differential-expression
Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.
npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-expression --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.
# Differential Expression ## Version Compatibility Reference examples assume: - `pydeseq2` 0.4+ - `pandas` 2.2+ - `numpy` 1.26+ - `matplotlib` 3.8+ Verify before use: - Python: `python -c "import pydeseq2, pandas; print(pydeseq2.__version__, pandas.__version__)"` ## Overview Use this skill for count-based DE from bulk RNA-seq or similar count matrices when the user needs: - robust model fitting - explicit contrasts - ranked gene tables - volcano and MA plots - pathway-ready output tables ## When To Use This Skill - raw count matrix and sample metadata are available - the task is condition, treatment, or genotype comparison - batch or pairing terms may need explicit modeling ## Quick Route - no replicates: do not pretend formal DE is robust - 2 replicates per group: possible but conservative interpretation - 3 or more replicates per group: standard starting point ## Progressive Disclosure - Read [technical_reference.md](technical_reference.md) for design formulas, confounding checks, and contrast logic. - Read [commands_and_thresholds.md](commands_and_thresholds.md) for PyDESeq2 code, recommended filters, and output file conventions. ## Prerequisites | Requirement | Recommendation |
- Version Compatibility
- Overview
- When To Use This Skill
- Quick Route
- Progressive Disclosure
- Prerequisites
- Expected Inputs
- Expected Outputs
- Starter Pattern
- Workflow
- 1. Validate the design
- 2. Fit a count-aware model
- 3. Apply explicit filtering and ranking
- 4. Visualize results
What does the differential-expression skill do?
Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-expression --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.