# Scanpy

Standard single-cell RNA-seq analysis pipeline.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Engineering
- License: SD-3-Clause license
- Last updated: 2026-07-20
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill scanpy -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill scanpy -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill scanpy -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill scanpy -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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('path/to/data.h5ad')

# From CSV
adata = sc.read_csv('path/to/data.csv')
```

### Understanding AnnData Structure

The AnnData object is the core data structure in scanpy:

```python
adata.X          # Expression matrix (cells × genes)
adata.obs        # Cell metadata (DataFrame)
adata.var        # Gene metadata (DataFrame)
adata.uns        # Unstructured annotations (dict)
adata.obsm       # Multi-dimensional cell data (PCA, UMAP)
adata.raw        # Raw data backup

# Access cell and gene names
adata.obs_names  # Cell barcodes
adata.var_names  # Gene names
```

## Standard Analysis Workflow

### 1. Quality Control

Identify and filter low-quality cells and genes:

```python
# Identify mitochondrial genes
adata.var['mt'] = adata.var_names.str.startswith('MT-')

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/05-生物信息与基因组学/scanpy/SKILL.md