🧬 Mastering Bulk RNA-seq Analysis in R – Post 14: From Gene Functions to Metabolic Maps with KEGG! Link to heading

🗺️ From Individual Jobs to Complete Workflows Link to heading

In Post 13, we discovered how GO enrichment transforms mysterious gene lists into biological processes. You learned that your treatment affects “immune response” and “cell cycle regulation.” But now you’re hungry for more detail: “How do these processes actually work? Which genes talk to which other genes? Where are the drug targets?”

This is where KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis takes over, transforming your analysis from understanding what happened to visualizing how it all connects. Think of it as upgrading from a simple map to a detailed GPS navigation system with turn-by-turn directions.

🎯 The Great GO vs KEGG Showdown Link to heading

Let’s settle this once and for all with a head-to-head comparison:

🔬 GO Tells You: “Individual Job Descriptions” Link to heading

Example: “This gene is involved in DNA repair”

  • What it’s great for: Understanding general biological processes
  • The limitation: Doesn’t show how genes work together
  • Perfect analogy: Reading individual job descriptions at a company

🗺️ KEGG Tells You: “Complete Workflow Diagrams” Link to heading

Example: “This gene catalyzes step 3 of DNA mismatch repair, which feeds into cell cycle checkpoint control, which can trigger apoptosis if damage is severe”

  • What it’s great for: Understanding molecular mechanisms and gene interactions
  • The superpower: Visual pathway diagrams showing gene relationships
  • Perfect analogy: Seeing the entire company organizational chart with workflow connections

The Power Combo Link to heading

Analysis Type Question Answered Best Use Case
GO Enrichment “What biological processes are affected?” Initial discovery, broad overview
KEGG Pathways “How do the affected genes work together?” Mechanism studies, drug target identification

🌟 What Makes KEGG Your Secret Weapon Link to heading

🛤️ Pathway-Centric Thinking Link to heading

KEGG organizes genes into functional modules rather than just categories:

  • Metabolic pathways: Glycolysis, TCA cycle, fatty acid synthesis
  • Signaling pathways: MAPK, Wnt, p53, insulin signaling
  • Disease pathways: Cancer, diabetes, neurodegeneration
  • Drug metabolism: How your treatment gets processed

🎯 Visual Pathway Diagrams Link to heading

Here’s KEGG’s killer feature: interactive pathway maps that show:

  • Which genes in your dataset are affected
  • How they connect to each other
  • Upstream and downstream relationships
  • Potential drug targets and intervention points

💊 Actionable Clinical Insights Link to heading

KEGG pathways directly translate to:

  • Drug targets: Genes you can potentially modulate
  • Biomarkers: Pathway activity as disease indicators
  • Mechanisms: Understanding how treatments work
  • Side effects: Predicting off-target pathway effects

🚀 Running KEGG Analysis: The Step-by-Step Guide Link to heading

Setup Your Analysis Arsenal Link to heading 

# Load the essentials
library(clusterProfiler)
library(pathview)  # For beautiful pathway diagrams
library(org.Hs.eg.db)
library(ggplot2)

The Core KEGG Workflow Link to heading 

# Start with your DE gene lists (from DESeq2)
upregulated_genes <- rownames(results[results$log2FoldChange > 1 & 
                                    results$padj < 0.05, ])

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

# Run KEGG enrichment
kegg_up <- enrichKEGG(gene = up_entrez$ENTREZID,
                      organism = 'hsa',  # Homo sapiens
                      pvalueCutoff = 0.05)

# Quick peek at results
head(kegg_up@result)

Create Stunning Visualizations Link to heading 

# Standard pathway overview
dotplot(kegg_up, showCategory = 15) +
  ggtitle("KEGG Pathway Enrichment: Upregulated Genes")

# The killer feature: actual pathway diagrams
pathview(gene.data = fold_changes,  # Your log2FC values
         pathway.id = "hsa04110",   # Cell cycle pathway
         species = "hsa")

📊 Reading KEGG Results Like a Pathway Detective Link to heading

Essential Columns Decoded Link to heading

Column What It Tells You What You Want
Description Pathway name Specific, relevant pathways
p.adjust Corrected significance < 0.05
Count Your genes in pathway 5-20 genes (sweet spot)
ID KEGG pathway identifier For pathway visualization

🔥 Example Results That Make You Smile Link to heading 

# Results that tell a clear story
#    ID        Description              p.adjust  Count
# 1  hsa04110  Cell cycle               2.1e-06   15
# 2  hsa04115  p53 signaling pathway   8.4e-05   8  
# 3  hsa04210  Apoptosis               1.2e-04   12

The biological story: “Your treatment disrupted cell cycle progression, activated p53 tumor suppressor responses, and triggered programmed cell death—a classic DNA damage response cascade!”

🎨 KEGG’s Visualization Superpowers Link to heading

🎯 Standard Plots for Overview Link to heading 

# Publication-ready dotplot
dotplot(kegg_results, showCategory = 20) +
  scale_color_gradient(low = "blue", high = "red") +
  theme_minimal() +
  labs(title = "KEGG Pathway Enrichment",
       x = "Gene Ratio",
       color = "p.adjust")

# Network view showing gene-pathway connections
cnetplot(kegg_results, showCategory = 10)

🗺️ The Crown Jewel: Pathway Diagrams Link to heading 

# Create pathway diagrams with your data overlaid
library(pathview)

# Prepare fold change data
fold_changes <- results$log2FoldChange
names(fold_changes) <- results$entrez_id

# Generate pathway diagram for cell cycle
pathview(gene.data = fold_changes,
         pathway.id = "hsa04110",  # Cell cycle
         species = "hsa",
         out.suffix = "my_experiment",
         kegg.native = TRUE)

This creates an actual KEGG pathway diagram with your genes colored by fold change—publication gold!

