Reactome Pathway Enrichment

Core Pattern - Over-Representation Analysis

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

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

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

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

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

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