pydeseq2
Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
npx skills add K-Dense-AI/scientific-agent-skills --skill pydeseq2 --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.
# PyDESeq2 ## Overview PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including formulaic single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData. ## When to Use This Skill This skill should be used when: - Analyzing bulk RNA-seq count data for differential expression - Comparing gene expression between experimental conditions (e.g., treated vs control) - Performing multi-factor designs accounting for batch effects or covariates - Converting R-based DESeq2 workflows to Python - Integrating differential expression analysis into Python-based pipelines - Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2" ## Quick Start Workflow For users who want to perform a standard differential expression analysis: ```python import pandas as pd from pydeseq2.dds import DeseqDataSet from pydeseq2.default_inference import DefaultInference from pydeseq2.ds import DeseqStats # 1. Load data counts_df = pd.read_csv("counts.csv", ind
- Overview
- When to Use This Skill
- Quick Start Workflow
- Core Workflow Steps
- Using the Analysis Script
- Result Interpretation
- Identifying Significant Genes
- Ranking and Sorting
- Quality Metrics
- Visualization Guidelines
- Volcano Plot
- MA Plot
- Troubleshooting Common Issues
- Data Format Problems
Basic usage python scripts/run_deseq2_analysis.py \ With additional options uv pip install pydeseq2==0.5.4
What does the pydeseq2 skill do?
Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pydeseq2 --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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.