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

PyDESeq2 is primarily designed for bulk RNA-seq. However, it is frequently used for 'pseudobulk' analysis where single-cell counts are aggregated by sample and cell type.

What visualization types can I generate?

The skill includes templates for Volcano plots, MA plots, and histograms for p-value and dispersion distributions.

What format should my input data be in?

The skill expects a count matrix (integer read counts) and a metadata DataFrame where the index matches the sample names in the count matrix.

How does PyDESeq2 compare to the original R DESeq2?

It is a native Python implementation that aims to replicate the original DESeq2 logic and results while providing better integration with pandas, NumPy, and AnnData.

Does this skill support batch effect correction?

Yes, you can specify multi-factor designs in the formula, such as '~batch + condition', to statistically control for technical variation.

PyDESeq2 Gene Expression Analysis

Name: PyDESeq2 Gene Expression Analysis
Author: Yezez9

byYezez9

0•

Data Science & ML

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

The PyDESeq2 skill provides a robust environment for transcriptomic research, enabling users to identify differentially expressed genes through automated workflows. It handles the entire pipeline from count normalization and dispersion fitting to Wald testing and FDR correction. Designed for bioinformatics experts and data scientists alike, it supports complex experimental designs, including multi-factor models and batch effect correction, while providing visualization templates for result interpretation. This skill is essential for migrating R-based DESeq2 workflows to Python or integrating RNA-seq analysis into broader machine learning pipelines.

Key Features

01Advanced Log Fold Change (LFC) shrinkage using the apeGLM algorithm

02Support for multi-factor designs and continuous covariates

030 GitHub stars

04Automated visualization tools for Volcano, MA, and Dispersion plots

05Complete DESeq2 pipeline including size factor estimation and dispersion priors

06Statistical testing via Wald tests with Benjamini-Hochberg FDR correction

Use Cases

01Correcting for technical batch effects in large-scale transcriptomic datasets

02Identifying biomarkers and differentially expressed genes between experimental conditions

03Integrating RNA-seq statistical results into Python-based data science workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yezez9/research-agent pydeseq2

For use in Claude.ai and ChatGPT