test_to_known_factors-ConsensusPartitionList-method {cola} | R Documentation

## Test correspondance between predicted classes and known factors

### Description

Test correspondance between predicted classes and known factors

### Usage

    ## S4 method for signature 'ConsensusPartitionList'
    test_to_known_factors(object, k, known = get_anno(object),
        silhouette_cutoff = 0.5, verbose = FALSE)

### Arguments

`object` | A `ConsensusPartitionList-class` object.
`k` | Number of subgroups. It uses all `k` if it is not set.
`known` | A vector or a data frame with known factors. By default it is the annotation table set in `consensus_partition` or `run_all_consensus_partition_methods`.
`silhouette_cutoff` | Cutoff for sihouette scores. Samples with value less than this are omit.
`verbose` | Whether to print messages.

### Details

The function basically sends each `ConsensusPartition-class` object to `test_to_known_factors,ConsensusPartition-method` and merges results afterwards.

### Value

A data frame with the following columns:

- number of samples used to test after filtered by `silhouette_cutoff`,

- p-values from the tests,

- number of subgroups.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### See Also

`test_between_factors`, `test_to_known_factors,ConsensusPartition-method`

### Examples

    data(cola_rl)
    test_to_known_factors(cola_rl, known = 1:40)

[Package cola version 1.4.1 Index]