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Perform Comprehensive GO and KEGG Pathway Enrichment Analysis

enrich_pathways.Rd

This function conducts a full suite of pathway enrichment analyses on a given differential expression (DE) result set. It performs Over-Representation Analysis (ORA) for significantly up- and down-regulated genes, as well as Gene Set Enrichment Analysis (GSEA) on the complete ranked list of genes. It analyzes both Gene Ontology (GO) terms and KEGG pathways.

Usage

enrich_pathways(
  DE,
  lfc_threshold = 1,
  fdr_threshold = 0.01,
  enrich_pvalue = 0.05,
  go_org = org.Hs.eg.db,
  kegg_org = "hsa",
  gene_col = "Genes"
)

Arguments

DE

A data frame containing differential expression results. Must include columns for gene identifiers, log fold change, and adjusted p-values.

lfc_threshold

A numeric value for the absolute log2 fold change threshold to define significant genes for ORA. Defaults to 1.

fdr_threshold

A numeric value for the adjusted p-value (FDR) threshold to define significant genes for ORA. Defaults to 0.01.

enrich_pvalue

A numeric value for the p-value cutoff used to determine significant enrichment for pathways/terms. Defaults to 0.05.

go_org

An annotation database object (e.g., org.Hs.eg.db for human) used for GO analysis and mapping gene IDs.

kegg_org

A character string specifying the KEGG organism code (e.g., 'hsa' for Homo sapiens).

gene_col

A character string indicating the name of the column in the DE data frame that contains the gene symbols/identifiers. Defaults to "Genes".

Value

A list containing two named elements:

results

A list of data frames with the detailed enrichment statistics for each analysis type (e.g., go_up, kegg_down, gse_go).

plots

A list of ggplot objects for visualizing the enrichment results (e.g., dot plots, bar plots).

The function returns NULL if the initial mapping of gene identifiers to Entrez IDs fails.

Examples

# Create a sample DE results dataframe
de_results <- data.frame(
  Genes = c("TP53", "EGFR", "BRCA1", "TNF", "MMP9", "IL6", "VEGFA", "JUN"),
  logFC = c(2.5, -1.8, 2.1, 1.6, -2.2, 3.0, -1.7, 2.2),
  adj.P.Val = c(0.001, 0.005, 0.002, 0.009, 0.003, 0.0001, 0.008, 0.001)
)

# This example requires an internet connection and the org.Hs.eg.db package.
if (FALSE) { # \dontrun{
if (requireNamespace("org.Hs.eg.db", quietly = TRUE)) {
  enrichment_output <- enrich_pathways(
    DE = de_results,
    lfc_threshold = 1.5,
    fdr_threshold = 0.01,
    go_org = org.Hs.eg.db,
    kegg_org = 'hsa'
  )

  # Check if the analysis produced results
  if (!is.null(enrichment_output)) {
    # View the head of the GO results for upregulated genes
    print(head(enrichment_output$results$go_up))

    # Display one of the generated plots
    if (!is.null(enrichment_output$plots$go_up_dotplot)) {
      print(enrichment_output$plots$go_up_dotplot)
    }
  }
}
} # }