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https://github.com/BIMSBbioinfo/netSmooth

netSmooth: A Network smoothing based method for Single Cell RNA-seq imputation
https://github.com/BIMSBbioinfo/netSmooth

bioinformatics genomics single-cell

Last synced: 23 days ago
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netSmooth: A Network smoothing based method for Single Cell RNA-seq imputation

Host: GitHub
URL: https://github.com/BIMSBbioinfo/netSmooth
Owner: BIMSBbioinfo
Created: 2017-10-09T17:15:02.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2024-05-15T09:53:28.000Z (about 2 months ago)
Last Synced: 2024-05-15T23:06:21.029Z (about 2 months ago)
Topics: bioinformatics, genomics, single-cell
Language: HTML
Homepage:
Size: 3.87 MB
Stars: 26
Watchers: 15
Forks: 6
Open Issues: 5
Metadata Files:
- Readme: readme.md

Lists

awesome_single_cell - netSmooth - [R] - netSmooth is a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. (Software packages / RNA-seq)
awesome-single-cell - netSmooth - [R] - netSmooth is a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. (Software packages / RNA-seq)
awesome-single-cell - netSmooth - [R] - netSmooth is a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. (Software packages / RNA-seq)

README

        


	



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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1119064.svg)](https://doi.org/10.5281/zenodo.1119064)

[![Build Status](http://bioconductor.org/shields/build/devel/bioc/netSmooth.svg)](https://travis-ci.org/BIMSBbioinfo/netSmooth) [![codecov](https://codecov.io/gh/BIMSBbioinfo/netSmooth/branch/master/graph/badge.svg)](https://codecov.io/gh/BIMSBbioinfo/netSmooth) [![BioC_years](http://www.bioconductor.org/shields/years-in-bioc/netSmooth.svg)](http://www.bioconductor.org/packages/release/bioc/html/netSmooth.html)

**_netSmooth_: A Network smoothing based method for single cell RNA-seq**

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_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.

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@jonathanronen, BIMSBbioinfo, 2017