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

In jokergoo/cola: A Framework for Consensus Partitioning

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 <[e-mail]>

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)

jokergoo/cola documentation built on Feb. 29, 2024, 1:41 a.m.