goat (version 1.1.2)

plot_heatmap: plot the geneset similarity matrix as a heatmap

Description

plot the geneset similarity matrix as a heatmap

Usage

plot_heatmap(
  x,
  output_dir,
  colors = grDevices::hcl.colors(100, "Viridis", rev = FALSE),
  fontsize = 10
)

Value

does not return a value, plots are printed to device or files depending on output_dir parameter

Arguments

x

result from cluster_genesets()

output_dir

set to NA to directly show the figures instead of writing them to file. Otherwise, this is the full path to the directory where the downloaded files should be stored. Directory is created if it does not exist. e.g. output_dir="~/data" on unix systems, output_dir="C:/data" on Windows, or set to output_dir=getwd() to write output to the current working directory

colors

a vector of 100 colors to be used for the heatmap (101 breaks are computed between 0 and the max value in the distance matrix)

fontsize

parameter sent to pheatmap::pheatmap(); control the size of labels in the plot, defaults to 10. Note that you can also change the plot device size, see examples

Examples

# \donttest{
# note; this example downloads data when first run, and typically takes ~60seconds

# store the downloaded files in the following directory. Here, the temporary file
# directory is used. Alternatively, consider storing this data in a more permanent location.
# e.g. output_dir="~/data/goat" on unix systems or output_dir="C:/data/goat" on Windows
output_dir = tempdir()

## first run the default example from test_genesets() to obtain geneset results
datasets = download_goat_manuscript_data(output_dir)
genelist = datasets$`Wingo 2020:mass-spec:PMID32424284`
genesets_asis = download_genesets_goatrepo(output_dir)
genesets_filtered = filter_genesets(genesets_asis, genelist)
result = test_genesets(genesets_filtered, genelist, method = "goat",
  score_type = "effectsize", padj_method = "bonferroni", padj_cutoff = 0.05)

# prior to running this function, cluster the genesets
clusters = cluster_genesets(result, genelist)

# use the plot heatmap function and try various color palettes
plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Viridis", rev = FALSE))
plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Inferno", rev = FALSE))
plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Lajolla", rev = TRUE))
plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Mako", rev = FALSE))
plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Turku", rev = TRUE))
plot_heatmap(clusters, output_dir, colors = hcl.colors(100, "Grays", rev = TRUE))
# }