<div align="center"> <img src="hex-netsmooth.png" alt="netsmooth"/> </div> --------- [](https://doi.org/10.5281/zenodo.1119064) [](https://travis-ci.org/BIMSBbioinfo/netSmooth) [](https://codecov.io/gh/BIMSBbioinfo/netSmooth) [](http://www.bioconductor.org/packages/release/bioc/html/netSmooth.html) **_netSmooth_: A Network smoothing based method for single cell RNA-seq** ----- _netSmooth_ is an R package for network smoothing of single cell RNA sequencing data. Using gene interaction networks such as protein- protein interactions as priors for gene co-expression, _netsmooth_ improves cell type identification from noisy, sparse scRNA-seq data. The smoothing method is suitable for other gene-based omics data sets such as proteomics, copy-number variation, etc. The algorithm uses a network-diffusion based approach which takes in a network (such as PPI network) and gene-expression matrix. The gene expression values in the matrix are smoothed using the interaction information in the network. The network-smoothing parameter is optimized using a robust clustering approach. For a detailed exposition, check out [our paper on F1000Research](https://f1000research.com/articles/7-8/v2). ### Installation _netSmooth_ is available via Bioconductor: if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("netSmooth") Alternatively, using `devtools`: library(devtools) install_github("BIMSBbioinfo/netSmooth") ### Usage For detailed usage information see [the vignette](http://htmlpreview.github.io/?https://github.com/BIMSBbioinfo/netSmooth/blob/master/vignettes/netSmoothIntro.html). In addition, the R package has full function documentation with examples. ### How to cite Please cite the _netSmooth_ paper: > Ronen J and Akalin A. _netSmooth_: Network-smoothing based imputation for single cell RNA-seq [version 2; referees: 2 approved]. F1000Research 2018, 7:8 (doi: 10.12688/f1000research.13511.2) ### License _netSmooth_ is available under a GPLv3 license. ### Contributing Fork and send a pull request. Or just e-mail us. ------------------------- @jonathanronen, BIMSBbioinfo, 2017