plot_differential_outcomes_heatmap: Plot differential outcomes: heatmap

In neurogenomics/MultiEWCE: Multi-Scale Target Explorer

Plot differential outcomes: heatmap

Description

Plot differential outcomes: heatmap

Usage

plot_differential_outcomes_heatmap(
  phenotypes = NULL,
  results = load_example_results(),
  celltype_col = "cl_name",
  outcome_var = "AgeOfDeath_score_mean",
  x_var = "celltype_symptom",
  title = "Differential outcomes by cell type",
  subtitle = paste0("Phenotype(s): ", paste(phenotypes, collapse = "; ")),
  fill_limits = NULL,
  print_phenotypes = TRUE,
  show_plot = TRUE,
  save_path = NULL,
  height = NULL,
  width = NULL,
  ...
)

Arguments

phenotypes	HPO phenotypes to include. Can be provided either as names (e.g. "Hypotonia") or HPO IDs (e.g. "HP:0001252").
results	The cell type-phenotype enrichment results generated by gen_results and merged together with merge_results
celltype_col	Name of the cell type column in the results.
outcome_var	Outcome variable to plot.
x_var	Variable to plot on the x-axis.
title	Title of the plot.
subtitle	Subtitle of the plot.
fill_limits	Min/max limits for the fill scale.
print_phenotypes	Print the phenotypes in the subtitle.
show_plot	Print the plot to the console.
save_path	Save the plot to a file. Set to NULL to not save the plot.
height	Height of the saved plot.
width	Width of the saved plot.
...	Arguments passed on to add_symptom_results annotLevels The annotation level to use within each CTD in ctd_list. keep_quantiles Quantiles to keep in each CellTypeDataset of the ctd_list. proportion_driver_genes_symptom_threshold The minimum proportion of overlap between symptom genes (genes annotated to a phenotype via a specific disease) and the driver genes (genes driving a signficant phenotype-cell type association). drop_subthreshold Drop rows that don't meet the proportion_driver_genes_symptom_threshold criterion. top_n Top N genes to keep when grouping by group_vars. ctd_list A named list of CellTypeDataset objects each created with generate_celltype_data. q_threshold The q value threshold to subset the results by. effect_threshold The minimum fold change in specific expression to subset the results by. phenotype_to_genes Output of load_phenotype_to_genes mapping phenotypes to gene annotations.

Examples

results <- load_example_results()
keep_descendants <- "Hypotonia" # HP:0001252
results2 <- HPOExplorer::filter_descendants(results,
                                            keep_descendants = keep_descendants)
results2 <- HPOExplorer::add_death(results2,
                                  allow.cartesian = TRUE,
                                  agg_by = c("disease_id","hpo_id"))
out <- plot_differential_outcomes_heatmap(results=results2)

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