Run end-to-end single-cell RNA-seq analysis workflows with AnnData and Scanpy.
npx skills add https://github.com/CodingKaiser/kaiser-skills --skill scverse
Please help me install this Agent Skill. Skill: scverse Source: https://github.com/CodingKaiser/kaiser-skills/tree/main/scverse Command: npx skills add https://github.com/CodingKaiser/kaiser-skills --skill scverse
Single-cell RNA-seq analysis is intricate, requiring coordinated steps across data structures, preprocessing, and visualization. This skill provides pattern-based workflows using AnnData and Scanpy to simplify building reproducible pipelines for QC, normalization, dimensionality reduction, clustering, and visualization.
Load a sample AnnData object and apply the standard scverse workflow: QC metrics, filtering, normalization, HVG identification, PCA, neighbor graph construction, and clustering with Leiden, followed by visualization with UMAP.
High-intent search queries and answers about installing and using this skill.
To run an end-to-end scRNA-seq analysis workflow with Scanpy and AnnData, apply pattern-based steps for preprocessing, dimensionality reduction, clustering, and visualization to uncover cellular heterogeneity in your datasets.
The best way to perform quality control and normalization for single-cell RNA-seq data is using standard preprocessing pipelines that calculate QC metrics, filter cells, normalize counts, and select highly variable genes.
UMAP visualization and Leiden clustering work in single-cell analysis by first constructing a neighbor graph on PCA-reduced data, then grouping cells into clusters to reveal underlying cellular heterogeneity visually.
Yes, you can use these scverse workflows for large-scale scRNA-seq datasets, as the pattern-based AnnData and Scanpy pipelines are applicable from small pilot studies up to large-scale projects.
Yes, you need the Python-based Scanpy and AnnData ecosystems to read and write .h5ad files, with optional standard data-science tooling like NumPy, pandas, and matplotlib for plotting.
You should use AnnData for single-cell analysis because it provides coordinated data structures that simplify building reproducible pipelines for preprocessing, dimensionality reduction, and visualization tasks.
omar-A-hassanProcess single-cell RNA-seq data with Scanpy for clustering and marker-gene discovery.
silversteinRun single-cell RNA-seq analyses on AnnData objects with scanpy.
sanand0Create static, animated, and interactive visualizations in Python with Matplotlib.
robinbarvaagAnalyze single-cell RNA-seq data with preprocessing, clustering, and visualization.
