Run GSEA Squared on GSEA results

Usage

run_GSEA_squared(
  df_GSEA,
  categories,
  cat_terms,
  rep0 = 2.2e-16,
  signlogp_base = 10,
  get_terms = FALSE,
  terms_filt_freq = c(5, 500),
  plot_type = c("jitter", "density"),
  plot_pval = TRUE,
  title = NULL,
  cat_colors = NULL,
  savename = NULL,
  plot_fmt = "png",
  height = 8,
  width = 8,
  seed = 13,
  verbose = TRUE
)

Arguments

df_GSEA: df/string; (path to) GSEA results with pathway and NES columns
categories: char vector; list of category names
cat_terms: char vector; list of category terms with each element being keywords separated by "|", ex. "CELL_CYCLE|MITOTIC|DNA_REPLICATION"
rep0: numeric; value to replace pval == 0 results with, use rep0 = 2.2e-16 (rounded .Machine$double.eps) to be same as original function
signlogp_base: integer; log base when calculating signed log_base p metric
get_terms: logical; TRUE to run Rubrary::get_GSEAsq_terms to output filtered table of terms and associated statistics
terms_filt_freq: num vector length 2; get_GSEAsq_terms arg, filt_freq[1] is minimum frequency, filt_freq[2] is maximum frequency
plot_type: c("jitter", "density"); GSEA squared output plot type
plot_pval: logical; TRUE to append p-value to category name. If ggtext package is installed, can do fancy markdown formatting
title: string; plot title
cat_colors: char vector; list of colors corresponding to category
savename: string; path to save outputs under (no extension)
plot_fmt: string; file extension to save plot as
height: numeric; output plot height
width: numeric; output plot width
seed: integer; randomization seed
verbose: logical; TRUE to output intermediate messages

Value

GSEAsq object with ggplot object plot, df of original GSEA input with categories pathways, df of categories KS statistics categories, and df of terms KS statistics terms if applicable

Examples

library(dplyr)
# Load data
airway_deseq_res <- Rubrary::airway_deseq_res
deseq_stats <- setNames(
  airway_deseq_res[,"sign_log_p"],
  airway_deseq_res[,"hgnc_symbol"]
)
pthwys <- Rubrary::GSEA_pathways
# Run (f)GSEA
gsea_results <- fgsea::fgsea(
  pathways = pthwys,
  stats = deseq_stats,
  eps = 0.0,
  minSize = 15,
  maxSize  = 500) %>%
  arrange(NES)
#> Warning: There are ties in the preranked stats (6.92% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
# Run GSEA Squared
GSEAsq_terms <- c("METABOLIC","DNA")
# Run GSEA squared
GSEAsq <- Rubrary::run_GSEA_squared(
  df_GSEA = gsea_results,
  get_terms = FALSE, verbose = FALSE,
  categories = GSEAsq_terms,
  cat_terms = GSEAsq_terms,
  plot_pval = TRUE,
  plot_type = "jitter"
)
names(GSEAsq) # Various outputs as list
#> [1] "plot"       "pathways"   "categories"
GSEAsq$plot