Automates single-cell RNA-seq workflows including QC, normalization, clustering, and annotation using scanpy Python package.
npx skills add https://github.com/yf8578/clawomics --skill scanpy-yf8578
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/yf8578/clawomics/tree/main/skills/scanpy Command: npx skills add https://github.com/yf8578/clawomics --skill scanpy-yf8578
This Skill streamlines the complex process of analyzing single-cell RNA-seq data, enabling researchers to derive meaningful biological insights from high-dimensional datasets.
Run the standard analysis workflow on your h5ad file.
High-intent search queries and answers about installing and using this skill.
Single-cell RNA-seq data analysis can be performed end-to-end by running quality control, normalization, dimensionality reduction, clustering, and cell type annotation to derive biological insights from high-dimensional datasets.
Identifying cell populations and marker genes in scRNA-seq data involves using standardized workflows for clustering and marker gene identification, automatically generating publication-quality visualizations of gene expression.
Scanpy can be used for quality control and normalization of single-cell genomics data, providing pre-built scripts and templates that guide researchers through these essential preprocessing steps reproducibly.
Single-cell RNA-seq analysis workflows support various input formats, including h5ad files, allowing researchers to run standard analysis pipelines on high-dimensional datasets to identify distinct cell types.
This single-cell analysis pipeline generates publication-quality visualizations for quality control, dimensionality reduction, and marker gene expression, streamlining the process of deriving biological insights from complex datasets.
ClawBioRun QC, clustering, marker detection, and differential expression on raw-count AnnData .h5ad files.
Hung-3008Analyze single-cell RNA-seq data to produce QC metrics, embeddings, clusters, and marker genes.
KetomihineOrganize, query, and visualize spatial omics data with a unified Python framework.
ya-wayCluster single-cell RNA-seq data with Seurat or Scanpy workflows.
