The function will take a metadata table and use Set2 palette when number of levels is > 3 or a set or orange/blue colors other wise.

degColors(ann, col_fun = FALSE, con_values = c("grey80", "black"),
  cat_values = c("orange", "steelblue"), palette = "Set2")

Arguments

ann

Data.frame with metadata information. Each column will be used to generate a palette suitable for the values in there.

col_fun

Whether to return a function for continuous variables (compatible with ComplexHeatmap::HeatmapAnnotation()) or the colors themself (comparible with [pheatmap::pheatmap())]).

[pheatmap::pheatmap())]: R:pheatmap::pheatmap())

con_values

Color to be used for continuous variables.

cat_values

Color to be used for 2-levels categorical variables.

palette

Palette to use from brewer.pal() for multi-levels categorical variables.

Examples

data(humanGender) library(DESeq2) library(ComplexHeatmap)
#> Loading required package: grid
#> ======================================== #> ComplexHeatmap version 2.0.0 #> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/ #> Github page: https://github.com/jokergoo/ComplexHeatmap #> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference #> #> If you use it in published research, please cite: #> Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional #> genomic data. Bioinformatics 2016. #> ========================================
idx <- c(1:10, 75:85) dse <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:10, idx], colData(humanGender)[idx,], design=~group) th <- HeatmapAnnotation(df = colData(dse), col = degColors(colData(dse), TRUE)) Heatmap(log2(counts(dse)+0.5), top_annotation = th)
custom <- degColors(colData(dse), TRUE, con_values = c("white", "red"), cat_values = c("white", "black"), palette = "Set1") th <- HeatmapAnnotation(df = colData(dse), col = custom) Heatmap(log2(counts(dse)+0.5), top_annotation = th)