Does it support Log Fold Change (LFC) shrinkage?

Yes, it includes support for apeGLM shrinkage, which is essential for reducing noise in low-count genes and producing high-quality visualizations.

How does it handle outlier detection?

The skill implements Cook's distance filtering and optional refitting, following the standard DESeq2 methodology to ensure robust statistical results.

Can I use this for multi-factor experimental designs?

Yes, it supports complex design formulas using Wilkinson notation, allowing you to account for batch effects, age, dosage, and interaction terms.

What input formats are compatible with this skill?

It is designed to work with pandas DataFrames for count matrices and metadata, but it also includes patterns for loading data from CSV, TSV, and AnnData (.h5ad) files.

Does this skill require R to be installed for DESeq2?

No, this skill utilizes PyDESeq2, which is a native Python implementation of the DESeq2 algorithm, allowing you to run the entire analysis without an R installation.

PyDESeq2 RNA-Seq Analysis

Name: PyDESeq2 RNA-Seq Analysis
Author: ovachiever

byovachiever

•

Data Science & ML

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

This skill enables Claude to execute full bioinformatic workflows for bulk RNA-seq data, providing a robust Python-based alternative to the traditional R-based DESeq2 environment. It facilitates data preparation, multi-factor experimental designs, Wald statistical testing with FDR correction, and advanced visualization techniques like volcano and MA plots. Ideal for researchers and bioinformaticians, it automates the identification of significantly regulated genes while accounting for batch effects and covariates, integrating seamlessly with pandas and AnnData structures for modern genomic pipelines.

Key Features

01Built-in visualization generation for Volcano plots, MA plots, and p-value distributions

02Support for complex multi-factor designs, interaction effects, and batch correction

03Native integration with pandas DataFrames and AnnData objects for data interoperability

04Complete DESeq2 workflow including normalization, dispersion estimation, and Wald tests

05Advanced LFC shrinkage using apeGLM for improved effect size estimation and ranking

065 GitHub stars

Use Cases

01Correcting for technical batch effects or continuous covariates in transcriptomic studies

02Identifying differentially expressed genes between experimental conditions like treated vs. control

03Porting legacy R-based DESeq2 pipelines into Python-centric data science environments

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ovachiever/droid-tings pydeseq2

For use in Claude.ai and ChatGPT

Download Skill