Dynamic Supervised Prelabel Diffusion for Single-Cell Clustering.

Accurate identification of cell types from single-cell RNA sequencing data remains challenging due to high dimensionality, sparsity, and the limited availability of expert annotations. We propose a dynamic supervised prelabel diffusion framework that leverages a small set of verified cell-type labels to guide clustering through iterative representation learning. The framework couples a purity-controlled diffusion mechanism with a supervised contrastive objective, forming a self-reinforcing loop in which improved cell representations enable more accurate and adaptive prelabel propagation, which in turn enriches the supervisory signal for subsequent training. An adaptive Leiden clustering strategy automatically matches the target number of cell types, eliminating the need for manual resolution tuning. Experiments on five benchmark datasets show that the proposed method consistently outperforms both unsupervised and semi-supervised baselines in clustering accuracy, normalized mutual information, and adjusted Rand index, while achieving substantially lower computational cost. These results demonstrate the effectiveness of dynamic prelabel diffusion as a principled semi-supervised strategy for single-cell clustering under limited annotation budgets.