differential-expression

Name: differential-expression
Availability: InStock
Author: zongtingwei

Perform count-based differential expression analysis on bulk RNA-seq data with PyDESeq2.

25|5|Updated Mar 22, 2026

Search Tags:#differential-expression #deseq2 #pydeseq2 #volcano-plot #bulk-rna-seq #gene-signatures

Ready to run

npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill differential-expression-zongtingwei

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: differential-expression
Source: https://github.com/zongtingwei/Bioclaw_Skills_Hub/tree/main/skills/transcriptomics/differential-expression
Command: npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill differential-expression-zongtingwei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Bulk transcriptomics differential expression analysis with count-aware modeling, design validation, contrasts, and publication-ready outputs.

Core Features & Use Cases

Design validation: checks replicate counts and confounding variables before fitting models.
Explicit contrasts & filtering: supports user-defined comparisons and thresholding for results.
Visualization & exports: generates volcano/MA plots and pathway-ready tables suitable for publication.

Quick Start

Provide a raw count matrix and sample metadata, specify the contrast, and run the skill to produce DE results and diagnostic plots.

Frequently Asked Questions about differential-expression

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, you provide a raw integer count matrix and sample metadata to the skill, which then applies count-aware modeling using PyDESeq2 to yield ranked genes and diagnostic plots.

Can I use PyDESeq2 for bulk RNA-seq differential expression with batch effects?▼

Yes, PyDESeq2 supports bulk RNA-seq differential expression analysis by validating design factors like condition and batch, checking for confounding variables before fitting models to generate accurate gene rankings.

How do I generate volcano plots from RNA-seq gene expression contrasts?▼

You generate volcano plots by specifying an explicit contrast for your count matrix; the skill automatically produces publication-ready volcano and MA plots alongside pathway-ready result tables.

What is the best way to validate sample metadata before fitting differential expression models?▼

The best way to validate sample metadata is using the skill's built-in design validation, which checks replicate counts and identifies confounding variables in your design factors before model fitting begins.

Do I need normalized counts or raw integer counts for bulk RNA-seq differential expression?▼

You need raw integer counts for bulk RNA-seq differential expression, as the skill applies count-aware modeling that requires unnormalized count matrices along with associated sample metadata.

Why does my differential expression analysis require explicit contrasts and thresholding?▼

Explicit contrasts and thresholding are required to filter and define specific comparisons between sample conditions, ensuring the differential expression results accurately reflect the biological question of interest.