## 4 Discussion  
Single-cell RNA sequencing is a powerful tool to study cell heterogeneity; however, its sparsity could hinder downstream analysis. To address this problem, we propose an imputation method called scCGImpute to recover gene expression and help improve downstream analysis. scCGImpute performs spectral clustering on the data that has been reduced in dimension and then uses the Gamma-Normal hybrid model to obtain dropout values set for each gene in each type. We use the averages of high-confidence cells in the same subpopulation as the initial imputation for low-confidence cells. The final imputation is then obtained through regression using random forests based on the initial imputation. The advantage of scCGImpute is that it uses both cell-to-cell similarity and gene-to-gene correlation to impute the scRNA-seq data while preserving the variability across cells. In addition, scCGImpute does not impute all zeros, which avoids the wrong imputation  
Figure 6: Stacked graph of three cluster evaluation results (_ARI_, _NMI_, and Purity) for four real datasets (Blakeley, Ting, Baron, and Zeisel); (**a**) [PERSON]; (**b**) Ting; (**c**) [PERSON]; (**d**) Zeisel. of biological zeros to a certain extent. The experiments of gene expression recovery and clustering analysis were performed on simulate and real data, and the results show that scCGImpute can recover gene expression and improve the result of cluster analysis more effectively than other methods. In the cluster analysis, scCGImpute was compared with eight methods designed from different perspectives, four of which were different model-based methods, and the experiment result shows that scCGImpute had better improvement than the other methods in the cluster analysis. In addition, in the simulated data, Splat is modeled based on the NB model, while scCGImpute is based on the Gamma-Normal mixed model. Furthermore, scCGImpute can also perform well, which proves that scCGImpute is robust to model distribution. Moreover, scCGImpute seems to be very good at handling scRNA-seq data with a high dropout rate. Even when the dropout rate exceeds 80%, scCGImpute can also achieve a good clustering effect. Even though scCGImpute already has good performance, there are still some areas that can be improved. First of all, since the size of the current scRNA-seq data has reached millions of cells, the computational complexity of dimensionality reduction by PCA will become higher and higher with the increase in the dataset. Although random PCA can be used when the dataset is too large, problems such as insufficient memory may still occur. To solve this problem, we can do feature extraction before working with big data, or use a distributed computing framework. Secondly, many people think that the data distribution of RNA-Seq is more consistent with the Poisson-Gamma distribution, so we can also use the Gamma-Poisson distribution to calculate the dropout rate of the data. In the future, we will pay attention to these questions and hope to achieve an approach that works better with large datasets. Methodology, T.L. and Y.L. ; software, T.L. ; data curation, T.L. ; writing--original draft, T.L. and Y.L. ; supervision, Y.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant No. 12001408). Not applicable. The dataset Blakeley is available at Gene Expression Omnibus (GEO) under accession code GSE66507. The dataset Ting is available at GEO under accession code GSE51372. The dataset Baron is available at GEO under accession code GSM2230757. The dataset Zeisel is available at [[http://linnarssonlab.org/cortex/](http://linnarssonlab.org/cortex/)]([http://linnarssonlab.org/cortex/](http://linnarssonlab.org/cortex/)), accessed on 2 November 2022. The Ziegenhain dataset is available at GEO under accession code GSE57590.