Imported from BioTender-max/awesome-bio-agent-skills (skills/omicsclaw/bulk-rnaseq-counts-to-de-deseq2/SKILL.md). Install upstream with npx skills add BioTender-max/awesome-bio-agent-skills --skill bulk-rnaseq-counts-to-de-deseq2. Copyright stays with the author.

DESeq2 Differential Expression Analysis

Core DESeq2 workflow for RNA-seq differential expression analysis with count data.

When to Use This Skill

Use DESeq2 when you have:

• ✅ Raw integer count data (not normalized TPM/FPKM)
• ✅ Biological replicates (≥2 per condition, ≥4 recommended)
• ✅ Need for log fold change shrinkage (ranking/visualization)
• ✅ Medium to large sample sizes (DESeq2's strength)

Don't use DESeq2 for:

• ❌ Normalized data (TPM/FPKM) → use limma-voom instead
• ❌ Very small samples (n=2-3) → consider edgeR quasi-likelihood

Quick Start (Example Data)

Test this skill with real RNA-seq data in ~2 minutes:

source("scripts/load_example_data.R")
data <- load_pasilla_data()  # Auto-installs pasilla package if needed (~2 min, ~50MB)
counts <- data$counts        # 14,599 genes × 7 samples
coldata <- data$coldata      # Metadata: treated vs untreated

# Run complete workflow
source("scripts/basic_workflow.R")  # Creates dds, res, resLFC objects + prints summary

What you get:

• Dataset: Drosophila pasilla gene RNAi knockdown (Brooks et al. 2011)
• Comparison: 3 treated vs 4 untreated samples
• Expected results: ~1,000 significant genes at padj < 0.1

For your own data: Replace data loading with your count matrix and metadata (see Inputs section).

Installation

Core packages (required):

# Set CRAN mirror first (required for installation)
options(repos = c(CRAN = "https://cloud.r-project.org"))

if (!require('BiocManager', quietly = TRUE))
    install.packages('BiocManager')
BiocManager::install(c('DESeq2', 'apeglm'))

Example data packages (optional - for testing/learning):

BiocManager::install(c('pasilla', 'airway'))  # ~70MB total, ~2-3 min

Visualization packages (required for QC plots):

# For publication-quality plots (required - generates PNG)
install.packages(c('ggplot2', 'ggprism', 'ggrepel'))

# For SVG export (optional - generates both PNG + SVG)
install.packages('svglite')

License: LGPL (>= 3) (commercial use permitted)

Inputs

Required:

• Count matrix: Raw integer counts (genes × samples)
  • Rows = genes (any identifier: Ensembl, symbols, etc.)
  • Columns = samples
  • Values = non-negative integers
• Sample metadata: Data frame with sample information
  • Row names must match count matrix column names
  • Required column: condition (factor with 2+ levels)
  • Optional: batch, covariates for complex designs

Alternative inputs:

• Salmon/Kallisto output (via tximport)
• SummarizedExperiment object
• featureCounts/HTSeq output
• Bioconductor data packages (pasilla, airway)

See references/deseq2-reference.md for loading examples.

Outputs

Primary results:

• deseq2_results.csv - Full differential expression table (baseMean, log2FC, lfcSE, pvalue, padj)
• deseq2_results_shrunk.csv - Shrunken LFC for visualization/ranking
• dds_object.rds - DESeqDataSet for further analysis

Normalized data:

• normalized_counts.csv - Size-factor normalized counts
• vst_transformed.csv / rlog_transformed.csv - Variance-stabilized values

QC plots (PNG always, SVG strongly preferred, 300 DPI):

• dispersion_plot.png / .svg - Dispersion estimates vs mean
• pca_plot.png / .svg - Principal component analysis
• ma_plot.png / .svg - Mean-average plot
• volcano_plot.png / .svg - Volcano plot (log2FC vs -log10 padj)
• ⚠️ SVG requires svglite package: install.packages('svglite') (falls back to PNG-only if unavailable)

Clarification Questions

⚠️ CRITICAL: Always ask question #1 first to check if user has provided input files before proceeding with analysis.

