test_to_known_factors-DownSamplingConsensusPartition-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 'DownSamplingConsensusPartition'
    test_to_known_factors(object, k, known = get_anno(object),
        p_cutoff = 0.05, verbose = FALSE)

### Arguments

`object` | A `DownSamplingConsensusPartition-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_by_down_sampling`.
`p_cutoff` | Cutoff for p-values for the class prediction. Samples with p-value higher 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 `p_cutoff`,

- p-values from the tests,

- number of subgroups.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### Examples

    data(golub_cola_ds)
    test_to_known_factors(golub_cola_ds, k = 3)
    test_to_known_factors(golub_cola_ds)

[Package cola version 1.6.0 Index]