Run end-to-end single-cell RNA-seq analysis with Scanpy on h5ad, 10X, or CSV datasets.
npx skills add https://github.com/must1f/Dissertaion-Project --skill scanpy-must1f
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/must1f/Dissertaion-Project/tree/main/.agents/skills/scanpy Command: npx skills add https://github.com/must1f/Dissertaion-Project --skill scanpy-must1f
Single-cell RNA-seq analysis requires assembling a multi-step pipeline for quality control, normalization, dimensionality reduction, clustering, marker identification, and visualization. This skill standardizes and accelerates scRNA-seq workflows in a reproducible, scalable manner.
Run the full Scanpy workflow on your scRNA-seq dataset to generate QC metrics, normalization, clustering, and visualization results.
High-intent search queries and answers about installing and using this skill.
End-to-end single-cell RNA-seq analysis is performed using Scanpy to execute quality control, normalization, PCA, UMAP, Leiden clustering, and differential expression on raw count data.
The best way to analyze scRNA-seq datasets in h5ad format is using Scanpy to generate highly variable genes, Leiden clustering, UMAP visualizations, and trajectory results.
Scanpy handles single-cell RNA-seq quality control by deriving QC metrics directly from the data, followed by normalization and highly variable gene selection for dimensionality reduction.
Yes, Scanpy performs scalable dimensionality reduction using PCA and UMAP, followed by Leiden clustering and differential expression analysis suitable for large scRNA-seq datasets.
Matplotlib is required for scRNA-seq visualization because Scanpy relies on it to render UMAP embeddings, Leiden clustering plots, and differential expression marker visualizations.
Scanpy standardizes scRNA-seq trajectory analysis and differential expression, but limitations depend on the input data quality and the complexity of the biological differentiation trajectories explored.
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