Before starting, gather:

1. Input Files (ASK THIS FIRST):

   • Do you have specific count matrix file(s) to analyze?
     • If uploaded: Is this the count matrix (genes × samples, raw integer counts)?
     • Expected formats: CSV/TSV, RDS (SummarizedExperiment), Salmon/Kallisto output
   • Or use example data for testing?
     • Use source("scripts/load_example_data.R"); data <- load_pasilla_data()
     • Requires installing pasilla package (~2 min, ~50MB)
   • ⚠️ If data is normalized (TPM/FPKM): Use limma-voom skill instead

2. Sample Metadata (if using own data):

   • What is the primary comparison (e.g., treated vs control)?
   • Which group is the reference/control?
   • Any covariates to adjust for (batch, sex, sequencing run)?
   • Validation: Confirm sample IDs match between count matrix columns and metadata rows

3. Experimental Design:

   • Simple: ~ condition | Multi-factor: ~ batch + condition | Paired: ~ individual + condition | Interaction: ~ genotype * treatment
   • See references/decision-guide.md#design-formulas

4. Sample Size Check:

   • n ≥ 4 per group (recommended) | n = 2-3 (consider edgeR) | n < 2 (insufficient)

5. Significance Thresholds:

   • Standard: padj < 0.05, |log2FC| ≥ 1 | Relaxed: padj < 0.1 | Stringent: padj < 0.01, |log2FC| ≥ 2

6. Analysis Goals:

   • Single pairwise comparison or multiple comparisons?
   • Need visualizations (volcano, heatmap)? → Use de-results-to-plots skill after
   • Need gene annotations? → Use de-results-to-gene-lists skill after

Typical Complete Workflow

This skill performs core differential expression analysis with QC plots. For a complete RNA-seq workflow:

1. This skill: Run DESeq2 → get dds, res, normalized counts, QC plots (PCA, MA, volcano, dispersion)
2. de-results-to-gene-lists: Filter significant genes → add annotations → export
3. de-results-to-plots (optional): Advanced visualizations (heatmaps, custom plots)

Quick start: "Run DESeq2 analysis and filter significant genes with annotations"

Why separate skills? Modular design works across DE methods (DESeq2, edgeR, limma). See Suggested Next Steps for details.

Standard Workflow

Note: Run from the OmicsClaw root directory and add the workflow scripts to sys.path:

import sys; import os; sys.path.insert(0, os.path.abspath('knowledge_base/scripts/bulk-rnaseq-counts-to-de-deseq2'))

🚨 MANDATORY: USE SCRIPTS EXACTLY AS SHOWN - DO NOT WRITE INLINE CODE 🚨

This skill uses low-freedom script execution. You must:

• ✅ Source the scripts using the exact commands below
• ✅ Wait for confirmation messages after each step
• ❌ NOT write inline DESeq2 code
• ❌ NOT rewrite plotting code
• ❌ NOT modify commands unless explicitly adapting for user-specific data

WHY USE SCRIPTS: They handle package installation, data validation, sample ID fixes, and error checking automatically. Writing inline code wastes time, introduces errors, and violates the skill design.

Step 1 - Load example data:

source("scripts/load_example_data.R")
data <- load_pasilla_data()
counts <- data$counts
coldata <- data$coldata

Step 2 - Run DESeq2 analysis:

source("scripts/basic_workflow.R")

DO NOT expand this into inline code. DO NOT write the DESeq2 steps manually. Just source the script.

Step 3 - Generate QC plots:

source("scripts/qc_plots.R")
run_all_qc(dds, res, output_dir = "results")

🚨 DO NOT write inline plotting code (ggsave, plotMA, etc.). Just source the script. 🚨

The script handles PNG + SVG export with graceful fallback for SVG dependencies.

Step 4 - Export results:

source("scripts/export_results.R")
export_all(dds, res, resLFC, output_dir = "results")

DO NOT write custom export code. Use export_all() to save all standard outputs including RDS and transformed counts.

✅ VERIFICATION - You should see these messages:

• After Step 1: "✓ Pasilla dataset loaded successfully" with dimensions
• After Step 2: "✓ Basic workflow completed successfully!" with summary statistics
• After Step 3: "✓ All QC plots generated successfully!" with file names
• After Step 4: "=== Export Complete ===" with list of 6-7 files saved

❌ IF YOU DON'T SEE THESE MESSAGES: You wrote inline code instead of using source(). Stop and use the commands above.

