Plot differential outcomes: heatmap

Plot differential outcomes: heatmap

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)
#> Translating ontology terms to ids.
#> Keeping descendants of 1 term(s).
#> 14 terms remain after filtering.
#> 2,814 associations remain after filtering.
results2 <- HPOExplorer::add_death(results2,
                                  allow.cartesian = TRUE,
                                  agg_by = c("disease_id","hpo_id"))
#> Annotating phenos with AgeOfDeath.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
out <- plot_differential_outcomes_heatmap(results=results2)
#> Adding symptom-level results.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Subsetting results by q_threshold and effect.
#> 74,847 associations remain after filtering.
#> Mapping cell types to cell ontology terms.
#> Adding stage information.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Loading ctd_DescartesHuman.rds
#> Loading ctd_HumanCellLandscape.rds