pydeseq2

Differential gene expression analysis on RNA-seq count data in Python can be performed using a DESeq2 implementation that handles normalization, dispersion estimation, and statistical testing. It processes bulk RNA-seq counts to identify significantly expressed genes.

To run DESeq2 analysis without R, you can use this Python-based alternative that executes the entire workflow including normalization, log fold change fitting, and statistical testing on bulk RNA-seq data via a simple command line interface.

Python DESeq2 supports loading RNA-seq data from AnnData formats, along with CSV and TSV files. This allows you to input bulk RNA-seq count matrices and metadata directly for differential expression testing.

You need pandas and scikit-learn installed to analyze differential expression, as they are required dependencies alongside numpy, scipy, anndata, and matplotlib for executing the DESeq2 workflow and processing the count data.

This differential expression tool is aimed at biological researchers analyzing bulk RNA-seq count data. It is not designed for single-cell RNA-seq analysis, focusing specifically on bulk count normalization and statistical testing.