trainModel-ConsensusMetaclusteringModel-method: Train A ConsensusMetaclusteringModel

In bhklab/PDATK: Pancreatic Ductal Adenocarcinoma Tool-Kit

Description

Since consensus clustering is an unsuperived learning method, there isn't really a 'training step' per se. Instead this method computes the consensus clusters and stores the results in the models slot.

Usage

## S4 method for signature 'ConsensusMetaclusteringModel'
trainModel(
  object,
  maxK = 5,
  reps = 10,
  distance = "pearson",
  clusterAlg = "hc",
  plot = NULL,
  ...
)

Arguments

object	A ConsensusMetaclusteringModel to train.
maxK	The maximum number of clusters to test. Defaults to 5.
reps	How many random samples should clustering be repeated on? Default is 10, but 1000+ is recommended for real world use.
distance	The distance method to use. Defaults to 'pearson'. See ?ConsensusClusterPlus::ConsensusClusterPlus for more options.
clusterAlg	The clustering algorithm to use. Defaults to 'hc'. See ?ConesnsusClusterPLus::ConsensusClusterPlus for more options.
plot	An optional path to output the plots generated by each call to ConsensusClusterPlus::ConsensusClusterPlus. Default is NULL, which suppresses all plots, otherwise passed to the clustering function.
...	Fall through parameters to BiocParallel::bplapply. This can be used to customize your parallelization using BPPARAM or to pass additional arguments to ConsensusClusterPlus.

Value

The ConsensusMetaclusteringModel with the clustering results in the models slot.

bhklab/PDATK documentation built on Dec. 27, 2021, 7:46 a.m.