test_to_known_factors-ConsensusPartitionList-method: Test correspondance between predicted classes and known...

In jokergoo/cola: A Framework for Consensus Partitioning

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.

If there are NA values, basically it means there are no efficient data points to perform the test.

Author(s)

Zuguang Gu <[e-mail]>

See Also

test_between_factors, test_to_known_factors,ConsensusPartition-method

Examples

data(golub_cola)
test_to_known_factors(golub_cola)

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