gabrielmoreira Version Compatibility
Reference examples tested with: DESeq2 1.42+, R stats (base), ReactomePA 1.46+, clusterProfiler 4.10+, ggplot2 3.5+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto 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.
Expression to Pathways Workflow
"Find enriched pathways from my differential expression results" → Orchestrate GO enrichment (clusterProfiler), GSEA, KEGG/Reactome pathway mapping, and enrichment visualization from DE gene lists or ranked gene lists.
Convert differential expression results into biological insights through functional enrichment analysis.
Method Selection
| Scenario | Method | Why |
|---|---|---|
| Have DE results with Wald stat / t-stat for all genes | GSEA (Step 4) | Uses full ranking; no arbitrary cutoff; ~35% higher F1 than ORA |
| Clear gene list from non-DE source (co-expression, GWAS) | ORA (Steps 1-3) | No ranking available |
| RNA-seq with known gene length bias | GOseq (goseq package) | Standard ORA ignores length bias |
| Bacterial / prokaryotic data | KEGG with locus tags | No org.*.eg.db; use keyType='kegg' |
| Multiple conditions to compare | compareCluster or mitch | Never compare p-values across separate enrichments |
When in doubt, run both ORA and GSEA and compare. Concordant results are more trustworthy.
Workflow Overview
DE Results (gene list or ranked list)
|
v
[1. Gene ID Conversion] --> Convert to Entrez/Ensembl
|
v
[2. Over-representation Analysis]
|
+---> GO Enrichment (BP, MF, CC)
|
+---> KEGG Pathways
|
+---> Reactome Pathways
|
v
[3. GSEA (ranked genes)]
|
v
[4. Visualization] -----> Dot plots, networks, bar plots
|
v
Functional annotations and pathway insights
Input Preparation
From DESeq2 Results
library(DESeq2)
library(clusterProfiler)
library(org.Hs.eg.db)
# Load DE results
res <- read.csv('deseq2_results.csv', row.names = 1)
# Significant genes for ORA
sig_genes <- rownames(subset(res, padj < 0.05 & abs(log2FoldChange) > 1))
# Background = all tested genes (NOT the full genome)
# Pre-filtering and independent filtering reduce the tested set; use only genes that were tested
background_genes <- rownames(res[!is.na(res$pvalue), ])
# Ranked list for GSEA — prefer Wald statistic (combines magnitude + precision)
# Alternatives: shrunken LFC, or sign(logFC) * -log10(PValue) for edgeR
ranked_genes <- res$stat
names(ranked_genes) <- rownames(res)
ranked_genes <- sort(ranked_genes[!is.na(ranked_genes)], decreasing = TRUE)
Gene ID Conversion
# Convert gene symbols to Entrez IDs
sig_entrez <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID',
OrgDb = org.Hs.eg.db)
# For ranked list
ranked_entrez <- bitr(names(ranked_genes), fromType = 'SYMBOL', toType = 'ENTREZID',
OrgDb = org.Hs.eg.db)
ranked_list <- ranked_genes[ranked_entrez$SYMBOL]
names(ranked_list) <- ranked_entrez$ENTREZID
Step 1: GO Over-representation Analysis
# Convert background genes too
bg_entrez <- bitr(background_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
# Biological Process — always specify universe (background)
go_bp <- enrichGO(gene = sig_entrez$ENTREZID,
universe = bg_entrez$ENTREZID,
OrgDb = org.Hs.eg.db,
pAdjustMethod = 'BH',
pvalueCutoff = 0.05,
qvalueCutoff = 0.1,
readable = TRUE)
# Molecular Function
go_mf <- enrichGO(gene = sig_entrez$ENTREZID,
universe = bg_entrez$ENTREZID,
OrgDb = org.Hs.eg.db,
pAdjustMethod = 'BH',
pvalueCutoff = 0.05,
readable = TRUE)
# Cellular Component
go_cc <- enrichGO(gene = sig_entrez$ENTREZID,
universe = bg_entrez$ENTREZID,
OrgDb = org.Hs.eg.db,
pAdjustMethod = 'BH',
pvalueCutoff = 0.05,
readable = TRUE)
# Simplify redundant terms
go_bp_simple <- simplify(go_bp, cutoff = 0.7, by = 'p.adjust')
Step 2: KEGG Pathway Enrichment
kegg <- enrichKEGG(gene = sig_entrez$ENTREZID,
organism = 'hsa',
