WGCNA: labels2colors – R documentation

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labels2colors

Convert numerical labels to colors.

Description

Converts a vector or array of numerical labels into a corresponding vector or array of colors corresponding to the labels.

Usage

labels2colors(labels, zeroIsGrey = TRUE, colorSeq = NULL, naColor = "grey",
              commonColorCode = TRUE)

Arguments

`labels`	Vector or matrix of non-negative integer or other (such as character) labels. See details.
`zeroIsGrey`	If TRUE, labels 0 will be assigned color grey. Otherwise, labels below 1 will trigger an error.
`colorSeq`	Color sequence corresponding to labels. If not given, a standard sequence will be used.
`naColor`	Color that will encode missing values.
`commonColorCode`	logical: if `labels` is a matrix, should each column have its own colors?

Details

If labels is numeric, it is used directly as index to the standard color sequence. If 0 is present among the labels and zeroIsGrey=TRUE, labels 0 are given grey color.

If labels is not numeric, its columns are turned into factors and the numeric representation of each factor is used to assign the corresponding colors. In this case commonColorCode governs whether each column gets its own color code, or whether the color code will be universal.

The standard sequence start with well-distinguishable colors, and after about 40 turns into a quasi-random sampling of all colors available in R with the exception of all shades of grey (and gray).

If the input labels have a dimension attribute, it is copied into the output, meaning the dimensions of the returned value are the same as those of the input labels.

Value

A vector or array of character strings of the same length or dimensions as labels.

Author(s)

Peter Langfelder, Peter.Langfelder@gmail.com

Examples

labels = c(0:20);
labels2colors(labels);
labels = matrix(letters[1:9], 3,3);
labels2colors(labels)
# Note the difference when commonColorCode = FALSE
labels2colors(labels, commonColorCode = FALSE)

WGCNA

Weighted Correlation Network Analysis

v1.70-3

GPL (>= 2)

Authors

Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Initial release

2021-02-17