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/rnaseq-de

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

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clawbio
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Install
$ npx -y skills add ClawBio/ClawBio --skill rnaseq-de --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition โ†’
  • You can call itInvoke it directly when you want it.
  • Slash command/rnaseq-de

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

rnaseq-de.SKILL.md
name: rnaseq-de
description: Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
license: MIT
metadata:
  version: 0.1.0
  tags:
  - rna-seq
  - differential expression
  - bulk
  - pseudo-bulk
  - transcriptomics
  - DESeq2
  - PyDESeq2
  - QC
  - PCA
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: ๐Ÿงฌ
    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

๐Ÿงฌ 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/
โ”œโ”€โ”€ report.md
โ”œโ”€โ”€ figures/
โ”‚   โ”œโ”€โ”€ pca.png
โ”‚   โ”œโ”€โ”€ volcano.png
โ”‚   โ””โ”€โ”€ ma_plot.png
โ”œโ”€โ”€ tables/
โ”‚   โ”œโ”€โ”€ qc_summary.csv
โ”‚   โ”œโ”€โ”€ normalized_counts.csv
โ”‚   โ””โ”€โ”€ de_results.csv
โ””โ”€โ”€ reproducibility/
    โ”œโ”€โ”€ commands.sh
    โ”œโ”€โ”€ environment.yml
    โ””โ”€โ”€ 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
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