pvalueCutoff = 0.05,
qvalueCutoff = 0.1)
# Convert KEGG IDs to readable names
kegg <- setReadable(kegg, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')
Step 3: Reactome Pathway Enrichment
library(ReactomePA)
reactome <- enrichPathway(gene = sig_entrez$ENTREZID,
organism = 'human',
pvalueCutoff = 0.05,
readable = TRUE)
Step 4: Gene Set Enrichment Analysis (GSEA)
# GO GSEA
gsea_go <- gseGO(geneList = ranked_list,
OrgDb = org.Hs.eg.db,
minGSSize = 10,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
# KEGG GSEA
gsea_kegg <- gseKEGG(geneList = ranked_list,
organism = 'hsa',
minGSSize = 10,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
Step 5: Visualization
library(enrichplot)
library(ggplot2)
# Dot plot
dotplot(go_bp_simple, showCategory = 20) +
ggtitle('GO Biological Process Enrichment')
ggsave('go_bp_dotplot.pdf', width = 10, height = 8)
# Bar plot
barplot(kegg, showCategory = 15) +
ggtitle('KEGG Pathway Enrichment')
ggsave('kegg_barplot.pdf', width = 9, height = 6)
# Enrichment map (network of related terms)
go_bp_simple <- pairwise_termsim(go_bp_simple)
emapplot(go_bp_simple, showCategory = 30) +
ggtitle('GO Term Similarity Network')
ggsave('go_network.pdf', width = 10, height = 10)
# Concept network (gene-term connections)
cnetplot(go_bp, showCategory = 5, categorySize = 'pvalue') +
ggtitle('Gene-Concept Network')
ggsave('cnet_plot.pdf', width = 12, height = 10)
# GSEA plot for specific pathway
gseaplot2(gsea_kegg, geneSetID = 1:3, pvalue_table = TRUE)
ggsave('gsea_plot.pdf', width = 10, height = 8)
# Ridge plot for GSEA
ridgeplot(gsea_go, showCategory = 15)
ggsave('gsea_ridge.pdf', width = 8, height = 10)
Step 6: Export Results
# Export enrichment results
write.csv(as.data.frame(go_bp), 'go_bp_enrichment.csv', row.names = FALSE)
write.csv(as.data.frame(kegg), 'kegg_enrichment.csv', row.names = FALSE)
write.csv(as.data.frame(reactome), 'reactome_enrichment.csv', row.names = FALSE)
write.csv(as.data.frame(gsea_go), 'gsea_go_results.csv', row.names = FALSE)
# Combine key results
combined <- rbind(
data.frame(Database = 'GO_BP', as.data.frame(go_bp_simple)[1:10,]),
data.frame(Database = 'KEGG', as.data.frame(kegg)[1:10,]),
data.frame(Database = 'Reactome', as.data.frame(reactome)[1:10,])
)
write.csv(combined, 'top_enriched_pathways.csv', row.names = FALSE)
Parameter Recommendations
| Analysis | Parameter | Value |
|---|---|---|
| enrichGO | pvalueCutoff | 0.05 |
| enrichGO | qvalueCutoff | 0.1 |
| simplify | cutoff | 0.7 |
| gseGO | minGSSize | 10 |
| gseGO | maxGSSize | 500 |
| GSEA | perm | 1000 (default) |
Troubleshooting
| Issue | Likely Cause | Solution |
|---|---|---|
| No enriched terms | Too few genes, wrong IDs | Check gene IDs, relax thresholds |
| All terms significant | Too many genes | Be more stringent with DE cutoffs |
| Gene ID conversion fails | Wrong organism, format | Check OrgDb package, gene format |
| GSEA no results | Poor ranking, small gene sets | Check ranked list, adjust minGSSize |
Complete Workflow Script
library(clusterProfiler)
library(org.Hs.eg.db)
library(ReactomePA)
library(enrichplot)
library(ggplot2)
# Configuration
de_file <- 'deseq2_results.csv'
output_dir <- 'pathway_analysis'
dir.create(output_dir, showWarnings = FALSE)
# Load and prepare data
res <- read.csv(de_file, row.names = 1)
sig_genes <- rownames(subset(res, padj < 0.05 & abs(log2FoldChange) > 1))
cat('Significant genes:', length(sig_genes), '\n')
# Convert IDs
sig_entrez <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
cat('Converted to Entrez:', nrow(sig_entrez), '\n')
# Ranked list for GSEA (Wald statistic preferred over LFC)
ranked <- res$stat
names(ranked) <- rownames(res)
ranked <- sort(ranked[!is.na(ranked)], decreasing = TRUE)
ranked_entrez <- bitr(names(ranked), fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
