Analyze single-cell RNA-seq data with Scanpy for QC, clustering, and visualization.
npx skills add https://github.com/JosephWoodall/noosphere --skill scanpy-josephwoodall
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/scanpy Command: npx skills add https://github.com/JosephWoodall/noosphere --skill scanpy-josephwoodall
Scanpy provides a scalable Python toolkit for analyzing single-cell RNA-seq data, enabling researchers to perform quality control, normalization, dimensionality reduction, clustering, and visualization within a unified workflow.
Execute a complete Scanpy workflow on your AnnData object to QC, normalize, reduce dimensions, cluster, annotate, and visualize.
High-intent search queries and answers about installing and using this skill.
To perform clustering and dimensionality reduction on scRNA-seq data, you can use Scanpy to execute quality control, normalization, PCA, and UMAP on an AnnData object, followed by Leiden or Louvain clustering to identify cell groups.
The best way to analyze h5ad files for marker gene identification is to load the AnnData object into a Python environment with Scanpy installed, then run preprocessing modules to normalize data and detect marker genes for each cluster.
Yes, you can use Scanpy for trajectory inference and cell-type annotation. The toolkit supports exploratory workflows on single-cell RNA-seq datasets, allowing you to map cellular transitions and assign cell types based on marker gene expression.
Yes, you need a Python environment with Scanpy installed to run single-cell RNA-seq visualization workflows. A valid AnnData object is also required to drive the core plotting, clustering, and processing modules for generating publication-ready plots.
Yes, this single-cell analysis workflow supports 10X format datasets. The Scanpy toolkit is designed to ingest 10X data, perform quality control, high variable gene selection, and generate visualizations suitable for exploratory analysis.
Your UMAP visualization may fail after neighborhood graph construction if the AnnData object lacks properly normalized high variable genes. Ensure the Scanpy preprocessing steps for scaling and PCA are completed before computing the neighborhood graph.
CHENyiru3Convert Python h5ad single-cell data into Seurat or SingleCellExperiment objects.
anthropicsFilter low-quality cells from single-cell RNA-seq data using MAD-based QC.
Hung-3008Analyze single-cell RNA-seq data to produce QC metrics, embeddings, clusters, and marker genes.
jimmc414Perform single-cell RNA-seq analysis with scanpy from QC to visualization.
