OpenSkillIndex← back to search
claude-codescience

Babysitter deseq2-differential-expression

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

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/bioinformatics/skills/deseq2-differential-expression" ~/.claude/skills/a5c-ai-babysitter-deseq2-differential-expression && rm -rf "$T"
manifest: library/specializations/domains/science/bioinformatics/skills/deseq2-differential-expression/SKILL.md
tags
#differential-expression#rna-seq#statistics#transcriptomics
source content

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)