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Data & ReproSingle-Cell & Spatial OmicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction
BI

bio-single-cell-clustering

Maintainer FreedomIntelligence · Last updated April 1, 2026

Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell data.

OpenClawNanoClawAnalysisReproductionbio-single-cell-clustering🧬 bioinformatics (gptomics bio-* suite)bioinformatics — single-cell & spatial omicsdimensionality

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

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

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Dimensionality reduction, neighbor graph construction, and clustering.
  • sc.tl.pca(adata, n_comps=50, svd_solver='arpack').

Source Doc

Excerpt From SKILL.md

Scanpy (Python)

Goal: Reduce dimensions, build neighbor graphs, cluster cells, and visualize with UMAP/tSNE using Scanpy.

Approach: Run PCA for dimensionality reduction, construct a k-NN graph, apply Leiden community detection, and compute UMAP embedding.

"Cluster cells and find groups" → Reduce dimensionality with PCA, build a neighborhood graph, partition cells into clusters, and embed in 2D for visualization.

Visualize variance explained

sc.pl.pca_variance_ratio(adata, n_pcs=50)

Visualize PCA

sc.pl.pca(adata, color='n_genes_by_counts')

Use cases

  • Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters.
  • Use when performing dimensionality reduction and clustering on single-cell data.

Not for

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

Upstream Related Skills

  • preprocessing - Data must be preprocessed before clustering
  • markers-annotation - Find markers for each cluster
  • data-io - Save clustered results

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