pyedger

A pure-Python port of Bioconductor edgeR (Robinson, McCarthy & Smyth, Bioinformatics 2010) — negative-binomial models for differential expression of count data.

• No rpy2, no R install — the edgeR negative-binomial GLM workflow reimplemented in NumPy / SciPy, vectorised over genes
• Matches Bioconductor edgeR 4.0 numerically (see Numerical parity)
• The canonical pipelines: DGEList → calcNormFactors → estimateDisp → glmQLFit → glmQLFTest and the classic exactTest
• TMM normalization, common / trended / tagwise dispersion, GLM and quasi-likelihood F-tests, filterByExpr, cpm / aveLogCPM
• Both Python-style (glm_fit, estimate_disp, top_tags) and R-style (glmFit, estimateDisp, topTags) names exported

This is a standalone mirror of the implementation developed in omicverse, where it powers the edgeR differential-expression backend of ov.bulk / pyDEG.

Install

pip install pyedger

Quick start

import numpy as np
import pyedger

# counts: genes x samples raw count matrix; group: per-sample condition labels
dge = pyedger.DGEList(counts=counts, group=group)
dge = pyedger.calcNormFactors(dge)                       # TMM normalization
keep = pyedger.filterByExpr(dge, group=group)
dge = dge[keep]

# Quasi-likelihood F-test workflow (the recommended edgeR pipeline)
dge = pyedger.estimateDisp(dge, design)
fit = pyedger.glmQLFit(dge, design)
qlf = pyedger.glmQLFTest(fit, coef=1)
res = pyedger.topTags(qlf, n=np.inf)
res.head()

Classic exact test

dge = pyedger.estimateDisp(dge, design)
et = pyedger.exactTest(dge, pair=("control", "treated"))
pyedger.topTags(et)

API

Python	R counterpart
DGEList	DGEList
calc_norm_factors / calcNormFactors	calcNormFactors (TMM)
filter_by_expr / filterByExpr	filterByExpr
estimate_disp / estimateDisp	estimateDisp
glm_fit / glmFit, glm_lrt / glmLRT	glmFit, glmLRT
glm_ql_fit / glmQLFit, glm_qlf_test / glmQLFTest	glmQLFit, glmQLFTest
exact_test / exactTest	exactTest
glm_treat / glmTreat	glmTreat
cpm, ave_log_cpm / aveLogCPM	cpm, aveLogCPM
top_tags / topTags	topTags
decide_tests_dge / decideTests	decideTestsDGE
DGEGLM, DGELRT, DGEExact, TestResults	the corresponding S4 classes

Numerical parity with edgeR

tests/test_r_parity.py checks pyedger against edgeR 4.0.16 (R 4.3.3, limma 3.58.1, locfit 1.5-9) on a paired design with zero-count library pairs; the reference values are regenerated by tests/data/make_reference.R. Since 0.1.1 the GLM kernels follow edgeR's compiled code step by step and run vectorised over all genes:

Step	Ported from	Agreement with R
glmFit (Levenberg, one-way shortcut, start values)	glm_levenberg.cpp, glm_one_group.cpp	~1e-9
estimateDisp with a design (Cox-Reid APL, zero-fit groups) and classic mode	R_compute_apl.cpp, adj_coxreid.cpp, q2qnbinom	~1e-10
Trend: locfitByCol	locfit tree evaluation + interpolation	bit-identical
maximizeInterpolant, aveLogCPM	interpolator.cpp, R_ave_log_cpm.cpp	~1e-13
squeezeVar / fitFDist with a covariate	limma (natural-spline trend)	~1e-14
glmQLFit(legacy=False) bias-adjusted deviance/df	ql_glm.c, ql_weights.c, .computePriorS2	~1e-10

glmQLFit(legacy=False) and estimateGLMCommonDisp depend on edgeR's optimize(tol=1e-5) over dispersion^(1/4), so R's own value is only defined to ~4e-5 relative; agreement there is at that level.

Defaults follow edgeR 4.0: glmQLFit(legacy=True, abundance_trend=True) and estimateDisp(prior_df=None) (prior df estimated).

Citation

Robinson, M.D., McCarthy, D.J., Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139–140 (2010).

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

License

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