Analyze scRNA-seq data with Scanpy workflows for QC through trajectory inference.
npx skills add https://github.com/ovachiever/droid-tings --skill scanpy
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/ovachiever/droid-tings/tree/main/skills/scanpy Command: npx skills add https://github.com/ovachiever/droid-tings --skill scanpy
Scanpy provides an end-to-end workflow for analyzing single-cell RNA-seq data, from QC to trajectory analysis, using the AnnData data structure and a comprehensive set of preprocessing and visualization tools.
Use cases include single-cell data exploration, publishing-quality plots, and reproducible scRNA-seq pipelines.
Load an h5ad dataset, run QC, normalization, HVG selection, PCA and UMAP, then cluster and annotate.
High-intent search queries and answers about installing and using this skill.
End-to-end single-cell RNA-seq analysis applies QC, normalization, dimensionality reduction, and clustering sequentially on .h5ad, 10X, or CSV data using Scanpy and AnnData. Load your dataset, run quality control and normalization, select highly variable genes, reduce dimensions with PCA/UMAP, cluster with Leiden, then annotate cell types using marker genes.
Scanpy supports .h5ad (AnnData's native format), 10X Genomics output, and CSV files for single-cell RNA-seq analysis. The AnnData data structure standardizes storage of expression matrices, metadata, and embeddings across preprocessing, clustering, and downstream analysis steps.
Yes, Scanpy enables both clustering via Leiden algorithm and trajectory inference on the same AnnData object. After QC, normalization, and dimensionality reduction, you can perform clustering to identify cell populations, then apply trajectory analysis to infer developmental paths and cell-state transitions.
Scanpy workflows require Python proficiency and familiarity with single-cell RNA-seq concepts like QC metrics, normalization, and clustering. The AnnData structure and Scanpy functions are designed for reproducible, publication-quality analysis but assume understanding of scRNA-seq biology and data preprocessing principles.
Scanpy integrates with Muon, part of the scverse ecosystem, to analyze multi-modal datasets combining RNA-seq with protein, chromatin, or other modalities. This integration allows joint dimensionality reduction, clustering, and cell-type annotation across modalities within a unified AnnData framework.
Scanpy provides comprehensive plotting functions that generate publication-ready visualizations of UMAP/t-SNE embeddings, clustered populations, marker genes, and cell-type annotations directly from AnnData objects. Customize colors, annotations, and styling to match journal requirements without exporting to external tools.
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