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Plot GSEA Enrichment Results

Source: R/plotGSEAenrichment.R
plotGSEAenrichment.Rd

This function generates enrichment plots for gene sets using the fgsea::plotEnrichment() function. It supports both individual plots (returned as a list) and a grid layout using ggpubr::ggarrange().

Usage

plotGSEAenrichment(
  GSEA_results,
  DEGList,
  gene_sets,
  widthTitle = 24,
  grid = FALSE,
  nrow = NULL,
  ncol = NULL,
  titlesize = 12
)

Arguments

GSEA_results

A named list of data frames containing GSEA results for each contrast. Each data frame should have a column named pathway specifying the gene set, and columns NES and padj for results. Output from runGSEA.

DEGList

A named list of data frames containing differentially expressed genes (DEGs) for each contrast. Each data frame must include a column named t with t-statistics for ranking genes. Output from calculateDE.

gene_sets

A named list of gene sets, where each entry is either:

  • A vector of gene names (unidirectional gene set)

  • A data frame with two columns: gene names and direction (+1 for enriched and -1 for depleted).

widthTitle

Integer. The maximum width (in characters) for wrapping plot titles. Default is 24.

grid

Logical. If TRUE, plots are arranged in a grid using ggpubr::ggarrange(). Default is FALSE.

nrow

Integer. Number of rows for the grid layout (used only if grid = TRUE). If NULL, it is auto-calculated.

ncol

Integer. Number of columns for the grid layout (used only if grid = TRUE). If NULL, it is auto-calculated.

titlesize

Integer. Font size for plot titles. Default is 12.

Value

If grid = FALSE, returns a named list of ggplot objects (each plot corresponding to a contrast-signature pair). If grid = TRUE, returns a single ggplot object with all enrichment plots arranged in a grid.

Examples

# Example GSEA results (mock data, missing columns if running by runGSEA)

GSEA_results <- list(
"Contrast1" = data.frame(
NES = rnorm(3),
  padj = runif(3),
  pathway = paste("Pathway", 1:3),
  stat_used = c("t", "B", "B")
),
"Contrast2" = data.frame(
  NES = rnorm(3),
  padj = runif(3),
  pathway = paste("Pathway", 4:6),
  stat_used = c("t", "B", "B")
)
)


# Generate the plot
plot <- plotCombinedGSEA(GSEA_results, sig_threshold = 0.05, PointSize = 4)
print(plot)