# Imputation Methods for scRNA Sequencing Data
## 2 Imputation Methods
### Data Smoothing Methods  
Based on data smoothing methods typically use gene expression values in similar cells to adjust all values (usually including zero and non-zero values). 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 smoothing see Table 4. \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 DEWAKSS & [[https://gitlab.com/](https://gitlab.com/)]([https://gitlab.com/](https://gitlab.com/))\(\chi\)parx/dewakss/ -/tree/Tjarnberg2020 branch (accessed on 1 October 2022) & Python & Python & NA & 2021 & [30] \\ G2S3 & [[https://github.com/zwang-lab/g2s3](https://github.com/zwang-lab/g2s3)]([https://github.com/zwang-lab/g2s3](https://github.com/zwang-lab/g2s3)) (accessed on 1 October 2022) & MATLAB/R & NULL & NA & 2021 & [55] \\ kNN-smoothing & [[https://github.com/yanailab/kn-smoothing](https://github.com/yanailab/kn-smoothing)]([https://github.com/yanailab/kn-smoothing](https://github.com/yanailab/kn-smoothing)) (accessed on 1 October 2022) & Python/R/MATLAB & NULL & NA & 2018 & [29] \\ netSmooth & [[https://github.com/BIMSBbioinfo/netSmooth](https://github.com/BIMSBbioinfo/netSmooth)]([https://github.com/BIMSBbioinfo/netSmooth](https://github.com/BIMSBbioinfo/netSmooth)) (accessed on 1 October 2022) & R & R & Linux, Mac and Windows & 2021 & [37] \\ PRIME & [[https://github.com/hyundoo/PRIME](https://github.com/hyundoo/PRIME)]([https://github.com/hyundoo/PRIME](https://github.com/hyundoo/PRIME)) (accessed on 1 October 2022) & R/C++ & R & NA & 2020 & [52] \\ RESCUE & [[https://github.com/season/rescue](https://github.com/season/rescue)]([https://github.com/season/rescue](https://github.com/season/rescue)) (accessed on 1 October 2022) & R/Python & R & NA & 2019 & [53] \\ scHinter & [[https://github.com/BMILAB/scHinter](https://github.com/BMILAB/scHinter)]([https://github.com/BMILAB/scHinter](https://github.com/BMILAB/scHinter)) (accessed on 1 October 2022) & MATLAB & NULL & NA & 2019 & [50] \\ scNPF & [[https://github.com/BMILAB/scNPF](https://github.com/BMILAB/scNPF)]([https://github.com/BMILAB/scNPF](https://github.com/BMILAB/scNPF)) (accessed on 1 October 2022) & R & R & NA & 2019 & [54] \\ MAGIC & [[https://github.com/DpeerLab/magic](https://github.com/DpeerLab/magic)]([https://github.com/DpeerLab/magic](https://github.com/DpeerLab/magic)) (accessed on 1 October 2022) & Jupyter Notebook/Python/MATLAB & Python! R & NA & 2018 & [36] \\ scTSSR & [[https://github.com/Zhangxf-ccnu/scTSSR](https://github.com/Zhangxf-ccnu/scTSSR)]([https://github.com/Zhangxf-ccnu/scTSSR](https://github.com/Zhangxf-ccnu/scTSSR)) (accessed on 1 October 2022) & R & R & NA & 2020 & [31] \\ DrImppute & [[https://github.com/gongx030/DrImpute](https://github.com/gongx030/DrImpute)]([https://github.com/gongx030/DrImpute](https://github.com/gongx030/DrImpute)) (accessed on 1 October 2022) & R/C++ & R & Linux & 2018 & [49] \\ DrImppute & [[https://github.com/gongx030/DrImpute](https://github.com/gongx030/DrImpute)]([https://github.com/gongx030/DrImpute](https://github.com/gongx030/DrImpute)) (accessed on 1 October 2022) & & & & & \\ \hline \hline \end{tabular}  
* The research did not mention. NULL: Null value.  
\end{table} 
Table 4: 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 smoothing. 1. MAGIC is a method for explicit and genome-wide inference of single-cell gene expression profiles. This method is based on the concept of thermal diffusion and calculates the dropout gene expression values by sharing information among similar cells.