# Make nice colors for metadata

`degColors.Rd`

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 2.1.0
    #> 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)