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bio-single-cell-clustering

Cluster single-cell RNA-seq data with Seurat or Scanpy workflows.

Updated Mar 30, 2026
Search Tags:#single-cell#clustering#pca#umap#seurat#scrna-seq#leiden
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-clustering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-single-cell-clustering
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-single-cell-clustering
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-single-cell-clustering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Dimensionality reduction, neighbor graph construction, and clustering of single-cell RNA-seq data across Seurat (R) and Scanpy (Python) pipelines to identify cell populations and visualizable embeddings.

Core Features & Use Cases

  • Supports both Seurat (R) and Scanpy (Python) for end-to-end clustering
  • Performs PCA-based dimensionality reduction, neighbor graph construction, and Leiden/Louvain clustering
  • Generates UMAP/tSNE embeddings and cluster visualizations
  • Suitable for exploratory analysis, multi-resolution clustering, and interoperable downstream workflows

Quick Start

Provide a preprocessed single-cell dataset (AnnData or Seurat object) and run the clustering workflow to obtain embeddings and cluster assignments.

Frequently Asked Questions about bio-single-cell-clustering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I cluster single-cell RNA-seq data using Seurat or Scanpy?▼

Single-cell RNA-seq clustering identifies cell populations by performing PCA dimensionality reduction, neighbor graph construction, and Leiden/Louvain clustering. This workflow supports both Seurat and Scanpy pipelines to produce UMAP embeddings and cluster assignments.

Can I generate UMAP embeddings and cluster visualizations from preprocessed scRNA-seq data?▼

Yes, you can generate UMAP and tSNE embeddings along with cluster visualizations by providing a preprocessed single-cell dataset like an AnnData or Seurat object to automatedly produce multi-resolution summary plots.

Does this single-cell clustering workflow support both Leiden and Louvain algorithms at multiple resolutions?▼

This single-cell clustering workflow supports Leiden and Louvain algorithms for multi-resolution clustering, allowing you to evaluate different granularities of cell population identification across Seurat and Scanpy environments.

What input format do I need to run PCA and UMAP on my single-cell dataset?▼

You need a preprocessed single-cell dataset formatted as an AnnData object for Python or a Seurat object for R. The workflow validates these inputs and dependencies before running PCA and generating UMAP embeddings.

Why does my scRNA-seq clustering fail during neighbor graph construction?▼

scRNA-seq clustering can fail during neighbor graph construction if input dependencies are missing or data is improperly preprocessed. This workflow validates inputs and dependencies upfront and includes error handling to surface these issues.

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