deseq2-differential-expression

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

509 stars

Best use case

deseq2-differential-expression is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using deseq2-differential-expression should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/deseq2-differential-expression/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/bioinformatics/skills/deseq2-differential-expression/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/deseq2-differential-expression/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How deseq2-differential-expression Compares

Feature / Agent	deseq2-differential-expression	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# DESeq2 Differential Expression Skill

## 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)

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