pydeseq2

You can perform end-to-end differential expression analysis on bulk RNA-seq count data by running the included analysis script with your counts and metadata, executing normalization, dispersion estimation, and Wald testing.

Yes, you can use this Python-based RNA-seq differential expression workflow to handle both single-factor and multi-factor designs, including the evaluation of batch effects and continuous covariates in your bulk RNA-seq studies.

LFC shrinkage in RNA-seq differential expression reduces noise in log2 fold-change estimates for visualization and ranking, while preserving unshrunk p-values ensures statistical significance remains accurate for identifying truly differentially expressed genes.

The best way to replace R-based DESeq2 pipelines is using this Python-native implementation, which provides the same end-to-end differential expression workflow for bulk RNA-seq data without requiring an R environment, leveraging PyDESeq2, pandas, and numpy.

You do not strictly need matplotlib for core RNA-seq differential expression testing, but it is an optional dependency used to generate plots and visualizations alongside your exported analysis results and intermediate objects.

Normalization and dispersion estimation for multi-factor designs require properly formatted count matrices and metadata inputs; ensuring your batch effects and continuous covariates are correctly specified in the metadata resolves common workflow execution errors.