rnaseq-de
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
Best use case
rnaseq-de is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
Teams using rnaseq-de should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/rnaseq-de/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rnaseq-de Compares
| Feature / Agent | rnaseq-de | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 🧬 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
├── 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
```bash
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 requiredRelated Skills
wes-clinical-report-es
Generates professional clinical PDF reports in Spanish from WES (Whole Exome Sequencing) data with clinical interpretation, pharmacogenomic alerts, and follow-up recommendations.
wes-clinical-report-en
Generates professional clinical PDF reports in English from WES (Whole Exome Sequencing) data with clinical interpretation summary, pharmacogenomic alerts, and follow-up recommendations.
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
variant-annotation
Annotate VCF variants with Ensembl VEP REST, ClinVar significance, gnomAD/population frequency context, and prioritized variant ranking.
ukb-navigator
Semantic search across UK Biobank's 12,000+ data fields and publications — find the right variables for your research question.
target-validation-scorer
Evidence-grounded target validation scoring with GO/NO-GO decisions for drug discovery campaigns
struct-predictor
Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs boltz predict, extracts per-residue pLDDT and PAE confidence, and writes a markdown report with figures.
soul2dna
Compile SOUL.md character profiles into synthetic diploid genomes (.genome.json) via trait-to-allele mapping
seq-wrangler
Sequence QC, alignment, and BAM processing. Wraps FastQC, BWA/Bowtie2, SAMtools for automated read-to-BAM pipelines.
scrna-orchestrator
Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional CellTypist annotation, optional latent downstream mode from integrated.h5ad/X_scvi, and optional dataset-level plus within-cluster contrastive marker analysis from raw-count .h5ad or 10x Matrix Market input.
scrna-embedding
Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
repro-enforcer
Export any bioinformatics analysis as a reproducible bundle with Conda environment, Singularity container definition, and Nextflow pipeline.