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".
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.
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.
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.
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)