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

Requires Python 3.11+. Current stable release: 0.12.16 (released 2026-05-18).

uv pip install "anndata==0.12.16"

# Lazy I/O and dask-backed operations
uv pip install "anndata[dask,lazy]==0.12.16"

# Development / docs (contributors)
uv pip install "anndata[dev,test,doc]==0.12.16"

Use unpinned installs only when intentionally tracking the latest compatible release.

Current API notes:

Use anndata.io for non-native read_* and write_* helpers. Top-level anndata.read_h5ad and anndata.read_zarr remain supported.

Avoid deprecated APIs: ad.read, AnnData.concatenate(), AnnData.*_keys(), and anndata.__version__. Prefer ad.read_h5ad, ad.concat, mapping .keys(), and importlib.metadata.version("anndata").

Treat anndata.experimental APIs as useful but unstable. Prefer them for large-data workflows only when their current caveats are acceptable.

Quick Start

Creating an AnnData object

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

# Native formats (read_h5ad/read_zarr remain at top-level)
adata = ad.read_h5ad('data.h5ad')
adata = ad.read_h5ad('large_data.h5ad', backed='r')  # lazy load for large files
adata = ad.read_zarr('data.zarr')

# Other formats: prefer anndata.io (top-level imports are deprecated)
from anndata.io import read_csv, read_loom, read_mtx

adata = read_csv('data.csv')
adata = read_loom('data.loom')

# 10X Genomics: use scanpy (not anndata) — see scanpy skill
import scanpy as sc
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')
adata = sc.read_10x_mtx('filtered_feature_bc_matrix/')

Writing data

# Write h5ad file
adata.write_h5ad('output.h5ad')

# Write with compression
adata.write_h5ad('output.h5ad', compression='gzip')

# Write other formats
adata.write_zarr('output.zarr')
adata.write_csvs('output_dir/')

Basic operations

# Subset by conditions
t_cells = adata[adata.obs['cell_type'] == 'T cell']

# Subset by indices
subset = adata[0:50, 0:100]

# Add metadata
adata.obs['quality_score'] = np.random.rand(adata.n_obs)
adata.var['highly_variable'] = np.random.rand(adata.n_vars) > 0.8

# Access dimensions
print(f"{adata.n_obs} observations × {adata.n_vars} variables")

Core Capabilities

1. Data Structure

Understand the AnnData object structure including X, obs, var, layers, obsm, varm, obsp, varp, uns, and raw components.

See: references/data_structure.md for comprehensive information on:

Core components (X, obs, var, layers, obsm, varm, obsp, varp, uns, raw)

Creating AnnData objects from various sources

Accessing and manipulating data components

Memory-efficient practices

2. Input/Output Operations

Read and write data in various formats with support for compression, backed mode, and cloud storage.

See: references/io_operations.md for details on:

Native formats (h5ad, zarr)

Alternative formats (CSV, MTX, Loom, 10X, Excel)

Backed mode for large datasets

Remote data access

Format conversion

Performance optimization

Common commands:

from anndata.io import read_mtx

# Read/write h5ad
adata = ad.read_h5ad('data.h5ad', backed='r')
adata.write_h5ad('output.h5ad', compression='gzip')

# 10X Genomics (via scanpy)
import scanpy as sc
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

# Read MTX format
adata = read_mtx('matrix.mtx').T

3. Concatenation

Combine multiple AnnData objects along observations or variables with flexible join strategies.

See: references/concatenation.md for comprehensive coverage of:

Basic concatenation (axis=0 for observations, axis=1 for variables)

Join types (inner, outer)

Merge strategies (same, unique, first, only)

Tracking data sources with labels

Lazy concatenation (AnnCollection)

On-disk concatenation for large datasets

Common commands:

# Concatenate observations (combine samples)
adata = ad.concat(
    [adata1, adata2, adata3],
    axis=0,
    join='inner',
    label='batch',
    keys=['batch1', 'batch2', 'batch3']
)

# Concatenate variables (combine modalities)
adata = ad.concat([adata_rna, adata_protein], axis=1)

# Lazy collection over backed AnnData objects (experimental)
from anndata.experimental import AnnCollection

backed_adatas = [
    ad.read_h5ad(path, backed='r')
    for path in ['data1.h5ad', 'data2.h5ad']
]
collection = AnnCollection(
    backed_adatas,
    join_obs='outer',
    join_vars='inner',
    label='dataset'
)

4. Data Manipulation

Transform, subset, filter, and reorganize data efficiently.

See: references/manipulation.md for detailed guidance on:

Subsetting (by indices, names, boolean masks, metadata conditions)

Transposition

Copying (full copies vs views)

Renaming (observations, variables, categories)

Type conversions (strings to categoricals, sparse/dense)

Adding/removing data components

Reordering

Quality control filtering

Common commands:

# Subset by metadata
filtered = adata[adata.obs['quality_score'] > 0.8]
hv_genes = adata[:, adata.var['highly_variable']]

# Transpose
adata_T = adata.T

# Copy vs view
view = adata[0:100, :]  # View (lightweight reference)
copy = adata[0:100, :].copy()  # Independent copy

# Convert strings to categoricals
adata.strings_to_categoricals()

5. Best Practices

Follow recommended patterns for memory efficiency, performance, and reproducibility.

