differential-expression

name	differential-expression
title	Differential Gene Expression Analysis
slug	differential-expression
description	Find differentially expressed marker genes between groups using scanpy rank_genes_groups with Wilcoxon test. The comparison target is flexible — between cell types, clusters, spatial domains, slices, ROIs, or any user-defined grouping.
filter_requirements	{"modalities":["gene"]}
prerequisites	["Understanding of what the user wants to compare (e.g., cell types within a slice, same cell type across slices, cells in different ROIs, etc.)"]
default_skill	true

Differential Gene Expression Analysis

Find differentially expressed genes (DEGs) 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 Function

import scanpy as sc

# Prepare adata with a 'group' column that defines the comparison
# Then simply:
sc.tl.rank_genes_groups(adata, groupby='group', method='wilcoxon')
de_df = sc.get.rank_genes_groups_df(adata, group=None)

Comparison Scenarios (for the agent)

The agent should understand the query, select the right cells, and build the comparison adata. Examples:

• Between cell types in one slice: Use groupby='celltype' directly on that slice's adata.
• One cell type vs another: Use groups=['TypeA'], reference='TypeB'.
• Same cell type across slices: Subset cells of that type from both slices, concatenate, label by slice origin, then groupby='slice'.
• Between ROIs: Subset cells from each ROI, concatenate, label by ROI name, then groupby='roi'.
• Tumor vs normal region: Label cells by region annotation, then groupby='region'.
• Any custom comparison: The agent creates a categorical column encoding the comparison, then calls rank_genes_groups.

Do not restrict to a fixed workflow. The agent should flexibly prepare the data based on the user's question.

Minimal Workflow Template

import scanpy as sc
import pandas as pd
import numpy as np

# Step 1: Prepare adata with comparison groups
# -- The agent constructs this based on the user's query --
# Example: adata with adata.obs['group'] as the comparison column

# Step 2: Normalize if needed
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

# Step 3: Run DE
sc.tl.rank_genes_groups(adata, groupby='group', method='wilcoxon', n_genes=100, use_raw=False)
de_df = sc.get.rank_genes_groups_df(adata, group=None)

# Step 4: Store results
slice_obj.adata.uns['rank_genes_groups'] = adata.uns['rank_genes_groups']
slice_obj.adata.uns['de_results'] = de_df

Parameter Guide

Parameter	Default	Options	Description
groupby	—	Any categorical obs column	Column defining groups to compare
method	'wilcoxon'	'wilcoxon', 't-test', 'logreg'	Statistical test
groups	all	List of group names	Specific groups to test
reference	'rest'	Group name or 'rest'	Reference group for comparison
n_genes	100	10-500	Top genes per group