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PyDESeq2

Maintainer K-Dense Inc. · Last updated April 1, 2026

PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.

Claude CodeOpenClawNanoClawAnalysisWritingpydeseq2bioinformaticspackagebioinformatics & genomics

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pydeseq2

Maintainer: K-Dense Inc.
License: MIT license
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.
counts_df = pd.read_csv("counts.csv", index_col=0).T # Transpose to samples × genes metadata = pd.read_csv("metadata.csv", index_col=0).

Source Doc

Excerpt From SKILL.md

When to Use This Skill

This skill should be used when:

Analyzing bulk RNA-seq count data for differential expression
Comparing gene expression between experimental conditions (e.g., treated vs control)
Performing multi-factor designs accounting for batch effects or covariates
Converting R-based DESeq2 workflows to Python
Integrating differential expression analysis into Python-based pipelines
Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2"

Quick Start Workflow

For users who want to perform a standard differential expression analysis:

import pandas as pd
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

## 2. Filter low-count genes

genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10]
counts_df = counts_df[genes_to_keep]