See: references/best_practices.md for guidelines on:

Memory management (sparse matrices, categoricals, backed mode)

Views vs copies

Data storage optimization

Performance optimization

Working with raw data

Metadata management

Reproducibility

Error handling

Integration with other tools

Common pitfalls and solutions

Key recommendations:

# Use sparse matrices for sparse data
from scipy.sparse import csr_matrix
adata.X = csr_matrix(adata.X)

# Convert strings to categoricals
adata.strings_to_categoricals()

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

# Store raw before filtering
adata.raw = adata.copy()
adata = adata[:, adata.var['highly_variable']]

Integration with Scverse Ecosystem

AnnData serves as the foundational data structure for the scverse ecosystem:

Scanpy (Single-cell analysis)

import scanpy as sc

# Preprocessing
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)

# Dimensionality reduction
sc.pp.pca(adata, n_comps=50)
sc.pp.neighbors(adata, n_neighbors=15)
sc.tl.umap(adata)
sc.tl.leiden(adata)

# Visualization
sc.pl.umap(adata, color=['cell_type', 'leiden'])

Muon (Multimodal data)

import muon as mu

# Combine RNA and protein data
mdata = mu.MuData({'rna': adata_rna, 'protein': adata_protein})

PyTorch integration

from anndata.experimental import AnnLoader

# Create DataLoader for deep learning
dataloader = AnnLoader(adata, batch_size=128, shuffle=True)

for batch in dataloader:
    X = batch.X
    # Train model

Common Workflows

Single-cell RNA-seq analysis

import anndata as ad
import scanpy as sc

# 1. Load data (10X via scanpy; anndata handles h5ad/zarr natively)
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

# 2. Quality control
adata.obs['n_genes'] = (adata.X > 0).sum(axis=1)
adata.obs['n_counts'] = adata.X.sum(axis=1)
adata = adata[adata.obs['n_genes'] > 200]
adata = adata[adata.obs['n_counts'] < 50000]

# 3. Store raw
adata.raw = adata.copy()

# 4. Normalize and filter
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
adata = adata[:, adata.var['highly_variable']]

# 5. Save processed data
adata.write_h5ad('processed.h5ad')

Batch integration

# Load multiple batches
adata1 = ad.read_h5ad('batch1.h5ad')
adata2 = ad.read_h5ad('batch2.h5ad')
adata3 = ad.read_h5ad('batch3.h5ad')

# Concatenate with batch labels
adata = ad.concat(
    [adata1, adata2, adata3],
    label='batch',
    keys=['batch1', 'batch2', 'batch3'],
    join='inner'
)

# Apply batch correction
import scanpy as sc
sc.pp.combat(adata, key='batch')

# Continue analysis
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)

Working with large datasets

# Open in backed mode
adata = ad.read_h5ad('100GB_dataset.h5ad', backed='r')

# Filter based on metadata (no data loading)
high_quality = adata[adata.obs['quality_score'] > 0.8]

# Load filtered subset
adata_subset = high_quality.to_memory()

# Process subset
process(adata_subset)

# Or process in chunks
chunk_size = 1000
for i in range(0, adata.n_obs, chunk_size):
    chunk = adata[i:i+chunk_size, :].to_memory()
    process(chunk)

Troubleshooting

Out of memory errors

Use backed mode or convert to sparse matrices:

# Backed mode
adata = ad.read_h5ad('file.h5ad', backed='r')

# Sparse matrices
from scipy.sparse import csr_matrix
adata.X = csr_matrix(adata.X)

Slow file reading

Use compression and appropriate formats:

# Optimize for storage
adata.strings_to_categoricals()
adata.write_h5ad('file.h5ad', compression='gzip')

# Use Zarr for cloud storage; v3 writes are opt-in in anndata 0.12
import anndata as ad

ad.settings.zarr_write_format = 3
ad.settings.auto_shard_zarr_v3 = True  # experimental; independent of zarr_write_format
adata.write_zarr('file.zarr', chunks=(1000, 1000))

Index alignment issues

Always align external data on index:

# Wrong
adata.obs['new_col'] = external_data['values']

# Correct
adata.obs['new_col'] = external_data.set_index('cell_id').loc[adata.obs_names, 'values']

Additional Resources

Official documentation: https://anndata.readthedocs.io/

Scanpy tutorials: https://scanpy.readthedocs.io/

Scverse ecosystem: https://scverse.org/

GitHub repository: https://github.com/scverse/anndata

anndata

Author

Category

Install

AnnData

Overview

When to Use This Skill

Installation

Quick Start

Creating an AnnData object

Reading data

Writing data

Basic operations

Core Capabilities

1. Data Structure

2. Input/Output Operations

3. Concatenation

4. Data Manipulation

5. Best Practices

Integration with Scverse Ecosystem

Scanpy (Single-cell analysis)

Muon (Multimodal data)

PyTorch integration

Common Workflows

Single-cell RNA-seq analysis

Batch integration

Working with large datasets

Troubleshooting

Out of memory errors

Slow file reading

Index alignment issues

Additional Resources