PyDESeq2 Gene Expression Analysis

About

The PyDESeq2 skill provides a comprehensive Python-based workflow for differential gene expression analysis, mirroring the industry-standard DESeq2 algorithm from R. It enables researchers and bioinformaticians to perform end-to-end transcriptomics tasks including data normalization, dispersion estimation, Wald testing, and log-fold change shrinkage. Designed to handle both simple case-control studies and complex multi-factor designs with batch effects, this skill integrates seamlessly with the Python data science ecosystem (pandas, AnnData) to turn raw count data into statistically significant biological insights.

Key Features

• Statistical testing using Wald tests with BH (FDR) p-value adjustment
• 16 GitHub stars
• Automated data normalization and genewise dispersion estimation
• Support for complex multi-factor designs and batch effect correction
• Optional apeGLM shrinkage for improved log-fold change estimation and visualization
• Full implementation of the DESeq2 pipeline for bulk RNA-seq data

Use Cases

• Identifying biomarkers by comparing gene expression between treated and control samples
• Converting established R-based bioinformatics pipelines to scalable Python environments
• Accounting for technical variation and covariates in large-scale transcriptomic studies