pydeseq2

Automate differential gene expression analysis of bulk RNA-seq data with PyDESeq2.

13|3|Updated Jun 10, 2026

Search Tags:#data analysis #DESeq2 #differential expression #RNA-seq #gene expression

Ready to run

npx skills add https://github.com/tassiovale/claude-code-kit --skill pydeseq2-tassiovale

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/pydeseq2
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill pydeseq2-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, scikit-learn, annData, formulaic, formulaic-contrasts, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies and automates the differential gene expression analysis using PyDESeq2, offering a streamlined process for RNA-seq data analysis and interpretation.

Core Features & Use Cases

Data Preparation: Automates the process of filtering low-count genes and removing samples with missing metadata.
DESeq2 Workflow: Manages the complete DESeq2 analysis pipeline, including normalization, fitting, and statistical testing.
Visualization: Provides visualization tools for result interpretation, including volcano and MA plots.
Use Case: Ideal for researchers analyzing bulk RNA-seq count data to compare gene expression between experimental conditions.

Quick Start

Run the following command to initiate a DESeq2 analysis: python run_deseq2_analysis.py --counts counts.csv --metadata metadata.csv --design "~condition" --contrast condition treated control --output results/

Frequently Asked Questions about pydeseq2

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

FAQPage Schema

How do I perform differential gene expression analysis on bulk RNA-seq data in Python?▼

Differential gene expression analysis for bulk RNA-seq data is automated using PyDESeq2, handling normalization, statistical testing, and visualization to compare gene expression between experimental conditions.

What's the best way to automate RNA-seq count data filtering and metadata cleanup before DESeq2 analysis?▼

Automating RNA-seq data preparation involves filtering low-count genes and removing samples with missing metadata, which this workflow handles natively before running the DESeq2 statistical testing pipeline.

Can I generate volcano and MA plots from RNA-seq differential expression results in Python?▼

Volcano and MA plots for interpreting RNA-seq differential expression results are generated automatically as part of the visualization workflow after the statistical testing completes.

Do I need pandas and scikit-learn to run PyDESeq2 for statistical testing on RNA-seq data?▼

Yes, PyDESeq2 requires pandas, numpy, scipy, scikit-learn, annData, and formulaic dependencies to perform normalization, statistical testing, and model fitting on bulk RNA-seq count data.

How does the DESeq2 design formula work for comparing treated versus control conditions in RNA-seq?▼

The DESeq2 design formula, such as "~condition", specifies the experimental variable, while the contrast parameter defines the specific groups like treated versus control for differential expression testing.