skills.rest
CHENyiru3
CHENyiru3COMMUNITY
Explore all skills by this author →
GitHub

pydeseq2

Quantify differential gene expression from bulk RNA-seq data using PyDESeq2.

Updated Apr 19, 2026
Search Tags:#differential-expression#wald-test#rnaseq#pydeseq2#fdr#volcano-plot
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill pydeseq2-chenyiru3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/compbio/multiomics/scRNA-seq/pydeseq2
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill pydeseq2-chenyiru3

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

PyDESeq2 provides a Python-based end-to-end pipeline for differential expression analysis on bulk RNA-seq data, replacing some R-based workflows with a native Python implementation to streamline analysis and integration into Python workflows.

Core Features & Use Cases

  • Differential expression analysis with single-factor and multi-factor designs, including covariates and batch effects.
  • Wald tests with Benjamini-Hochberg correction, optional LFC shrinkage, and seamless data handling with pandas and AnnData.
  • Real-world use case: analyze a tumor versus normal comparison, obtain per-gene statistics, and generate visualizations for downstream interpretation.

Quick Start

Load counts and metadata, initialize DeseqDataSet with design '~condition', run the DESeq2 pipeline, and inspect 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 in Python can be performed using PyDESeq2 to normalize counts, estimate dispersion, and run Wald tests. It supports single-factor and multi-factor designs with covariates and batch effects.

What is the best way to handle batch effects in RNA-seq differential expression analysis?▼

Handling batch effects in RNA-seq differential expression analysis is achieved by adding batch variables to the multi-factor design formula. PyDESeq2 supports covariates and batch effects to ensure robust normalization and dispersion estimation.

Does PyDESeq2 support LFC shrinkage and FDR correction for RNA-seq data?▼

PyDESeq2 supports LFC shrinkage and FDR correction for RNA-seq data by applying Benjamini-Hochberg correction to Wald test results. This ensures accurate multiple testing correction and optional log2 fold change shrinkage.

Can I use AnnData and pandas for downstream RNA-seq differential expression analysis?▼

You can use AnnData and pandas for downstream RNA-seq differential expression analysis because PyDESeq2 integrates seamlessly with both frameworks. This allows efficient data handling and visualization generation for interpreting results.

How do I generate a volcano plot from RNA-seq differential expression results?▼

Generating a volcano plot from RNA-seq differential expression results involves using matplotlib with the per-gene statistics output. PyDESeq2 provides the necessary Wald test p-values and log2 fold changes for visualization.

Related Skills

Explore More Data & Analytics Skills→
robotlearning123robotlearning123

pydeseq2

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

Community
Intermediate
StarlitnightlyStarlitnightly
1.2k

data-viz-plots

Generate publication-quality plots with matplotlib and seaborn.

Community
Advanced
MubasherMohammedMubasherMohammed

diff-visualizer

Generate visualizations and reports for bulk and single-cell RNA-seq differential expression.

Community
Advanced
ovachieverovachiever
52

pydeseq2

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

Community
Advanced