DECODE to NetDECODE

Author: seandavi

README.md

@@ -31,7 +31,6 @@ List of software packages (and the people developing these methods) for single-c
 - [Clustergrammer](https://github.com/maayanlab/clustergrammer) - [Python, JavaScript] - Interative web-based heatmap for visualizing and analyzing high dimensional biological data, including single-cell RNA-seq. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see [example notebook](http://nbviewer.jupyter.org/github/MaayanLab/CCLE_Clustergrammer/blob/master/notebooks/Clustergrammer_CCLE_Notebook.ipynb).
 - [CytoGuide](https://cyteguide.cytosplore.org/) - [C++,D3] - [CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis](http://ieeexplore.ieee.org/document/8017575/)
 - [DECENT](https://github.com/cz-ye/DECENT) - [R] - The unique features of scRNA-seq data have led to the development of novel methods for differential expression (DE) analysis. However, few of the existing DE methods for scRNA-seq data estimate the number of molecules pre-dropout and therefore do not explicitly distinguish technical and biological zeroes. We develop DECENT, a DE method for scRNA-seq data that adjusts for the imperfect capture efficiency by estimating the number of molecules pre-dropout.
-- [DECODE](https://github.com/shmohammadi86/DECODE) - [R] - We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene's local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors.
 - [DESCEND](https://github.com/jingshuw/descend) - [R] - DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts. It provides estimates of several distribution based statistics (five distribution measurements and the coefficients of covariates (such as batches or cell size)).
 - [destiny](http://bioconductor.org/packages/destiny/) - [R] - Diffusion maps are spectral method for non-linear dimension reduction introduced by Coifman et al.(2005). Diffusion maps are based on a distance metric (diffusion distance) which is conceptually relevant to how differentiating cells follow noisy diffusion-like dynamics, moving from a pluripotent state towards more differentiated states.
 - [DensityPath](https://doi.org/10.1101/276311) - [.] - DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data
@@ -64,6 +63,7 @@ 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.
+- [NetDECODE](https://github.com/shmohammadi86/NetDECODE) - [R] - We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene's local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors.
 - [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: <a href="http://bit.ly/2Msh7qv">Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures</a>
 - [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.