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bio-single-cell-markers-annotation

Maintainer FreedomIntelligence · Last updated April 1, 2026

Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.

OpenClawNanoClawAnalysisReproductionbio-single-cell-markers-annotation🧬 bioinformatics (gptomics bio-* suite)bioinformatics — single-cell & spatial omicsfind

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-markers-annotation

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Find differentially expressed genes between clusters and annotate cell types.
  • sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon').

Source Doc

Excerpt From SKILL.md

Scanpy (Python)

Goal: Identify cluster-specific marker genes, score gene sets, and annotate cell types using Scanpy.

Approach: Perform differential expression testing between clusters with Wilcoxon rank-sum, visualize markers with dot plots and heatmaps, and assign cell type labels manually.

"Find marker genes for each cluster" → Test each cluster against all others for differentially expressed genes and rank by statistical significance and fold change.

View top markers

sc.pl.rank_genes_groups(adata, n_genes=10, sharey=False)

Get results as DataFrame

markers = sc.get.rank_genes_groups_df(adata, group=None) print(markers.head(20))

Use cases

  • Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels.
  • Use when finding marker genes and annotating clusters.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • clustering - Must cluster before finding markers
  • preprocessing - Data must be normalized
  • data-io - Export annotated data

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