{"slug":"alterlab-anndata","title":"alterlab-anndata","summary":"Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handl","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-23T18:56:52.442937Z","repo":{"url":"https://github.com/AlterLab-IEU/AlterLab-Academic-Skills","stars":71,"forks":14,"license":"MIT","updatedAt":"2026-10-01T06:58:53Z"},"bodyHtml":"
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name: alterlab-anndata\ndescription: Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the QC, normalization, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for RNA velocity from spliced/unspliced layers prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.\nlicense: MIT\nallowed-tools: Read Write Edit Bash(python:) Bash(uv:)\ncompatibility: \"Runs under uv run python with anndata >= 0.13 (current 0.13.4 as of 2026-09, requires Python >= 3.12, zarr >= 3.1) installed in the project env; no API key or account required. The 0.11/0.12 APIs still work except where flagged below.\"\nmetadata:\nskill-author: AlterLab\nversion: \"1.1.0\"\nlast_updated: \"2026-09-23\"

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AnnData

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Overview

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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.

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When to Use This Skill

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Use this skill when:

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Does NOT Trigger

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ScenarioUse Instead
QC, normalization, HVG, clustering, UMAP, or marker genesalterlab-scanpy
RNA velocity from spliced/unspliced layersalterlab-scvelo
Deep generative models / batch integration (scVI, scANVI)alterlab-scvi-tools
Querying the public CELLxGENE Census for reference cellsalterlab-cellxgene
Chunked n-dimensional arrays outside the AnnData modelalterlab-zarr
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Installation

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uv pip install anndata          # 0.13.x as of 2026-09; needs Python >= 3.12\n\n# Lazy/backed reads (ad.experimental.read_lazy) need dask + xarray:\nuv pip install 'anndata[lazy]'   # or 'anndata[dask]' for dask-backed arrays only\n
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What changed in 0.13 (breaking)

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These bite existing scripts, so check them before running older code:

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Quick Start

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Creating an AnnData object

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import anndata as ad\nimport numpy as np\nimport pandas as pd\n\n# Minimal creation\nX = np.random.rand(100, 2000)  # 100 cells × 2000 genes\nadata = ad.AnnData(X)\n\n# With metadata\nobs = pd.DataFrame({\n    'cell_type': ['T cell', 'B cell'] * 50,\n    'sample': ['A', 'B'] * 50\n}, index=[f'cell_{i}' for i in range(100)])\n\nvar = pd.DataFrame({\n    'gene_name': [f'Gene_{i}' for i in range(2000)]\n}, index=[f'ENSG{i:05d}' for i in range(2000)])\n\nadata = ad.AnnData(X=X, obs=obs, var=var)\n
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Reading data

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import scanpy as sc  # 10x readers live in scanpy, not anndata\n\n# Read h5ad file\nadata = ad.read_h5ad('data.h5ad')\n\n# Read with backed mode (for large files)\nadata = ad.read_h5ad('large_data.h5ad', backed='r')\n\n# Read other formats (these live under ad.io as of anndata 0.11)\nadata = ad.io.read_csv('data.csv')\nadata = ad.io.read_mtx('matrix.mtx')\nadata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')\n
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API namespaces (anndata >= 0.11): all format readers/writers live in the\nanndata.io module (ad.io.read_csv, ad.io.read_mtx, ad.io.read_loom,\nad.io.read_elem, ...). The top-level ad.read_csv-style aliases still resolve\nbut emit a FutureWarning (upgraded from DeprecationWarning in 0.12), and\nanndata.read was removed in 0.12. Exceptions: ad.read_h5ad, ad.read_zarr,\nadata.write_h5ad, and adata.write_zarr stay top-level with no warning.

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10x readers (read_10x_h5, read_10x_mtx) live in scanpy\n(sc.read_10x_h5), not anndata — this skill defers analysis-specific I/O to scanpy.

