pyedger: A Python Implementation of edgeR for Differential Expression Analysis

We present pyedger, a Python implementation of the edgeR workflow for differential expression analysis of count data, covering TMM normalisation, filterByExpr, dispersion estimation, the quasi-likelihood (QL) F-test, the likelihood-ratio test and the classic exact test. pyedger follows edgeR's compiled code and its limma and locfit dependencies step by step and is versioned against a specific edgeR release. On eight TCGA tumour–normal comparisons (4,972 samples) and 60 small-sample analyses it returns the same differentially expressed genes as edgeR 4.10.5 in all three tests, P-values within a relative difference of 1.2 × 10⁻⁵ and identical gene-set enrichment results, while running 1.1–1.3 times faster than edgeR on one thread and 3.4–3.5 times faster on eight. pyedger is the edgeR backend of OmicVerse and is released under LGPL-3.0-or-later at https://github.com/omicverse/py-edgeR and on PyPI.