{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"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.","skillfed_tags":["rna-seq","bioinformatics","statistics"],"use_cases":["Compare gene expression between treatment and control groups in bulk RNA-seq experiments","Analyze multi-factor designs (e.g., treatment \u00d7 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"],"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.\n\nThe 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.","worth_installing":"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\u2014verify feature completeness against your specific experimental design first."},"id":"pydeseq2","links":{"html":"https://skillfed.io/packages/pydeseq2","md":"https://skillfed.io/packages/pydeseq2.md","pypi":"https://pypi.org/project/pydeseq2/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-23","license_spdx":null,"license_treatment":"permissive","name":"pydeseq2","python_support":"supports_current","summary":"A python implementation of DESeq2."},"popularity":{"monthly_downloads":178023,"position":10202,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.4"}