# Imputation Methods for scRNA Sequencing Data
## 1 Introduction  
Bulk cell RNA sequencing (RNA-seq) data are usually used in transcriptome analysis, such as transcriptional structure, splicing patterns, genes, as well as transcriptional expression levels [1]. However, in the traditional bulk cell transcriptome sequencing, gene expression is measured by the average reading of a large number of cells, which masks the heterogeneity between cells. Fortunately, scRNA-seq technology provides a more accurate insight into the function of individual cells, and it is also used to reveal the dynamics of heterogeneous cell populations and complex tissues [2; 3; 4; 5; 6; 7; 8]. Single-cell transcriptomic analysis has provided unprecedented opportunities to study complex phenomena, such as cancer, tumor, development, and microbial ecology in biological systems [9]. [PERSON] et al. used scRNA-seq technology to obtain high-quality transcripts from mouse pancreatic circulating tumor cells (CTCS), and compared them with scRNA-seq data from human pancreatic cancer patients. They found that the gene SPARC is very much related to the invasion and metastasis of pancreatic cancer [10]. Furthermore, assessing gene expression differences between individual cells has the potential to identify rare populations that are not detected from bulk cell analysis, and the ability to study outlier cells within a population can help people understand the drug resistance in cancer and tumor treatment [11]. In 2015, [PERSON] et al. executed scRNA-seq analysis of cells isolated from a xenograft tumor of a lung adenocarcinoma (LUAD) patient, and they found a subpopulation of tumor cells associated with anticancer drug resistance [12]. Moreover, single cell sequencing can be used to analyze individual cell in tumors and investigate what roles they play, so as to provide new insights into treatment [13]. In 2017, [PERSON] et al. found that the cells exhibiting p-EMT activationwere located at the outer area of primary tumors and promote the invasion and metastasis of tumor cells [14]. Part of the application of scRNA-seq is shown in Figure 1[15]. In scRNA-seq datasets, more than 50% of the expression in the count matrix is usually found that to be equal to 0 [16; 17]. As we know, a lot of technical noise can affect the scRNA-seq data, and one important feature is called \"dropout\", in which the gene is moderately expressed in one cell but not detected in another [18]. This is because most scRNA-seq techniques usually require heavy amplification due to the picogram level of RNAs in a single cell after reverse transcription. RNAs transcripts may be lost in the reverse transcription and amplification steps, so they will not be detected in subsequent sequencing [6]. The dropout events caused by technology improve the cell-to-cell variability, leading to signal influence on each gene, and obscuration of gene-gene and cell-cell real relationships. Thus, the presence of dropout values will greatly reduce the accuracy of the downstream analysis [19; 20]. Moreover, by the selective gene expression, there are many truly unexpressed genes in single cell, which results in a confusion of biological zeros with the technology-induced zeros. Distinguishing between these two cases is a very important but yet not fully resolved problem. In recent years, there are many imputation methods are proposed to recover gene expression data [21; 22; 23; 24; 25]. These methods can be roughly divided into the following categories: probabilistic models are used in some methods to identify the zeros are technique-induced dropout values rather than true zeros, they usually only impute dropout zeros, while not involving other values that are not affected by the dropout events. In this category, we find sclmpute [4], SAVER [26], SAVER-X [27], CIDR [28], etc. sclmpute estimated dropout rates by using the Gamma-Normal mixture model and population-specific thresholds while SAVER using Possion-Gamma model to pool expression values across genes within each cell [3; 15].