Curriculum-guided divergence scheduling improves single-cell clustering robustness.

Abstract

Deep clustering of single-cell RNA-seq data faces significant challenges due to extreme sparsity and noise. We present DAGCL (Dynamic Attention-enhanced Graph Embedding with Curriculum Learning), a dynamic graph embedding framework that reframes representation learning as a coarse-to-fine evolutionary process. Unlike conventional static paradigms, DAGCL employs a curriculum-guided scheduling mechanism that actively modulates both attention intensity and supervision stringency throughout training. This strategy aligns model complexity with feature maturity, effectively mitigating early-stage confirmation bias. To further stabilize optimization, we incorporate an entropy-regularized Sinkhorn projection that enforces globally balanced soft assignments. Extensive experiments on 27 benchmarks demonstrate that DAGCL consistently outperforms baselines in clustering accuracy and robustness. Our work establishes a principled strategy for unsupervised learning where structural constraints and supervisory pressure co-evolve with learned representations.