Bio Pathway Gsea

Gene Set Enrichment Analysis (GSEA)

Core Concept

GSEA uses all genes ranked by a statistic (log2FC, signed p-value) rather than a subset of significant genes. It finds gene sets where members are enriched at the top or bottom of the ranked list.

Prepare Ranked Gene List

library(clusterProfiler)
library(org.Hs.eg.db)

de_results <- read.csv('de_results.csv')

# Create named vector: values = statistic, names = gene IDs
gene_list <- de_results$log2FoldChange
names(gene_list) <- de_results$gene_id

# Sort in decreasing order (REQUIRED)
gene_list <- sort(gene_list, decreasing = TRUE)

Convert Gene IDs for GSEA

# Convert symbols to Entrez IDs
gene_ids <- bitr(names(gene_list), fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)

# Create ranked list with Entrez IDs
gene_list_entrez <- gene_list[names(gene_list) %in% gene_ids$SYMBOL]
names(gene_list_entrez) <- gene_ids$ENTREZID[match(names(gene_list_entrez), gene_ids$SYMBOL)]
gene_list_entrez <- sort(gene_list_entrez, decreasing = TRUE)

Alternative Ranking Statistics

# Signed p-value (recommended for detecting both up and down)
gene_list <- -log10(de_results$pvalue) * sign(de_results$log2FoldChange)
names(gene_list) <- de_results$gene_id
gene_list <- sort(gene_list, decreasing = TRUE)

# Wald statistic (from DESeq2)
gene_list <- de_results$stat
names(gene_list) <- de_results$gene_id
gene_list <- sort(gene_list, decreasing = TRUE)

GSEA with GO

gse_go <- gseGO(
    geneList = gene_list_entrez,
    OrgDb = org.Hs.eg.db,
                        # BP, MF, CC, or ALL
    minGSSize = 10,
    maxGSSize = 500,
    pvalueCutoff = 0.05,
    verbose = FALSE,
    pAdjustMethod = 'BH'
)

# Make readable
gse_go <- setReadable(gse_go, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')

GSEA with KEGG

gse_kegg <- gseKEGG(
    geneList = gene_list_entrez,
    organism = 'hsa',
    minGSSize = 10,
    maxGSSize = 500,
    pvalueCutoff = 0.05,
    verbose = FALSE
)

# Make readable
gse_kegg <- setReadable(gse_kegg, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')

GSEA with Custom Gene Sets

# Read GMT file (Gene Matrix Transposed)
gene_sets <- read.gmt('msigdb_hallmarks.gmt')

gse_custom <- GSEA(
    geneList = gene_list_entrez,
    TERM2GENE = gene_sets,
    minGSSize = 10,
    maxGSSize = 500,
    pvalueCutoff = 0.05
)

MSigDB Gene Sets

# Use msigdbr package for MSigDB gene sets
library(msigdbr)

# Hallmark gene sets
hallmarks <- msigdbr(species = 'Homo sapiens', category = 'H')
hallmarks_t2g <- hallmarks[, c('gs_name', 'entrez_gene')]

gse_hallmark <- GSEA(
    geneList = gene_list_entrez,
    TERM2GENE = hallmarks_t2g,
    pvalueCutoff = 0.05
)

# Other categories: C1 (positional), C2 (curated), C3 (motif), C5 (GO), C6 (oncogenic), C7 (immunologic)

Understanding Results

# View results
head(gse_go)
results <- as.data.frame(gse_go)

# Key columns:
# - NES: Normalized Enrichment Score (positive = upregulated, negative = downregulated)
# - pvalue: Nominal p-value
# - p.adjust: FDR-adjusted p-value
# - core_enrichment: Leading edge genes

Interpreting NES (Normalized Enrichment Score)

Key Parameters

Export Results

results_df <- as.data.frame(gse_go)
write.csv(results_df, 'gsea_go_results.csv', row.names = FALSE)

# Get leading edge genes for a term
leading_edge <- strsplit(results_df$core_enrichment[1], '/')[[1]]

Notes

• Must be sorted - gene list must be sorted in decreasing order
• Named vector - names are gene IDs, values are statistics
• No arbitrary cutoffs - uses all genes, not just significant ones
• NES sign matters - positive = upregulated enrichment
• Leading edge - core_enrichment contains driving genes

Related Skills

• go-enrichment - Over-representation analysis for GO
• kegg-pathways - Over-representation analysis for KEGG
• enrichment-visualization - GSEA plots, ridge plots