Bulk RNA-seq DE with PyDESeq2: load counts, normalize, fit negative binomial models, Wald test (BH-FDR), LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, multiple contrasts.
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PyDESeq2 Differential Expression Analysis
Overview
PyDESeq2 is a Python reimplementation of the R DESeq2 package for differential gene expression analysis from bulk RNA-seq count data. It fits negative binomial generalized linear models per gene, estimates dispersion with empirical Bayes shrinkage, and performs Wald tests with Benjamini-Hochberg FDR correction. This skill covers the full pipeline from raw counts to publication-ready result tables and visualizations.
When to Use
Ide
[Description truncada. Veja o README completo no GitHub.]
