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Run Gene Set Enrichment Analysis (GSEA)

Source: R/05_pathway_analysis.R
run_gsea.Rd

Performs pre-ranked GSEA using fgsea on differential expression results. Genes are ranked by their test statistic (DESeq2 Wald stat or -log10(pvalue) * sign(log2FC)). Gene sets come from MSigDB via msigdbr.

Usage

run_gsea(
  de_results,
  gene_sets = "hallmark",
  gene_id_type = "ensembl_gene",
  min_size = 15L,
  max_size = 500L
)

Arguments

de_results

Data frame of DE results. Must contain gene identifiers (as rownames or in a gene/gene_id column) and at least one of: stat (DESeq2 Wald statistic), or log2FoldChange/logFC + pvalue/PValue.

gene_sets

Character string specifying the MSigDB collection: "hallmark" (default), "kegg", "reactome", "go_bp", "go_mf", "go_cc", "c2", "c7". Alternatively, a named list of character vectors (custom gene sets).

gene_id_type

Type of gene identifiers: "ensembl_gene" (default), "gene_symbol", or "entrez_gene".

min_size

Minimum gene set size (default: 15).

max_size

Maximum gene set size (default: 500).

Value

List with components:

results

Data frame with pathway, NES, pval, padj, size, leadingEdge columns.

n_gene_sets

Number of gene sets tested.

n_significant

Number significant at padj < 0.05.

top_gene_sets

Top 20 enriched gene sets as a data frame.

ranked_genes

Named numeric vector of gene ranks used.

collection

Gene set collection used.

See also

Other pathway: annotate_de_results(), annotate_genes(), plot_enrichment_barplot(), plot_enrichment_dotplot(), plot_gsea_running_score(), run_ora(), run_pathway_analysis()