skills.rest
qinyan-ai
qinyan-aiCOMMUNITY
Explore all skills by this author →
GitHub

pydeseq2

Identify differential gene expression in bulk RNA-seq data with PyDESeq2.

48|6|Updated Mar 9, 2026
Search Tags:#genomics#differential-expression#wald-test#rnaseq#pydeseq2#fdr
One-click install
npx skills add https://github.com/qinyan-ai/qinyan-academic-skills --skill pydeseq2-qinyan-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/qinyan-ai/qinyan-academic-skills/tree/main/skills/05-%E7%94%9F%E7%89%A9%E4%BF%A1%E6%81%AF%E4%B8%8E%E5%9F%BA%E5%9B%A0%E7%BB%84%E5%AD%A6/pydeseq2
Command: npx skills add https://github.com/qinyan-ai/qinyan-academic-skills --skill pydeseq2-qinyan-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Differential gene expression analysis on bulk RNA-seq data to identify condition-associated gene changes using a Python-based DESeq2 reimplementation.

Core Features & Use Cases

  • Wald tests for differential expression with multiple design formulas (single-factor and multi-factor)
  • FDR control with Benjamini–Hochberg correction, and optional LFC shrinkage for visualization and ranking
  • End-to-end workflow from counts loading to result export and basic visualizations, via a CLI script

Quick Start

Run a standard differential expression workflow on your RNA-seq dataset using the included script to load counts and metadata, fit the model, and save the 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 expression analysis on bulk RNA-seq data in Python?▼

Differential expression analysis on bulk RNA-seq data uses PyDESeq2 to fit models and identify condition-associated gene changes via Wald tests. It processes counts and metadata to output a results table with FDR correction.

Can I analyze multi-factor RNA-seq experimental designs with Wald tests?▼

Yes, analyzing multi-factor RNA-seq experimental designs with Wald tests is fully supported. You can specify multiple design formulas to identify condition-associated gene changes while applying Benjamini-Hochberg FDR correction.

What is LFC shrinkage used for in RNA-seq differential expression analysis?▼

LFC shrinkage in RNA-seq differential expression analysis is an optional step used for visualization and ranking. It refines log2 fold change estimates to produce more stable plots and exportable result tables.

How do I run an end-to-end RNA-seq differential expression workflow from counts to plots?▼

Running an end-to-end RNA-seq differential expression workflow uses the included script to load counts and metadata, fit the model, apply FDR correction, and save result tables alongside optional matplotlib visualizations.

Does PyDESeq2 require pandas and numpy for bulk RNA-seq differential analysis?▼

Yes, PyDESeq2 requires pandas and numpy for bulk RNA-seq differential analysis, alongside optional matplotlib for generating basic visualizations and plots.

Related Skills

Explore More Education & Research Skills→
Victory-HugoVictory-Hugo
1

pydeseq2

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

Community
Intermediate
gexijingexijin
1

microarray-analysis

Analyze GEO microarray data with QC, normalization, differential expression, and enrichment.

Community
Advanced
cupcake777cupcake777
1

plotting-library

Generate publication-ready charts from runnable matplotlib templates with a unified style.

Community
Intermediate
robotlearning123robotlearning123

pydeseq2

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

Community
Intermediate