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Writing data

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# Write h5ad file\nadata.write_h5ad('output.h5ad')\n\n# Write with compression\nadata.write_h5ad('output.h5ad', compression='gzip')\n\n# Write other formats\nadata.write_zarr('output.zarr')                  # zarr v3, sharded by default\nadata.write_csvs('output_dir/', skip_data=False)  # skip_data defaults to True (obs/var only)\n
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Basic operations

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# Subset by conditions\nt_cells = adata[adata.obs['cell_type'] == 'T cell']\n\n# Subset by indices\nsubset = adata[0:50, 0:100]\n\n# Add metadata\nadata.obs['quality_score'] = np.random.rand(adata.n_obs)\nadata.var['highly_variable'] = np.random.rand(adata.n_vars) > 0.8\n\n# Access dimensions\nprint(f\"{adata.n_obs} observations × {adata.n_vars} variables\")\n
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Core Capabilities

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1. Data Structure

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

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

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2. Input/Output Operations

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

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See: references/io_operations.md for details on:

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Common commands:

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# Read/write h5ad\nadata = ad.read_h5ad('data.h5ad', backed='r')\nadata.write_h5ad('output.h5ad', compression='gzip')\n\n# Read 10X data (10x readers live in scanpy, not anndata)\nimport scanpy as sc\nadata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')\n\n# Read MTX format (.mtx is variables x observations; transpose so cells are rows)\nadata = ad.io.read_mtx('matrix.mtx').T\n
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3. Concatenation

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

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

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Common commands:

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# Concatenate observations (combine samples)\nadata = ad.concat(\n    [adata1, adata2, adata3],\n    axis=0,\n    join='inner',\n    label='batch',\n    keys=['batch1', 'batch2', 'batch3']\n)\n\n# Concatenate variables (combine modalities)\nadata = ad.concat([adata_rna, adata_protein], axis=1)\n\n# Lazy concatenation — AnnCollection takes AnnData objects (open them backed), not paths\nfrom anndata.experimental import AnnCollection\nadatas = {p: ad.read_h5ad(f'{p}.h5ad', backed='r') for p in ('data1', 'data2')}\ncollection = AnnCollection(adatas, join_obs='outer', label='dataset')\n
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4. Data Manipulation

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Transform, subset, filter, and reorganize data efficiently.

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

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Common commands:

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# Subset by metadata\nfiltered = adata[adata.obs['quality_score'] > 0.8]\nhv_genes = adata[:, adata.var['highly_variable']]\n\n# Transpose\nadata_T = adata.T\n\n# Copy vs view\nview = adata[0:100, :]  # View (lightweight reference; writes to it copy-on-write in 0.13+)\ncopy = adata[0:100, :].copy()  # Independent copy\n\n# Convert strings to categoricals\nadata.strings_to_categoricals()\n
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5. Best Practices

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

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See: references/best_practices.md for guidelines on:

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Key recommendations:

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# Use sparse matrices for sparse data\nfrom scipy.sparse import csr_matrix\nadata.X = csr_matrix(adata.X)\n\n# Convert strings to categoricals\nadata.strings_to_categoricals()\n\n# Use backed mode for large files\nadata = ad.read_h5ad('large.h5ad', backed='r')\n\n# Store raw before filtering\nadata.raw = adata.copy()\nadata = adata[:, adata.var['highly_variable']]\n
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Integration with Scverse Ecosystem

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

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Scanpy (Single-cell analysis)

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AnnData is scanpy's native object — once built/loaded, pass it straight in.\nPreprocessing, dimensionality reduction, clustering, and plotting\n(sc.pp.normalize_total, sc.pp.highly_variable_genes, sc.pp.pca,\nsc.pp.neighbors, sc.tl.umap, sc.tl.leiden, sc.pl.*) are scanpy's\njob, not anndata's — defer the analysis workflow there.

