bio-workflows-expression-to-pathways

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Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.

mdbabumiamssm By mdbabumiamssm schedule Updated 2/4/2026
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name: bio-workflows-expression-to-pathways description: Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment. tool_type: r primary_tool: clusterProfiler workflow: true depends_on: - pathway-analysis/go-enrichment - pathway-analysis/kegg-pathways - pathway-analysis/reactome-pathways - pathway-analysis/gsea - pathway-analysis/enrichment-visualization qc_checkpoints: - input_validation: "Valid gene IDs, sufficient DE genes" - enrichment_qc: "Reasonable number of terms, p-values not all significant" measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

Expression to Pathways Workflow

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
    |
    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))

# 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,
                  ont = 'BP',
                  pAdjustMethod = 'BH',
                  pvalueCutoff = 0.05,
                  qvalueCutoff = 0.1,
                  readable = TRUE)

# Molecular Function
go_mf <- enrichGO(gene = sig_entrez$ENTREZID,
                  OrgDb = org.Hs.eg.db,
                  ont = 'MF',
                  pAdjustMethod = 'BH',
                  pvalueCutoff = 0.05,
                  readable = TRUE)

# Cellular Component
go_cc <- enrichGO(gene = sig_entrez$ENTREZID,
                  OrgDb = org.Hs.eg.db,
                  ont = 'CC',
                  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,
                 ont = 'BP',
                 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, ont = 'BP', 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, ont = 'BP', 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
Install via CLI
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill bio-workflows-expression-to-pathways
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