Plot a Hierarchical Clustering Dendrogram of Samples

Performs hierarchical clustering on the samples based on their protein abundance profiles and generates a dendrogram plot using the ggdendro package.

Usage

plot_hierarchical_cluster(
  object,
  dist_method = "euclidean",
  hclust_method = "complete"
)

# S4 method for class 'SummarizedExperiment'
plot_hierarchical_cluster(
  object,
  dist_method = "euclidean",
  hclust_method = "complete"
)

Arguments

object

A SummarizedExperiment object. The data should be imputed.

dist_method

The distance measure to be used by stats::dist. Common options include "euclidean", "maximum", "manhattan". Defaults to "euclidean".

hclust_method

The agglomeration method to be used by stats::hclust. Common options include "complete", "ward.D2", "average". Defaults to "complete".

Value

A ggplot object representing the dendrogram, which can be further customized.

Details

This function expects clean, imputed data. Missing values (NA) will cause an error. For meaningful biological results, it is highly recommended to use data that has been log-transformed and normalized before clustering.

Functions

• plot_hierarchical_cluster(SummarizedExperiment): Method for SummarizedExperiment objects.

Examples

# Create a sample SummarizedExperiment object
raw_data <- data.frame(
  Gene = c("GENEA", "GENEB", "GENEC", "GENED"),
  SampleA = c(10, 20, 15, 12),
  SampleB = c(11, 21, 16, 13), # Similar to A
  SampleC = c(25, 10, 30, 5),
  SampleD = c(26, 11, 31, 6)  # Similar to C
)
se <- create_se(raw_data)
#> `intensity_cols` not provided. Detecting numeric columns as intensity data.
#> Warning: `sample_metadata` not provided. Generating a basic version from column names.

# Run with default methods. We expect A/B and C/D to cluster together.
p1 <- plot_hierarchical_cluster(se)
if (interactive()) {
  print(p1)
}

# Run with different methods
p2 <- plot_hierarchical_cluster(se, dist_method = "manhattan", hclust_method = "ward.D2")
if (interactive()) {
  print(p2)
}