DESeq2 Differential Expression Skill

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DESeq2 differential expression analysis skill with normalization, statistical modeling, and visualization

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the DESeq2 Differential Expression Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/bioinformatics/skills/deseq2-differential-expression/SKILL.md and read by ahel’s review.

Purpose

Provide DESeq2 differential expression analysis with normalization, statistical modeling, and visualization.

Capabilities

Size factor normalization
Negative binomial modeling
Shrinkage estimation
Batch effect modeling
Multi-factor designs
Result visualization (MA plots, volcano plots)

Usage Guidelines

Design experiments with appropriate replication
Include batch effects in model when present
Apply appropriate shrinkage estimators
Use multiple testing correction
Generate publication-quality visualizations
Document analysis parameters and thresholds

Dependencies

DESeq2
edgeR
limma-voom

Process Integration

RNA-seq Differential Expression Analysis (rnaseq-differential-expression)
Single-Cell RNA-seq Analysis (scrnaseq-analysis)
CRISPR Screen Analysis (crispr-screen-analysis)

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: deseq2-differential-expression
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter