Bio Pathway Reactome

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

When to Use Reactome

Reactome pathways are curated by PhD-level biologists and externally peer-reviewed, making them the highest-quality curated pathway database. Human is the primary species; all others are computationally inferred.

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

Supported Organisms

Interpretation Notes

• Reactome is very granular -- some pathways contain only 2-3 genes. Use minGSSize = 10 to filter these out.
• The deep hierarchy means parent pathways will often appear alongside child pathways. Look for the most specific (deepest) enriched pathway.
• Always specify a background universe (all tested genes) to avoid inflated significance.
• Examine fold enrichment (GeneRatio / BgRatio), not just p-values.
• For non-human species, note that annotations are orthology-inferred and may not capture species-specific pathway biology.

Related Skills

• go-enrichment - Gene Ontology enrichment
• kegg-pathways - KEGG pathway enrichment
• wikipathways - WikiPathways enrichment
• gsea - Gene Set Enrichment Analysis
• enrichment-visualization - Visualization functions

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