tools-only 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.
Workflow Overview
DE Results (gene list or ranked list)
|
v
[1. Gene ID Conversion] --> Convert to Entrez/Ensembl
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v
[2. Over-representation Analysis]
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+---> GO Enrichment (BP, MF, CC)
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+---> KEGG Pathways
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+---> Reactome Pathways
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[3. GSEA (ranked genes)]
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v
[4. Visualization] -----> Dot plots, networks, bar plots
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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))
# All genes for background
all_genes <- rownames(res)
# Ranked list for GSEA (by stat or log2FC)
ranked_genes <- res$log2FoldChange
names(ranked_genes) <- rownames(res)
ranked_genes <- sort(ranked_genes, decreasing = TRUE)
ranked_genes <- ranked_genes[!is.na(ranked_genes)]
Gene ID Conversion
# Convert gene symbols to Entrez IDs
sig_entrez <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID',
OrgDb = org.Hs.eg.db)
# For ranked list
ranked_entrez <- bitr(names(ranked_genes), fromType = 'SYMBOL', toType = 'ENTREZID',
OrgDb = org.Hs.eg.db)
ranked_list <- ranked_genes[ranked_entrez$SYMBOL]
names(ranked_list) <- ranked_entrez$ENTREZID
Step 1: GO Over-representation Analysis
# Biological Process
go_bp <- enrichGO(gene = sig_entrez$ENTREZID,
OrgDb = org.Hs.eg.db,
pAdjustMethod = 'BH',
pvalueCutoff = 0.05,
qvalueCutoff = 0.1,
readable = TRUE)
# Molecular Function
go_mf <- enrichGO(gene = sig_entrez$ENTREZID,
OrgDb = org.Hs.eg.db,
pAdjustMethod = 'BH',
pvalueCutoff = 0.05,
readable = TRUE)
# Cellular Component
go_cc <- enrichGO(gene = sig_entrez$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
ranked <- res$log2FoldChange
names(ranked) <- rownames(res)
ranked <- sort(ranked[!is.na(ranked)], decreasing = TRUE)
ranked_entrez <- bitr(names(ranked), fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
ranked_list <- ranked[ranked_entrez$SYMBOL]
names(ranked_list) <- ranked_entrez$ENTREZID
# GO enrichment
go_bp <- enrichGO(sig_entrez$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')
Related Skills
- 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