What is the primary purpose of the PyDESeq2 skill?

It is used to identify genes that are significantly differentially expressed between different groups of samples in RNA-seq data using a Python implementation of the DESeq2 algorithm.

Can this skill handle batch effects in biological data?

Yes, it supports multi-factor design formulas which allow you to statistically account for technical batch effects or other covariates like age or sex.

Does it require specific input formats?

It typically requires a count matrix (samples vs genes) as a pandas DataFrame and a metadata table describing the experimental conditions.

Can I visualize the results using this skill?

Yes, the skill includes templates for creating standard bioinformatics visualizations including volcano plots, MA plots, and dispersion distribution histograms.

How does it handle low-count genes?

The skill provides patterns for pre-filtering low-count genes to improve statistical power and reduce the multiple testing burden.

PyDESeq2 Analysis

Name: PyDESeq2 Analysis
Author: Microck

byMicrock

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Data Science & ML

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

About

This skill empowers Claude to conduct end-to-end differential expression workflows using PyDESeq2, providing a Python-native alternative to the classic R-based DESeq2 package. It facilitates the transformation of raw count matrices and metadata into statistically rigorous results by handling normalization, dispersion estimation, and Wald testing. Whether managing complex multi-factor experimental designs or generating publication-ready visualizations like volcano plots, this skill streamlines bioinformatics pipelines and ensures best practices in genomic data analysis within a Python environment.

Key Features

Automated visualization guidance for MA and volcano plots
Support for multi-factor designs and batch effect correction
Log2 Fold Change (LFC) shrinkage via apeGLM for noise reduction
End-to-end DESeq2 workflow from count normalization to statistical testing
81 GitHub stars
Advanced outlier detection and refitting using Cook’s distance

Use Cases

Migrating legacy R-based bioinformatics workflows into modern Python data science pipelines
Correcting for technical batch effects or continuous covariates in genomic studies
Identifying differentially expressed genes between experimental conditions in bulk RNA-seq

About

Key Features

Automated visualization guidance for MA and volcano plots
Support for multi-factor designs and batch effect correction
Log2 Fold Change (LFC) shrinkage via apeGLM for noise reduction
End-to-end DESeq2 workflow from count normalization to statistical testing
81 GitHub stars
Advanced outlier detection and refitting using Cook’s distance

Use Cases

Migrating legacy R-based bioinformatics workflows into modern Python data science pipelines
Correcting for technical batch effects or continuous covariates in genomic studies
Identifying differentially expressed genes between experimental conditions in bulk RNA-seq