scDGCN: A Graph-Based Deep Clustering Framework for Single-Cell Transcriptomics via Neighborhood-Enhanced Context Representation Learning.

Abstract

Self-supervised and context-aware representation learning offers a powerful strategy for extracting robust features from high-dimensional, sparse, and noisy single-cell data. Here, we present scDGCN, a graph-based deep clustering framework for single-cell RNA sequencing (scRNA-seq) analysis. scDGCN models intercellular similarity as a graph and leverages local neighborhood context to guide expression reconstruction, thereby learning robust cell representations. The method integrates a network enhancement module to denoise the cell similarity graph, a neighborhood-enhanced autoencoder that reconstructs gene expression using topological neighborhood context, and a graph neural network to preserve structural semantics among cells. By embedding each cell's expression profile within its neighbors, scDGCN captures subtle transcriptional variation and identifies cell subtypes without labeled references. Across ten scRNA-seq datasets from diverse human and mouse tissues, scDGCN achieves competitive adjusted Rand index (ARI), normalized mutual information (NMI), and clustering accuracy (ACC), supporting unsupervised identification of cellular subpopulations. These results demonstrate that combining graph denoising, neighborhood-enhanced context reconstruction, and graph-structured representation learning improves unsupervised single-cell clustering and facilitates high-resolution cell atlas construction.