PAC: The proportion of ambiguous clustering (PAC score)

In jokergoo/cola: A Framework for Consensus Partitioning

The proportion of ambiguous clustering (PAC score)

Description

The proportion of ambiguous clustering (PAC score)

Usage

PAC(consensus_mat, x1 = 0.1, x2 = 0.9, class = NULL)

Arguments

consensus_mat	A consensus matrix.
x1	Lower bound to define "ambiguous clustering".
x2	Upper bound to define "ambihuous clustering".
class	Subgroup labels. If it is provided, samples with silhouette score less than the 5^th percential are removed from PAC calculation.

Details

The PAC score is defined as F(x2) - F(x1) where F(x) is the CDF of the consensus matrix.

Value

A single numeric vaule.

See

See https://www.nature.com/articles/srep06207 for explanation of PAC score.

Author(s)

Zuguang Gu <[e-mail]>

Examples

data(golub_cola)
PAC(get_consensus(golub_cola[1, 1], k = 2))
PAC(get_consensus(golub_cola[1, 1], k = 3))
PAC(get_consensus(golub_cola[1, 1], k = 4))
PAC(get_consensus(golub_cola[1, 1], k = 5))
PAC(get_consensus(golub_cola[1, 1], k = 6))

# with specifying `class`
PAC(get_consensus(golub_cola[1, 1], k = 2), 
    class = get_classes(golub_cola[1, 1], k = 2)[, 1])

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