skills.rest
Starlitnightly
StarlitnightlyCOMMUNITY
Explore all skills by this author →
GitHub

Bulk RNA-seq DESeq2 analysis with omicverse

Run PyDESeq2 analysis on bulk RNA-seq count matrices with omicverse.

1.2k|145|Updated Mar 22, 2021
Search Tags:#bioinformatics#visualization#bulk RNA-seq#GSEA#DESeq2#omicverse#differential expression#DEG
One-click install
npx skills add https://github.com/Starlitnightly/omicverse --skill bulk-rna-seq-deseq2-analysis-with-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Bulk RNA-seq DESeq2 analysis with omicverse
Source: https://github.com/Starlitnightly/omicverse/tree/main/.claude/skills/bulk-deseq2-analysis
Command: npx skills add https://github.com/Starlitnightly/omicverse --skill bulk-rna-seq-deseq2-analysis-with-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires omicverse, scanpy, matplotlib, pandas, gseapy, and includes references (resource) components.

What problem does it solves? Conducting robust differential expression analysis for bulk RNA-seq data using DESeq2 involves several steps, from gene ID mapping to statistical testing and visualization, which can be intricate. This Skill automates the PyDESeq2 workflow within omicverse, simplifying the entire process.

Core Features & Use Cases

  • Gene ID Mapping: Convert gene identifiers (e.g., Ensembl IDs) to gene symbols for consistent analysis.
  • DESeq2 Differential Expression: Perform DESeq2-based differential expression testing between treatment and control groups.
  • Visualization & Filtering: Filter results by fold-change and p-value, and visualize with volcano plots and boxplots.
  • Gene Set Enrichment Analysis (GSEA): Conduct GSEA on ranked genes to identify enriched biological pathways.
  • Use Case: Analyze a bulk RNA-seq experiment comparing drug-treated vs. untreated samples, identify genes differentially expressed using DESeq2, visualize the results, and then perform GSEA to find enriched biological pathways.

Quick Start

Run DESeq2 differential expression on my bulk RNA-seq counts, generate a volcano plot, and perform GSEA on WikiPathways.

Frequently Asked Questions about Bulk RNA-seq DESeq2 analysis with omicverse

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

FAQPage Schema
How do I perform differential expression analysis on bulk RNA-seq data using DESeq2?▼

DESeq2 differential expression analysis identifies genes with significant expression changes between conditions. This Skill automates the PyDESeq2 workflow via omicverse, handling gene ID mapping, statistical testing, and visualization of treatment versus control comparisons in a single pipeline.

Can I convert Ensembl gene IDs to gene symbols in my RNA-seq analysis?▼

Yes. This Skill maps Ensembl IDs to gene symbols as a preprocessing step before DESeq2 analysis, ensuring consistent gene identification and enabling downstream enrichment work across treatment and control datasets.

How do I generate volcano plots and filter DESeq2 results by fold-change and p-value?▼

After running DESeq2 differential expression testing, this Skill filters genes by your chosen fold-change and p-value thresholds and produces volcano plots and boxplots to visualize significant differentially expressed genes.

What is gene set enrichment analysis and how does it work with DESeq2 results?▼

Gene set enrichment analysis (GSEA) identifies biological pathways and processes enriched in ranked gene lists. This Skill ranks DESeq2 results and performs GSEA against WikiPathways to reveal pathway-level insights beyond individual gene changes.

Do I need to handle duplicate gene symbols when converting from Ensembl IDs?▼

Yes. This Skill addresses duplicate gene symbol handling as part of the ID mapping process, ensuring clean DESeq2 input and preventing ambiguous gene assignments that could skew differential expression results.

What should I do if DESeq2 returns a size-factor error during analysis?▼

This Skill includes troubleshooting guidance for common size-factor errors and species-specific ID mapping issues, helping resolve calculation failures that block DESeq2 normalization and statistical testing.

Related Skills

Explore More Education & Research Skills→
omicverseomicverse
32

bulk-rna-seq-differential-expression-with-omicverse

Analyze bulk RNA-seq count matrices for differential expression and pathway enrichment.

Official
Advanced
ya-wayya-way

bio-workflows-expression-to-pathways

Translate differential expression results into GO, KEGG, and Reactome enrichment analyses with visualization.

Community
Advanced
MubasherMohammedMubasherMohammed

diff-visualizer

Generate visualizations and reports for bulk and single-cell RNA-seq differential expression.

Community
Advanced
MubasherMohammedMubasherMohammed

de-summary

Summarize differential expression results from DESeq2, edgeR, or limma outputs.

Community
Intermediate