AnnData — Annotated Data Matrices for Single-Cell Genomics

AnnData — Annotated Data Matrices for Single-Cell Genomics

Overview

AnnData provides the standard data structure for single-cell genomics in the scverse ecosystem. It stores an observations-by-variables matrix (X) alongside cell metadata (obs), gene metadata (var), layers, embeddings (obsm/varm), graphs (obsp/varp), and unstructured metadata (uns). Supports sparse matrices, H5AD/Zarr storage, backed mode for large files, and integration with Scanpy, scvi-tools, and Muon.

When to Use

• Constructing annotated matrices from raw count data with cell/gene metadata
• Reading/writing .h5ad or .zarr files for single-cell experiments
• Subsetting cells by quality metrics, gene sets, or metadata conditions
• Concatenating multiple experimental batches with consistent metadata
• Storing multiple data layers (raw counts, normalized, scaled) in one object
• Working with large datasets exceeding RAM (backed mode, lazy concatenation)
• Preparing data for Scanpy or scvi-tools pipelines
• For single-cell analysis (clustering, DE, visualization), use scanpy instead
• For probabilistic models, use scvi-tools instead

Prerequisites

• Python packages: anndata, scipy, pandas, numpy
• Optional: scanpy (analysis), zarr (cloud storage), h5py (HDF5 backend)
• Data requirements: count matrices (dense or sparse), cell/gene metadata tables

pip install "anndata>=0.10"
# Full ecosystem
pip install anndata scanpy zarr

Quick Start

import anndata as ad
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix

counts = csr_matrix(np.random.poisson(0.5, (500, 2000)).astype(np.float32))
obs = pd.DataFrame({"cell_type": np.random.choice(["T", "B", "NK"], 500)},