# Imputation Methods for scRNA Sequencing Data
## 1 Introduction
SAVER-X performs single-cell analysis by exploiting expression recovery from external data, the approach that combines a Bayesian hierarchical model with a trainable deep autoencoder [27]. CIDR is an ultra-fast algorithm, which estimates the relationship between dropout rate and gene expression level by identifying the neighbors of dropout value [28]. Another class of methods can reconstruct the expression value from the simplified representation of the observed data matrix, including scRMD [6], McImpute [3], etc. scRMD imputes the gene expression value by robust matrix decomposition [6]. McImpute models the gene expression matrix as a low-rank matrix, takes the preprocessed gene expression matrix as the input of the nuclear norm minimization algorithm and recovers the gene expression value of the complete matrix by solving non-convex optimization problems [3]. Some methods adjust the expression values of every cell by utilizing expression levels of \"similar\" cells. These methods typically change all expression values,  
Figure 1: Application of scRNA-seq data [15]. containing the dropout values and true zero values as well as the observed non-zero values. For example, the kNN-smoothing uses a Possion distribution and aggregated information from similar cells [29]. With regard to DEWAKSS denoise expression data using weighted affinity kernels and self-supervised, ref. [30] used a self-supervised technique to tune the parameters. Some authors use scTSSR to impute the \"dropout\" values [31]. Some methods identify potential spatial representations of cells based on deep learning theory, the observed gene expression matrix is then reconstructed from the estimated potential space. DCA is a neural network-based method that uses deep autoencoding networks for unsupervised learning [32]. DeepImpute and scVI are imputation algorithms based on deep neural network to learn the distribution patterns in the data to accurately impute the dropout values [22; 33]. In addition, a recent study proposed a new algorithm called scMOO [34]. These methods recover the gene expression data in various degrees and improve the accuracy of downstream analysis. Using scRNA-seq data, people can perform downstream analyses, such as cell clustering, differential expression analysis, identification of cell-type specific genes, and reconstruction of differentiation trajectory, which can help people to find rare cell subpopulation within seemingly similar cells. This is particularly advantageous for studying cell-to-cell heterogeneity and cell development dynamics. These downstream analyses are all highly dependent on the accuracy of the gene expression measurements, so it is imperative to impute the dropouts events caused by technology in the scRNA-seq data by imputation methods [4; 6]. However, for massive biological datasets, there is still no universal and effective imputation algorithm to eliminate batch effects and find rare cell types, and there is no clear selection strategy for the imputation method necessary for different characteristics of datasets and specific research problems. Therefore, we classify, analyze, and compare the current advanced single-cell transcriptome sequencing data imputation methods from different perspectives and put forward corresponding recommendations for researchers, it can ensure the accuracy of downstream analysis.