test_to_known_factors-ConsensusPartition-method: Test correspondance between predicted subgroups and known...
In cola: A Framework for Consensus Partitioning

Description Usage Arguments Details Value Author(s) Examples

Test correspondance between predicted subgroups and known factors

1
2
3

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

`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.

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

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.

Zuguang Gu <z.gu@dkfz.de>

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)