python-limma 0.1.0

Pure-Python port of Bioconductor limma — linear models & empirical-Bayes moderated statistics for differential expression (voom, lmFit, eBayes, topTable).

limmapy

A pure-Python port of Bioconductor limma (Ritchie et al., Nucleic Acids Research 2015) — linear models and empirical-Bayes moderated statistics for differential expression.

• No rpy2, no R install — the core limma linear-model workflow reimplemented in NumPy / SciPy
• The canonical pipeline: voom → lmFit → contrasts.fit → eBayes → topTable
• Smyth 2004 empirical-Bayes variance moderation; treat, decideTests, duplicateCorrelation, removeBatchEffect
• Both Python-style (lm_fit, ebayes, top_table) and R-style (lmFit, eBayes, topTable) names exported
• pandas / numpy-friendly

The import name is pylimma; the PyPI distribution name is python-limma (pip install python-limma). The names pylimma / limmapy were unavailable on PyPI, so the distribution carries the omicverse-ecosystem name.

This is a standalone mirror of the implementation developed in omicverse. It powers omicverse's edgeR / limma-voom differential-expression backends and the pydeqms proteomics workflow.

Install

pip install python-limma

Quick start

import numpy as np
import pylimma

# expr: genes x samples log-expression matrix; design: samples x coefficients
fit = pylimma.lmFit(expr, design)
fit = pylimma.eBayes(fit)
res = pylimma.topTable(fit, coef=1, number=np.inf)
res.head()

RNA-seq with voom

import pylimma
v = pylimma.voom(counts, design)            # EList: mean-variance weights
fit = pylimma.eBayes(pylimma.lmFit(v.E, design, weights=v.weights))
pylimma.topTable(fit, coef=1)

API

Python	R counterpart
lm_fit / lmFit	lmFit
ebayes / eBayes	eBayes
contrasts_fit	contrasts.fit
top_table / topTable	topTable
voom	voom
treat	treat
decide_tests / decideTests	decideTests
duplicate_correlation / duplicateCorrelation	duplicateCorrelation
remove_batch_effect / removeBatchEffect	removeBatchEffect
MArrayLM, EList, TestResults	the corresponding S4 classes

Citation

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47 (2015).

…and acknowledge omicverse / this repo for the Python port.

License

LGPL-3.0-or-later — matches the upstream Bioconductor package.