PyDESeq2 (Claude Skill)

Claude skill guiding bulk RNA-seq differential expression analysis with PyDESeq2, the Python reimplementation of DESeq2’s negative-binomial GLM workflow.

   
Type Claude Skill
Supplier K-Dense Inc. (community OSS)
Availability GA — actively maintained 2025–2026
Pricing Free / OSS
Capabilities Read/Write — Claude executes PyDESeq2 via Python/Bash

How to install

  • Also packaged in the SciAgent-Skills collection (jaechang-hits (community OSS, CC BY 4.0)): clone jaechang-hits/SciAgent-Skills and run /plugin install sciagent-skills in Claude Code (or copy skills/genomics-bioinformatics/rnaseq/pydeseq2-differential-expression into ~/.claude/skills/).
  • Claude Code / Claude.ai — Skills CLI (recommended): 
    npx skills add K-Dense-AI/scientific-agent-skills

    Installs the K-Dense collection; enable the pydeseq2 skill when prompted (also works in Cursor/Codex via the Agent Skills spec; requires Node ≥ 18).

  • Claude Code / Claude Desktop — manual clone: 
    git clone https://github.com/K-Dense-AI/scientific-agent-skills
cp -r scientific-agent-skills/skills/pydeseq2 ~/.claude/skills/
pip install pydeseq2

What it does

Recipes covering:

  • Size-factor estimation for library-size normalisation
  • Dispersion estimation and shrinkage
  • Wald test and likelihood ratio test for differential expression
  • Benjamini–Hochberg FDR correction, filtering, and ranking of results
  • Multi-factor designs, batch effects, and replicate handling
  • Producing log2 fold-change estimates, p-values, and adjusted p-values per gene as a pandas DataFrame

Primary use cases: Bulk RNA-seq DE between conditions, biomarker discovery, batch-corrected expression studies, downstream input to enrichment workflows.

Notes

Pairs with the AnnData and Scanpy skills for end-to-end transcriptomics. For pseudobulk single-cell DE, combine with Scanpy/Scanpy-MCP to aggregate counts before passing to PyDESeq2.

Sources

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