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differential-expression

Identify differentially expressed genes across defined groups in AnnData objects.

3|Updated Apr 3, 2026
Search Tags:#biology#scanpy#differential-expression#gene-expression#adata#deg-analysis#rank_genes_groups
One-click install
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill differential-expression
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: differential-expression
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/differential-expression
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill differential-expression

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Find differentially expressed genes (DEGs) between groups using sc.tl.rank_genes_groups.

The comparison target is completely flexible. The agent must understand the user's intent and prepare the data accordingly — the key is to construct an adata with a categorical column that defines the two (or more) groups to compare.

Core Features & Use Cases

  • Flexible group definitions: compare cell types, clusters, slices, ROIs, or user-defined groupings.
  • Output: ranked DE results per group, ready for downstream interpretation and visualization.

Quick Start

Prepare an AnnData object with a grouping column and run rank_genes_groups to obtain ranked DE genes.

Frequently Asked Questions about differential-expression

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

FAQPage Schema
How do I find differentially expressed genes between cell clusters using scanpy?▼

To find differentially expressed genes, this Skill runs sc.tl.rank_genes_groups on an AnnData object, requiring a categorical group annotation column to produce ranked DE gene results for your defined cell clusters.

Can I compare spatial domains or custom groupings for marker gene discovery?▼

Yes, marker gene discovery supports flexible group definitions, allowing you to compare spatial domains, custom groupings, cell types, or slices by applying rank_genes_groups to your categorical adata annotations.

What do I need to prepare in my AnnData object before running DE analysis?▼

Before running DE analysis, you need an AnnData object containing a categorical group annotation column that defines the two or more groups you intend to compare for differentially expressed genes.

Does this differential expression method work with multi-slice datasets?▼

Yes, this differential expression analysis applies to both single-slice and multi-slice datasets, enabling comparisons across various groupings like cell types or spatial domains using rank_genes_groups.

What is the best way to get ranked DE gene results for downstream visualization?▼

The best way to get ranked DE gene results is to define a categorical grouping column in your AnnData object and run rank_genes_groups, which outputs ranked DE results ready for downstream interpretation and visualization.

Why are my differentially expressed genes not showing up after running rank_genes_groups?▼

Differentially expressed genes may not show up if your AnnData object lacks a valid categorical group annotation column, which is strictly required to define the groups for the DE comparison.

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