Deep clustering of single-cell RNA-seq using adversarial graph contrastive learning

Single-cell technologies enable the exploration of biological insights at cellular resolution. One of the crucial tasks in the analysis of single-cell RNA sequencing (scRNA-seq) data is to classify cells into groups of cell types. Recent developments in scRNA-seq clustering methods utilize the strength of contrastive learning and graph-based deep learning to learn high-quality representations which are beneficial for classifying cells. However, the unique characteristics of the scRNA-seq data still pose many computational challenges. This study proposes a novel method for clustering scRNA-seq data using adversarial graph contrastive learning, called scAGCL. The proposed algorithm creates a cell-cell graph and then generates a meaningful representation for clustering based on a contrastive learning process with the support of an adversarial attack on both the graph structures and node features. In addition, a subgraph sampling technique is used to increase the scalability of the method. Experiments on real scRNA-seq datasets demonstrate that the proposed method outperforms seven state-of-the-art algorithms. Furthermore, scAGCL also shows the ability to support the identification of marker genes for cell types. The source code of the proposed method and all datasets used in this paper can be downloaded at https://github.com/levinhcntt/scAGCL.