add netSmooth

README.md

@@ -42,6 +42,7 @@ List of software packages (and the people developing these methods) for single-c
 - [MAST](https://github.com/RGLab/MAST) - [R] - Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data
 - [MIMOSCA](https://github.com/asncd/MIMOSCA) - [python] - A repository for the design and analysis of pooled single cell RNA-seq perturbation experiments (Perturb-seq).
 - [Monocle](http://cole-trapnell-lab.github.io/monocle-release/) - [R] - Differential expression and time-series analysis for single-cell RNA-Seq.
+- [netSmooth](https://github.com/BIMSBbioinfo/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. 
 - [NetworkInference](https://github.com/Tchanders/NetworkInference.jl) - [Julia] - Fast implementation of single-cell network inference algorithms: [Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures]( http://www.cell.com/cell-systems/fulltext/S2405-4712(17)30386-1 )
 - [nimfa](https://github.com/ccshao/nimfa) - [Python] - Nimfa is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface to matrix factorization algorithms and methods.
 - [OEFinder](https://github.com/lengning/OEFinder) - [R] - Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.