Perform single-cell RNA-seq analysis with scanpy from QC to visualization.
npx skills add https://github.com/jimmc414/Kosmos --skill scanpy-jimmc414
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/scanpy Command: npx skills add https://github.com/jimmc414/Kosmos --skill scanpy-jimmc414
This Skill streamlines the complex process of analyzing single-cell RNA-seq data, enabling researchers to derive meaningful biological insights from high-throughput experiments.
Use the scanpy skill to load the data from 'path/to/data.h5ad', perform quality control, normalize, identify highly variable genes, run PCA and UMAP, cluster cells using Leiden, and find marker genes for each cluster.
High-intent search queries and answers about installing and using this skill.
Single-cell RNA-seq data analysis involves quality control, normalization, dimensionality reduction, clustering, and marker gene identification to identify distinct cell populations and characterize their relationships.
Quality control and normalization for scRNA-seq datasets are handled automatically, alongside highly variable gene identification, PCA, UMAP, and Leiden clustering to prepare data for downstream biological insights.
Yes, you can generate publication-quality plots for scRNA-seq data, including embeddings like UMAP and PCA, quality control metrics, and gene expression visualizations using matplotlib.
Yes, the single-cell RNA-seq workflow supports clustering cells using the Leiden algorithm and automatically finding marker genes for each identified cell cluster.
Single-cell RNA-seq data analysis requires Python libraries including scanpy, pandas, numpy, and matplotlib to execute the complete analytical workflow and generate visualizations.
The best way to identify distinct cell populations in scRNA-seq data is by running a comprehensive workflow that performs dimensionality reduction, Leiden clustering, and marker gene identification automatically.
ya-wayExtract per-cluster marker genes from scRNA-seq h5ad data into JSON.
TianGzlabAutomate cell type annotation for single-cell omics data using marker genes, CellTypist, SingleR, or scmap.
KetomihineAnalyze single-cell RNA-seq .h5ad data with Scanpy from QC to clustering.
SologaAnalyze single-cell omics data with deep generative models.
