scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scanpy --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 Analysis ## Overview Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis. ## When to Use This Skill This skill should be used when: - Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats) - Performing quality control on scRNA-seq datasets - Creating UMAP, t-SNE, or PCA visualizations - Identifying cell clusters and finding marker genes - Annotating cell types based on gene expression - Conducting trajectory inference or pseudotime analysis - Generating publication-quality single-cell plots ## Quick Start ### Basic Import and Setup ```python import scanpy as sc import pandas as pd import numpy as np # Configure settings sc.settings.verbosity = 3 sc.settings.set_figure_params(dpi=80, facecolor='white') sc.settings.figdir = './figures/' ``` ### Loading Data ```python # From 10X Genomics adata = sc.read_10x_mtx('path/to/data/') adata = sc.read_10x_h5('path/to/data.h5') # From h5ad (AnnData format) adata = sc.read_h5ad('p
- Overview
- When to Use This Skill
- Quick Start
- Basic Import and Setup
- Loading Data
- Understanding AnnData Structure
- Standard Analysis Workflow
- 1. Quality Control
- 2. Normalization and Preprocessing
- 3. Dimensionality Reduction
- 4. Clustering
- 5. Marker Gene Identification
- 6. Cell Type Annotation
- 7. Save Results
python scripts/qc_analysis.py input_file.h5ad --output filtered.h5ad python scripts/qc_analysis.py input.h5ad --output filtered.h5ad \ cp assets/analysis_template.py my_analysis.py Edit parameters and run python my_analysis.py
What does the scanpy skill do?
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scanpy --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.