Pydeseq2

If you're doing differential gene expression analysis in Python instead of R, this handles the full DESeq2 workflow from count matrices through statistical testing. You get Wald tests with FDR correction, batch effect control through multi-factor designs, optional apeGLM shrinkage for cleaner fold changes, and straightforward pandas integration. The workflow is pretty standard: load your counts and metadata, specify a design formula, fit the model with dds.deseq2(), then run statistical tests with contrast specifications. Works well for moving R-based RNA-seq pipelines to Python or when you need differential expression as part of a larger computational biology workflow. The main thing is making sure your count matrix is in the right orientation (samples by genes) and your metadata aligns properly.