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 majiayu000/claude-skill-registry --skill scanpy-k-dense-ai-scientific-agent-ski-2 --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. Current stable release: **scanpy 1.12.x** (January 2026). ## Installation Requires Python **3.12+** (scanpy 1.12 dropped Python ≤3.11) and anndata **≥0.10**. ```bash uv pip install "scanpy[leiden]" ``` The `[leiden]` extra installs `python-igraph` and `leidenalg`, required for Leiden clustering. For reproducible environments, pin a version: `uv pip install "scanpy[leiden]==1.12.1"`. For large or out-of-core datasets, many functions support [Dask](https://docs.dask.org/) arrays (experimental): ```bash uv pip install "scanpy[leiden]" dask ``` See the [Using dask with Scanpy](https://scanpy.scverse.org/en/stable/tutorials/experimental/dask.html) tutorial. For GPU-accelerated scanpy-like operations, use [rapids-singlecell](https://rapids-singlecell.readthedocs.io/) as a separate package. For AnnData structure and I/O details, use the **anndata** skill. Fo
- Overview
- Installation
- 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
uv pip install "scanpy[leiden]" uv pip install "scanpy[leiden]" dask python skills/scanpy/scripts/qc_analysis.py input_file.h5ad --output filtered.h5ad python skills/scanpy/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 majiayu000/claude-skill-registry --skill scanpy-k-dense-ai-scientific-agent-ski-2 --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 majiayu000/claude-skill-registry, a repository with 534 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.