Version Compatibility
Reference examples tested with: R stats (base), ReactomePA 1.46+, clusterProfiler 4.10+
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.
Reactome Pathway Enrichment
Core Pattern - Over-Representation Analysis
Goal: Identify Reactome pathways over-represented in a gene list from differential expression or other analyses.
Approach: Test for enrichment using the hypergeometric test via ReactomePA enrichPathway against curated peer-reviewed pathways.
"Run pathway enrichment against Reactome" → Test whether genes in curated Reactome pathways are over-represented among significant genes.
library(ReactomePA)
library(org.Hs.eg.db)
pathway_result <- enrichPathway(
gene = entrez_ids, # Character vector of Entrez IDs
organism = 'human', # human, rat, mouse, celegans, yeast, zebrafish, fly
pvalueCutoff = 0.05,
pAdjustMethod = 'BH',
readable = TRUE # Convert to gene symbols
)
head(as.data.frame(pathway_result))
Prepare Gene List from DE Results
Goal: Extract significant Entrez gene IDs from differential expression results for Reactome enrichment.
Approach: Filter by significance and fold change, then convert symbols to Entrez IDs using bitr.
library(clusterProfiler)
de_results <- read.csv('de_results.csv')
sig_genes <- de_results[de_results$padj < 0.05 & abs(de_results$log2FoldChange) > 1, 'gene_symbol']
gene_ids <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
entrez_ids <- gene_ids$ENTREZID
GSEA on Reactome Pathways
Goal: Detect coordinated expression changes in Reactome pathways using all genes ranked by a statistic.
Approach: Create a sorted named vector from DE results and run gsePathway for rank-based enrichment.
# Create ranked gene list (named vector sorted by statistic)
gene_list <- de_results$log2FoldChange
names(gene_list) <- de_results$entrez_id
gene_list <- sort(gene_list, decreasing = TRUE)
gsea_result <- gsePathway(
geneList = gene_list,
organism = 'human',
pvalueCutoff = 0.05,
pAdjustMethod = 'BH',
verbose = FALSE
)
head(as.data.frame(gsea_result))
With Background Universe
Goal: Restrict enrichment testing to only genes that were actually measured in the experiment.
Approach: Pass all tested gene IDs as the universe parameter to enrichPathway.
all_genes <- de_results$entrez_id # All tested genes
pathway_result <- enrichPathway(
gene = entrez_ids,
universe = all_genes, # Background gene set
organism = 'human',
pvalueCutoff = 0.05,
readable = TRUE
)
Visualization
Goal: Create publication-quality plots of Reactome enrichment results.
Approach: Use enrichplot functions (dotplot, barplot, emapplot, cnetplot, gseaplot2) on enrichment result objects.
library(enrichplot)
# Dot plot
dotplot(pathway_result, showCategory = 15)
# Bar plot
barplot(pathway_result, showCategory = 15)
# Enrichment map (requires pairwise_termsim first)
pathway_result <- pairwise_termsim(pathway_result)
emapplot(pathway_result)
# Gene-concept network
cnetplot(pathway_result, categorySize = 'pvalue')
# GSEA plot
gseaplot2(gsea_result, geneSetID = 1:3)
View Pathway in Browser
# Open pathway in Reactome browser
viewPathway('R-HSA-109582', organism = 'human') # Uses pathway ID
# Get pathway ID from results
top_pathway_id <- pathway_result@result$ID[1]
viewPathway(top_pathway_id, organism = 'human')
Export Results
results_df <- as.data.frame(pathway_result)
write.csv(results_df, 'reactome_enrichment.csv', row.names = FALSE)
# Key columns: ID, Description, GeneRatio, BgRatio, pvalue, p.adjust, geneID, Count
Different Organisms
# Mouse
pathway_mouse <- enrichPathway(gene = mouse_entrez, organism = 'mouse', readable = TRUE)
# Rat
pathway_rat <- enrichPathway(gene = rat_entrez, organism = 'rat', readable = TRUE)
# Zebrafish
pathway_zfish <- enrichPathway(gene = zfish_entrez, organism = 'zebrafish', readable = TRUE)
# Supported: human, rat, mouse, celegans, yeast, zebrafish, fly
Compare Clusters
Goal: Compare Reactome pathway enrichment across multiple gene lists (e.g., upregulated vs downregulated).
Approach: Use compareCluster with enrichPathway to run enrichment per group and visualize side by side.
# Compare pathways across multiple gene lists
gene_clusters <- list(
upregulated = up_genes,
downregulated = down_genes
)
compare_result <- compareCluster(
geneClusters = gene_clusters,
fun = 'enrichPathway',
organism = 'human',
pvalueCutoff = 0.05
)
dotplot(compare_result)
Key Parameters
| Parameter | Default | Description |
|---|
| gene | required | Vector of Entrez IDs |
| organism | human | Species name |
| pvalueCutoff | 0.05 | P-value threshold |
| pAdjustMethod | BH | Adjustment method |
| universe | NULL | Background genes |
| minGSSize | 10 | Min genes per pathway |
| maxGSSize | 500 | Max genes per pathway |
| readable | FALSE | Convert to symbols |
Supported Organisms
| Organism | Name | OrgDb |
|---|
| Human | human | org.Hs.eg.db |
| Mouse | mouse | org.Mm.eg.db |
| Rat | rat | org.Rn.eg.db |
| Zebrafish | zebrafish | org.Dr.eg.db |
| Fly | fly | org.Dm.eg.db |
| C. elegans | celegans | org.Ce.eg.db |
| Yeast | yeast | org.Sc.sgd.db |
Related Skills
- go-enrichment - Gene Ontology enrichment
- kegg-pathways - KEGG pathway enrichment
- wikipathways - WikiPathways enrichment
- gsea - Gene Set Enrichment Analysis
- enrichment-visualization - Visualization functions