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

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

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling of cell heterogeneity. However, analyzing this noisy, high-dimensional matrix remains challenging. We present scVAG, an integrated deep learning framework combining Variational-Autoencoder (VAE) and Graph Attent...

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Bibliographic Details
Main Authors: Seyedpouria Laghaee, Morteza Eskandarian, Mohammadamin Fereidoon, Somayyeh Koohi
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Heliyon
Subjects:
Single-cell RNA sequencing
Variational-autoencoder
Graph attention autoencoder
Clustering
Dimensionality reduction
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024167631
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Summary:Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling of cell heterogeneity. However, analyzing this noisy, high-dimensional matrix remains challenging. We present scVAG, an integrated deep learning framework combining Variational-Autoencoder (VAE) and Graph Attention Autoencoder (GATE) for enhanced single-cell clustering. Building upon scGAC, our approach replaces its restrictive linear principal component analysis (PCA) with nonlinear dimensionality reduction better suited for scRNA-seq data. Specifically, we integrate VAE and GATE to enable more flexible latent space encoding. Extensive experiments on 20 datasets demonstrate scVAG's superior performance over previous state-of-the-art methods including scGAC, SCEA, SC3, Seurat, scGNN, scASGC, DESC, NIC, scLDS2, DRJCC, sLMIC, and jSRC. On average, scVAG improves clustering accuracy by 5 percent in ARI and 4 percent in NMI parameters. Visualizations highlight scVAG's capacity to recover interpretable biological structures. Our VAE-GATE pipeline extracts intricate expression patterns into compact representations that precisely delineate cell subpopulations consistent with ground truth labels. Overall, scVAG establishes a robust architecture for elucidating cell taxonomies from noisy transcriptomic inputs.
ISSN:2405-8440

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