What is PyDESeq2 used for?

PyDESeq2 is a Python-based implementation of the DESeq2 algorithm, used primarily for differential gene expression analysis in bulk RNA-seq data to identify genes that change significantly between experimental conditions.

How are p-values adjusted for multiple testing?

The skill follows standard DESeq2 protocols, applying the Benjamini-Hochberg (FDR) correction to raw p-values to minimize false discovery rates.

Can I handle batch effects with this skill?

Yes, the skill provides specific patterns for multi-factor designs (e.g., '~batch + condition') to account for and control technical variation or known covariates.

Do I need R installed to use this skill?

No, this skill utilizes the native Python implementation of DESeq2, allowing for a complete bioinformatics workflow within a Python environment without needing an R installation.

Does this skill include data visualization?

Absolutely. It includes code snippets and best practices for generating standard bioinformatics plots, such as Volcano plots and MA plots, to visualize results.

PyDESeq2 Gene Expression Analysis

Name: PyDESeq2 Gene Expression Analysis
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Performs differential gene expression analysis for bulk RNA-seq data using the Python implementation of DESeq2.

About

This skill enables Claude to conduct comprehensive differential gene expression analysis directly in Python, mimicking the gold-standard DESeq2 workflow originally from R. It supports the entire bioinformatics pipeline, from raw count normalization and low-count filtering to statistical Wald tests, multi-factor designs for batch effect correction, and advanced log fold change (LFC) shrinkage with apeGLM. Designed for computational biologists and researchers, it provides structured guidance for identifying significant genes, generating publication-quality volcano and MA plots, and integrating RNA-seq results into broader Python-based data science workflows.

Key Features

Log Fold Change (LFC) shrinkage using apeGLM
Automated visualization including Volcano and MA plots
Statistical Wald tests with Benjamini-Hochberg FDR correction
End-to-end RNA-seq differential expression workflows
Multi-factor design support for batch effect correction
3 GitHub stars

Use Cases

Migrating R-based DESeq2 bioinformatics pipelines to a Python ecosystem
Accounting for technical variation and batch effects in transcriptomic studies
Identifying differentially expressed genes between treated and control samples

About

Key Features

Log Fold Change (LFC) shrinkage using apeGLM
Automated visualization including Volcano and MA plots
Statistical Wald tests with Benjamini-Hochberg FDR correction
End-to-end RNA-seq differential expression workflows
Multi-factor design support for batch effect correction
3 GitHub stars

Use Cases

Migrating R-based DESeq2 bioinformatics pipelines to a Python ecosystem
Accounting for technical variation and batch effects in transcriptomic studies
Identifying differentially expressed genes between treated and control samples