scVAG: Unified single-cell clustering via variational-autoencoder integration with Graph Attention Autoencoder.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptional data, revealing cellular heterogeneity.
• Analyzing noisy, high-dimensional scRNA-seq data for accurate cell clustering remains a significant challenge in transcriptomics.

Purpose of the Study:

• To introduce scVAG, an integrated deep learning framework designed for enhanced single-cell clustering.
• To overcome limitations of linear dimensionality reduction in scRNA-seq analysis by employing nonlinear methods.

Main Methods:

• scVAG integrates Variational-Autoencoder (VAE) and Graph Attention Autoencoder (GATE) for flexible latent space encoding.
• The framework replaces traditional linear principal component analysis (PCA) with nonlinear dimensionality reduction techniques tailored for scRNA-seq data.

Main Results:

• scVAG demonstrated superior performance across 20 datasets compared to state-of-the-art methods, including scGAC, Seurat, and SC3.
• The method achieved an average improvement of 5% in Adjusted Rand Index (ARI) and 4% in Normalized Mutual Information (NMI) for clustering accuracy.
• Visualizations confirmed scVAG's ability to identify interpretable biological structures and delineate cell subpopulations accurately.

Conclusions:

• scVAG offers a robust deep learning architecture for precise cell clustering from noisy transcriptomic data.
• The VAE-GATE pipeline effectively extracts complex expression patterns into compact representations for elucidating cell taxonomies.