pydeseq2

What it is and what it does

PyDESeq2 is a Python port of the widely-used R package DESeq2, designed to make differential expression analysis accessible to Python users working with bulk RNA-seq data. It implements statistical methods for comparing gene expression across experimental conditions, handling count normalization, dispersion estimation, and hypothesis testing. The package works with data in AnnData format and depends on standard scientific Python libraries: numpy, pandas, scipy, scikit-learn, matplotlib, and formulaic for design matrix specification.

The implementation currently covers single-factor and multi-factor experimental designs using Wald tests, matching the default behavior of DESeq2 v1.34.0. As a re-implementation from scratch rather than a direct wrapper, it may produce slightly different numerical results or lack some advanced features of the original R package. The project is actively maintained by the scverse community and welcomes feature requests via issue tracking.

Use it for:

• Compare gene expression between treatment and control groups in bulk RNA-seq experiments
• Analyze multi-factor designs (e.g., treatment × genotype) to identify condition-specific effects
• Normalize and standardize RNA-seq count data for downstream statistical testing
• Integrate differential expression results into Python-based bioinformatics pipelines using AnnData
• Identify significantly differentially expressed genes with multiple testing correction

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

PyDESeq2 performs differential expression analysis on bulk RNA-seq data using a Python implementation of the DESeq2 statistical method, supporting single-factor and multi-factor analysis with Wald tests.

Yes, if you work with bulk RNA-seq data in Python and need DESeq2-compatible statistical analysis. The package has low install friction, active maintenance, permissive licensing, and no known vulnerabilities. Install with caution if you require exact numerical equivalence to the original R DESeq2 or advanced features beyond Wald tests—verify feature completeness against your specific experimental design first.

Install

pydeseq2 on PyPI

pip install pydeseq2

uv add pydeseq2

poetry add pydeseq2

Installing pydeseq2

Before you install

Low install friction with a pure-wheel distribution. Actively maintained as of August 2026 with recent commits; transferred to scverse community maintenance in December 2025. Tested against Python 3.11–3.13 with current versions of its eight runtime dependencies.

License in practice

MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for academic, commercial, and proprietary projects.

Quickstart

pip install pydeseq2

import anndata
from pydeseq2.dds import DeseqDataSet

# Load count data into AnnData object
ads = anndata.read_h5ad('counts.h5ad')

# Initialize and run DESeq2 analysis
dds = DeseqDataSet(adata=ads, design_factors=['condition'])
dds.deseq2()

Requires Python 3.11 or higher; input data must be in AnnData format or convertible to it.

Verify before relying

• Completeness of feature parity with DESeq2 v1.34.0 beyond single/multi-factor Wald tests
• Performance characteristics on large-scale datasets (sample size, gene count thresholds)
• Availability of downstream analysis tools (e.g., visualization, result filtering) within the package

Package facts

Evidence: pydeseq2-0.5.4-py3-none-any.whl

Tags

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