edgePython

edgePython is a Python implementation of the Bioconductor edgeR package for differential analysis of genomics count data. It also includes a new single-cell differential expression method that extends the NEBULA-LN negative binomial mixed model with edgeR's TMM normalization and empirical Bayes dispersion shrinkage.

Installation

From PyPI:

pip install edgepython

With optional extras from PyPI:

pip install "edgepython[all]"

From source:

pip install .

With optional extras:

pip install .[all]

Quick Start

import numpy as np
import edgepython as ep

# genes x samples count matrix
counts = np.random.poisson(lam=10, size=(1000, 6))
group = np.array(["A", "A", "A", "B", "B", "B"])

y = ep.make_dgelist(counts=counts, group=group)
y = ep.calc_norm_factors(y)
y = ep.estimate_disp(y)

design = np.column_stack([np.ones(6), (group == "B").astype(float)])
fit = ep.glm_ql_fit(y, design)
res = ep.glm_ql_ftest(fit, coef=1)
top = ep.top_tags(res, n=10)
print(top["table"].head())

Features

Data Structures

DGEList-style data structures (make_dgelist, cbind_dgelist, rbind_dgelist, valid_dgelist) with accessor functions (get_counts, get_dispersion, get_norm_lib_sizes, get_offset).

Normalization

TMM, TMMwsp, RLE, and upper-quartile normalization via calc_norm_factors. Normalized expression values via cpm, rpkm, tpm, ave_log_cpm, cpm_by_group, and rpkm_by_group.

Filtering

Gene filtering by expression level via filter_by_expr.

Dispersion Estimation

Common, trended, and tagwise dispersion estimation (estimate_disp, estimate_common_disp, estimate_trended_disp, estimate_tagwise_disp) with GLM variants (estimate_glm_common_disp, estimate_glm_trended_disp, estimate_glm_tagwise_disp). Weighted likelihood empirical Bayes shrinkage via WLEB.

Differential Expression Testing

• Exact test: exact_test for two-group comparisons with exact negative binomial tests, plus helpers (exact_test_double_tail, equalize_lib_sizes, q2q_nbinom, split_into_groups).
• GLM fitting: glm_fit, glm_ql_fit for generalized linear model fitting.
• GLM testing: likelihood ratio tests (glm_lrt), quasi-likelihood F-tests (glm_ql_ftest), and fold-change threshold testing (glm_treat).
• Results: top_tags for extracting top DE genes with p-value adjustment, decide_tests for classifying genes as up/down/unchanged.

Gene Set Testing

Competitive and self-contained gene set tests: camera, fry, roast, mroast, romer. Gene ontology and KEGG pathway enrichment via goana and kegga.

Differential Splicing

Differential exon and transcript usage testing via diff_splice (GLM-based with LRT or QL tests), diff_splice_dge (exact test for two-group comparisons), and splice_variants (chi-squared tests for homogeneity of proportions across exons).

Quantification Uncertainty

Reading quantification output with bootstrap or Gibbs sampling uncertainty from Salmon (catch_salmon), kallisto (catch_kallisto), and RSEM (catch_rsem). Overdispersion estimates from quantification uncertainty are used for differential transcript expression following the approach of Baldoni et al. (2024).

I/O

• Universal reader: read_data with auto-detection for kallisto (H5/TSV), Salmon, oarfish, RSEM, 10X CellRanger, CSV/TSV count tables, AnnData (.h5ad), and RDS files.
• Specialized readers: read_dge (collates per-sample count files), read_10x (10X Genomics output), feature_counts_to_dgelist (featureCounts output), read_bismark2dge (Bismark methylation coverage).
• Single-cell aggregation: seurat_to_pb for pseudo-bulk aggregation.
• Export: to_anndata for converting DGEList and results to AnnData format.

Visualization

plot_md (mean-difference plots), plot_bcv (biological coefficient of variation), plot_mds (multidimensional scaling), plot_ql_disp (quasi-likelihood dispersion), plot_smear (smear plots), ma_plot (MA plots), and gof (goodness of fit).

Single-Cell Mixed Model

NEBULA-LN-style negative binomial gamma mixed model for multi-subject single-cell data: glm_sc_fit, shrink_sc_disp, glm_sc_test.

ChIP-Seq

ChIP-seq normalization to matched input controls via normalize_chip_to_input and calc_norm_offsets_for_chip.

