pydeseq2

Analyze bulk RNA-seq count data for differential gene expression with PyDESeq2.

74|5|Updated Dec 10, 2025

Search Tags:#bioinformatics #metadata #differential expression #rnaseq #pydeseq2 #wald test #lfc shrinkage

Ready to run

npx skills add https://github.com/dralkh/seerai --skill pydeseq2-dralkh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/dralkh/seerai/tree/main/skills/pydeseq2
Command: npx skills add https://github.com/dralkh/seerai --skill pydeseq2-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pydeseq2, pandas, matplotlib, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

It removes the manual friction from bulk RNA-seq differential expression analysis by guiding you from raw count matrices and sample metadata to statistically tested gene-level results.

Core Features & Use Cases

End-to-end DE analysis: Run normalization, dispersion fitting, Wald tests, multiple testing correction, and optional log fold-change shrinkage in one workflow.
Flexible experimental designs: Handle single-group comparisons, batch-adjusted models, interaction effects, and continuous covariates using formula-based design strings.
Practical research output: Export significant genes, ranked results, and portable AnnData files for downstream plotting, reporting, or collaboration.

Quick Start

Use this skill to analyze your RNA-seq count matrix and metadata with PyDESeq2 and return the full differential expression results along with the most significant genes.

Frequently Asked Questions about pydeseq2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run differential expression analysis on bulk RNA-seq count data?▼

To run differential expression analysis on bulk RNA-seq count data, provide a samples-by-genes count matrix and matched metadata. The workflow applies normalization, dispersion fitting, and Wald tests to return statistically tested gene-level results.

Can I adjust for batch effects in RNA-seq differential expression workflows?▼

Yes, you can adjust for batch effects in RNA-seq differential expression workflows. The analysis supports flexible experimental designs using formula-based design strings to handle batch-adjusted models, interaction effects, and continuous covariates.

What is LFC shrinkage and when should I use it in RNA-seq analysis?▼

LFC shrinkage in RNA-seq analysis visually stabilizes log fold-change estimates for genes with low counts or high dispersion. Apply it during differential expression testing to generate more accurate effect sizes for downstream ranking and visualization.

Does PyDESeq2 support exporting results as H5AD files for downstream analysis?▼

Yes, PyDESeq2 supports exporting results as H5AD files for downstream analysis. The workflow can output portable AnnData files alongside CSV files containing significant genes and ranked differential expression results.

What input formats are required for differential gene expression testing?▼

Differential gene expression testing requires samples-by-genes count matrices and matched metadata as input. You must also provide explicit contrasts and formulaic design strings to define the statistical model for the workflow.