Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats s…
---
name: scanpy
description: Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
license: BSD-3-Clause
metadata: {"version": "1.3", "skill-author": "K-Dense Inc."}
---
# 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):
… load the full skill through Skill Me