⚠️ CRITICAL - DO NOT:

• ❌ Write inline data loading code → STOP: This violates the skill design. Use source("scripts/load_example_data.R") instead. Inline loading causes sample ID mismatches and missing validations.
• ❌ Write inline DESeq2 workflow code → STOP: This violates the skill design. Use source("scripts/basic_workflow.R") instead. Inline workflow wastes time and introduces bugs.
• ❌ Write inline plotting code (ggsave, plotMA, etc.) → STOP: This violates the skill design. Use source("scripts/qc_plots.R") and run_all_qc() instead. If scripts fail, fix the script, don't rewrite inline.
• ❌ Write custom export code → STOP: This violates the skill design. Use source("scripts/export_results.R") and export_all() instead. Custom export code misses RDS objects and transformed counts needed downstream.
• ❌ Try to install svglite → script handles SVG fallback automatically
• ❌ Use absolute paths for scripts → Always use relative paths scripts/file.R
  • ❌ WRONG: source("/mnt/knowhow/workflows/bulk-rnaseq-counts-to-de-deseq2/scripts/load_example_data.R")
  • ❌ WRONG: setwd("/absolute/path/to/skill")
  • ✅ CORRECT: source("scripts/load_example_data.R") (skill should already be working directory)

⚠️ IF SCRIPTS FAIL - Script Failure Hierarchy:

1. Fix and Retry (90%) - Install missing package, re-run script
2. Modify Script (5%) - Edit the script file itself, document changes
3. Use as Reference (4%) - Read script, adapt approach, cite source
4. Write from Scratch (1%) - Only if genuinely impossible, explain why

NEVER skip directly to writing inline code without trying the script first.

📁 Output Directory Paths:

• ✅ Recommended: Use relative paths like output_dir = "results" (creates folder in working directory)
• ✅ Also valid: Environment-specific absolute paths like output_dir = "/mnt/results" (containerized environments only)

✅ When to read references for adaptation (NOT rewriting):

• Design formulas (multi-factor, interactions): Read references/comprehensive-reference.md#design-formulas to understand patterns
• Result extraction (specific contrasts): Read references/comprehensive-reference.md#extracting-results
• Shrinkage methods (ashr vs apeglm): Read references/comprehensive-reference.md#log-fold-change-shrinkage

❌ When NOT to write custom inline code:

• Unless user explicitly says: "show me the complete inline workflow without using scripts"
• The scripts already handle 95% of use cases - use them first, customize only if truly needed

What the scripts provide:

• scripts/load_example_data.R - load_pasilla_data(), load_airway_data(), validate_input_data()
• scripts/basic_workflow.R - Complete DESeq2 pipeline with validation and error messages
• scripts/qc_plots.R - Publication-quality plots with ggplot2/ggprism/ggrepel (PNG + SVG if svglite installed)
• scripts/export_results.R - export_all() saves all outputs (CSV, RDS, transformed counts)

When Scripts Fail

When a script fails, follow this hierarchy:

1. Fix and Retry (Preferred)

• Read the error message - Understand what went wrong
• Install missing packages - Use BiocManager::install() or install.packages()
• Update dependencies - If version conflicts, update packages: install.packages('package_name')
• Check your data - Ensure count matrices are integer counts and metadata is properly formatted
• Re-run the script - After fixing the issue, source the script again

2. Modify the Script (If Fix Doesn't Work)

• Edit the script file to fix the issue (e.g., change default parameters, add data validation)
• Document your changes - Add comment: # Modified: [what and why]
• Source the modified script - Use the edited version

3. Use Script as Reference (If Can't Modify Script)

• Read the script to understand the approach and logic
• Adapt the approach to your specific situation (different data format, missing dependencies)
• Cite the source - Comment: # Adapted from scripts/basic_workflow.R
• Explain the deviation - Why the original script couldn't be used

4. Write From Scratch (Absolute Last Resort)

• Only if steps 1-3 are impossible (e.g., script fundamentally incompatible with environment)
• Explain to user why scripts couldn't be used
• Document the deviation - Note what approach you're taking instead

⚠️ DO NOT skip straight to step 4 - Always attempt steps 1-3 first. Scripts are designed, tested, and documented. Inline code should be a last resort, not a first choice.

