alterlab-pydeseq2

# PyDESeq2 ## Overview PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. It supports complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple-testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData. ## When to Use This Skill Use this skill when: - Analyzing bulk RNA-seq count data for differential expression - Comparing gene expression between experimental conditions (e.g., treated vs control) - Performing multi-factor designs accounting for batch effects or covariates - Converting R-based DESeq2 workflows to Python - Integrating differential expression analysis into Python-based pipelines - Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2" ## Installation and Requirements ```bash uv pip install "pydeseq2>=0.5,<0.6" ``` **System requirements (pydeseq2 0.5.x):** Python ≥3.11; numpy ≥2.0, pandas ≥2.2, scipy ≥1.12, scikit-learn ≥1.4, anndata ≥0.11, formulaic ≥1.0.2 (parses the `~` design formula), matplotlib ≥3.9. These are pulled in automatically as dependencies. **API note (0.4+):** parallelism is configured through an `inference` object, not a bare `n_cpus=` kwarg: ```python from pydeseq2.default_inference import DefaultInference inference = DefaultInference(n_cpus=8) dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition", inference=inferenc...