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Open Source Science

https://github.com/cbg-ethz/clustnet

Network-based clustering

https://github.com/cbg-ethz/clustnet

Science Score: 13.0%

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Keywords

bayesian-network bayesian-networks clustering dag genomics mixture-model network-clustering
Last synced: 5 months ago · JSON representation

Repository

Network-based clustering

Basic Info
  • Host: GitHub
  • Owner: cbg-ethz
  • Language: R
  • Default Branch: main
  • Homepage:
  • Size: 17.3 MB
Statistics
  • Stars: 7
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
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Topics
bayesian-network bayesian-networks clustering dag genomics mixture-model network-clustering
Created almost 4 years ago · Last pushed about 2 years ago
Metadata Files
Readme Changelog

README.md

clustNet: Network-based clustering with covariate adjustment

License: GPL v3

clustNet is an R package for network-based clustering of categorical data using a Bayesian network mixture model and optional covariate adjustment.

Installation

The package requires Rgraphviz and RBGL, which can be installed from Bioconductor as follows:

{r eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("Rgraphviz", "RBGL"))

The latest stable version of clustNet is available on CRAN and can be installed with

{r eval=FALSE} install.packages("clustNet") from within an R session. On a normal computer, this should take around 5-60 seconds, depending on how many of the required packages are already installed.

BiocManager::install("remotes")

Being hosted on GitHub, it is also possible to use the install_github tool from an R session to install the latest development version:

{r eval=FALSE} library("devtools") install_github("cbg-ethz/clustNet")

clustNet requires R >= 3.5.

Example

```{r eval=FALSE} library(clustNet)

Simulate data

kclust <- 3 # numer of clusters ss <- c(400, 500, 600) # samples in each cluster simulationdata <- sampleData(kclust = kclust, nvars = 20, nsamples = ss) sampleddata <- simulationdata$sampled_data

Network-based clustering

clusterresults <- getclusters(sampleddata, kclust = k_clust)

Load additional pacakges to visualize the networks

library(ggplot2) library(ggraph) library(igraph) library(ggpubr)

Visualize networks

plotclusters(clusterresults)

Load additional pacakges to create a 2d dimensionality reduction

library(car) library(ks) library(graphics) library(stats)

Plot a 2d dimensionality reduction

densityplot(clusterresults)

```

On a normal computer, the clustering should take around 2-4 minutes.

Owner

  • Name: Computational Biology Group (CBG)
  • Login: cbg-ethz
  • Kind: organization
  • Location: Basel, Switzerland

Beerenwinkel Lab at ETH Zurich

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  • fritzbayer (1)
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