Example decision tree:

• Missing package? → Step 1 (install and retry)
• Script has bug? → Step 2 (fix script and re-run)
• User's data format differs? → Step 3 (adapt script logic)
• Can't install required packages? → Step 4 (explain and provide alternative)

Design Formulas

Common patterns: ~ condition (simple), ~ batch + condition (batch correction), ~ individual + condition (paired), ~ genotype * treatment (interaction).

⚠️ Design must not be confounded - ensure batches exist in both conditions.

To understand patterns and choose the appropriate design formula for your experimental setup: Read references/comprehensive-reference.md#design-formulas and adapt the syntax to your specific experimental design.

Extracting Results

Extract comparisons using results() with either coefficient name (name = 'condition_treated_vs_control') or contrast (contrast = c('condition', 'treated', 'control')).

Use resultsNames(dds) to see available coefficients.

For standard extraction patterns: Use scripts/extract_results.R (execute as-is).

To understand extraction methods and choose the appropriate approach for your comparison: Read references/comprehensive-reference.md#extracting-results and adapt the syntax to your specific contrast needs.

Log Fold Change Shrinkage

⚠️ REQUIRED for visualization/ranking. Use shrunk LFC for MA/volcano plots and gene ranking; use unshrunk for hypothesis testing.

Apply shrinkage with lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'apeglm'). Use apeglm method (recommended), ashr (faster for large datasets), or normal (legacy).

For standard shrinkage: Use scripts/extract_results.R apply_lfc_shrinkage() (execute as-is).

To understand shrinkage methods and choose the appropriate approach for your analysis: Read references/comprehensive-reference.md#log-fold-change-shrinkage to compare methods and adapt the syntax to your specific use case.

Normalization & Transformations

source("scripts/transformations.R")
transformed <- transform_counts(dds, method = "auto")  # Auto-selects vst/rlog by sample size

Script: scripts/transformations.R Decision: vst() for >30 samples (fast), rlog() for <30 samples (accurate). See references/decision-guide.md#transformation

Quality Control

🚨 REQUIRED: Use provided script (DO NOT write inline plotting code)

CRITICAL: Source the script and call run_all_qc(). DO NOT reimplement plotting.

source("scripts/qc_plots.R")
run_all_qc(dds, res, output_dir = "qc_plots")  # Auto-generates all QC plots

What you get automatically:

• dispersion_plot.svg - Gene-wise dispersion vs mean expression (ggplot2 + ggprism theme)
• pca_plot.svg - Sample clustering with labeled samples (ggrepel prevents overlaps)
• ma_plot.svg - Log fold change vs expression with top genes labeled (ggrepel)
• volcano_plot.svg - Log2 fold change vs adjusted p-value with top genes labeled (ggrepel)
• Automatic quality checks printed to console
• Publication-ready plots styled with ggprism themes

Features built-in:

• ✅ ggplot2 for customizable, high-quality plots
• ✅ ggprism themes for publication-ready styling
• ✅ ggrepel for non-overlapping text labels
• ✅ Auto-selects vst/rlog by sample size
• ✅ Saves as SVG (vector) or PNG (raster) with 300 DPI

⚠️ DO NOT write inline plotting code - scripts handle all visualization needs

Script: scripts/qc_plots.R - Complete QC plotting functions

For custom plot styling: See references/qc-guide.md#custom-plots (only if user explicitly requests customization)

Key checks: Dispersion trend fit, PCA clustering by condition, symmetric MA plot. See references/qc-guide.md

Exporting Results

source("scripts/export_results.R")
export_all(dds, res, res_shrunk, output_dir = "deseq2_results")

Script: scripts/export_results.R - Exports results, shrunk LFC, normalized/transformed counts, significant genes

Decision Points

Decision 1: Transformation Method

When: Before creating PCA plots and heatmaps

Options:

• vst(): Use for >30 samples (fast, suitable for large datasets)
• rlog(): Use for <30 samples (better for small samples, slower)

See references/decision-guide.md#decision-point-1 for detailed guidance.

Decision 2: LFC Shrinkage Method

When: Before ranking genes or creating MA/volcano plots

Options:

• apeglm (recommended): Best shrinkage, preserves large LFC
• ashr: Good for large datasets or when apeglm is slow
• normal: Legacy method, not recommended

See references/decision-guide.md#decision-point-2 for detailed guidance.

