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

ABSTRACT: 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

SUBMITTER: Van Vinh L 

PROVIDER: S-EPMC12368859 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature