scanpy-scrna-seq
scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference. Standard scRNA-seq exploration.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scanpy-scrna-seq --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Scanpy Single-Cell RNA-seq Analysis ## Overview Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data built on the AnnData format. This skill covers the end-to-end standard workflow: quality control, normalization, highly variable gene selection, dimensionality reduction, clustering, marker gene identification, and cell type annotation. It produces annotated datasets and publication-quality visualizations. ## When to Use - Analyzing single-cell RNA-seq count matrices (10X Genomics, h5ad, CSV, loom) - Performing quality control filtering on scRNA-seq datasets (mitochondrial %, gene counts) - Running dimensionality reduction: PCA, UMAP, t-SNE - Identifying cell clusters via Leiden community detection - Finding differentially expressed marker genes per cluster (Wilcoxon, t-test, logistic regression) - Annotating cell types from known marker gene panels - Conducting trajectory inference and pseudotime analysis (PAGA, diffusion pseudotime) - Generating publication-quality single-cell plots (dot plots, heatmaps, stacked violins) - Comparing gene expression across experimental conditions within cell types - Use **Seurat** (R/Bioconductor) instead for scRNA-seq analy
- Overview
- When to Use
- Prerequisites
- Workflow
- Step 1: Setup and Data Loading
- Step 2: Quality Control
- Step 3: Normalization and Feature Selection
- Step 4: Scaling and Regression
- Step 5: Dimensionality Reduction
- Step 6: Clustering
- Step 7: Marker Gene Identification
- Step 8: Cell Type Annotation and Export
- Key Parameters
- Common Recipes
pip install "scanpy[leiden]" anndata
What does the scanpy-scrna-seq skill do?
scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference. Standard scRNA-seq exploration.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scanpy-scrna-seq --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.