Decision 3: Design Formula

When: Before creating DESeqDataSet

Options:

• ~ condition: Simple design, no known batch effects
• ~ batch + condition: Known batch effects (requires balanced design)
• ~ individual + condition: Paired samples
• ~ genotype * treatment: Test interactions

Check PCA first - if samples cluster by batch, add batch to design.

See references/decision-guide.md#decision-point-3 for detailed guidance.

Common Issues

See references/troubleshooting.md for comprehensive troubleshooting guide.

Best Practices

1. 🚨 CRITICAL: Use source() commands from Standard Workflow - DO NOT write inline code
   • Verify you see success messages: "✓ Pasilla dataset loaded successfully", "✓ Basic workflow completed successfully!"
   • Scripts handle all package installation, validation, and error checking automatically
2. ✅ REQUIRED: Validate sample IDs match between counts and metadata (scripts do this automatically, or use validate_input_data())
3. ✅ REQUIRED: Pre-filter low-count genes before DESeq() (basic_workflow.R does this)
4. ✅ REQUIRED: Set reference level explicitly with relevel() (basic_workflow.R does this)
5. ✅ REQUIRED: Apply LFC shrinkage for visualization/ranking, use unshrunk for testing (basic_workflow.R does this)
6. ✅ Use padj (not pvalue) for significance calling
7. ✅ Check QC plots before trusting results (PCA, dispersion, MA) - use run_all_qc()
8. ✅ Use vst() for >30 samples, rlog() for <30 samples (qc_plots.R auto-selects)
9. ✅ Document design formula and report DESeq2 version

Suggested Next Steps

After completing DESeq2 analysis, you'll typically want to:

1. Filter and Export Results (de-results-to-gene-lists skill)

RECOMMENDED NEXT STEP - Use the de-results-to-gene-lists skill to:

• Filter significant genes (padj < 0.05, |log2FC| > 1)
• Add gene annotations (symbols, descriptions, IDs)
• Export to CSV, Excel, or gene list formats
• Create ranked gene lists for GSEA

Example prompt: "Filter the DESeq2 results to get significant genes with padj < 0.05 and |log2FC| > 1, add gene annotations, and export to CSV and Excel"

Inputs needed: The res and dds objects from this analysis

2. Create Advanced Visualizations (de-results-to-plots skill)

OPTIONAL - This skill already generates basic QC plots (PCA, MA, volcano, dispersion). Use the de-results-to-plots skill for:

• Publication-quality visualizations with advanced customization
• Heatmaps of top differentially expressed genes
• Sample distance matrices
• Expression plots for specific genes of interest

Example prompt: "Create a heatmap of the top 50 significant genes and expression plots for genes FBgn0039155, FBgn0025111"

Inputs needed: The res, dds, and transformed count data from this analysis

3. Functional Enrichment Analysis

After filtering significant genes (using de-results-to-gene-lists):

• pathway-analysis - GO/KEGG enrichment of gene lists
• gsea - Gene set enrichment on ranked genes

4. Quality Control

If you see issues in QC plots:

• Batch effects in PCA: Re-run with ~ batch + condition design
• Poor sample clustering: Check sample metadata for swaps/errors
• High dispersion: May indicate low quality samples

Related Skills

Alternative methods (use instead of this skill):

• edger - Use for small samples (n=2-3) or many contrasts (coming soon)
• limma-voom - Use for normalized data (TPM/FPKM) (coming soon)

References

Detailed documentation:

• references/deseq2-reference.md - Complete code patterns and examples
• references/decision-guide.md - Detailed decision-making guidance
• references/troubleshooting.md - Comprehensive error solutions

Scripts:

• scripts/basic_workflow.R - Complete example workflow
• scripts/qc_plots.R - Quality control functions
• scripts/extract_results.R - Results extraction functions
• scripts/export_results.R - Export functions
• scripts/transformations.R - Transformation functions

Official documentation:

• DESeq2 Bioconductor: http://bioconductor.org/packages/DESeq2
• DESeq2 paper: Love et al. (2014) Genome Biology

License: LGPL (>= 3) (commercial use permitted)