Skip to contents

Consensus Clustering for Glycomics and Glycoproteomics Data

gly_consensus_clustering.Rd

Perform consensus clustering on the expression data using ConsensusClusterPlus. The function returns cluster assignments for all k values from 2 to max_k in long format. Setting output_file to visualize consensus CDF curves and consensus matrices, which helps you decide the optimal number of clusters. See this tutorial for more information.

Usage

gly_consensus_clustering(
  exp,
  on = "sample",
  max_k = 9,
  reps = 1000,
  p_item = 0.8,
  cluster_alg = "hc",
  scale = TRUE,
  output_file = NULL,
  add_info = TRUE,
  ...
)

gly_consensus_clustering_(
  expr_mat,
  on = "sample",
  max_k = 9,
  reps = 1000,
  p_item = 0.8,
  cluster_alg = "hc",
  scale = TRUE,
  output_file = NULL,
  ...
)

Arguments

exp

A glyexp::experiment() object containing expression matrix and sample information.

on

A character string specifying what to cluster. Either "sample" (default) to cluster samples/observations, or "variable" to cluster variables/features.

max_k

Maximum number of clusters to test. Default is 9. Passed to the maxK argument of ConsensusClusterPlus::ConsensusClusterPlus().

reps

Number of resampling iterations. Default is 1000. Passed to the reps argument of ConsensusClusterPlus::ConsensusClusterPlus().

p_item

Proportion of items to sample. Default is 0.8. Passed to the pItem argument of ConsensusClusterPlus::ConsensusClusterPlus().

cluster_alg

Clustering algorithm to use. Default is "hc" (hierarchical clustering). Other options include "km" (k-means), "pam" (partitioning around medoids). Passed to the clusterAlg argument of ConsensusClusterPlus::ConsensusClusterPlus().

scale

A logical indicating whether to scale the data. Default is TRUE.

output_file

A character string specifying the output file path for plots. If NULL (default), no plots are saved and no files are generated. If provided, the consensus clustering plot will be saved directly to this file path. The function will create any necessary parent directories.

add_info

A logical value. If TRUE (default), sample and variable information from the experiment will be added to the result tibbles. If FALSE, only the consensus clustering results are returned. Only applicable to gly_consensus_clustering().

...

Additional arguments passed to ConsensusClusterPlus::ConsensusClusterPlus().

expr_mat

A numeric matrix with variables as rows and samples as columns.

Value

A list with two elements:

  • tidy_result: A tibble with consensus clustering results containing the following columns:

    • variable or sample: Variable or sample name (depending on on parameter)

    • k: Number of clusters

    • cluster: Cluster assignment for the corresponding k

  • raw_result: The raw ConsensusClusterPlus object The list has classes glystats_cc_res and glystats_res.

Details

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

gly_consensus_clustering() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_consensus_clustering_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Consensus Clustering Process:

  1. Perform consensus clustering for k = 2 to max_k using ConsensusClusterPlus

  2. Return cluster assignments for all k values in long format

Required packages

This function requires the following packages to be installed:

  • ConsensusClusterPlus for consensus clustering

See also

ConsensusClusterPlus::ConsensusClusterPlus()