Version Compatibility

Reference examples tested with: DESeq2 1.42+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

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

Goal: Create a sorted named vector of gene-level statistics suitable for GSEA input.

Approach: Extract fold changes (or other statistics) from DE results, name by gene ID, and sort in decreasing order.

"Run GSEA on my differential expression results" → Rank all genes by expression statistic and test whether predefined gene sets cluster toward the extremes of the ranked 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

Goal: Map gene symbols to Entrez IDs while preserving the ranked statistic values.

Approach: Use bitr for ID conversion, then rebuild the named sorted vector with Entrez IDs as names.

# 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)