Pydeseq2

If you're doing differential expression analysis on bulk RNA-seq data in Python, this wraps the DESeq2 methodology into a pandas-friendly workflow. It handles the full pipeline from count normalization through Wald tests and FDR correction, with support for multi-factor designs when you need to control for batch effects or covariates. The workflow is straightforward: load your counts matrix and metadata, specify a design formula, run the fitting, then pull results with adjusted p-values. It includes optional apeGLM shrinkage for cleaner fold change estimates in visualizations. Good fit if you're migrating R-based DESeq2 workflows to Python or building RNA-seq pipelines that need to stay in the Python ecosystem.

Install to Claude Code

npx -y skills add k-dense-ai/scientific-agent-skills --skill pydeseq2 --agent claude-code