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数据与复现
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bio-single-cell-clustering
维护者 FreedomIntelligence · 最近更新 2026年4月1日
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.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-clustering
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Dimensionality reduction，neighbor graph construction，、 聚类。
- sc.tl.pca(adata，n_comps=50，svd_solver='arpack')。
原始文档
SKILL.md 摘录
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')
适用场景
- 可用于running PCA，computing neighbors，聚类 ，支持 Leiden/Louvain algorithms，generating UMAP/tSNE embeddings，、 visualizing clusters。
- 适合在performing dimensionality reduction 、 聚类 on single-cell data时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- preprocessing - Data must be preprocessed before clustering
- markers-annotation - Find markers for each cluster
- data-io - Save clustered results
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