/rnaseq-de
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
Claude Code
$ npx -y skills add ClawBio/ClawBio --skill rnaseq-de --agent claude-code
Codex
$ npx skills add ClawBio/ClawBio --skill rnaseq-de -a codex -y
Cursor
$ npx skills add ClawBio/ClawBio --skill rnaseq-de -a cursor -y
Gemini CLI
$ npx skills add ClawBio/ClawBio --skill rnaseq-de -a gemini-cli -y
GitHub Copilot
$ npx skills add ClawBio/ClawBio --skill rnaseq-de -a github-copilot -y
OpenCode
$ npx skills add ClawBio/ClawBio --skill rnaseq-de -a opencode -y
Windsurf
$ npx skills add ClawBio/ClawBio --skill rnaseq-de -a windsurf -y
SKILL.md
rnaseq-de.SKILL.mdname: 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: 🧬
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.sha256Usage
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
Read more
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: 🧬
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.sha256Usage
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
🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free.
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Questions about rnaseq-de.
- Which coding agents can use rnaseq-de?
- rnaseq-de installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

