Perform single-cell RNA-seq analysis with scanpy, including QC, clustering, and annotation.
npx skills add https://github.com/Sologa/codex-pipeline --skill scanpy-sologa
Please help me install this Agent Skill. Skill: scanpy Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/scanpy Command: npx skills add https://github.com/Sologa/codex-pipeline --skill scanpy-sologa
This Skill streamlines the complex and computationally intensive process of analyzing single-cell RNA-seq data, enabling researchers to gain insights into cellular heterogeneity and function.
Use the scanpy skill to perform a standard single-cell RNA-seq analysis on the provided h5ad file.
High-intent search queries and answers about installing and using this skill.
Single-cell RNA-seq analysis involves quality control, normalization, dimensionality reduction via PCA and UMAP, Leiden clustering, and marker gene identification to annotate cell types.
The standard scRNA-seq workflow uses PCA for initial dimensionality reduction, followed by UMAP for visualization, and applies Leiden clustering to identify distinct cellular populations.
Yes, you can process h5ad files to perform comprehensive single-cell RNA-seq analysis, which includes identifying marker genes and generating publication-quality visualizations.
Single-cell RNA-seq analysis supports various input formats, with h5ad being standard, and applies quality control and normalization steps to prepare data for downstream clustering.
To visualize cellular heterogeneity in scRNA-seq data, apply UMAP for dimensionality reduction and generate publication-quality figures that highlight distinct cell populations and marker genes.
CHENyiru3Convert Python h5ad single-cell data into Seurat or SingleCellExperiment objects.
anthropicsFilter low-quality cells from single-cell RNA-seq data using MAD-based QC.
TianGzlabCompute RNA velocity vectors from spatial transcriptomics AnnData objects.
ya-wayProcess 10X Cell Ranger scRNA-seq data into annotated cell types.
