# Imputation Methods for scRNA Sequencing Data
## 3 Conclusions and Discussion  
We review most imputation methods used in recent references. And we find that these methods can be divided into four categories. The first category includes imputation methods that use probability models to directly represent sparsity. In the imputation function, it may not be possible to distinguish between biological zeros and technical zeros,  
Figure 11: The framework of DCA (**a**) and scVI (**b**) method [32,84]. usually only technical zeros are considered. This method produces fewer false positives, but it depends on the homogeneity or heterogeneity of the data. The second category includes methods for smoothing or adjusting zero and non-zero values through average expression values or their diffusion. This method helps to reduce noise, but it may produce many false positives. Interestingly, in datasets with small effect size genes, the first kind of method may be better than the second kind of algorithm. The third category is based on low-rank matrix, and the fourth category is based on deep learning neural network. The third and fourth methods are based on data reconstruction. The method based on low-rank matrix captures the linear relationship, while the deep learning method deals with the nonlinear relationship. Through the analysis of various imputation methods, we find that CIDR, MISC, PRIME, RESCUE, RIA, TRANSLATE, and others provide the best performance in terms of computing time. In terms of memory consumption, SCC, G2S3, and others provide the highest memory efficiency. In terms of scalability, AutomInpute, GraphSCI, netSmooth, SAUCIE, sclmpute, TRANSLATE, MAGIC, kNN-smoothing etc. demonstrate high scalability as the number of cells in the dataset increases. Based on a comprehensive comparison of time, memory, and scalability, ENHANCE, MAGIC, SAVER, SAVER-X, and scVI provide the best overall performance. We note that most methods seem to be difficulty to deal with non-UMI datasets, especially the full-length scRNA-seq datasets. This is because comparing with the large number of variables such as genes, the observations such as cells are always small. At this point, although the bayNorm is originally designed for the scRNA-seq protocol that includes UMI, it also applies to non-UMI data. In addition, we also note that methods using biological variability and technical noise assumptions (SAVER, SAVER-X, scVI, etc.) usually show better performance, which is because they use a priori knowledge in their algorithms. It is also shown that model-based methods usually perform well in imputation and expression recovery, but they are usually costly and poor in the enhancement of cell similarity. Techniques based on matrix theory show good performance in characterizing cell similarity, as well as noteworthy scalability, even in the case of big datasets. At last, data smoothing methods usually show good performance, but there are significant differences according to specific tasks. We believe that the best imputation method depends on genes and datasets. That is, there is no single best way to perform. For example, a network-based approach may perform well in cell types that meet the co-expression model assumptions, while it may fail (for the same gene) in other cell types that do not meet these assumptions. Therefore, the best imputation method depends on genes and datasets. We should no longer look for a single best imputation method. Instead, the future task will be to find the best way for a specific combination of genes and experimental conditions. Finally, we point out that imputation methods for precision-imputing of scRNA-seq data in the presence of dropout values pave the way for specific downstream analyses, for example, where improved reliability and accuracy of scRNA-seq data enable more accurate investigation of single-cell genotypes and phenotypes. Moreover, this allows one to better estimate metabolic fluxes from scRNA-seq data, and act into cancer metabolic studies.