PAC {cola} | R Documentation

## 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 <z.gu@dkfz.de>

### 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])

[Package cola version 1.6.0 Index]