process.custom.config.matrix: Process a custom configuration matrix.

In NKI-CCB/iTOP: Inferring the Topology of Omics Data

Description

This function can be used to process a custom-made configuration matrix (i.e. similarity matrix) for use with the RV coefficient. The function can perform two tasks: centering and preparation for the modified RV coefficient, both of which we will briefly explain here.

Usage

process.custom.config.matrix(S, center = TRUE, mod.rv = TRUE)

Arguments

S	A configuration matrix.
center	Should the configuration matrix be centered using kernel centering?
mod.rv	Should the configuration matrix be prepared for the modified RV coefficient?

Details

The RV coefficient often results in values very close to one when both datasets are not centered around zero, even for orthogonal data. For inner product similarity and Jaccard similarity, we recommend using centering. However, for some other similarity measures, centering may not be beneficial (for example, because the measure itself is already centered, such as in the case of Pearson correlation). For more information on centering of binary (and other non-continuous) data, for which we used kernel centering of the configuration matrix, we refer to our manuscript: Aben et al., 2018, doi.org/10.1101/293993.

The modified RV coefficient was proposed for high-dimensional data, as the regular RV coefficient would result in values close to one even for orthogonal data. We recommend always using the modified RV coefficient.

Value

The processed configuration matrix.

Examples

set.seed(2)
n = 100
p = 100
x = matrix(rnorm(n*p)+10, n, p)
S = x%*%t(x)
S_dash = process.custom.config.matrix(S, center=TRUE, mod.rv=TRUE)

NKI-CCB/iTOP documentation built on May 28, 2019, 3:33 p.m.