## 1 Introduction
After distinguishing between true zeros and dropout zeros, scRecover applied the result to existing imputation methods to impute. Several approaches also take advantage of both cell-level and gene-level information. For example, SAVER [17] assumes that the count of each gene in each cell follows a Poison-Gamma mixed distribution, also known as the NB model. SAVER performs Poisson LASSO regression between the genes to estimate the prior mean, and then, after estimating the prior variance, SAVER uses the posterior mean derived from the posterior distribution of the true expression as the recovered expression value. However, if SAVER adjusts all gene expression levels, it may introduce new biases and eliminate biological zeros. MAGIC [22] shares information across similar cells through data diffusion to denoise the cell count matrix and impute missing values. Like SAVER, MAGIC adjusts all gene expression levels. Some methods, such as scRMD [24] and ALRA [25], do not use the information between genes or cells but reconstruct data matrices for imputation. scRMD models the dropout imputation problem as a robust matrix decomposition based on the low-rankness assumption and the sparsity assumption. ALRA selectively computes technical zeros using the nonnegative and low-rank structure of the gene expression matrix. ALRA first computes the low-rank approximation of the data matrix using singular vector decomposition (SVD). The biological zeros in the matrix are then restored by thresholding the entries of the matrix. In addition, a few methods incorporate external references to the current datasets [29; 30; 31; 32]. Here, we propose a model-based imputation algorithm that borrows information from similar cells and related genes simultaneously. The framework of scCGImpute is presented in Figure 1. Firstly, the gene expression matrix is preprocessed with library size normalization and logarithmic transformation. Secondly, the preprocessed scRNA-seq data profile is used for PCA dimension reduction and spectral clustering. Thirdly, after the clustering results are obtained for each subpopulation, the Gamma-Normal mixing model is used to identify the dropout value of each gene. And then the average of the high confidence part for each gene in each subpopulation is used as the imputed value of the low confidence part. The imputation results obtained by cell similarity are used to calculate the relationships between genes in each subpopulation. Finally, based on the data that already containedinformation on cell similarity in the previous step, for each gene in each subpopulation, the highly related genes are selected for the random forest regression. Thus, scCGImpute preserves heterogeneity across cells. And we used the above eight methods from different angles with scCGImpute to do the cluster analysis. They are model-based imputation methods scImpute (Gamma-Normal mixed model), SAVER (NB model), scRecover (ZINB model), and VIPER (Sparse non-negative regression model), data-smoothing methods MAGIC, matrix decomposition methods in data reconstruction scRMD and ALRA, and machine learning methods in data reconstruction DeepImpute. By comparing the results on the simulated data and the real datasets, it is proven that scCGImpute can recover gene expression and promote the recognition of cell subpopulations.