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https://github.com/cbg-ethz/graphclust_neurips
Network-Based Clustering of Pan-Cancer Data Accounting for Clinical Covariates
https://github.com/cbg-ethz/graphclust_neurips
clustering genomics graphs networks
Last synced: about 2 months ago
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Network-Based Clustering of Pan-Cancer Data Accounting for Clinical Covariates
- Host: GitHub
- URL: https://github.com/cbg-ethz/graphclust_neurips
- Owner: cbg-ethz
- License: gpl-3.0
- Created: 2022-09-27T16:06:28.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-27T16:04:06.000Z (about 1 year ago)
- Last Synced: 2024-08-09T00:21:50.574Z (5 months ago)
- Topics: clustering, genomics, graphs, networks
- Language: R
- Homepage:
- Size: 23.6 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Network-Based Clustering of Pan-Cancer Data Accounting for Clinical Covariates
-----------
[](https://www.gnu.org/licenses/gpl-3.0)
This repository contains the code to reproduce the results of the NeurIPS 2022 LMRL workshop paper "[Network-Based Clustering of Pan-Cancer Data Accounting for Clinical Covariates](https://openreview.net/pdf?id=mnvPgQTt2Xs)".
Installation
-----------
In order to install the package, it suffices to launch
`
R CMD INSTALL path/to/graphClust
`
from a terminal, or `make install` from within the package source folder.
Being hosted on GitHub, it is possible to use the `install_github`
tool from an R session:
```
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("Rgraphviz", "RBGL"))
library("devtools")
install_github("cbg-ethz/graphClust_NeurIPS")
```
`graphClust` requires R `>= 3.5`, and depends on
`pcalg`, `reshape2`, `BiDAG` (>= 2.0.2),
`RBGL`, `clue` and `grDevices`.
Simulations
-----------
**Figure 2** can be reproduced by running the script `simulations/figure_2-simulation.R`. Analogously, **Figure 4** in the appendix can be reproduced by running the script `simulations/figure_4-simulation.R`. The simulations can be modified and executed in the `simulations/cluster-scripts` folder.
Pan-Cancer Data
-----------
**Figure 3** can be reproduced by runnign the script `tcga_analysis/figure_3-km_plot.R`. The results of **Table 1** can be reproduced by runnign the script `tcga_analysis/table_1-cox_analysis.R`. A reproducability analysis for a range of different seeds can be found in `tcga_analysis/reproducability_different_seeds`. The hyperparameters of the cluster algorithms can be modified and executed in the `tcga_analysis/clustering folder` folder.
Example
-------
```{r eval=FALSE}
library(graphClust)
# Simulate binary data from 3 clusters
k_clust <- 3
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$sampled_data
# Network-based clustering
cluster_res <- get_clusters(sampled_data, k_clust = k_clust)
# Calculate the ARI
library(mclust)
adjustedRandIndex(simulation_data$cluster_membership, cluster_res_t$clustermembership)
# Visualize the networks
library(ggplot2)
library(ggraph)
library(igraph)
library(ggpubr)
graphClust::plot_clusters(cluster_res_t)
# Visualize a single network
my_graph <- igraph::graph_from_adjacency_matrix(cluster_res_t$DAGs[[1]], mode="directed")
graphClust::nice_DAG_plot(my_graph)
```