Analyze single-cell RNA-seq data with scanpy for QC, clustering, and annotation.
npx skills add https://github.com/sanand0/scientific-research --skill scanpy-sanand0
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/scanpy Command: npx skills add https://github.com/sanand0/scientific-research --skill scanpy-sanand0
This Skill streamlines the complex process of analyzing single-cell RNA-sequencing (scRNA-seq) data, enabling researchers to derive meaningful biological insights from high-dimensional datasets.
Use the scanpy skill to load the AnnData object from 'data/raw_counts.h5ad' and perform a standard QC and normalization workflow.
High-intent search queries and answers about installing and using this skill.
Single-cell RNA-seq data analysis is streamlined through a comprehensive pipeline handling quality control, normalization, dimensionality reduction, clustering, and marker gene identification to annotate cell types.
Yes, scRNA-seq analysis supports various input formats including .h5ad, 10X, and CSV, allowing you to load single-cell RNA-seq datasets directly into the workflow for immediate processing.
To visualize cell populations and gene expression patterns, this scRNA-seq workflow generates publication-ready visualizations that explore data structure and relationships across different cell types.
Dimensionality reduction and clustering for scRNA-seq datasets work by processing normalized high-dimensional data to identify distinct cell populations and reveal underlying biological structures.
No specific external dependencies are required to run scRNA-seq quality control, as the environment provides the necessary Python package infrastructure to execute normalization and analysis scripts.
Unlike other single-cell genomics tools, this approach provides an end-to-end Python-based workflow specifically designed for scRNA-seq, integrating quality control, clustering, and visualization in one pipeline.
robotlearning123Manage and analyze AnnData objects for single-cell genomics research.
TianGzlabAnnotate cell types in spatial transcriptomics AnnData objects using marker-based scoring, Tangram, scANVI, or CellAssign.
LigphiDonkAutomate end-to-end single-cell bioinformatics analysis with HTML report generation.
tassiovaleStandardize single-cell RNA-seq workflows with Scanpy for QC, clustering, and annotation.
