# Imputation Methods for scRNA Sequencing Data
## 2 Imputation Methods
### Model-Based Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Availability**} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Pkg**} & \multicolumn{2}{c}{**Operative System**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref**} \\  & & & & & & **Compatibility** \\ \hline \multirow{3}{*}{bayNorm} & [[https://bioconductor.org/packages/](https://bioconductor.org/packages/)]([https://bioconductor.org/packages/](https://bioconductor.org/packages/)) & & & Linux, Mac and & & \\  & release/bioc/html/bayNorm.html & R & R & Windows & 2020 & [38] \\  & (accessed on 1 October 2022) & NA & NA & NA & 2016 & [35] \\  & [[https://github.com/songfd2018/](https://github.com/songfd2018/)]([https://github.com/songfd2018/](https://github.com/songfd2018/)) & & & Ubuntu 18.04, Mac & & \\ \multirow{3}{*}{BUSseq} & BUSseq-Rpackage (accessed on 1 October 2022) & C++ & R & OX X 10.04 Mojave & 2020 & [41] \\  & [[https://github.com/VCCRI/CIDR](https://github.com/VCCRI/CIDR)]([https://github.com/VCCRI/CIDR](https://github.com/VCCRI/CIDR)) & & & and Windows 10 & 2017 & [28] \\  & (accessed on 1 October 2022) & C++/R & R & Windows 10 and & 2018 & [26] \\ \multirow{3}{*}{SAVER} & https: & & & Ubuntu 20.04 LTS & 2018 & [26] \\  & https: & & & Ubuntu 20.04 LTS & 2019 & [27] \\ \multirow{3}{*}{SAVER-X} & https: & & & & & \\  & (accessed on 1 October 2022) & R & R & NA & 2018 & [4] \\  & https: & & & & & \\  & (accessed on 1 October 2022) & R/HTML/Jupyter & & & & \\ \multirow{3}{*}{scImpute} & https: & & & & & \\  & (accessed on 1 October 2022) & Notebook & & & 2018 & [4] \\  & [[https://github.com/XuegongLab/](https://github.com/XuegongLab/)]([https://github.com/XuegongLab/](https://github.com/XuegongLab/)) & & & & & \\ \multirow{3}{*}{scRecover} & scRecover (accessed on 1 October 2022) & R & R & Univ, Mac and & & \\  & 2022) & & & & & \\ SCRIBE & NA & NA & NA & NA & 2019 & [44] \\ URSM & NA & NA & NA & NA & 2018 & [45] \\ \multirow{3}{*}{VIPER} & https: & & & & & \\  & (accessed on 1 October 2022) & C++/R & R & NA & 2018 & [48] \\ \multirow{3}{*}{SCC} & https: & & & & & \\  & (accessed on 1 October 2022) & R & NULL & NA & 2021 & [78] \\  & https: & & & & & \\  & & & & & & \\  & & & & & & \\ \multirow{3}{*}{JOINT} & georgetown/JOINT (accessed on 1 October 2022) & Python & NULL & NA & 2021 & [79] \\ \multirow{3}{*}{SIMPLEs} & https: & & & & & \\ \multirow{3}{*}{MISC} & https: & & & & & \\  & (accessed on 1 October 2022) & R & NULL & NA & 2020 & [46] \\ \multirow{3}{*}{MISC} & NA & NA & NA & NA & 2018 & [42] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The availability links, programming language, whether a package is available, year of presentation, operative system, distribution compatibility and reference of the representative methods based on model. 3. SAVER-X combines a Bayesian hierarchical model with a pre-trained depth autoencoder by recovering the expression of external data for single cell analysis. The pre-training of automatic encoder in SAVER-X includes the sharing network between humans and mice. Specifically, for the target data with UMI counting matrix, SAVER-X trains the target data using an autoencoder without selecting a pre-training model, then uses cross validation to filter unpredictable genes, and uses empirical Bayesian shrinkage to estimate the final denoising value [27]. 4. BISCUIT is a hierarchical Bayesian mixture model with a specific cell scaling, which is realized by incorporating the parameters representing technical variation into the hierarchical Dirichlet process hybrid model, and infers cell clusters according to similar gene expression and determines the technical variation of each cell. In addition, the model can impute dropout values based on cells with similar co-expression patterns [35]. 5. VIPER borrows information from cells with similar expression patterns to impute the expression measurements of interested cells.