PyDESeq2 is a Python implementation of the popular DESeq2 R package, specifically designed for performing differential gene expression analysis on bulk RNA-seq count data.

What input data is required?

It requires a raw count matrix (non-normalized integers) where rows are samples and columns are genes, along with a metadata DataFrame containing experimental factors.

Does it support LFC shrinkage?

Yes, the skill includes patterns for apeGLM shrinkage, which reduces noise in fold change estimates for genes with low counts or high dispersion.

Can I use this for single-cell RNA-seq?

PyDESeq2 is optimized for bulk RNA-seq. However, it is frequently used in single-cell workflows for 'pseudobulk' analysis, where counts are aggregated by cluster or sample.

How does this skill handle batch effects?

You can account for batch effects by including them in the design formula, such as '~batch + condition', which allows the model to control for technical variation.

PyDESeq2 RNA-Seq Analysis

Name: PyDESeq2 RNA-Seq Analysis
Author: sanand0

bysanand0

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Data Science & ML

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

About

PyDESeq2 provides a comprehensive Python-based workflow for identifying differentially expressed genes in bulk RNA-seq datasets. This skill enables Claude to guide researchers through the entire analytical pipeline—from count matrix preparation and experimental design specification to statistical testing with Wald tests and FDR correction. It supports complex multi-factor designs, batch effect correction, and advanced visualization techniques like volcano and MA plots, making it an essential tool for bioinformaticians transitioning R-based workflows to Python-integrated environments.

Key Features

Statistical outlier detection with Cook's distance and automatic refitting
Support for single-factor and complex multi-factor experimental designs
Log Fold Change (LFC) shrinkage via apeGLM for improved ranking and visualization
3 GitHub stars
Seamless integration with pandas and AnnData for high-performance data handling
Automated differential expression workflows using Wald tests and FDR correction

Use Cases

Controlling for technical batch effects and continuous covariates in transcriptomics studies
Generating publication-ready genomic visualizations directly within Python-based pipelines
Identifying genes significantly regulated between experimental conditions (e.g., treated vs. control)

About

Key Features

Statistical outlier detection with Cook's distance and automatic refitting
Support for single-factor and complex multi-factor experimental designs
Log Fold Change (LFC) shrinkage via apeGLM for improved ranking and visualization
3 GitHub stars
Seamless integration with pandas and AnnData for high-performance data handling
Automated differential expression workflows using Wald tests and FDR correction

Use Cases

Controlling for technical batch effects and continuous covariates in transcriptomics studies
Generating publication-ready genomic visualizations directly within Python-based pipelines
Identifying genes significantly regulated between experimental conditions (e.g., treated vs. control)