https://github.com/cbg-ethz/clustnet
Network-based clustering
https://github.com/cbg-ethz/clustnet
bayesian-network bayesian-networks clustering dag genomics mixture-model network-clustering
Last synced: 8 days ago
JSON representation
Network-based clustering
- Host: GitHub
- URL: https://github.com/cbg-ethz/clustnet
- Owner: cbg-ethz
- Created: 2022-03-11T09:26:38.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-02-15T08:40:38.000Z (about 2 years ago)
- Last Synced: 2025-09-09T23:08:42.962Z (6 months ago)
- Topics: bayesian-network, bayesian-networks, clustering, dag, genomics, mixture-model, network-clustering
- Language: R
- Homepage:
- Size: 17.3 MB
- Stars: 7
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
Awesome Lists containing this project
README
clustNet: Network-based clustering with covariate adjustment
-----------
[](https://www.gnu.org/licenses/gpl-3.0)
`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
k_clust <- 3 # numer of clusters
ss <- c(400, 500, 600) # samples in each cluster
simulation_data <- sampleData(k_clust = k_clust, n_vars = 20, n_samples = ss)
sampled_data <- simulation_data$sampled_data
# Network-based clustering
cluster_results <- get_clusters(sampled_data, k_clust = k_clust)
# Load additional pacakges to visualize the networks
library(ggplot2)
library(ggraph)
library(igraph)
library(ggpubr)
# Visualize networks
plot_clusters(cluster_results)
# Load additional pacakges to create a 2d dimensionality reduction
library(car)
library(ks)
library(graphics)
library(stats)
# Plot a 2d dimensionality reduction
density_plot(cluster_results)
```
On a normal computer, the clustering should take around 2-4 minutes.