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scientific-gene-expression-transcriptomics

Automate bulk RNA-seq analysis from GEO retrieval to GSEA enrichment interpretation.

3|1|Updated Feb 11, 2026
Search Tags:#differential-expression#gsea#deseq2#transcriptomics#eqtl#geo-datasets#bulk-rna-seq
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-gene-expression-transcriptomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-gene-expression-transcriptomics
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-gene-expression-transcriptomics
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-gene-expression-transcriptomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides an end-to-end workflow for bulk RNA-seq transcriptomics, integrating data retrieval from GEO, preprocessing, differential expression analysis (via PyDESeq2), GTEx reference querying, eQTL lookups, and gene-set enrichment (GSEA/ORA) to enable rapid, reproducible interpretation of transcriptomic studies.

Core Features & Use Cases

  • GEO dataset retrieval and preprocessing for bulk RNA-seq and microarray data.
  • Differential expression analysis using DESeq2 (via PyDESeq2) and downstream enrichment analyses (GSEA/ORA).
  • GTEx tissue expression context and eQTL lookups to help interpret tissue-specific patterns.
  • Expression Atlas integration for baseline and differential expression exploration.
  • End-to-end pipeline outputs including ranked gene lists, enrichment reports, and publication-ready plots.

Quick Start

Run the end-to-end bulk RNA-seq pipeline to fetch GEO data, preprocess counts, perform differential expression with PyDESeq2, and interpret results with GTEx references and GSEA.

Frequently Asked Questions about scientific-gene-expression-transcriptomics

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

FAQPage Schema
How do I perform end-to-end bulk RNA-seq differential expression analysis from GEO datasets?▼

You can automate bulk RNA-seq differential expression by retrieving GEO datasets, preprocessing counts, and running PyDESeq2 to output ranked gene lists and publication-ready plots.

Can I use DESeq2 for differential expression in Python without R?▼

Yes, PyDESeq2 enables differential expression analysis directly in Python, providing ranked gene lists and visualizations while avoiding R environment dependencies.

How do I add tissue expression context and eQTL lookups to my transcriptomics pipeline?▼

You can integrate GTEx tissue expression context and eQTL lookups into your bulk RNA-seq pipeline to interpret tissue-specific patterns alongside differential expression results.

What's the best way to run GSEA and ORA on differential expression results?▼

Use gseapy to perform GSEA and ORA on your PyDESeq2 differential expression outputs, generating comprehensive enrichment reports for bulk RNA-seq experiments.

Do I need specific Python libraries to run this transcriptomics analysis pipeline?▼

Yes, the pipeline requires GEOparse for data retrieval, PyDESeq2 for differential expression, gseapy for enrichment, plus standard plotting and data manipulation libraries.

When should I use this bulk RNA-seq pipeline instead of analyzing microarray data separately?▼

This pipeline handles both bulk RNA-seq and microarray data retrieval from GEO, but its PyDESeq2 differential expression and GTEx integration features are specifically optimized for bulk RNA-seq transcriptomics studies.

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