bio-single-cell-clustering
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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-single-cell-clustering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Single-Cell Clustering Dimensionality reduction, neighbor graph construction, and clustering. ## 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. ### Required Imports ```python import scanpy as sc import matplotlib.pyplot as plt ``` ### PCA ```python # Run PCA sc.tl.pca(adata, n_comps=50, svd_solver='arpack') # Visualize v
- Version Compatibility
- Scanpy (Python)
- Required Imports
- PCA
- Determine Number of PCs
- Compute Neighbors
- Clustering (Leiden - Recommended)
- Clustering (Louvain)
- UMAP
- tSNE
- Complete Clustering Pipeline
- Exploring Different Resolutions
- PAGA (Trajectory Inference)
- Seurat (R)
What does the bio-single-cell-clustering skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-single-cell-clustering --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.