Pylimma: A Python Implementation of Limma for Differential Expression Analysis

We present pylimma, a Python implementation of limma's linear-model workflow for differential expression analysis, covering voom precision weights, weighted linear model fits, empirical-Bayes moderated t- and F-statistics with the B-statistic, tests relative to a fold-change threshold (treat), contrasts and multiple testing. pylimma follows limma's algorithms step by step and is versioned against a specific limma release. On eight TCGA tumour–normal comparisons (4,972 samples) and 60 small-sample analyses, the limma-voom pipeline in pylimma returns the same differentially expressed genes as limma 3.68.5 with both the moderated t-test and treat, P-values within a relative difference of 1.6 × 10⁻⁴ (median 2.1 × 10⁻¹⁰) and identical gene-set enrichment results, while running 1.2–1.3 times faster than limma on one thread. pylimma is the limma backend of OmicVerse and is released under LGPL-3.0-or-later at https://github.com/omicverse/py-limma and on PyPI as python-limma.