Plot cell type severity — plot_celltype

Plot the phenotype severity scores (generated by GPT-4) aggregated by the cell types each phenotype is significantly associated with.

plot_celltype_severity(
  results,
  cl = get_cl(),
  q_threshold = 0.05,
  run_enrichment = TRUE,
  top_n = 3,
  types = c("dot", "bar")[1],
  run_prune_ancestors = FALSE,
  nonsig_fill = ggplot2::alpha("grey90", 0.001),
  force_new = FALSE,
  base_size = 8,
  save_path = tempfile(fileext = ".rds"),
  workers = 1
)

Arguments

results: The cell type-phenotype enrichment results generated by gen_results and merged together with merge_results
cl: Cell Ontology (CL) object from KGExplorer::get_ontology("cl").
q_threshold: The q value threshold to subset the results by.
run_enrichment: Instead of simply taking the top N results, run a series of one-sided Wilcoxan rank-sum tests to determine whether the distribution of ordinal severity values (never=0, rarely=1, often=2, always=3) are significantly different between a given cell type and all other cell types. Tests are repeated across each GPT annotation separately using group_by and wilcox_test.
top_n: Top and bottom number of cell types to show per annotation (used in dot plot only).
types: Which types of plots of create.
run_prune_ancestors: Prune redundant ancestral terms if any of their descendants are present. Passes to prune_ancestors.
nonsig_fill: Fill colour for non-significant results.
force_new: Run a new set of enrichment tests even when cached results are found. Only used when run_enrichment=FALSE.
base_size: base font size, given in pts.
save_path: Path to save results to.
workers: Number (>1) or proportion (<1) of worker cores to use.

Value

Named list of ggplot and data.table objects.

Examples

set.seed(2025)
results <- load_example_results()
results <- results[sample(seq(nrow(results)), 5000),]
out <- plot_celltype_severity(results, types=c("bar","dot"))
#> Mapping cell types to cell ontology terms.
#> Adding stage information.
#> Translating ontology terms to ids.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2025-05-06
#> 151 phenotypes do not have matching HPO IDs.
#> Reading in GPT annotations for 16,982 phenotypes.
#> Creating bar plot.
#> Creating dot plot.
#> 1 core(s) assigned as workers (3 reserved).
#> Running Wilcoxon rank-sum tests:
#> 
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#> 
#> Caching Wilcoxon rank-sum test results: /tmp/RtmpsW0tFt/file100e527c6380.rds