K-means Clustering for Glycomics and Glycoproteomics Data

Perform k-means clustering on the expression data. The function uses stats::kmeans() to perform clustering and provides tidy results with cluster assignments.

Usage

gly_kmeans(
  exp,
  on = "variable",
  centers = 3,
  scale = TRUE,
  add_info = TRUE,
  ...,
  log_transform = TRUE
)

Arguments

exp: A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object, or another SummarizedExperiment containing an expression matrix and sample information.
on: A character string specifying what to cluster. Either "variable" (default) to cluster variables/features, or "sample" to cluster samples/observations.
centers: Either the number of clusters (integer) or a set of initial cluster centers. Default is 3.
scale: A logical indicating whether to scale the data before clustering. Default is TRUE.
add_info: A logical value. If TRUE (default), sample information from the experiment will be added to the result tibbles. If FALSE, only the clustering results are returned.
...: Additional arguments passed to stats::kmeans().
log_transform: A single logical value indicating whether to transform expression values using log2(x + 1e-6) before analysis. Default is TRUE. Set to FALSE for data that are already log-transformed.

Value

A list with three elements:

tidy_result: A tibble with cluster assignments containing the following columns:
- variable or sample: Variable or sample name (depending on on parameter)
- cluster: Cluster assignment
raw_result: The raw kmeans object from stats::kmeans().
meta_data: A list containing metadata from the input experiment

Details

Set log_transform = FALSE to use the expression values as supplied, for example when they are already log-transformed.

By default, the function performs log2 transformation on the expression data (log2(x + 1e-6)) before clustering. When on = "variable" (default), variables are clustered based on their expression patterns across samples. When on = "sample", samples are clustered based on their expression profiles across variables.

Data Preparation: Data is log2-transformed when log_transform = TRUE and optionally scaled before clustering.

Clustering Method: K-means clustering is performed using stats::kmeans() with the specified parameters.

Required packages