# Imputation Methods for scRNA Sequencing Data
## 2 Imputation Methods  
For the presence of dropout values of scRNA-seq data, many methods first pre-process and normalize the data, to eliminate the adverse effects of outliers. Then, an important process is to distinguish these zero values is true zero or dropout value caused by technology. Finally, these methods choose a certain strategy to impute the dropout value. As described in [21; 22; 23; 24; 25], classifying and comparing currently popular imputation methods is very necessary to help users provide advice when facing different datasets and different needs. Most of the current methods can be divided into the following four cases: (1) Model-based methods: these methods assume the statistical model of technical and biological variability and noise distribution, and impute by estimating the parameters of the distribution. (2) Low-rank matrix-based methods: the method based on the low rank matrix identifies the potential spatial representation of the cell by capturing the linear relationship, and then reconstructs the expression matrix from the low rank that is no longer sparse. (3) Data smoothing methods: the method based on data smoothing typically uses gene expression values in similar cells to adjust all values (usually including zero and non-zero values). (4) Deep learning methods: the method based on deep learning identifies the potential spatial representation of cells based on the deep learning method, and by using estimated potential space to reconstruct the observed expression matrix [21; 22; 23; 24; 25]. For detailed division, see Figure 2[35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77]. The second classification angle divides imputation methods into statistical methods and machine learning methods. Most of the statistical methods make assumptions based on the dataset itself, and then use the original dataset to fill the \"dropout\" data accordingly. This kind of method does not consider the category of the data object itself, and the filling value is often affected by other types of objects, and the accuracy of the filling result is poor. The common methods include the EM (expectation maximization) filling algorithm, regression analysis, multiple imputation, and so on. Generally speaking, the machine learning method is to classify or cluster the \"dropout\" dataset and then fill it. Such methods have sprung up with the upsurge of machine learning in recent years. Representative methods are K nearest neighbor filling, K-means filling, Bayesian network, and so on. Among them, the classification method classifies the \"dropout\" attributes as the target, and then fills them in each category, but when there are too many \"dropout\" attributes, it is easy to lead to too many categories and low efficiency, meanwhile, the clustering method first clusters the original data to multiple clusters, while according to the similar objects in the cluster to fill, the number of \"dropout\" attributes will not affect the number of clusters, this kind of method has a wide range of applications, and is also the focus of the current research. The third classification divides imputation methods into (1) global methods and (2) local methods according to the way imputation information is used from observed data, and the fourth classification divides imputation methods into two categories according to whether they are based on Bayesian or not [21]. Using the second method for classification (Table 1) can be divided into statistical methods and machine learning methods. Statistical methods also include mean/mode complete, expectation maximization imputation, hot deck imputation, cold deck imputation, regression imputation. The mean/mode complete include bayNorm. The expectation maximization imputation includes SCRIBE, UKSM, SCC [78], JOINT [79], SIMPLEs, ZIFA. The hot deck imputation includes G2S3, scHinter, scNPF, MAGIC, CMF-Impute, deepMc, PBLR, Randomly, WEDGE, FRMC, scRMD and SDImpute.