Agent skill · research science · k-dense-ai
pydeseq2
Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
Why this skill is useful
Includes a complete command-line script for differential expression analysis that automates complex statistical workflows not easily inferred by the AI.
What it needs
Requires pandas, pydeseq2, python installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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: Core Workflow Steps The six steps, with code, are in references/coreworkflowsteps.md: 1. Data preparation — raw integer counts with genes as columns and samples as rows, and matching metadata. Never feed normalized or transformed values to DESeq2. 2. Design specification — the design factors and the reference level for each. 3. DESeq2 fitting — size factors, dispersions, and the GLM fit. 4. Statistical testing — Wald tests for a named contrast. 5. Optional LFC shrinkage — for ranking and visualization. 6. Result export — the results table with adjusted p-values. Multi-factor designs, contrasts, and interaction terms are in references/analysispatterns.md. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/pydeseq2