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

uv pip install anndata
With optional dependencies

uv pip install anndata[dev,test,doc]

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

# 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

# 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:

# Read/write h5ad
adata = ad.read_h5ad('data.h5ad', backed='r')
adata.write_h5ad('output.h5ad', compression='gzip')
Read 10X data

adata = ad.read_10x_h5('filtered_feature_bc_matrix.h5')
Read MTX format

adata = ad.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 concatenation

from anndata.experimental import AnnCollection
collection = AnnCollection(
    ['data1.h5ad', 'data2.h5ad'],
    join_obs='outer',
    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

adata = ad.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

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

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