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pydeseq2

Run Python-based differential expression analysis for bulk RNA-seq data with configurable designs.

15|2|Updated Dec 17, 2025
Search Tags:#python#bioinformatics#differential-expression#deseq2#rnaseq#bulk-rna-seq
One-click install
npx skills add https://github.com/rubensliv/k-dense-ai --skill pydeseq2-rubensliv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydeseq2
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/pydeseq2
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill pydeseq2-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

PyDESeq2 provides a Python-based pipeline for differential expression analysis of bulk RNA-seq data, replicating key DESeq2 functionality.

Core Features & Use Cases

  • End-to-end analysis: load counts and sample metadata, specify a design formula, run normalization, dispersion estimation, and statistical testing.
  • Multi-factor designs: handle batch effects and covariates, including interaction terms.
  • LFC shrinkage & visualization: optional shrinkage for improved visualization and ranking, with supporting plots and exports to CSV/AnnData.
  • Use Case: convert R-based DESeq2 workflows to Python pipelines for reproducible analyses in Python-centric environments.

Quick Start

Run a standard differential expression analysis by passing counts.csv, metadata.csv, a design string, and a contrast triple to produce results.

Frequently Asked Questions about pydeseq2

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

FAQPage Schema
How do I run differential expression analysis for bulk RNA-seq in Python?▼

Differential expression analysis for bulk RNA-seq in Python is executed by passing a counts DataFrame, metadata DataFrame, design formula, and contrast to generate a results DataFrame with padj values and an AnnData object for downstream use.

Can I handle batch effects and multi-factor designs in a Python DESeq2 workflow?▼

Multi-factor designs in a Python DESeq2 workflow support batch effects, covariates, and interaction terms by specifying a design formula alongside the metadata DataFrame to isolate specific contrasts during statistical testing.

What is LFC shrinkage and when is it needed for RNA-seq data?▼

LFC shrinkage in RNA-seq data is an optional post-processing step that refines log2 fold change estimates for improved visualization and ranking, outputting a results DataFrame with adjusted values.

Does this Python pipeline support multi-factor designs with interaction terms?▼

Yes, this Python differential expression pipeline supports multi-factor designs including interaction terms. You specify these using a design formula string alongside your samples and factors metadata DataFrame to model complex experimental setups.

What data formats do I need to run a DESeq2-style analysis in Python?▼

Running a DESeq2-style analysis in Python requires a counts DataFrame mapping samples to genes and a metadata DataFrame mapping samples to factors, plus a design string, outputting a results DataFrame and an AnnData object.

Why convert an R-based DESeq2 workflow to Python for RNA-seq analysis?▼

Converting an R-based DESeq2 workflow to Python for RNA-seq analysis enables reproducible pipelines within Python-centric environments, leveraging pandas DataFrames and exporting AnnData objects for native downstream integration.

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