pydeseq2-differential-expression
Bulk RNA-seq DE with PyDESeq2: load counts, normalize, fit negative binomial models, Wald test (BH-FDR), LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, multiple contrasts.
npx skills add BioTender-max/awesome-bio-agent-skills --skill pydeseq2-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.
# PyDESeq2 Differential Expression Analysis ## Overview PyDESeq2 is a Python reimplementation of the R DESeq2 package for differential gene expression analysis from bulk RNA-seq count data. It fits negative binomial generalized linear models per gene, estimates dispersion with empirical Bayes shrinkage, and performs Wald tests with Benjamini-Hochberg FDR correction. This skill covers the full pipeline from raw counts to publication-ready result tables and visualizations. ## When to Use - Identifying differentially expressed genes between two or more experimental conditions from bulk RNA-seq - Performing two-group comparisons (e.g., treated vs control) with proper statistical testing - Running multi-factor designs that account for batch effects or covariates (e.g., `~batch + condition`) - Applying log2 fold change shrinkage (apeGLM) for ranking and visualization - Generating volcano plots, MA plots, and heatmaps from differential expression results - Converting R-based DESeq2 workflows to a pure Python environment - Integrating DE analysis into larger Python bioinformatics pipelines (e.g., with scanpy, pandas) - Use **DESeq2** (R/Bioconductor) or **edgeR** instead for the reference
- Overview
- When to Use
- Prerequisites
- Workflow
- Step 1: Data Loading and Validation
- Step 2: Gene Filtering
- Step 3: DeseqDataSet Initialization and Fitting
- Step 4: Statistical Testing (Wald Test)
- Step 5: LFC Shrinkage (Optional)
- Step 6: Result Filtering and Export
- Step 7: Visualization — Volcano Plot
- Step 8: Visualization — MA Plot
- Key Parameters
- Common Recipes
pip install pydeseq2 matplotlib seaborn
What does the pydeseq2-differential-expression skill do?
Bulk RNA-seq DE with PyDESeq2: load counts, normalize, fit negative binomial models, Wald test (BH-FDR), LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, multiple contrasts.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill pydeseq2-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.