Skills pydeseq2
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pydeseq2

Safe 📁 Filesystem access🌐 Network access

Analyze RNA-seq differential gene expression with PyDESeq2

Also available from: davila7

PyDESeq2 enables differential gene expression analysis from bulk RNA-seq count data. Perform statistical testing, multiple comparison correction, and generate publication-ready volcano and MA plots for your genomics research.

Supports: Claude Codex Code(CC)
🥉 75 Bronze
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Using "pydeseq2". Analyze my RNA-seq data and show top differentially expressed genes

Expected outcome:

  • Analysis complete. Found 847 significant genes (padj < 0.05)
  • Top upregulated genes:
  • - GeneX: log2FC = 4.2, padj = 1.3e-15
  • - GeneY: log2FC = 3.8, padj = 2.7e-12
  • - GeneZ: log2FC = 3.5, padj = 5.1e-11
  • Top downregulated genes:
  • - GeneA: log2FC = -3.9, padj = 8.2e-14
  • - GeneB: log2FC = -3.1, padj = 3.4e-10
  • Results saved to deseq2_results.csv

Security Audit

Safe
v4 • 1/17/2026

All 429 static findings are false positives. The 'weak cryptographic algorithm' flags incorrectly match 'DES' in 'DESeq2' (a statistical method name, not cryptography). The 'external_commands' flags misinterpret markdown code fences as shell execution. Filesystem access is standard data I/O for bioinformatics workflows. Network access involves only documentation URLs. This is a legitimate scientific computing skill with no malicious code.

5
Files scanned
1,961
Lines analyzed
2
findings
4
Total audits

Risk Factors

📁 Filesystem access (2)
scripts/run_deseq2_analysis.py:180-185 SKILL.md:211-213
🌐 Network access (1)
SKILL.md:553-556
Audited by: claude View Audit History →

Quality Score

64
Architecture
100
Maintainability
83
Content
30
Community
100
Security
78
Spec Compliance

What You Can Build

Compare treated vs control

Identify differentially expressed genes between experimental conditions using proper statistical testing and FDR correction for publication-ready results.

RNA-seq thesis analysis

Process RNA-seq count data, perform differential expression analysis, and generate publication-quality figures for thesis or research papers.

Batch RNA-seq processing

Automate differential expression analysis across multiple conditions or timepoints using the included command-line script.

Try These Prompts

Basic DE analysis 
Load my RNA-seq data from counts.csv and metadata.csv, then perform differential expression analysis comparing treated vs control samples using PyDESeq2
Multi-factor design 
Analyze my RNA-seq data accounting for batch effects using design formula ~batch + condition, then test for treatment vs control differences
Generate visualizations 
Run PyDESeq2 analysis on my data and create volcano and MA plots highlighting significant genes with padj < 0.05
Advanced filtering 
Load RNA-seq data, filter genes with fewer than 20 total counts, use multi-factor design ~age + sex + condition, and identify genes with |log2FC| > 1 and padj < 0.01

Best Practices

  • Always transpose count matrix if genes are rows (use .T to get samples × genes format)
  • Filter low-count genes before analysis to improve statistical power
  • Use adjusted p-values (padj) not raw p-values for determining significance
  • Check that sample names match exactly between counts and metadata files

Avoid

  • Never use raw p-values for multiple testing - always use FDR-corrected padj values
  • Do not apply LFC shrinkage before statistical testing - use after for visualization only
  • Avoid complex multi-factor designs without sufficient sample size per condition
  • Never transpose metadata - only transpose count matrix if needed

Frequently Asked Questions

Why do I get an index mismatch error?
Sample names in counts and metadata files do not match. Ensure both files use identical sample identifiers in the same format.
Should I transpose my count matrix?
If your CSV has genes as rows and samples as columns, transpose with .T to get the required samples × genes format.
What is the difference between pvalue and padj?
pvalue is the raw statistical p-value; padj is the FDR-corrected value for multiple testing. Use padj < 0.05 for significance.
When should I use LFC shrinkage?
Apply LFC shrinkage after statistical testing for visualization, ranking genes, or creating heatmaps. Do not use for significance determination.
How do I handle batch effects in my analysis?
Include batch in your design formula as ~batch + condition. This controls for technical variation while testing biological differences.
Why are no genes significant in my analysis?
Check your sample size, effect sizes, and biological variability. Small studies or subtle effects may yield few significant genes.

Developer Details

Author

K-Dense-AI

License

MIT license

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pydeseq2

Ref

main

File structure

📁 references/

📄 api_reference.md

📄 workflow_guide.md

📁 scripts/

📄 run_deseq2_analysis.py

📄 SKILL.md