GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) analysis is challenged by high dimensionality, sparsity, and complex cell relationships.
• Existing methods often fail to preserve global structure, model cellular dynamics, and handle technical noise effectively.

Purpose of the Study:

• To develop a novel computational framework for comprehensive single-cell analysis.
• To improve cell clustering, dimensionality reduction, and pseudotime trajectory analysis in scRNA-seq and scATAC-seq data.

Main Methods:

• Introduced GNODEVAE, a novel architecture integrating Graph Attention Networks (GAT), Neural Ordinary Differential Equations (NODE), and Variational Autoencoders (VAE).
• Evaluated GAT performance across 10 graph convolutional layers, demonstrating its superiority.
• Systematically compared GNODEVAE against 18 existing methods across 50 diverse single-cell datasets.

Main Results:

• GNODEVAE consistently outperformed major benchmark method categories, including dimensionality reduction techniques, VAE variants, and graph-based models.
• Achieved significant advantages in reconstruction clustering quality (ARI) and clustering geometry quality (ASW) over standard VGAE and all benchmark methods.
• Demonstrated superior performance in gene dynamics clustering compared to Diffusion map and Palantir.

Conclusions:

• GNODEVAE provides a robust computational framework combining neighborhood-awareness, dynamic modeling, and probabilistic expressiveness for single-cell multi-omics analysis.
• Its consistent superior performance across diverse datasets highlights its versatility for scRNA-seq and scATAC-seq data mining.
• Establishes a new standard for cell clustering, dimensionality reduction, and pseudotime analysis.