dendro_heatmap: Dendrogram with amalgamated heatmap (Comparisons' comparability for transitivity evaluation)

Description

dendro_heatmap creates a dendrogram alongside the heatmap of Gower dissimilarities among the trials in the network for a specific linkage method and number of clusters.

Usage

dendro_heatmap(input, label_size = 12, axis_text_size = 10)

Value

dendro_heatmap uses the heatmaply

function of the R-package

heatmaply to create a cluster heatmap for a selected linkage method and number of clusters. The function uses different colours to indicate the clusters directly on the dendrogram, specified using the R-package

dendextend. The names of the leaves refer to the trials and corresponding pairwise comparison.

@details The function inherits the linkage method and number of optimal clusters by the comp_clustering function.

Remember: when using the comp_clustering function, inspect the average silhouette width for a wide range of clusters to decide on the optimal number of clusters.

Arguments

input: An object of S3 class comp_clustering. See 'Value' in comp_clustering.
label_size: A positive integer for the font size of the heatmap elements. label_size determines the size argument found in the geom's aesthetic properties in the R-package ggplot2.
axis_text_size: A positive integer for the font size of row and column names of the heatmap. axis_text_size determines the axis.text argument found in the theme's properties in the R-package ggplot2.

Author

Loukia M. Spineli

Examples

Run this code

# \donttest{
# Fictional dataset
data_set <- data.frame(Trial_name = as.character(1:7),
                      arm1 = c("1", "1", "1", "1", "1", "2", "2"),
                      arm2 = c("2", "2", "2", "3", "3", "3", "3"),
                      sample = c(140, 145, 150, 40, 45, 75, 80),
                      age = c(18, 18, 18, 48, 48, 35, 35),
                      blinding = factor(c("yes", "yes", "yes", "no", "no", "no", "no")))

# Apply hierarchical clustering (informative = FALSE)
hier <- comp_clustering(input = data_set,
                        drug_names = c("A", "B", "C"),
                        threshold = 0.13,  # General research setting
                        informative = FALSE,
                        optimal_clusters = 3,
                        get_plots = TRUE)

# Create the dendrogram with integrated heatmap
dendro_heatmap(hier)
# }