cfDiffusion: diffusion-based efficient generation of high quality scRNA-seq data with classifier-free guidance.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity crucial for understanding biological processes.
• Data variability in scRNA-seq across cell types can hinder downstream analysis accuracy.
• Existing scRNA-seq data simulation methods struggle with multi-attribute cells and high training costs.

Purpose of the Study:

• To introduce cfDiffusion, a novel diffusion model-based method for simulating scRNA-seq data.
• To address limitations of traditional simulation approaches, particularly for multi-attribute cells.
• To improve the efficiency and expressiveness of scRNA-seq data generation.

Main Methods:

• Utilized diffusion models with Classifier-Free Guidance for reduced training costs.
• Integrated a high-level feature caching mechanism to shorten inference times.
• Developed cfDiffusion for simulating multi-attribute single-cell data and pseudo-time series.

Main Results:

• cfDiffusion demonstrated superior expressiveness and efficiency in generating multi-attribute single-cell data compared to scDiffusion.
• The method consistently outperformed state-of-the-art models across various performance metrics on diverse datasets.
• Classifier-Free Guidance significantly reduced model development training costs.

Conclusions:

• cfDiffusion offers an efficient and expressive solution for simulating complex, multi-attribute scRNA-seq data.
• The method facilitates advanced biological analyses, including cell differentiation tracking and intercellular communication studies.
• cfDiffusion advances the field of single-cell data simulation, improving the reliability of downstream analyses.