Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data

Recently, deep cell clustering, which employs deep neural networks to learn cell representation for clustering purposes, has attracted increasing research interests. Traditional deep cell clustering models for single-cell RNA sequencing data rely only on the cell's internal features for learning the representation and suffer from the insufficient problem of representation learning. In this article, we introduce a deep structural enhanced network for cell clustering, namely, Deep Structure-Enhanced Cell Clustering (scDSEC). The scDSEC model uses the internal features of the cells as a foundation and enhances them by incorporating the external structural semantics of the cells. An integrated reinforcement enhancement strategy is designed, in which a complete cell representation, captured by fusing cell internal information and external information, and an enhanced cell internal representation, captured with the help of complete cell representation, are learned in a layer-by-layer reinforcement manner. Experimental results show that the scDSEC model outperforms various existing mainstream deep cell clustering algorithms in terms of performance.