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pydeseq2

combined

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis. Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.

When to use

• Analyzing bulk RNA-seq count data for differential expression
• Comparing gene expression between experimental conditions (e.g., treated vs control)
• Performing multi-factor designs accounting for batch effects or covariates
• Converting R-based DESeq2 workflows to Python
• Integrating differential expression analysis into Python-based pipelines
• Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2"
• **Case vs control bulk RNA-seq** from a raw integer count matrix (e.g., treated vs control).
• **Multi-factor designs** to adjust for batch effects or covariates (e.g., `~ batch + condition`, `~ age + condition`).

Skill page

Conflict

The K-Dense-AI copy is a rigorous, version-pinned (PyDESeq2 0.5.4) skill that includes a CLI driver, strict data validation rules (rejecting silent intersections), and specific output artifacts. The other three copies (Vibe-Skills, medical-research-skills, OpenClaw-Medical-Skills) are nearly identical to each other, providing a standard Python API workflow with inline code examples and a manual volcano plot script. The K-Dense copy lacks the specific Python API code patterns found in the others, while the others lack the strict validation logic, CLI usage, and version constraints of the K-Dense copy. Combining them provides both the robust procedural guardrails and the practical code implementation.