⚠️ KEGG Pitfalls and How to Dodge Them Link to heading

🚩 Red Flags in Your Results Link to heading

Only extremely broad pathways showing up

  • Terms like “Metabolic pathways” or “Signal transduction”
  • These are too general to be actionable

Pathways with only 1-2 genes

  • Probably statistical noise
  • Focus on pathways with 5+ genes

Results completely unrelated to your treatment

  • Double-check your gene ID conversion
  • Verify your experimental design captured the expected biology

😅 Common Mistakes That Waste Time Link to heading

Not converting gene IDs properly

# Always check conversion success
conversion_stats <- bitr(your_genes, fromType = "ENSEMBL", 
                        toType = "ENTREZID", OrgDb = org.Hs.eg.db)
success_rate <- nrow(conversion_stats) / length(your_genes)
cat("Gene ID conversion success:", round(success_rate * 100, 1), "%\n")

Forgetting to separate up and down-regulated genes

  • Opposite pathways can mask each other
  • Always analyze directional changes separately

Ignoring pathway diagrams

  • The visual maps are KEGG’s unique value
  • Don’t just look at the enrichment tables!

🧪 Real-World Applications: When KEGG Shines Link to heading

💊 Drug Discovery and Development Link to heading

Scenario: Testing a new cancer therapeutic

KEGG reveals:

  • Which pathways your drug affects
  • Potential off-target effects
  • Combination therapy opportunities
  • Biomarkers for patient stratification

🔬 Disease Mechanism Studies Link to heading

Scenario: Understanding diabetes progression

KEGG shows:

  • Insulin signaling pathway disruption
  • Metabolic pathway alterations
  • Inflammatory cascade activation
  • Connections between pathways

🎯 Precision Medicine Applications Link to heading

Scenario: Personalizing treatment approaches

KEGG enables:

  • Patient-specific pathway activity profiles
  • Targeted therapy selection
  • Resistance mechanism prediction
  • Response biomarker identification

🔥 The GO + KEGG Power Combo Strategy Link to heading

Step 1: Start with GO for the Big Picture Link to heading 

# Get the biological overview
go_results <- enrichGO(your_genes, ont = "BP")

Answers: “What general processes are affected?”

Step 2: Dive Deep with KEGG for Mechanisms Link to heading 

# Understand the molecular details
kegg_results <- enrichKEGG(your_genes, organism = 'hsa')

Answers: “How do these processes actually work mechanistically?”

Step 3: Cross-Reference for Validation Link to heading

Look for consistency between GO and KEGG results:

  • GO shows “cell cycle” → KEGG should show specific cell cycle pathways
  • GO shows “immune response” → KEGG should show immune signaling pathways
  • Contradictions suggest you need to investigate further

Decision Framework Link to heading

Research Goal Primary Tool Why
Initial discovery GO enrichment Broad overview, hypothesis generation
Mechanism studies KEGG pathways Detailed molecular interactions
Drug development KEGG pathways Targetable pathways and interactions
Biomarker discovery Both GO + KEGG Comprehensive functional annotation

🎯 Quality Control: Validating Your KEGG Results Link to heading

The Three Essential Checks Link to heading

1. Biological Coherence Test

  • Do the enriched pathways make sense for your treatment?
  • Can you explain the pathway connections to a colleague?

2. Literature Validation Test

  • Do similar studies show related pathway changes?
  • Are there published pathway maps that support your findings?

3. Pathway Diagram Reality Check

# Always examine the actual pathway diagrams
pathview(gene.data = your_fold_changes,
         pathway.id = top_pathway_id,
         species = "hsa")
  • Do your affected genes cluster in logical pathway modules?
  • Are the connections biologically plausible?

🌟 The Complete Functional Analysis Workflow Link to heading

Your Step-by-Step Protocol Link to heading

  1. Start with differential expression (DESeq2)
  2. Run GO enrichment for biological process overview
  3. Follow with KEGG pathways for mechanistic insights
  4. Create pathway diagrams for top KEGG hits
  5. Cross-validate GO and KEGG results
  6. Literature mining to confirm biological relevance

From Genes to Actionable Biology Link to heading

Before functional analysis: “We found 347 genes that changed expression”

After GO + KEGG analysis: “Our treatment specifically disrupted cell cycle checkpoint pathways while activating p53-mediated apoptosis, suggesting a targeted DNA damage response that could be enhanced with checkpoint inhibitor combinations”

That’s the transformation from data to discovery!

🧪 What’s Next? Link to heading

Post 15: Reactome Pathways for Human-Curated Precision will introduce you to Reactome, a manually curated pathway database that offers even more detailed pathway models and human-specific annotations. Perfect for when you need the highest quality pathway information!

Ready to explore the gold standard of human pathway curation?

💬 Share Your Thoughts! Link to heading

What’s the most interesting pathway connection you’ve discovered using KEGG? Any surprising pathway crosstalk that changed your research direction? Drop your pathway stories below!

#RNAseq #KEGG #PathwayAnalysis #ClusterProfiler #EnrichmentAnalysis #MetabolicPathways #SystemsBiology #DESeq2 #Bioinformatics #RStats