Abstract: One of the most important aspects of single-cell data analysis is the clustering of individual cells into clusters of subpopulations.However,due to the limitation of sequencing principle and sequencing platform,the obtained single cell dataset ge-nerally has high-dimensional sparsity,high variance noise and a large amount of data loss,which lead to many challenges in cluster analysis and application of single cell data.Single-cell clustering methods proposed in recent years mainly model the relationship between cell and gene expression,ignoring the full mining of the potential characteristic relationship between cells and the remo-val of noise,resulting in unsatisfactory clustering results,which hinders the later analysis of data.In view of the above problems,a self-optimized single-cell clustering algorithm(scZDGAC) combining zero expansion negative binomial(ZINB) model with graph attention autoencoder is proposed.The algorithm firstly uses ZINB model combined with extensible DCA denoising algorithm,better fit data feature distribution through ZINB distribution,to improve the denoising performance of autoencoder,and reduce the impact of noise and data loss on the output of KNN algorithm.And then using the graph attention autoencoder to spread the information between cells of different weights,which can better capture the potential features between cells for clustering.Finally,scZDGAC uses the self-optimization method to make the originally two independent clustering modules and feature modules benefit from each other,and constantly update the clustering center iteratively to further improve the clustering performance.In order to evaluate the clustering results,this paper uses adjusted RAND index(ARI) and standardized mutual information(NMI) as two general evaluation indicators.Compared with six single cell datasets of different scales,experimental results show that the clustering performance of the proposed clustering algorithm has greatly improved.

Key words: Deep clustering, scRNA-seq, ZINB model, Self-optimization, DCA, Graph attention autoencoder