How does it handle experimental bias?

It supports multi-factor design formulas that allow you to account for batch effects, age, or other covariates alongside your primary experimental condition.

What is the primary function of the PyDESeq2 skill?

The PyDESeq2 skill automates the identification of differentially expressed genes from bulk RNA-seq count data using a Python implementation of the DESeq2 framework.

Can I use this with AnnData objects?

Yes, the skill includes implementation patterns for converting AnnData objects into the pandas format required by PyDESeq2.

Does it provide data visualization?

Yes, it includes built-in guidelines and code snippets for generating Volcano plots and MA plots to visualize the results of your analysis.

What statistical tests does this skill use?

It utilizes Wald tests for statistical significance and applies Benjamini-Hochberg FDR correction to provide adjusted p-values.

PyDESeq2 Gene Analysis

Name: PyDESeq2 Gene Analysis
Author: Sologa

bySologa

Data Science & ML

Performs differential gene expression analysis for bulk RNA-seq data using Python-based DESeq2 workflows.

About

PyDESeq2 provides a specialized workflow for identifying differentially expressed genes directly within Python environments. By implementing the industry-standard DESeq2 statistical framework, it enables researchers to execute complete pipelines from data normalization and dispersion estimation to Wald tests and FDR correction. This skill is ideal for bioinformaticians looking to integrate robust transcriptomics analysis into Python-based data science stacks, supporting complex experimental designs, batch effect correction, and high-quality visualizations like Volcano and MA plots.

Key Features

Built-in visualization templates for Volcano and MA plots
Support for single-factor and multi-factor experimental designs
Automated differential expression analysis (DEA) for bulk RNA-seq
Statistical validation via Wald tests and FDR correction
Integrated apeGLM shrinkage for improved log fold change estimation
0 GitHub stars

Use Cases

Identifying biomarkers by comparing treated vs. control samples
Adjusting for technical batch effects in large transcriptomic studies
Migrating R-based DESeq2 pipelines to Python for better ML integration

About

Key Features

Built-in visualization templates for Volcano and MA plots
Support for single-factor and multi-factor experimental designs
Automated differential expression analysis (DEA) for bulk RNA-seq
Statistical validation via Wald tests and FDR correction
Integrated apeGLM shrinkage for improved log fold change estimation
0 GitHub stars

Use Cases

Identifying biomarkers by comparing treated vs. control samples
Adjusting for technical batch effects in large transcriptomic studies
Migrating R-based DESeq2 pipelines to Python for better ML integration