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import scanpy as sc\n\nsc.pp.filter_cells(adata, min_genes=200)  # scanpy mutates the AnnData in place\n# ... continue the analysis pipeline in scanpy\n
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Muon (Multimodal data)

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import muon as mu\n\n# Combine RNA and protein data\nmdata = mu.MuData({'rna': adata_rna, 'protein': adata_protein})\n
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PyTorch integration

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from anndata.experimental import AnnLoader\n\n# Create DataLoader for deep learning (also accepts an AnnCollection)\ndataloader = AnnLoader(adata, batch_size=128, shuffle=True)\n\nfor batch in dataloader:\n    X = batch[\"X\"]      # dict-style access; tensors, not attributes\n    labels = batch[\"obs\"][\"cell_type\"]\n    # Train model\n
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Common Workflows

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Single-cell data lifecycle (the anndata-owned parts)

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Load, compute simple QC metrics on obs/var, snapshot raw, subset, and write.\nThe normalize/log1p/HVG/cluster steps belong to scanpy — hand off there.

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import anndata as ad\nimport scanpy as sc\n\n# 1. Load (10x readers live in scanpy)\nadata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')\n\n# 2. Quick QC metrics on obs/var, then mask-subset (pure anndata wrangling)\nadata.obs['n_genes'] = (adata.X > 0).sum(axis=1)\nadata.obs['n_counts'] = adata.X.sum(axis=1)\nadata = adata[(adata.obs['n_genes'] > 200) & (adata.obs['n_counts'] < 50000)].copy()\n\n# 3. Snapshot raw before any gene filtering\nadata.raw = adata.copy()\n\n# 4. Hand off normalization / HVG / clustering to scanpy, then come back:\n#    sc.pp.normalize_total / sc.pp.log1p / sc.pp.highly_variable_genes / ...\nadata = adata[:, adata.var['highly_variable']].copy()  # subset is anndata's job\n\n# 5. Save processed data\nadata.write_h5ad('processed.h5ad', compression='gzip')\n
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Batch integration (concatenate, then defer correction)

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# Load and concatenate batches with source labels — this is anndata's job\nadatas = [ad.read_h5ad(p) for p in ['batch1.h5ad', 'batch2.h5ad', 'batch3.h5ad']]\nadata = ad.concat(\n    adatas,\n    label='batch',\n    keys=['batch1', 'batch2', 'batch3'],\n    join='inner',\n)\n\n# Batch correction and downstream analysis (combat / pca / neighbors / umap)\n# are scanpy territory — pass `adata` to scanpy from here.\n
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Working with large datasets

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# Open in backed mode\nadata = ad.read_h5ad('100GB_dataset.h5ad', backed='r')\n\n# Filter based on metadata (no data loading)\nhigh_quality = adata[adata.obs['quality_score'] > 0.8]\n\n# Load filtered subset\nadata_subset = high_quality.to_memory()\n\n# Process subset\nprocess(adata_subset)\n\n# Or process in chunks\nchunk_size = 1000\nfor i in range(0, adata.n_obs, chunk_size):\n    chunk = adata[i:i+chunk_size, :].to_memory()\n    process(chunk)\n
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Troubleshooting

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Out of memory errors

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Use backed mode or convert to sparse matrices:

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# Backed mode\nadata = ad.read_h5ad('file.h5ad', backed='r')\n\n# Sparse matrices\nfrom scipy.sparse import csr_matrix\nadata.X = csr_matrix(adata.X)\n
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Slow file reading

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Use compression and appropriate formats:

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# Optimize for storage\nadata.strings_to_categoricals()\nadata.write_h5ad('file.h5ad', compression='gzip')\n\n# Use Zarr for cloud storage\nadata.write_zarr('file.zarr', chunks=(1000, 1000))\n
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Index alignment issues

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Always align external data on index:

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# Wrong\nadata.obs['new_col'] = external_data['values']\n\n# Correct\nadata.obs['new_col'] = external_data.set_index('cell_id').loc[adata.obs_names, 'values']\n
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Additional Resources

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Part of the AlterLab Academic Skills suite.

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