Deep Clustering for scRNA-seq Analysis via Graph Attention Networks and Variational Autoencoders

Recent advances in single-cell RNA sequencing(scRNA-seq) technologies have shown significant improvements in cellular-level biological research. Clustering individual cells into subpopulations is a pivotal procedure of scRNA-seq data analysis. The high sparsity and dimensionality of scRNA-seq data still limit the performance of cell clustering. Recent graph-based scRNA-seq clustering methods overlook the expression of cells, leading to suboptimal representation of cell embeddings. In this study, we propose single-cell Combined Graph Attentional Clustering (scCAT), a novel unsupervised clustering method for scRNA-seq data. scCAT first learns cell embeddings through a joint dimensionality reduction (JDR). Then, a graph attention autoencoder (GATE) is designed to analyze the cell topological information. Finally, the clustering operation is executed through a self-optimizing unit. Experimental results on five scRNA-seq datasets show that scCAT significantly improves clustering accuracy over existing methods with an average increase of 11.2% in the Adjusted Rand Index (ARI) and 9.6% in the Normalized Mutual Information (NMI) parameters.