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pydeseq2

Fit DESeq2-like models to bulk RNA-seq counts and run Wald tests with FDR correction.

Updated May 24, 2026
Search Tags:#python#volcano plot#differential expression#bulk rna-seq#fdr correction#wald test#deSeq2
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill pydeseq2-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/pydeseq2
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill pydeseq2-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

PyDESeq2 turns bulk RNA-seq count matrices into differential expression results by estimating normalization factors and dispersions, running Wald tests, and controlling false discoveries with FDR correction.

Core Features & Use Cases

  • End-to-end DE analysis for bulk RNA-seq: Takes a counts table and sample metadata, fits the DESeq2 model, and produces gene-level statistics (log2 fold change, p-values, and FDR-adjusted p-values).
  • Flexible experimental designs: Supports single-factor and multi-factor models (e.g., batch effects, covariates, and interaction terms) using Wilkinson-style design formulas.
  • Interpretation-ready outputs: Generates results dataframes suitable for filtering by padj, ranking by significance/effect size, and creating volcano/MA plots; also supports optional LFC shrinkage for cleaner visualization.
  • Use Case: Compare treated vs control bulk RNA-seq samples while accounting for batch effects, then export significant genes (padj < 0.05) for downstream pathway or validation work.

Quick Start

Use the pydeseq2 skill to run differential expression on your bulk RNA-seq counts from counts.csv with sample annotations in metadata.csv for a treated vs control contrast, producing results as a CSV of tested 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?▼

Bulk RNA-seq differential expression analysis takes a non-negative integer counts table and sample metadata, fits a DESeq2-like model, runs Wald tests, and outputs log2FoldChange, p-values, and FDR-adjusted p-values for each gene.

Can I use differential expression analysis for multi-factor designs with batch effects?▼

Yes, differential expression analysis supports multi-factor designs including batch effects and covariates by using Wilkinson-style formula designs to accurately model and isolate the treatment effect from confounding variables.

What's the best way to prepare bulk RNA-seq counts for differential gene expression testing?▼

Prepare bulk RNA-seq counts as non-negative integers with consistent sample and metadata indices, then pair them with a metadata table containing experimental conditions to run Wald tests and FDR correction.

How do I identify significant genes from bulk RNA-seq differential expression results?▼

Identify significant genes from bulk RNA-seq differential expression results by filtering the output dataframe using an FDR-adjusted p-value threshold, typically padj < 0.05, and ranking by log2 fold change.

Does differential expression analysis support LFC shrinkage for volcano plots?▼

Yes, differential expression analysis supports optional log2 fold change shrinkage to produce cleaner, interpretation-ready results dataframes suitable for generating volcano and MA plots for visualizing gene-level statistics.

Why does my bulk RNA-seq differential expression model require a Wilkinson formula design?▼

A Wilkinson formula design is required to explicitly define the relationships between sample metadata variables, enabling the DESeq2-like model to correctly estimate dispersions and run Wald tests across single-factor or multi-factor experimental setups.

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