Abstract: Cell clustering,a core task in single-cell RNA sequencing(scRNA-seq) analysis,plays a crucial role in scRNA-seq data analysis.In recent years,single-cell deep embedding representation models have gained popularity due to their ability to simultaneously learn feature representation and perform clustering.However,these models still face several significant challenges,including massive data,widespread dropout events,and complex noise patterns in the transcriptome.This paper proposes a graph-structure-aware single-cell transcriptomic embedding clustering model(scGAEC).This model innovatively integrates a contrastive learning mechanism with a self-developed graph-aware approach for more in-depth embedding representation.It also designs a decoder based on the zero-inflated negative binomial(ZINB) model to reconstruct gene expression information.By employing a joint mutual supervision strategy,the model optimizes clustering loss,contrastive loss,ZINB loss,and gene expression matrix reconstruction loss in a coordinated manner,thereby enhancing clustering performance and deep learning of latent representation.Experimental results demonstrate that scGAEC achieves average performance improvements of 30.63% in NMI and 52.17% in ARI over six competing models across four single-cell RNA sequencing datasets from different sequencing platforms,significantly outperforming various state-of-the-art methods.

Key words: Clustering, Single-cell RNA clustering, Graph-structure aware, Contrastive learning, Zero-inflated negative binomial model