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

In cola: A Framework for Consensus Partitioning

Description

Test correspondance between predicted classes and known factors

Usage

## S4 method for signature 'HierarchicalPartition'
test_to_known_factors(object, known = get_anno(object[1]),
    depth = 2:max_depth(object), verbose = FALSE)

Arguments

object	A HierarchicalPartition-class object.
depth	Depth of the hierarchy.
known	A vector or a data frame with known factors. By default it is the annotation table set in hierarchical_partition.
verbose	Whether to print messages.

Value

A data frame with columns:

• number of samples

• p-values from the tests

• number of classes

The classifications are extracted for each depth.

Author(s)

Zuguang Gu <[e-mail]>

Examples

data(golub_cola_rh)
# golub_cola_rh already has known annotations, so test_to_known_factors()
# can be directly applied
test_to_known_factors(golub_cola_rh)

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