/rnaseq-de
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
$ npx -y skills add ClawBio/ClawBio --skill rnaseq-de --agent claude-codeHow 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.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: ๐งฌ
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
- pydeseq2๐งฌ 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/
โโโ report.md
โโโ figures/
โ โโโ pca.png
โ โโโ volcano.png
โ โโโ ma_plot.png
โโโ tables/
โ โโโ qc_summary.csv
โ โโโ normalized_counts.csv
โ โโโ de_results.csv
โโโ reproducibility/
โโโ commands.sh
โโโ environment.yml
โโโ 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: ๐งฌ
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
- pydeseq2๐งฌ 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/
โโโ report.md
โโโ figures/
โ โโโ pca.png
โ โโโ volcano.png
โ โโโ ma_plot.png
โโโ tables/
โ โโโ qc_summary.csv
โ โโโ normalized_counts.csv
โ โโโ de_results.csv
โโโ reproducibility/
โโโ commands.sh
โโโ environment.yml
โโโ 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
๐ฆ ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free.
Other skills on clawbio.
- /affinity-proteomics
Unified analysis pipeline for affinity-based proteomics platforms โ Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer,
Open skill - /analyze-fasta
Synthetic ~120 aa protein sequence (CC0, no real organism)
Open skill - /ancestry-risk-profiler
Synthetic South Asian 23andMe profile with T2D, CAD, and hypertension risk alleles
Open skill - /archaic-introgression
Genomic coordinates of introgressed segments
Open skill - /article-data-fetcher
A test DOI pointing to a public GEO dataset
Open skill - /bgpt-mcp
Structured paper data with 25+ fields per result
Open skill

