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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.
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基于 SOC 职业分类
| 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 |
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.
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)
The agent should understand the query, select the right cells, and build the comparison adata. Examples:
groupby='celltype' directly on that slice's adata.groups=['TypeA'], reference='TypeB'.groupby='slice'.groupby='roi'.groupby='region'.rank_genes_groups.Do not restrict to a fixed workflow. The agent should flexibly prepare the data based on the user's question.
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 | 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 |
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