How does PyDESeq2 compare to the original R version?

It is a faithful Python port that implements the same statistical model (Negative Binomial distribution) and estimation procedures as the original R-based DESeq2.

Can I use this with AnnData objects?

Absolutely. The skill provides patterns for loading data directly from AnnData and exporting results back into picklable formats for further analysis.

Does this skill support multi-factor experimental designs?

Yes, PyDESeq2 supports complex formulas including multiple factors, interaction effects, and continuous covariates using R-style Wilkinson notation.

What visualizations can I generate with this skill?

The skill includes guidance and scripts for generating Volcano plots, MA plots, p-value histograms, and dispersion distribution plots.

Does it include log-fold change (LFC) shrinkage?

Yes, it supports apeGLM shrinkage, which is recommended for ranking genes and producing cleaner visualizations by reducing noise in genes with low counts.

PyDESeq2 Gene Expression Analysis

Name: PyDESeq2 Gene Expression Analysis
Author: plurigrid

byplurigrid

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数据科学与机器学习

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

PyDESeq2 is a robust Python implementation of the widely used DESeq2 algorithm, specifically designed for the statistical analysis of transcriptomics data. This skill enables users to execute end-to-end bioinformatics workflows—from preprocessing raw count matrices and defining complex experimental designs (including batch effect corrections) to performing Wald tests and generating publication-ready visualizations like volcano and MA plots. It is an essential tool for bioinformaticians and data scientists who need to transition R-based genomic workflows into Python-centric pipelines without sacrificing the statistical rigor of the original DESeq2 methodology.

主要功能

01Implementation of apeGLM shrinkage for improved log-fold change estimates

02Automated differential expression workflows using Wald tests and FDR correction

03Seamless integration with pandas DataFrames and AnnData objects

048 GitHub stars

05Built-in visualization templates for MA plots, volcano plots, and dispersion analysis

06Support for complex multi-factor designs including batch effects and covariates

使用场景

01Identifying differentially expressed genes between treated and control experimental groups

02Correcting for technical batch effects and confounding variables in large-scale RNA-seq studies

03Ranking gene candidates for downstream functional analysis or experimental validation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi pydeseq2

For use in Claude.ai and ChatGPT

Download Skill