test_to_known_factors-ConsensusPartition-method {cola} | R Documentation

## Test correspondance between predicted subgroups and known factors

### Description

Test correspondance between predicted subgroups and known factors

### Usage

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

### Arguments

`object` | A `ConsensusPartition-class` object.
`k` | Number of subgroups. It uses all `k` if it is not specified.
`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 it are omit.
`verbose` | Whether to print messages.

### Details

The test is performed by `test_between_factors` between the predicted classes and user's annotation table.

### 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>

### Examples

    data(golub_cola)
    res = golub_cola["ATC:skmeans"]
    anno = get_anno(res)
    anno
    test_to_known_factors(res, k = 3)
    # or explicitly specify known argument
    test_to_known_factors(res, k = 3, known = anno)

[Package cola version 1.8.1 Index]