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lib-pydeseq2

Perform differential gene expression analysis on bulk RNA-seq count data.

Updated Mar 13, 2026
Search Tags:#bioinformatics#genomics#gene analysis#differential expression#rna-seq#pydeseq2
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pydeseq2
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lib-pydeseq2
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-pydeseq2
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pydeseq2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pydeseq2, pandas, numpy, scipy, scikit-learn, anndata, matplotlib, seaborn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of identifying differentially expressed genes from bulk RNA-seq count data, enabling researchers to pinpoint genes that change significantly between experimental conditions.

Core Features & Use Cases

  • Differential Expression Analysis: Perform standard DESeq2 workflows for RNA-seq data.
  • Statistical Testing: Identify significant genes using Wald tests with FDR correction.
  • Visualization: Generate volcano and MA plots for interpreting results.
  • Use Case: Analyze RNA-seq data from a drug treatment experiment to find genes that are significantly upregulated or downregulated in the treated group compared to the control group.

Quick Start

Use the lib-pydeseq2 skill to analyze RNA-seq counts using a simple condition design and identify genes differentially expressed between treated and control samples.

Frequently Asked Questions about lib-pydeseq2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform differential gene expression analysis on bulk RNA-seq count data?▼

Differential gene expression analysis on bulk RNA-seq count data is performed by fitting generalized linear models, estimating dispersions, and running Wald tests with Benjamini-Hochberg correction to identify significantly changing genes.

Can I use multi-factor experimental designs for RNA-seq differential expression?▼

Yes, multi-factor experimental designs are supported for RNA-seq differential expression alongside single-factor designs, allowing you to control for covariates while identifying differentially expressed genes.

What's the best way to visualize RNA-seq differential expression results?▼

Visualizing RNA-seq differential expression results is best done using volcano and MA plots, with log fold change shrinkage applied to stabilize estimates for clearer interpretation of gene expression changes.

Does this RNA-seq analysis approach support LFC shrinkage for plotting?▼

Yes, LFC shrinkage is supported for visualization in this RNA-seq analysis approach, generating standard volcano and MA plots while applying Wald tests with FDR correction to identify significant genes.

What statistical methods are used to identify differentially expressed genes?▼

Identifying differentially expressed genes uses Wald tests with Benjamini-Hochberg FDR correction, combined with generalized linear model fitting and dispersion estimation on raw RNA-seq count matrices.

Do I need pre-processed count matrices to run RNA-seq differential expression?▼

Yes, you need pre-processed bulk RNA-seq count matrices as input to run differential expression analysis, which then applies generalized linear models and statistical testing to pinpoint significant genes.

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