trainModel,ConsensusMetaclusteringModel-method {PDATK} | R Documentation

## Train A ConsensusMetaclusteringModel

### 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.

[Package PDATK version 1.2.0 Index]