In recent years,the rapid development of single-cell RNA sequencing(scRNA-seq)technology has made it possible to research the heterogeneity of tissues and organs at the single-cell level.To accurately identify cell types in scRNA-seq data,based on dual autoencoder combined with variational Bayesian Gaussian mixture mode,a new clustering method,sc-VBDAE,is proposed.First,through the encoding and decoding process in adversarial autoencoder network,the scRNA-seq data is reconstructed.Then,the autoencoder network is used to reduce the dimensionality of the data,so as to obtain low-dimensional and effective scRNA-seq data.Finally,the variational Bayesian Gaussian mixture model is used to cluster the cells and visualize the clustering results.The experimental results on ten scRNA-seq datasets show that the ARI index of the proposed method is superior to other methods on six datasets,and the ARI index value on Biase and Klein datasets reaches 0.90 or above.
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