pydeseq2

# PyDESeq2 ## Overview PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute 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 This skill should be used 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" ## Quick Start Workflow For users who want to perform a standard differential expression analysis: ```python import pandas as pd from pydeseq2.dds import DeseqDataSet from pydeseq2.ds import DeseqStats # 1. Load data counts_df = pd.read_csv("counts.csv", index_col=0).T # Transpose to samples × genes metadata = pd.read_csv("metadata.csv", index_col=0) # 2. Filter low-count genes genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10] counts_df = counts_df[genes_to_keep] # 3. Initialize and fit DESeq2 dds = DeseqDataSet( counts=counts_df, metadata=metadata, design="~condition", refit_c...