pydeseq2

Perform DESeq2 differential expression analysis on bulk RNA-seq count data.

Updated Dec 17, 2025

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

Ready to run

npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill pydeseq2-robotlearning123

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/robotlearning123/claude-scientific-skills/tree/main/scientific-skills/pydeseq2
Command: npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill pydeseq2-robotlearning123

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of analyzing bulk RNA-seq count data to identify differentially expressed genes using the DESeq2 algorithm implemented in Python.

Core Features & Use Cases

DESeq2 Analysis: Execute the entire DESeq2 workflow, including normalization, estimation of dispersions, fitting log fold changes, and statistical testing.
Data Loading: Handle various data formats including CSV, TSV, and AnnData.
Use Case: Ideal for researchers who need to perform differential expression analysis on their RNA-seq data, offering a convenient Python-based alternative to the R-based DESeq2 package.

Quick Start

Execute the command 'deseq2 -c counts.csv -m metadata.csv -d "~condition" -o results/' to analyze your differential expression.

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 RNA-seq count data in Python?▼

Differential gene expression analysis on RNA-seq count data in Python can be performed using a DESeq2 implementation that handles normalization, dispersion estimation, and statistical testing. It processes bulk RNA-seq counts to identify significantly expressed genes.

What is the best way to run DESeq2 analysis without using R?▼

To run DESeq2 analysis without R, you can use this Python-based alternative that executes the entire workflow including normalization, log fold change fitting, and statistical testing on bulk RNA-seq data via a simple command line interface.

Does Python DESeq2 support loading RNA-seq data from AnnData formats?▼

Python DESeq2 supports loading RNA-seq data from AnnData formats, along with CSV and TSV files. This allows you to input bulk RNA-seq count matrices and metadata directly for differential expression testing.

Do I need pandas and scikit-learn installed to analyze differential expression with this tool?▼

You need pandas and scikit-learn installed to analyze differential expression, as they are required dependencies alongside numpy, scipy, anndata, and matplotlib for executing the DESeq2 workflow and processing the count data.

Can I use this differential expression tool for single-cell RNA-seq data?▼

This differential expression tool is aimed at biological researchers analyzing bulk RNA-seq count data. It is not designed for single-cell RNA-seq analysis, focusing specifically on bulk count normalization and statistical testing.