pachterlab

pachterlab / edgePython

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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.

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Found Feb 20, 2026 at 29 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

edgePython is a Python reimplementation of the edgeR Bioconductor package for differential expression analysis of count data from bulk and single-cell RNA-seq experiments.

How It Works

1
🔍 Discover edgePython

You hear about edgePython, a friendly tool that helps biologists spot genes changing between conditions in sequencing experiments.

2
📦 Get it running

Install it quickly with a single command, no hassle.

3
📁 Load your data

Point it to your count data files from sequencing runs.

4
🔬 Run the analysis

Watch it normalize counts, estimate variability, and test for gene differences across groups.

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📊 Explore results

View tables of top changed genes with clear p-values and fold-changes.

6
📈 Create visuals

Generate plots like volcano or MDS to see patterns clearly.

🎉 Share discoveries

Export results and confidently publish your gene expression findings.

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AI-Generated Review

What is edgePython?

edgePython ports the Bioconductor edgeR package to Python for differential expression analysis of genomics count data using negative binomial models. It handles bulk RNA-seq with TMM normalization, empirical Bayes dispersion shrinkage, exact tests, and GLM-based methods, while extending to single-cell data via a NEBULA-LN mixed model boosted by edgeR's techniques. Install via pip and run workflows like loading counts, normalizing, estimating dispersion, and testing DE in a few lines.

Why is it gaining traction?

It delivers edgeR's battle-tested accuracy in Python—no more R dependency for DE pipelines—plus single-cell support that beats vanilla NEBULA on benchmarks. Developers get seamless I/O for Salmon, kallisto, 10x, and AnnData, gene set tests like camera and fry, and Colab-ready examples matching edgeR results. Speed rivals R on bulk and scRNA-seq, with visualization built-in.

Who should use this?

Bioinformaticians analyzing bulk or single-cell RNA-seq for differential expression, especially those building Python workflows around Scanpy or pandas. It's ideal for researchers validating edgeR results in Python or needing TMM-normalized binomial mixed models across multi-subject scRNA-seq datasets.

Verdict

Solid for genomics DE analysis if you trust Pachter's direction and AI-generated code—preprint benchmarks hold up—but with 25 stars and 1.0% credibility, treat as experimental. Run pytest, check examples, and pair with mature tools until adoption grows.

(198 words)

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