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MubasherMohammed
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rnaseq-de

Execute differential expression analysis on RNA-seq count matrices with quality control, PCA/volcano plots, and markdown reports.

Updated May 10, 2026
Search Tags:#quality control#volcano plot#PCA#differential expression#RNA-seq
One-click install
npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill rnaseq-de
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rnaseq-de
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/rnaseq-de
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill rnaseq-de

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, scikit-learn, pydeseq2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines RNA-seq differential expression analysis, offering a streamlined approach with built-in quality control, visualization, and report generation.

Core Features & Use Cases

  • Differential Expression Analysis: Conduct differential expression testing on bulk RNA-seq data.
  • Quality Control: Includes pre-processing checks like library size normalization and filtering of low-count genes.
  • Visualization: Provides PCA and volcano plots for visualization of results.
  • Report Generation: Automatically generates a comprehensive markdown report including reproducibility information.

Quick Start

Run the rnaseq-de skill with the command: python rnaseq_de.py --counts counts.csv --metadata metadata.csv --formula "~ condition" --contrast "condition,treated,control" --output report_dir

Frequently Asked Questions about rnaseq-de

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

FAQPage Schema
How do I perform RNA-seq differential expression analysis with quality control in Python?▼

RNA-seq differential expression analysis is performed by processing count matrices with pandas and pydeseq2, applying library size normalization, filtering low-count genes for quality control, and generating a markdown report.

What is the best way to visualize RNA-seq differential expression results using PCA and volcano plots?▼

Visualizing RNA-seq differential expression results involves generating PCA plots to assess sample clustering and volcano plots to identify significantly changing genes, utilizing matplotlib and scikit-learn for graphical rendering.

Do I need pydeseq2 and pandas to run bulk RNA-seq differential expression testing?▼

Yes, you need pydeseq2 and pandas along with numpy, matplotlib, and scikit-learn installed in your Python environment to execute the bulk RNA-seq differential expression testing and visualization workflows.

How do I specify experimental conditions and contrasts for RNA-seq count matrix processing?▼

Specifying experimental conditions for RNA-seq count matrix processing requires providing a metadata CSV file, defining a design formula such as tilde condition, and setting a contrast variable like treated versus control.

Does this RNA-seq analysis approach automatically handle low-count gene filtering and library size normalization?▼

Yes, this RNA-seq analysis approach automatically handles low-count gene filtering and library size normalization as built-in pre-processing quality control checks before conducting the actual differential expression testing.

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