scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data

TL;DR

scASDC is a novel deep clustering method that combines graph neural networks, autoencoders, and attention mechanisms to improve the accuracy and robustness of single-cell RNA-seq data clustering.

Contribution

The paper introduces a new deep clustering framework integrating GCN, ZINB autoencoder, attention fusion, and self-supervised learning for single-cell data analysis.

Findings

Outperforms existing clustering methods on benchmark datasets.

Effectively captures high-order structural relationships between cells.

Enhances clustering robustness and biological interpretability.

Abstract

Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity. However, the high sparsity and complex noise patterns inherent in scRNA-seq data present significant challenges for traditional clustering methods. To address these issues, we propose a deep clustering method, Attention-Enhanced Structural Deep Embedding Graph Clustering (scASDC), which integrates multiple advanced modules to improve clustering accuracy and robustness.Our approach employs a multi-layer graph convolutional network (GCN) to capture high-order structural relationships between cells, termed as the graph autoencoder module. To mitigate the oversmoothing issue in GCNs, we introduce a ZINB-based autoencoder module that extracts content information from the data and learns latent representations of gene expression. These modules are further integrated through an attention fusion mechanism, ensuring effective combination of gene expression and structural information at each layer of the GCN. Additionally, a self-supervised learning module is incorporated to enhance the robustness of the learned embeddings. Extensive experiments demonstrate that scASDC outperforms existing state-of-the-art methods, providing a robust and effective solution for single-cell clustering tasks. Our method paves the way for more accurate and meaningful analysis of single-cell RNA sequencing data, contributing to better understanding of cellular heterogeneity and biological processes. All code and public datasets used in this paper are available at \url{https://github.com/wenwenmin/scASDC} and \url{https://zenodo.org/records/12814320}.