Single-cell marker gene clustering: A unified deep learning framework for marker gene-based clustering of single-cell...

Single-cell RNA sequencing (scRNA-seq) has transformed the study of cellular heterogeneity by making it possible to classify individual cells and their functional states. However, the analysis remains difficult because high dropout rates lead to sparse and noisy expression data. Existing marker gene selection methods are often fragmented, typically relying on a single strategy that overlooks complementary biological signals. Without a unifying framework for denoising and marker identification, clustering can suffer in accuracy, stability, and interpretability. This makes it more difficult to define cell subpopulations and extract meaningful biological insights clearly. We introduce single cell marker gene clustering (scMGC), a unified framework that integrates a denoising autoencoder with multi-method marker selection for scRNA-seq clustering. The autoencoder reduces dropout noise, whereas a unified marker gene scoring system balances the contributions of multiple methods to select reliable markers. These markers are then applied in graph-based clustering to uncover accurate and interpretable cell subpopulations. scMGC was benchmarked against seven state-of-the-art methods across seven scRNA-seq datasets. On average, it outperformed competing approaches by 31.3% in adjusted rand index and 28.2% in normalized mutual information, showing consistent improvements in clustering accuracy. Enrichment and disease association analyses further validated that the discovered clusters are both biologically meaningful and clinically relevant. The codes and datasets used are available on the GitHub website (srniloy/scMGC).