tooluniverse-rnaseq-deseq2

Name: tooluniverse-rnaseq-deseq2
Availability: InStock
Author: teng

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

2|Updated May 7, 2026

Search Tags:#gene annotation #differential expression #deseq2 #rnaseq #enrichment analysis

Ready to run

npx skills add https://github.com/Teng-bio/codex-skills-hub --skill tooluniverse-rnaseq-deseq2

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tooluniverse-rnaseq-deseq2
Source: https://github.com/Teng-bio/codex-skills-hub/tree/main/skills/global/tooluniverse-rnaseq-deseq2
Command: npx skills add https://github.com/Teng-bio/codex-skills-hub --skill tooluniverse-rnaseq-deseq2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pydeseq2, gseapy, tooluniverse, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of RNA-seq differential expression analysis, providing a robust and automated way to identify differentially expressed genes using DESeq2, while offering additional features like gene annotation and enrichment analysis.

Core Features & Use Cases

DESeq2 Analysis: Perform differential expression analysis using DESeq2 with normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering.
Multi-Factor Design Support: Handle multi-factor designs, multiple contrasts, batch effects, and gene enrichment.
Flexible Input Formats: Supports CSV/TSV/H5AD input formats and any organism type.
Use Case: When you have RNA-seq count matrices and want to identify differentially expressed genes, perform differential expression with statistical rigor, or integrate with gene enrichment tools.

Quick Start

Use the tooluniverse-rnaseq-deseq2 skill with the 'analyze_rnaseq_data' command, providing the path to your count matrix file and metadata file.

Frequently Asked Questions about tooluniverse-rnaseq-deseq2

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

FAQPage Schema

How do I perform RNA-seq differential expression analysis with DESeq2?▼

RNA-seq differential expression analysis with DESeq2 requires a count matrix and metadata file in CSV, TSV, or H5AD format. The process automates normalization, dispersion estimation, Wald testing, and LFC shrinkage to identify differentially expressed genes.

Can I handle batch effects and multi-factor experimental designs in RNA-seq differential expression?▼

Yes, RNA-seq differential expression analysis supports multi-factor designs and multiple contrasts. You can account for batch effects by specifying them in your metadata file alongside your primary experimental condition variables.

What is the best way to integrate gene enrichment and annotation with differential expression results?▼

Gene enrichment and annotation integration uses gseapy and tooluniverse dependencies to automatically map differentially expressed genes to functional annotations and perform enrichment analysis directly after the DESeq2 statistical testing workflow.

Does DESeq2 differential expression analysis support H5AD input format?▼

Yes, DESeq2 differential expression analysis supports H5AD input format along with CSV and TSV files. You can provide your RNA-seq count matrix in any of these formats for processing with the pydeseq2 implementation.

What are the limitations of using pydeseq2 for RNA-seq differential expression?▼

Pydeseq2 for RNA-seq differential expression requires input count matrices rather than raw sequencing reads, meaning prior alignment and quantification must be completed. It relies on Python implementations of the DESeq2 statistical framework for analysis.

When do I need LFC shrinkage in RNA-seq differential expression analysis?▼

LFC shrinkage in RNA-seq differential expression analysis is needed when estimating log2 fold changes for genes with low counts or high variance. It stabilizes effect size estimates for more accurate ranking and visualization of differentially expressed genes.