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rnaseq-de

Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.

Published by ClawBio ↗ClawBio/ClawBio

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rnaseq-de

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Use rnaseq-de to perform a research-only differential expression analysis using [count matrix and sample metadata] with the formula [formula] and contrast [contrast], returning a report with QC, PCA, result tables, plots, and reproducibility files.

Required inputs

  • [count matrix and sample metadata]
  • [formula]
  • [contrast]

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SKILL.md

name: rnaseq-dedescription: Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.license: MITmetadata:   version: 0.1.0  tags:   - rna-seq  - differential expression  - bulk  - pseudo-bulk  - transcriptomics  - DESeq2  - PyDESeq2  - QC  - PCA  openclaw:     requires:       bins:       - python3    always: false    emoji: 🧬    homepage: https://github.com/ClawBio/ClawBio    os:     - darwin    - linux    install:     - kind: uv      package: pandas    - kind: uv      package: numpy    - kind: uv      package: matplotlib    - kind: uv      package: scikit-learn    trigger_keywords:     - rna-seq    - differential expression    - bulk RNA    - pseudo-bulk    - volcano plot    - ma plot    - count matrix    - DESeq2    - pydeseq2

🧬 RNA-seq Differential Expression

This skill performs differential expression on bulk RNA-seq or pseudo-bulk count matrices.

Core Capabilities

  1. Input validation for count matrix and sample metadata
  2. Pre-DE QC (library size, detected genes, low-count filtering)
  3. PCA visualisation on normalized expression
  4. Differential expression from formula + contrast
  5. Volcano and MA plots
  6. Markdown report with reproducibility files

Input Contract

  • Count matrix (.csv or .tsv): rows are genes, columns are samples, first column is gene identifier
  • Metadata table (.csv or .tsv): one row per sample, must include sample_id
  • Formula: e.g. ~ condition or ~ batch + condition
  • Contrast: factor,numerator,denominator (e.g. condition,treated,control)

Output Structure

rnaseq_de_report/
├── report.md
├── result.json
├── figures/
│   ├── pca.png
│   ├── volcano.png
│   └── ma_plot.png
├── tables/
│   ├── qc_summary.csv
│   ├── normalized_counts.csv
│   └── de_results.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Usage

python rnaseq_de.py \
  --counts counts.csv \
  --metadata metadata.csv \
  --formula "~ batch + condition" \
  --contrast "condition,treated,control" \
  --output report_dir

Safety

  • Local-only processing
  • Warn before overwriting existing output
  • Report-level disclaimer required