Methylation/RRBS

Bismark coverage file reader (read_bismark2dge) and methylation-specific design matrix construction (model_matrix_meth).

Utilities

Design matrix construction (model_matrix), prior count addition (add_prior_count), predicted fold changes (pred_fc), Good-Turing smoothing (good_turing), count thinning/downsampling (thin_counts), Gini coefficient (gini), sum technical replicates (sum_tech_reps), negative binomial z-scores (zscore_nbinom), nearest TSS annotation (nearest_tss), and variance shrinkage (squeeze_var).

Examples

The examples/mammary directory contains two notebooks for the GSE60450 mouse mammary dataset (Fu et al. 2015):

• mouse_mammary_tutorial.ipynb — edgePython-only tutorial (Colab-ready)
• mouse_mammary_R_vs_Python.ipynb — side-by-side edgeR vs edgePython comparison

The examples/hoxa1 directory contains two notebooks for the GSE37704 HOXA1 knockdown dataset (Trapnell et al. 2013), with transcript-level quantification by kallisto:

• hoxa1_tutorial.ipynb — edgePython-only tutorial with scaled analysis using bootstrap overdispersion (Colab-ready)
• hoxa1_R_vs_Python.ipynb — side-by-side edgeR vs edgePython comparison reproducing Figure 1 panels

The examples/clytia directory contains a notebook for the Clytia hemisphaerica single-cell RNA-seq dataset (Chari et al. 2021), demonstrating the NEBULA-LN mixed model with empirical Bayes dispersion shrinkage:

• clytia_tutorial.ipynb — single-cell differential expression of fed vs starved gastrodigestive cells across 10 organisms, reproducing Figure 2 panels (Colab-ready)

Development

Run tests:

pytest -q

Authorship

The code was written primarily by Claude (Anthropic) and Codex (OpenAI). The project was directed by Lior Pachter.

A detailed description of the project, methods, and benchmarks is available in the associated preprint:

Pachter, L., Differential analysis of genomics count data with edge* (2026). bioRxiv. https://doi.org/10.64898/2026.02.16.706223

edgeR

edgePython is based on the edgeR Bioconductor package. The edgeR publications are:

• Robinson MD, Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23(21), 2881-2887. doi:10.1093/bioinformatics/btm453

• Robinson MD, Smyth GK (2007). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9(2), 321-332. doi:10.1093/biostatistics/kxm030

• Robinson MD, McCarthy DJ, Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616

• Robinson MD, Oshlack A (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(3), R25. doi:10.1186/gb-2010-11-3-r25

• McCarthy DJ, Chen Y, Smyth GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), 4288-4297. doi:10.1093/nar/gks042

• Chen Y, Lun ATL, Smyth GK (2014). Differential expression analysis of complex RNA-seq experiments using edgeR. In Statistical Analysis of Next Generation Sequencing Data, Springer, 51-74. doi:10.1007/978-3-319-07212-8_3

• Zhou X, Lindsay H, Robinson MD (2014). Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Research, 42(11), e91. doi:10.1093/nar/gku310

• Dai Z, Sheridan JM, Gearing LJ, Moore DL, Su S, Wormald S, Wilcox S, O'Connor L, Dickins RA, Blewitt ME, Ritchie ME (2014). edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens. F1000Research, 3, 95. doi:10.12688/f1000research.3928.2

• Lun ATL, Chen Y, Smyth GK (2016). It's DE-licious: A recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. In Statistical Genomics, Springer, 391-416. doi:10.1007/978-1-4939-3578-9_19

• Chen Y, Lun ATL, Smyth GK (2016). From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research, 5, 1438. doi:10.12688/f1000research.8987.2

• Chen Y, Pal B, Visvader JE, Smyth GK (2018). Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR. F1000Research, 6, 2055. doi:10.12688/f1000research.13196.2

• Baldoni PL, Chen Y, Hediyeh-zadeh S, Liao Y, Dong X, Ritchie ME, Shi W, Smyth GK (2024). Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR. Nucleic Acids Research, 52(3), e13. doi:10.1093/nar/gkad1167

• Chen Y, Chen L, Lun ATL, Baldoni PL, Smyth GK (2025). edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Research, 53(2), gkaf018. doi:10.1093/nar/gkaf018

The single-cell mixed model in edgePython is based on NEBULA:

• He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM (2021). NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications Biology, 4, 629. doi:10.1038/s42003-021-02146-6

License

This project is licensed under the GNU General Public License v3.0.