Agent skill · research science · aiskillstore
pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Why this skill is useful
Provides specific workflows and commands for differential gene expression analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires anndata, pandas installed locally. About 7k tokens when loaded. Last updated 2026-08-07. 405 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 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 Step 1: Data Preparation Input requirements: Count matrix: Samples × genes DataFrame with non-negative integer read counts Metadata: Samples × variables DataFrame with experimental factors Common data loading patterns: Data filtering: Step 2: Design Specification The design formula specifies how gene expression is modeled. Single-factor designs: Multi-factor designs: Design formula guidelines: Use Wilkinson formula notation (R-style) Put adjustment variables (e.g., batch) before the main variable of interest Ensure variables exist as columns in the metadata DataFrame Use appropriate data types (categorical for discrete variables) Step 3: DESeq2 Fitting Initialize the DeseqDataSet and run the complete pipeline: What deseq2() does: 1. Computes size factors (normalization) 2. Fits genewise dispersions 3. Fits dispersion trend curve 4. Computes dispersion priors 5. Fits MAP dispersions (shrinkage) 6. Fits log fold changes 7. Calculates Cook's distances (outlier detection) 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pydeseq2