correlation_heatmap: Plot heatmap

In neurogenomics/MultiEWCE: Multi-Scale Target Explorer

Plot heatmap

Description

Plot a phenotype x phenotype correlation matrix based on genetic overlap.

Usage

correlation_heatmap(
  top_targets,
  row_side_vars = c("ancestor_name", "ontLvl"),
  col_side_vars = c("n_celltypes", "n_genes"),
  row_labels = "hpo_name",
  phenotype_to_genes = HPOExplorer::load_phenotype_to_genes(),
  col = pals::gnuplot(),
  show_plot = TRUE,
  save_path = tempfile(fileext = "correlation_heatmap.pdf"),
  height = 10,
  width = height * 1.3,
  fontsize = 7,
  seed = 2023,
  verbose = TRUE
)

Arguments

top_targets	data.table of prioritised targets generated by prioritise_targets.
row_side_vars	Variables to include in row-side metadata annotations.
col_side_vars	Variables to include in column-side metadata annotations.
row_labels	Optional row labels which are put as row names in the heatmap.
phenotype_to_genes	Output of load_phenotype_to_genes mapping phenotypes to gene annotations.
col	A vector of colors if the color mapping is discrete or a color mapping function if the matrix is continuous numbers (should be generated by colorRamp2). If the matrix is continuous, the value can also be a vector of colors so that colors can be interpolated. Pass to ColorMapping. For more details and examples, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#colors .
show_plot	Show the plot.
save_path	Path to save interactive plot to as a self-contained HTML file.
height	Height of the heatmap body.
width	Width of the heatmap body.
fontsize	Axis labels font size.
seed	Set the seed for reproducible clustering.
verbose	Print messages.

Value

Plot

Examples

top_targets <- MSTExplorer::example_targets$top_targets[seq(100),]
hm <- correlation_heatmap(top_targets = top_targets)

neurogenomics/MultiEWCE documentation built on April 22, 2024, 6:22 a.m.