GDSim shows high consistency with real data in cell subtype clustering.
• Differential gene expression analysis on GDSim-generated data aligns well with analyses performed on real datasets.

Conclusions:

• GDSim is an effective tool for simulating scRNA-seq data, addressing limitations of data scarcity.
• The method accurately preserves key data distribution characteristics and biological insights.
• GDSim facilitates more robust downstream analyses, including cell clustering and differential gene expression, thereby advancing scRNA-seq applications.