# AnnData Single-Cell Data

Reads and writes annotated matrices and h5ad files in single-cell analysis.

- Repository: https://github.com/synthetic-sciences/openscience (4321 stars)
- Category: Data and ML
- License: BSD-3-Clause license
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add synthetic-sciences/openscience --skill anndata -g -a claude-code -y`
Codex: `npx skills add synthetic-sciences/openscience --skill anndata -g -a codex -y`
Cursor: `npx skills add synthetic-sciences/openscience --skill anndata -g -a cursor -y`
Gemini CLI: `npx skills add synthetic-sciences/openscience --skill anndata -g -a gemini-cli -y`

## Description

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill, for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

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

# AnnData

## Overview

AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs), variable metadata (var), and multi-dimensional annotations (obsm, varm, obsp, varp, uns). Originally designed for single-cell genomics through Scanpy, it now serves as a general-purpose framework for any annotated data requiring efficient storage, manipulation, and analysis.

## When to Use This Skill

Use this skill when:
- Creating, reading, or writing AnnData objects
- Working with h5ad, zarr, or other genomics data formats
- Performing single-cell RNA-seq analysis
- Managing large datasets with sparse matrices or backed mode
- Concatenating multiple datasets or experimental batches
- Subsetting, filtering, or transforming annotated data
- Integrating with scanpy, scvi-tools, or other scverse ecosystem tools

## Installation

```bash
uv pip install anndata

# With optional dependencies
uv pip install anndata[dev,test,doc]
```

## Quick Start

### Creating an AnnData object
```python
import anndata as ad
import numpy as np
import pandas as pd

# Minimal creation
X = np.random.rand(100, 2000)  # 100 cells × 2000 genes
adata = ad.AnnData(X)

# With metadata
obs = pd.DataFrame({
    'cell_type': ['T cell', 'B cell'] * 50,
    'sample': ['A', 'B'] * 50
}, index=[f'cell_{i}' for i in range(100)])

var = pd.DataFrame({
    'gene_name': [f'Gene_{i}' for i in range(2000)]
}, index=[f'ENSG{i:05d}' for i in range(2000)])

adata = ad.AnnData(X=X, obs=obs, var=var)
```

### Reading data
```python
# Read h5ad file
adata = ad.read_h5ad('data.h5ad')

# Read with backed mode (for large files)
adata = ad.read_h5ad('large_data.h5ad', backed='r')

# Read other formats
adata = ad.read_csv('data.csv')
adata = ad.read_loom('data.loom')
adata = ad.read_10x_h5('filtered_feature_bc_matrix.h5')
```

### Writing data
```python
# Write h5ad file
adata.write_h5ad('output.h5ad')

The rest: https://github.com/synthetic-sciences/openscience/blob/HEAD/backend/cli/skills/biology/anndata/SKILL.md