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PyDESeq2 is a robust Python implementation of DESeq2, designed for comprehensive differential expression analysis using bulk RNA-seq count data. It supports complex formulaic designs, performs Wald tests with multiple testing correction (FDR), and includes advanced features like LFC shrinkage, enabling researchers to analyze gene expression changes between various experimental conditions within Python pipelines.

This guide provides a rigorous framework for conducting mechanism (mediation/moderation) and heterogeneity analyses for empirical research papers, particularly adhering to high academic standards. It helps researchers move beyond simple correlation to establish causal pathways ('why') and identify differential effects ('for whom') by incorporating theoretically grounded intermediate variables and performing sophisticated econometric tests like causal mediation and interaction effects.