ranked_list <- ranked[ranked_entrez$SYMBOL]
names(ranked_list) <- ranked_entrez$ENTREZID
# Background genes (all tested, not full genome)
bg_entrez <- bitr(rownames(res[!is.na(res$pvalue), ]), fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
# GO enrichment with background
go_bp <- enrichGO(sig_entrez$ENTREZID, universe = bg_entrez$ENTREZID, OrgDb = org.Hs.eg.db, readable = TRUE)
go_bp_simple <- simplify(go_bp, cutoff = 0.7)
# KEGG
kegg <- enrichKEGG(sig_entrez$ENTREZID, organism = 'hsa')
kegg <- setReadable(kegg, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')
# Reactome
reactome <- enrichPathway(sig_entrez$ENTREZID, organism = 'human', readable = TRUE)
# GSEA
gsea_go <- gseGO(ranked_list, OrgDb = org.Hs.eg.db, verbose = FALSE)
# Plots
pdf(file.path(output_dir, 'enrichment_plots.pdf'), width = 10, height = 8)
print(dotplot(go_bp_simple, showCategory = 20) + ggtitle('GO Biological Process'))
print(barplot(kegg, showCategory = 15) + ggtitle('KEGG Pathways'))
if (nrow(as.data.frame(reactome)) > 0) {
print(dotplot(reactome, showCategory = 15) + ggtitle('Reactome Pathways'))
}
dev.off()
# Export
write.csv(as.data.frame(go_bp_simple), file.path(output_dir, 'go_bp.csv'), row.names = FALSE)
write.csv(as.data.frame(kegg), file.path(output_dir, 'kegg.csv'), row.names = FALSE)
write.csv(as.data.frame(reactome), file.path(output_dir, 'reactome.csv'), row.names = FALSE)
cat('\nResults saved to:', output_dir, '\n')
cat('GO BP terms:', nrow(as.data.frame(go_bp_simple)), '\n')
cat('KEGG pathways:', nrow(as.data.frame(kegg)), '\n')
cat('Reactome pathways:', nrow(as.data.frame(reactome)), '\n')
Prokaryotic Organisms
For bacteria/archaea, standard org.db annotation packages are unavailable. Use KEGG directly with strain-specific organism codes:
# Find organism code
search_kegg_organism('Pseudomonas aeruginosa', by = 'scientific_name')
# KEGG ORA with bacterial organism code (e.g., 'pae' for P. aeruginosa PAO1)
kegg_bac <- enrichKEGG(gene = sig_gene_ids, organism = 'pae', keyType = 'kegg',
pvalueCutoff = 0.05)
# For organisms without KEGG annotation, use KEGG Orthology
# Map genes to KO IDs via eggNOG-mapper or KOALA, then:
kegg_ko <- enrichKEGG(gene = ko_ids, organism = 'ko', keyType = 'kegg')
GO enrichment for prokaryotes: use enricher() with custom GO-to-gene mapping from eggNOG-mapper or InterProScan output, rather than org.db packages.
Multi-Condition Enrichment Comparison
When comparing enrichment across conditions (e.g., treatment A vs B vs C):
# compareCluster: run ORA across multiple gene lists
gene_clusters <- list(
ConditionA = sig_genes_A,
ConditionB = sig_genes_B,
ConditionC = sig_genes_C
)
cc <- compareCluster(gene_clusters, fun = 'enrichKEGG', organism = 'hsa')
dotplot(cc, showCategory = 10) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Do not compare raw -log10(p-values) across conditions — they scale with sample size. Compare NES (normalized enrichment scores) for GSEA, or use compareCluster for ORA.
Related Skills
- database-access/biomart-queries - Bulk SYMBOL -> Entrez / HGNC / UniProt ID mapping
- database-access/uniprot-access - UniProt ID-mapping (alternative to bitr) for obsolete-accession resolution
- database-access/interaction-databases - Layer STRING / OmniPath interactions on enriched gene sets
- database-access/ortholog-inference - Pull pre-computed orthologs for cross-species enrichment
- pathway-analysis/go-enrichment - GO enrichment details
- pathway-analysis/kegg-pathways - KEGG analysis
- pathway-analysis/reactome-pathways - Reactome analysis
- pathway-analysis/gsea - GSEA methods
- pathway-analysis/enrichment-visualization - Visualization options
- differential-expression/de-results - Preparing gene lists for enrichment