Does this skill support AnnData objects?

Yes, the skill provides patterns for loading data from AnnData (.h5ad) files and converting them into the required format for PyDESeq2 analysis.

Can I visualize the results within my workflow?

Yes, the skill includes specific implementation patterns for generating Volcano plots and MA plots using Matplotlib to visualize significance and fold changes.

What is the PyDESeq2 skill used for?

It is used for differential gene expression analysis (DGEA) using the Python implementation of the DESeq2 algorithm, which is standard for analyzing bulk RNA-seq count data.

What kind of statistical tests does it perform?

It performs Wald tests to calculate p-values and applies Benjamini-Hochberg (FDR) correction to identify significant differentially expressed genes.

Can I use this skill to account for batch effects?

Yes, it supports multi-factor designs. You can specify formulas like '~batch + condition' to control for technical variations while testing for your variable of interest.

PyDESeq2 Analysis

Name: PyDESeq2 Analysis
Author: K-Dense-AI

byK-Dense-AI

•

2,188

Data Science & ML

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

About

PyDESeq2 is a robust scientific tool designed for bioinformatics workflows, enabling users to identify differentially expressed (DE) genes within Python-based environments. It supports complex experimental designs, including multi-factor models that account for batch effects or covariates, and provides statistically rigorous results via Wald tests and Benjamini-Hochberg FDR correction. By integrating seamlessly with pandas and AnnData, this skill allows researchers to execute complete transcriptomic analysis pipelines—from data normalization and dispersion estimation to LFC shrinkage and visualization—directly within their Python development workflow.

Key Features

2,188 GitHub stars
Statistically robust Wald tests with independent filtering and FDR correction
Supports single-factor and multi-factor experimental designs using Wilkinson notation
Optional apeGLM shrinkage for improved log fold change estimation in visualizations
Automated data normalization, dispersion estimation, and MAP shrinkage
Seamless integration with pandas DataFrames and AnnData objects for bioinformatics

Use Cases

Comparing gene expression levels between experimental conditions (e.g., treated vs. control)
Controlling for technical variation and batch effects in bulk RNA-seq studies
Generating publication-ready volcano and MA plots for transcriptomic data

About

Key Features

2,188 GitHub stars
Statistically robust Wald tests with independent filtering and FDR correction
Supports single-factor and multi-factor experimental designs using Wilkinson notation
Optional apeGLM shrinkage for improved log fold change estimation in visualizations
Automated data normalization, dispersion estimation, and MAP shrinkage
Seamless integration with pandas DataFrames and AnnData objects for bioinformatics

Use Cases

Comparing gene expression levels between experimental conditions (e.g., treated vs. control)
Controlling for technical variation and batch effects in bulk RNA-seq studies
Generating publication-ready volcano and MA plots for transcriptomic data