🧬 Mastering Bulk RNA-seq Analysis in R – Post 15: Human-Curated Pathway Precision with Reactome! Link to heading

🎯 Meet the Gold Standard of Human Pathway Analysis Link to heading

We’ve journeyed through GO enrichment (Post 13) for biological processes and KEGG pathways (Post 14) for metabolic maps. Now it’s time to meet the aristocrat of pathway databases: Reactome—the hand-crafted, artisan-quality pathway resource that takes human-focused analysis to the next level.

Imagine if Wikipedia was written entirely by PhD experts who fact-checked every single sentence against peer-reviewed literature. That’s essentially what Reactome represents in the pathway analysis world. Every connection, every interaction, every pathway step has been manually curated and validated by human experts.

For human disease research, drug development, and clinical applications, Reactome often provides the most actionable and reliable pathway insights you can get.

🔬 The Database Hierarchy: From Good to Great Link to heading

Let’s put this in perspective with a clear comparison:

The Database Family Tree Link to heading

What This Means in Practice Link to heading

GO tells you: “This gene is involved in immune response”

KEGG tells you: “This gene participates in the cytokine signaling pathway”

Reactome tells you: “This gene encodes IL-6 which binds to IL-6 receptor, activates JAK1/JAK2/TYK2 kinases, leading to STAT3 phosphorylation and nuclear translocation, ultimately driving inflammatory gene expression—and here are the 15 papers that prove each step”

✨ What Makes Reactome Your Secret Weapon Link to heading

🧬 Human-Focused Excellence Link to heading

Exclusively human pathways

• No model organism data to confuse interpretations
• Direct relevance to human disease and therapeutics
• Species-specific molecular interactions

Expert-curated quality

• Every pathway manually reviewed by PhD-level scientists
• Regular updates based on latest research
• Quality control that puts other databases to shame

🌳 Hierarchical Precision Link to heading

Reactome organizes pathways in logical, hierarchical trees:

Top level: “Signal Transduction” Mid level: “Immune System” Specific level: “Interleukin-6 family signaling” Detailed level: “IL6-mediated signaling events”

This structure lets you zoom in and out depending on your research needs.

📚 Literature Integration Link to heading

Every single pathway step includes:

• Direct links to supporting publications
• Evidence codes for different types of experimental support
• Regular updates as new research emerges
• Cross-references to other major databases

🚀 Running Reactome Analysis: The Expert’s Guide Link to heading

Setup Your Analysis Environment Link to heading

# Install the Reactome-specific package
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ReactomePA")

# Load essential libraries
library(ReactomePA)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ggplot2)

The Core Reactome Workflow Link to heading

# Start with your DE gene lists
upregulated_genes <- rownames(results[results$log2FoldChange > 1 & 
                                    results$padj < 0.05, ])

# Convert to Entrez IDs (Reactome requirement)
up_entrez <- bitr(upregulated_genes, 
                  fromType = "ENSEMBL", 
                  toType = "ENTREZID", 
                  OrgDb = org.Hs.eg.db)

# Run Reactome pathway enrichment
reactome_up <- enrichPathway(gene = up_entrez$ENTREZID,
                            pvalueCutoff = 0.05,
                            readable = TRUE)

# Explore the results
head(reactome_up@result, 10)

Unlock the Hierarchical Power Link to heading

# Explore pathway hierarchy
reactome_up <- pairwise_termsim(reactome_up)  # Calculate similarities

# Visualize pathway relationships
emapplot(reactome_up, showCategory = 20) +
  ggtitle("Reactome Pathway Network")

# Tree-like visualization of hierarchy
treeplot(reactome_up, showCategory = 15)

📊 Reading Reactome Results Like a Clinical Expert Link to heading

Essential Columns Decoded Link to heading

🔥 Results That Make Clinicians Excited Link to heading

# Example results with clinical implications
#    Description                           p.adjust  Count
# 1  FGFR2 mutant receptor activation      1.2e-08   12
# 2  PI3K/AKT signaling in cancer         3.4e-06   23  
# 3  TP53 Regulates Transcription         8.9e-05   18

Clinical translation: “The treatment specifically affects oncogenic FGFR2 signaling, activates tumor suppressor pathways via p53, and modulates cancer-associated PI3K/AKT signaling—suggesting potential for combination therapy with FGFR inhibitors”

🧪 When Reactome Becomes Your Go-To Choice Link to heading

🔬 Human Disease Studies Link to heading

Perfect for:

• Cancer research with known oncogenes/tumor suppressors
• Neurological disorders with well-characterized pathways
• Metabolic diseases with human-specific pathway variants
• Immune system studies requiring precise human interactions

Example application:

# Focus on disease-relevant pathways
disease_pathways <- enrichPathway(disease_genes, 
                                 pvalueCutoff = 0.01,
                                 minGSSize = 5,
                                 maxGSSize = 500)

# Filter for cancer-related pathways
cancer_pathways <- disease_pathways@result[
  grepl("cancer|oncogene|tumor|apoptosis", 
        disease_pathways@result$Description, ignore.case = TRUE), ]

