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

Targets AnnData 0.13.4 (current PyPI release reviewed 2026-09-30), requiring Python 3.12+. Small synthetic checks cover dense/sparse matrices, native I/O, metadata, concatenation, and lazy reads. File paths, biological analysis, remote stores, and third-party integration examples are illustrative unless stated otherwise.

uv pip install "anndata==0.13.4"

# Lazy I/O and dask-backed operations
uv pip install "anndata[dask,lazy]==0.13.4"

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.
• Use ad.concat; AnnData.concatenate() was removed in 0.13. Avoid old ad.read, deprecated AnnData.*_keys() helpers and anndata.__version__; prefer explicit readers, mapping .keys(), and importlib.metadata.version("anndata").
• In 0.13, .X is also layers[None]; layer iteration includes None. Use key is not None when selecting named layers. View .X writes now use copy-on-write.
• Zarr v3 and automatic sharding are the defaults; the Python dependency is Zarr >=3. Dense H5AD X remains writable with backed="r+", but backed sparse item assignment is unsupported in 0.13.
• AnnLoader and Loom reading/writing are deprecated. AnnCollection and other experimental APIs need the caveats in the references.

These changes are documented in the official release notes. The live docs header still displayed 0.13.3.post0 at review; behavior below was also checked against installed 0.13.4 source.

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')
source = ad.read_h5ad('large_data.h5ad', backed='r')  # X backed; metadata/layers can load
try:
    subset = source[:100, :].to_memory()
finally:
    source.file.close()
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/', skip_data=False)  # Lossy; may densify X

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
source = ad.read_h5ad('data.h5ad', backed='r')
try:
    source.write_h5ad('output.h5ad', compression='gzip')
finally:
    source.file.close()

# 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 an independent in-memory object; .raw is not retained
adata_T = adata.copy().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()

# Materialize a manageable backed subset before modifying it
source = ad.read_h5ad('large.h5ad', backed='r')
try:
    adata = source[:1000, :].to_memory()
finally:
    source.file.close()

# Snapshot current X/var before feature filtering (not automatically raw counts)
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)

Illustrative analysis; requires Scanpy and its selected clustering backend. Choose QC thresholds and representations for the assay, and keep count provenance.

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

anndata.experimental.AnnLoader is deprecated since 0.12.17. Follow the official annbatch migration tutorial for annbatch.Loader; no training run or GPU compatibility is claimed here.

Third-party storage compatibility

AnnData 0.13.4 exposes (None, X) in layers.items(). TileDB-SOMA 2.3.0 from_anndata can fail when treating that key as a URI name. Do not claim this version pair ingests successfully or delete layers[None] as a workaround (that removes X). Use an independently tested compatible environment and verify values, identifiers, named layers, and provenance after any conversion.

Common Workflows

Single-cell RNA-seq analysis

import anndata as ad
import numpy as np
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'] = np.asarray((adata.X > 0).sum(axis=1)).ravel()
adata.obs['n_counts'] = np.asarray(adata.X.sum(axis=1)).ravel()
adata = adata[adata.obs['n_genes'] > 200]
adata = adata[adata.obs['n_counts'] < 50000]

# 3. Preserve counts explicitly, then normalize X
adata = adata.copy()
adata.layers['counts'] = adata.X.copy()
adata.uns['matrix_semantics'] = {'counts': 'untransformed counts'}

# 4. Normalize and filter
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata.copy()  # Snapshot log-normalized X with all genes
adata.uns['matrix_semantics']['raw'] = 'log1p library-size normalized expression'
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
adata = adata[:, adata.var['highly_variable']].copy()

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

Combining batches

# 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'
)

# Inspect retained features and provenance before choosing an integration method.
assert adata.obs['batch'].notna().all()
# Concatenation alone does not correct batch effects; see the scanpy skill.

Working with large datasets

In H5AD backed mode, r+ supports in-place dense X updates, not sparse X item assignment in 0.13, nor arbitrary edits to obs, var, or uns. Write those edits to a new file and reopen it to verify they survived. Close the source with adata.file.close() when finished; materialize any needed subsets before closing. See the backed I/O contract.

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

# Filter on already-loaded metadata without loading all X
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)
adata.file.close()

Troubleshooting

Out of memory errors

Use backed mode and materialize a subset that fits memory:

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

# Materialize only a manageable subset; converting already-loaded huge arrays
# to sparse does not undo the peak memory cost.
subset = adata[:1000, :].to_memory()
adata.file.close()

Slow file reading

Benchmark chunk layout and compression for the access pattern; gzip reduces size but can slow reads:

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

# Zarr v3 and automatic sharding are defaults in 0.13.4
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