What visualization types are included?

The skill provides implementation patterns for Volcano plots (significance vs. effect size) and MA plots (log fold change vs. mean expression).

How does it handle multiple testing correction?

It automatically applies the Benjamini-Hochberg procedure to calculate adjusted p-values (padj), which controls the False Discovery Rate (FDR).

Does this skill handle batch effects?

Yes, it supports multi-factor designs. By including variables in your design formula (e.g., '~batch + treatment'), you can statistically control for technical variation.

What is the benefit of using PyDESeq2 instead of the R version?

PyDESeq2 allows you to stay within the Python ecosystem, enabling direct integration with modern data science tools like pandas, scikit-learn, and scanpy/AnnData without needing R-to-Python bridges.

Can I use this for single-cell RNA-seq?

While PyDESeq2 is specifically optimized for bulk RNA-seq data, it can be used for 'pseudobulk' analysis where single-cell counts are aggregated by sample and cell type.

PyDESeq2 RNA-seq Analysis

Name: PyDESeq2 RNA-seq Analysis
Author: x-cmd

byx-cmd

•

Ciencia de Datos y ML

Performs robust differential gene expression analysis on bulk RNA-seq data using the PyDESeq2 framework.

Acerca de

PyDESeq2 is a specialized Claude Code skill designed for bioinformaticians and data scientists to conduct comprehensive differential expression analysis within Python-based workflows. It provides a structured approach to modeling gene expression data, accounting for experimental factors and batch effects, and executing statistical tests like the Wald test with FDR correction. This skill streamlines the transition from traditional R-based DESeq2 workflows to modern Python environments, enabling seamless integration with pandas, NumPy, and AnnData for sophisticated biological interpretation and visualization.

Características Principales

Statistical Wald testing with Benjamini-Hochberg FDR correction
8 GitHub stars
Multi-factor experimental design modeling with batch effect correction
Log Fold Change (LFC) shrinkage using apeGLM for better visualization
Automated normalization and dispersion shrinkage estimation
Integrated visualization patterns for Volcano and MA plots

Casos de Uso

Identifying significantly upregulated and downregulated genes between experimental conditions
Converting legacy R/DESeq2 bioinformatics pipelines into Python-based data science stacks
Controlling for technical variation and covariates in complex RNA-seq studies

Acerca de

Características Principales

Statistical Wald testing with Benjamini-Hochberg FDR correction
8 GitHub stars
Multi-factor experimental design modeling with batch effect correction
Log Fold Change (LFC) shrinkage using apeGLM for better visualization
Automated normalization and dispersion shrinkage estimation
Integrated visualization patterns for Volcano and MA plots

Casos de Uso

Identifying significantly upregulated and downregulated genes between experimental conditions
Converting legacy R/DESeq2 bioinformatics pipelines into Python-based data science stacks
Controlling for technical variation and covariates in complex RNA-seq studies