Perform Gene Set Enrichment Analysis (GSEA)

Conducts Gene Set Enrichment Analysis to identify significantly enriched gene sets from differential gene expression data. Supports MSigDB gene sets or custom gene signatures, and generates comprehensive visualizations and statistical results.

Usage

sig_gsea(
  deg,
  genesets = NULL,
  path = NULL,
  gene_symbol = "symbol",
  logfc = "log2FoldChange",
  org = c("hsa", "mus"),
  msigdb = TRUE,
  category = "H",
  subcategory = NULL,
  palette_bar = "jama",
  palette_gsea = 2,
  cols_gsea = NULL,
  cols_bar = NULL,
  show_bar = 10,
  show_col = FALSE,
  show_plot = FALSE,
  show_gsea = 8,
  show_path_n = 20,
  plot_single_sig = FALSE,
  project = "custom_sig",
  minGSSize = 10,
  maxGSSize = 500,
  verbose = TRUE,
  seed = FALSE,
  fig.type = "pdf",
  print_bar = TRUE
)

Arguments

deg: Data frame containing differential expression results with gene symbols and log fold changes.
genesets: List of custom gene sets for enrichment analysis. If `NULL`, MSigDB gene sets are used based on `org` and `category`. Default is `NULL`.
path: Character string specifying the directory path for saving results. Default is `NULL`.
gene_symbol: Character string specifying the column name in `deg` containing gene symbols. Default is `"symbol"`.
logfc: Character string specifying the column name in `deg` containing log fold change values. Default is `"log2FoldChange"`.
org: Character string specifying the organism. Options are `"hsa"` (Homo sapiens) or `"mus"` (Mus musculus). Default is `"hsa"`.
msigdb: Logical indicating whether to use MSigDB gene sets. Default is `TRUE`.
category: Character string specifying the MSigDB category (e.g., `"H"` for Hallmark, `"C2"` for curated gene sets). Default is `"H"`.
subcategory: Character string specifying the MSigDB subcategory to filter gene sets. Default is `NULL`.
palette_bar: Character string or integer specifying the color palette for bar plots. Default is `"jama"`.
palette_gsea: Integer specifying the color palette for GSEA plots. Default is `2`.
cols_gsea: Character vector specifying custom colors for GSEA enrichment plots. If `NULL`, colors are automatically generated. Default is `NULL`.
cols_bar: Character vector specifying custom colors for the enrichment bar plot. If `NULL`, colors are automatically generated. Default is `NULL`.
show_bar: Integer specifying the number of top enriched gene sets to display in the bar plot. Default is `10`.
show_col: Logical indicating whether to display color names in the bar plot. Default is `FALSE`.
show_plot: Logical indicating whether to display GSEA enrichment plots. Default is `FALSE`.
show_gsea: Integer specifying the number of top significant gene sets for which to generate GSEA plots. Default is `8`.
show_path_n: Integer specifying the number of pathways to display in GSEA plots. Default is `20`.
plot_single_sig: Logical indicating whether to generate separate plots for each significant gene set. Default is `TRUE`.
project: Character string specifying the project name for output files. Default is `"custom_sig"`.
minGSSize: Integer specifying the minimum gene set size for analysis. Default is `10`.
maxGSSize: Integer specifying the maximum gene set size for analysis. Default is `500`.
verbose: Logical indicating whether to display progress messages. Default is `TRUE`.
seed: Logical indicating whether to set a random seed for reproducibility. Default is `FALSE`.
fig.type: Character string specifying the file format for saving plots (e.g., `"pdf"`, `"png"`). Default is `"pdf"`.
print_bar: Logical indicating whether to save and print the bar plot. Default is `TRUE`.

Value

List containing:

up: Data frame of up-regulated enriched gene sets
down: Data frame of down-regulated enriched gene sets
all: Complete GSEA results
plot_top: GSEA enrichment plot for top gene sets

Author

Dongqiang Zeng

Examples

set.seed(123)
genes <- c(
  "TP53", "BRCA1", "EGFR", "MYC", "KRAS", "PTEN", "APC", "RB1",
  "CDKN2A", "VHL", "ATM", "ATR", "CHEK2", "PALB2", "RAD51", "MDM2",
  "CDK4", "CDK6", "CCND1", "CCNE1", "CDK2", "E2F1", "E2F2", "E2F3",
  "ARF1", "ARF3", "ARF4", "ARF5", "ARF6", "GSK3B", "AKT1", "AKT2",
  "PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG", "PIK3R1", "PIK3R2", "PIK3R3"
)
deg <- data.frame(
  symbol = genes,
  log2FoldChange = rnorm(length(genes), mean = 0, sd = 2),
  padj = runif(length(genes), 0, 0.1)
)
signature <- list(
  DNA_Repair = c(
    "TP53", "BRCA1", "ATM",
    "ATR", "CHEK2", "PALB2", "RAD51"
  ),
  Cell_Cycle = c(
    "TP53", "MYC",
    "RB1", "CDKN2A", "CDK4",
    "CDK6", "CCND1", "CCNE1",
    "CDK2", "E2F1", "E2F2", "E2F3"
  ),
  PI3K_AKT = c(
    "AKT1", "AKT2",
    "PIK3CA", "PIK3CB", "PIK3CD",
    "PIK3CG", "PIK3R1", "PIK3R2", "PIK3R3"
  )
)
res <- sig_gsea(
  deg = deg,
  genesets = signature,
  path = tempdir(),
  show_plot = FALSE,
  print_bar = FALSE
)
#> ℹ Mapping gene symbols to Entrez IDs for "hsa"
#> 
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning: number of columns of result is not a multiple of vector length (arg 1)
#> ✔ Signature data saved to sig.csv
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning: 3.57% of input gene IDs are fail to map...
#> ℹ Running GSEA analysis...
#> using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> ✔ GSEA results written to: /tmp/RtmpMGdUJv/1-H_GSEA_significant_results.csv
#> ℹ Most significant gene sets: Cell_Cycle
print(names(res))
#> [1] "up"       "down"     "all"      "plot_top"