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You can install the released version of immunaut from CRAN with:
install.packages("immunaut")Or you can install immunaut directly from GitHub with use of following commands:
# install.packages("devtools")
devtools::install_github("atomiclaboratory/immunaut", subdir = 'R-package')library("immunaut")
# Generate a demo dataset with 1000 subjects, 200 features, 4 clusters, and a 10% probability of missing values
dataset <- generate_demo_data(n_subjects = 1000, n_features = 200,
desired_number_clusters = 4, # Approximate number of clusters
cluster_overlap_sd = 35, # Standard deviation for cluster overlap
missing_prob = 0.1) # Probability of missing values
# Generate a file header for the dataset to use in downstream analysis
file_header <- generate_file_header(dataset)
settings <- list(
fileHeader = file_header,
seed = 1337,
selectedColumns = colnames(dataset), # Columns selected for analysis
# Exclude outcome, age, and gender columns from the analysis
excludedColumns = c("outcome", "age", "gender"),
removeNA = TRUE,
clusterType = "Louvain",
target_clusters_range = c(3,4),
resolution_increments =c(0.01, 0.1, 0.2, 0.3, 0.4),
min_modularities = c(0.5, 0.6, 0.7, 0.8),
pickBestClusterMethod = "Modularity",
weights = list(AUROC = 0.9, modularity = 0.05, silhouette = 0.05),
preProcessDataset = c("scale", "center", "medianImpute", "corr", "zv", "nzv"),
selectedPartitionSplit = 0.7, # Use the current partition split
selectedPackages = c("rf", "gcvEarth"),
trainingTimeout = 180
)# Perform t-SNE and Louvain clustering using the 'immunaut' function
result <- immunaut(dataset, settings)
# Plot the clustered t-SNE results using ggplot2
p <- plot_clustered_tsne(result$tsne_clust$info.norm,
result$tsne_clust$cluster_data,
result$settings)
print(p) # Display the plot
# Extract the dataset with the applied clustering from the result
dataset_ml <- result$dataset$dataset_ml
# Run the auto_simon_ml function to train machine learning models on the dataset
model_results <- auto_simon_ml(dataset_ml, settings)
# Extract the names of the models
model_names <- names(model_results$models)
# Create a data frame to store the model names and their corresponding AUROC values
model_auroc_table <- data.frame(
Model = character(),
AUROC = numeric(),
stringsAsFactors = FALSE
)
# Loop through the models and extract AUROC values (One-vs-Rest) for Multiclass Models
for (model_name in model_names) {
auroc_value <- model_results$models[[model_name]][["predictions"]][["AUROC"]]
# Add the model name and its AUROC to the table
model_auroc_table <- rbind(model_auroc_table, data.frame(Model = model_name, AUROC = auroc_value))
}
library(ggplot2)
# Create a bar chart with AUROC values
ggplot(model_auroc_table, aes(x = Model, y = AUROC, fill = Model)) +
geom_bar(stat = "identity") + # Create bars
geom_text(aes(label = round(AUROC, 3)), vjust = -0.5) + # Add AUROC values above bars
ggtitle("AUROC for Models") +
xlab("Model") +
ylab("AUROC") +
theme_minimal() + # Use a minimal theme
scale_fill_brewer(palette = "Set3")
# Update settings for DBSCAN clustering
settings$clusterType <- "Density"
settings$minPtsAdjustmentFactor <- 1.5
settings$epsQuantile <- 0.9
# Run t-SNE and DBSCAN clustering
dbscan_result <- immunaut(dataset, settings)
#> [1] "====> Density-based clustering"# Update settings for Mclust clustering
settings$clusterType <- "Mclust"
settings$clustGroups <- 3 # Specify the number of clusters for Mclust
# Run t-SNE and Mclust clustering
mclust_result <- immunaut(dataset, settings)
#> [1] "==> cluster_tsne_mclust clustGroups: 3"
#> fitting ...
#> | | | 0% | |========= | 7% | |================== | 13% | |========================== | 20% | |=================================== | 27% | |============================================ | 33% | |===================================================== | 40% | |============================================================== | 47% | |====================================================================== | 53% | |=============================================================================== | 60% | |======================================================================================== | 67% | |================================================================================================= | 73% | |========================================================================================================== | 80% | |================================================================================================================== | 87% | |=========================================================================================================================== | 93% | |====================================================================================================================================| 100%# Update settings for Hierarchical clustering
settings$clusterType <- "Hierarchical"
settings$clustLinkage <- "ward.D2"
settings$clustGroups <- 3
# Run t-SNE and Mclust clustering
hierarchical_result <- immunaut(dataset, settings)
#> [1] "====> Noise indices: 25"
#> [1] "====> Noise indices done"These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.