tooluniverse-rnaseq-deseq2
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-rnaseq-deseq2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RNA-seq Differential Expression Analysis (DESeq2) Comprehensive differential expression analysis of RNA-seq count data using PyDESeq2, with integrated enrichment analysis (gseapy) and gene annotation via ToolUniverse. **BixBench Coverage**: Validated on 53 BixBench questions across 15 computational biology projects covering RNA-seq, miRNA-seq, and differential expression analysis tasks. --- ## Core Principles 1. **Data-first approach** - Load and validate count data and metadata BEFORE any analysis 2. **Statistical rigor** - Always use proper normalization, dispersion estimation, and multiple testing correction 3. **Flexible design** - Support single-factor, multi-factor, and interaction designs 4. **Threshold awareness** - Apply user-specified thresholds exactly (padj, log2FC, baseMean) 5. **Reproducible** - Set random seeds, document all parameters, output complete results 6. **Question-driven** - Parse what the user is actually asking and extract the specific answer 7. **Enrichment integration** - Chain DESeq2 results into pathway/GO enrichment when requested 8. **English-first queries** - Use English gene/pathway names in all tool calls --- ## When to Use This Skill Apply whe
- Core Principles
- When to Use This Skill
- Required Packages
- Analysis Workflow
- Step 1: Question Parsing
- Step 1.5: Design Formula Decision Tree ⚠️ CRITICAL
- Step 2: Data Loading & Validation
- Step 2.5: Inspect Metadata Structure ⚠️ REQUIRED
- Step 3: Run PyDESeq2
- Step 4: Filter Results
- Step 5: Dispersion Analysis (if asked)
- Step 6: Enrichment Analysis (optional)
- Step 7: Gene Annotation with ToolUniverse (optional)
- Output Formatting
pip install pydeseq2 gseapy pandas numpy scipy anndata
What does the tooluniverse-rnaseq-deseq2 skill do?
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-rnaseq-deseq2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.