test_to_known_factors-DownSamplingConsensusPartition-method: Test correspondance between predicted subgroups and known...

In cola: A Framework for Consensus Partitioning

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 <[e-mail]>

Examples

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

cola documentation built on Nov. 8, 2020, 8:12 p.m.