Plot a Hierarchical Clustering Dendrogram of Samples
plot_hierarchical_cluster.Rd
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"
.
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.
Examples
# Create a sample ProtData 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
)
pd_obj <- create_protdata(dat = raw_data)
# Run with default methods. We expect A/B and C/D to cluster together.
p1 <- plot_hierarchical_cluster(pd_obj)
#> Error: unable to find an inherited method for function ‘plot_hierarchical_cluster’ for signature ‘object = "ProtData"’
if (interactive()) {
print(p1)
}
# Run with different methods
p2 <- plot_hierarchical_cluster(pd_obj, dist_method = "manhattan", hclust_method = "ward.D2")
#> Error: unable to find an inherited method for function ‘plot_hierarchical_cluster’ for signature ‘object = "ProtData"’
if (interactive()) {
print(p2)
}