pydeseq2

Runs differential expression analysis on bulk RNA-seq count data with PyDESeq2, the Python port of DESeq2. Covers formulaic single- and multi-factor designs, contrasts, Wald tests, Benjamini-Hochberg FDR correction, optional apeGLM LFC shrinkage, pandas and AnnData (H5AD) integration, CSV export, volcano and MA plots, and a command-line script. Use when comparing gene expression between conditions such as treated vs control, adjusting for batch or covariates, porting an R DESeq2 workflow to Python, or building a Python pipeline for differential expression from raw integer counts. Not for single-cell data or for R-based DESeq2 itself.

Version

1.4

License

MIT

Compatibility

Requires Python >=3.11 and PyDESeq2 0.5.4-compatible dependencies. Examples target PyDESeq2 0.5.x, formulaic design strings, explicit contrasts, and uv-based installs.