sc-dynamic-clustering

What problem does it solve?

Clustering of scRNA-seq data often requires manual tuning of dimensionality reduction and clustering parameters; this Skill automates HVG selection, PCA, UMAP embedding, and graph-based clustering to accelerate discovery.

Core Features & Use Cases

• HVG selection and PCA/UMAP embedding for neighborhood graph construction.
• Leiden clustering with fallback to Louvain and automatic summary of cluster sizes.
• Use Case: when comparing multiple resolutions to identify stable subpopulations in qc_filtered data.

Quick Start

Provide a cleaned AnnData file, specify n_pcs and resolution, and set an output directory to run iterative clustering and generate a summary.