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pydeseq2

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

8|Updated Jan 13, 2026
Search Tags:#bioinformatics#genomics#differential expression#rna-seq#gene expression#deseq2
One-click install
npx skills add https://github.com/hxk622/TokenDance --skill pydeseq2-hxk622
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/bioinformatics/pydeseq2
Command: npx skills add https://github.com/hxk622/TokenDance --skill pydeseq2-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of differential gene expression analysis from bulk RNA-seq count data, enabling researchers to identify genes that change significantly between experimental conditions.

Core Features & Use Cases

  • Differential Expression Analysis: Perform standard DESeq2 workflows in Python for RNA-seq data.
  • Data Handling: Load, filter, and prepare count matrices and metadata.
  • Statistical Testing: Conduct Wald tests, apply FDR correction, and optionally perform LFC shrinkage.
  • Visualization: Generate volcano and MA plots to interpret results.
  • Use Case: A biologist has RNA-seq data from treated and control cell lines and needs to find genes that are upregulated or downregulated in the treated group. This Skill can process their count data and metadata to produce a list of significant genes with associated statistics.

Quick Start

Use the pydeseq2 skill to analyze RNA-seq count data from 'counts.csv' and 'metadata.csv' to find differentially expressed genes between 'treated' and 'control' conditions.

Frequently Asked Questions about pydeseq2

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

FAQPage Schema
How do I perform differential gene expression analysis on bulk RNA-seq count data?▼

Differential gene expression analysis on bulk RNA-seq count data is performed using a Python implementation of DESeq2, which handles data loading, filtering, and Wald tests. You need a counts matrix and metadata file to identify significant expression changes between conditions.

Can I use multi-factor experimental designs for RNA-seq differential expression?▼

Yes, multi-factor experimental designs are supported for RNA-seq differential expression analysis. The workflow accommodates complex metadata structures, allowing you to control for multiple variables while calculating Wald tests and applying FDR correction.

How do I generate volcano and MA plots from RNA-seq differential expression results?▼

Volcano and MA plots are generated from RNA-seq differential expression results to visualize significant gene expression changes. The workflow calculates statistics and optionally performs LFC shrinkage to produce the data required for these visualizations.

Does this differential expression workflow support pandas DataFrames and AnnData objects?▼

Yes, the differential expression workflow supports integration with both pandas DataFrames and AnnData objects. This allows you to load, filter, and prepare count matrices and metadata directly from standard Python bioinformatics data structures.

What is the best way to identify upregulated and downregulated genes from treated and control conditions?▼

To identify upregulated and downregulated genes from treated and control conditions, apply Wald tests with FDR correction to your RNA-seq count data. Optional LFC shrinkage refines the log2 fold change estimates to produce a list of significant genes.

When should I apply LFC shrinkage in differential gene expression analysis?▼

LFC shrinkage in differential gene expression analysis should be applied when estimating log2 fold changes for genes with low counts. It refines the effect size estimates, producing more stable volcano and MA plots for interpreting significant expression changes.

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