This function performs hierarchical clustering on specified metadata scores within the provided expression dataset. It updates the dataset's sample annotations with the clustering results.
Usage
cluster_metadata(
exp_data,
k,
metadata_name = "pathway_scores",
pca = TRUE,
n_pcs = 10,
features = NULL,
dist_method = "euclidean",
hc_method = "complete"
)Arguments
- exp_data
A SummarizedExperiment object containing expression data and metadata.
- k
An integer specifying the number of clusters to generate.
- metadata_name
A character string specifying the name of the metadata variable to use for clustering. Defaults to "pathway_scores".
- pca
Logical. If TRUE, principal component analysis (PCA) is applied before clustering. Defaults to TRUE.
- n_pcs
An integer specifying the number of principal components to retain if PCA is applied. Defaults to 10.
- features
A character vector of feature names to use for clustering. If NULL, all features are used. Defaults to NULL.
- dist_method
A character string specifying the distance metric to use for hierarchical clustering. Defaults to "euclidean".
- hc_method
A character string specifying the hierarchical clustering linkage method. Defaults to "complete".