💊 Drug Development and Target Identification Link to heading

Advantages:

• Direct mapping to known drug targets
• Human-specific molecular interactions
• Pathway druggability assessment
• Off-target effect prediction

Practical workflow:

# Identify druggable pathways
drug_targets <- reactome_results@result[
  reactome_results@result$Count >= 5 & 
  reactome_results@result$p.adjust < 0.01, ]

# Cross-reference with known drug databases
# (integrate with DrugBank, ChEMBL, etc.)

🎯 Precision Medicine Applications Link to heading

Clinical value:

• Patient-specific pathway activity profiles
• Biomarker pathway identification
• Treatment response prediction
• Resistance mechanism elucidation

⚠️ Reactome Pitfalls and Quality Control Link to heading

🚩 Warning Signs to Watch For Link to heading

Only finding top-level broad pathways

• Terms like “Signal transduction” or “Metabolism”
• Need to explore deeper hierarchy levels
• Consider adjusting statistical thresholds

Ignoring the pathway hierarchy

• Missing the forest for the trees
• Always check parent-child pathway relationships
• Look for patterns across hierarchy levels

Not leveraging disease connections

• Reactome’s disease annotations are gold
• Always check if enriched pathways link to relevant diseases
• Cross-reference with clinical literature

😅 Common Analysis Mistakes Link to heading

Treating Reactome like GO

# Don't just look at the top results
head(reactome_results@result)  # This misses the hierarchy!

# Do explore the pathway relationships
emapplot(reactome_results, showCategory = 30)
treeplot(reactome_results, showCategory = 20)

Ignoring pathway overlap

• Reactome pathways can be hierarchically related
• High similarity between pathways might indicate broader biological themes
• Use pathway similarity analysis to identify meta-themes

🔥 The Triple Database Power Strategy Link to heading

Your Complete Functional Analysis Workflow Link to heading

Step 1: Start with GO for biological overview

go_results <- enrichGO(your_genes, ont = "BP")

Answers: “What general biological processes are affected?”

Step 2: Add KEGG for metabolic insights

kegg_results <- enrichKEGG(your_genes, organism = 'hsa')

Answers: “How do these processes connect metabolically?”

Step 3: Finish with Reactome for clinical precision

reactome_results <- enrichPathway(your_genes)

Answers: “What are the human-specific, clinically relevant mechanisms?”

Cross-Database Validation Strategy Link to heading

# Compare results across databases
go_terms <- go_results@result$Description
kegg_terms <- kegg_results@result$Description  
reactome_terms <- reactome_results@result$Description

# Look for convergent themes
# Example: All three show immune/inflammatory processes

Decision Matrix for Database Choice Link to heading

🎯 Quality Control: Validating Reactome Results Link to heading

The Clinical Relevance Test Link to heading

Literature validation:

• Do the enriched pathways appear in similar disease studies?
• Are there clinical trials targeting these pathways?
• Do the pathways connect to known biomarkers or therapeutic targets?

Pathway coherence check:

# Check pathway relationships
reactome_sim <- pairwise_termsim(reactome_results)
emapplot(reactome_sim, showCategory = 25)

# Look for logical pathway clusters
# Related pathways should group together

The Hierarchy Exploration Protocol Link to heading

# Always explore multiple hierarchy levels
upregulated_broad <- enrichPathway(up_genes, pvalueCutoff = 0.1)
upregulated_specific <- enrichPathway(up_genes, pvalueCutoff = 0.01)

# Compare broad vs specific pathway terms
# Specific terms should be subsets of broader ones

🌟 The Complete Picture: GO + KEGG + Reactome Link to heading

From Data to Clinical Insight Link to heading

Your gene list journey:

1. DESeq2: “347 genes changed expression”
2. GO enrichment: “Immune response and cell cycle processes affected”
3. KEGG pathways: “Cytokine signaling and cell cycle checkpoints disrupted”
4. Reactome precision: “Specifically, IL-6/JAK/STAT3 inflammatory signaling activated while p53-mediated cell cycle checkpoints triggered, suggesting combination IL-6 blockade with cell cycle inhibitors could be therapeutic”

That’s the transformation from genes to actionable clinical strategy!

🧪 What’s Next? Link to heading

Post 16: GSEA Introduction - When Gene Lists Aren’t Enough! will introduce Gene Set Enrichment Analysis, a powerful approach that considers ALL genes in your dataset, not just the significantly differentially expressed ones. Perfect for detecting subtle but coordinated pathway changes!

Ready to move beyond arbitrary cutoffs and analyze your entire gene expression profile?

💬 Share Your Thoughts! Link to heading

Have you used Reactome for clinical research? What pathway discoveries have influenced your therapeutic strategies? Share your Reactome success stories below!

#RNAseq #Reactome #PathwayAnalysis #ReactomePA #EnrichmentAnalysis #HumanPathways #DiseaseResearch #PrecisionMedicine #DESeq2 #Bioinformatics #RStats