Does this skill support multi-factor experimental designs?

Yes, it supports complex R-style design formulas (e.g., '~batch + condition'), allowing you to control for confounding variables or analyze interaction effects.

What visualizations are included in this skill?

The skill provides ready-to-use boilerplate for creating Volcano plots, MA plots, dispersion distributions, and p-value histograms using Matplotlib.

How does PyDESeq2 handle low-count genes?

The skill includes workflow steps for filtering low-count genes and applies apeGLM shrinkage to stabilize log2 fold change estimates for genes with high dispersion.

Can I use this with AnnData objects from Scanpy?

Absolutely. The skill provides specific patterns for extracting count matrices and metadata from AnnData (.h5ad) files for use in the DESeq2 pipeline.

Is the statistical output compatible with the original R DESeq2?

Yes, PyDESeq2 is a faithful Python implementation of the DESeq2 algorithm, producing comparable results for Wald tests and normalization factors.

PyDESeq2 Differential Expression

Name: PyDESeq2 Differential Expression
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Performs robust differential gene expression analysis for bulk RNA-seq data using the Python implementation of DESeq2.

About

This skill empowers Claude to execute end-to-end differential expression workflows directly in Python, bridging the gap for researchers moving away from R-centric pipelines. It provides structured guidance for data preparation, multi-factor experimental design, Wald statistical testing, and FDR correction. By integrating with pandas and AnnData, it facilitates the identification of significant genes, application of apeGLM shrinkage for effect size estimation, and the generation of essential genomic visualizations like Volcano and MA plots within a unified Python environment.

Key Features

1 GitHub stars
ApeGLM shrinkage for reliable log fold change visualization and ranking
Automated DESeq2 pipeline including normalization and dispersion estimation
Multi-factor design modeling to account for batch effects and covariates
Wald test statistics with Benjamini-Hochberg multiple testing correction
Seamless integration with pandas DataFrames and AnnData objects

Use Cases

Accounting for technical variation and batch effects in complex RNA-seq experiments
Identifying differentially expressed genes between case and control groups
Migrating legacy R-based DESeq2 workflows to modern Python-based bioinformatics stacks

About

Key Features

1 GitHub stars
ApeGLM shrinkage for reliable log fold change visualization and ranking
Automated DESeq2 pipeline including normalization and dispersion estimation
Multi-factor design modeling to account for batch effects and covariates
Wald test statistics with Benjamini-Hochberg multiple testing correction
Seamless integration with pandas DataFrames and AnnData objects

Use Cases

Accounting for technical variation and batch effects in complex RNA-seq experiments
Identifying differentially expressed genes between case and control groups
Migrating legacy R-based DESeq2 workflows to modern Python-based bioinformatics stacks