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alterlab-anndata

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Resumen

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.

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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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          # 0.11+ (the API namespaces below assume >= 0.11)

# Optional extra for Dask-backed lazy reads (ad.experimental.read_lazy)
uv pip install 'anndata[dask]'

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

import scanpy as sc  # 10x readers live in scanpy, not anndata

# 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 (these live under ad.io as of anndata 0.11)
adata = ad.io.read_csv('data.csv')
adata = ad.io.read_loom('data.loom')
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

API namespaces (anndata >= 0.11): all format readers/writers moved to the anndata.io module (ad.io.read_csv, ad.io.read_mtx, ad.io.read_loom, ad.io.read_elem, ...). The top-level ad.read_csv-style aliases still work but emit a DeprecationWarning. Exceptions: ad.read_h5ad, ad.read_zarr, adata.write_h5ad, and adata.write_zarr stay top-level with no warning.

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

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 (10x readers live in scanpy, not anndata)
import scanpy as sc
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

# Read MTX format (.mtx is variables x observations; transpose so cells are rows)
adata = ad.io.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)

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

import scanpy as sc

sc.pp.filter_cells(adata, min_genes=200)  # scanpy mutates the AnnData in place
# ... continue the analysis pipeline in scanpy

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 (also accepts an AnnCollection)
dataloader = AnnLoader(adata, batch_size=128, shuffle=True)

for batch in dataloader:
    X = batch["X"]      # dict-style access; tensors, not attributes
    labels = batch["obs"]["cell_type"]
    # Train model

Common Workflows

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

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

import anndata as ad
import scanpy as sc

# 1. Load (10x readers live in scanpy)
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

# 2. Quick QC metrics on obs/var, then mask-subset (pure anndata wrangling)
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.obs['n_counts'] < 50000)].copy()

# 3. Snapshot raw before any gene filtering
adata.raw = adata.copy()

# 4. Hand off normalization / HVG / clustering to scanpy, then come back:
#    sc.pp.normalize_total / sc.pp.log1p / sc.pp.highly_variable_genes / ...
adata = adata[:, adata.var['highly_variable']].copy()  # subset is anndata's job

# 5. Save processed data
adata.write_h5ad('processed.h5ad', compression='gzip')

Batch integration (concatenate, then defer correction)

# Load and concatenate batches with source labels — this is anndata's job
adatas = [ad.read_h5ad(p) for p in ['batch1.h5ad', 'batch2.h5ad', 'batch3.h5ad']]
adata = ad.concat(
    adatas,
    label='batch',
    keys=['batch1', 'batch2', 'batch3'],
    join='inner',
)

# Batch correction and downstream analysis (combat / pca / neighbors / umap)
# are scanpy territory — pass `adata` to scanpy from here.

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.re

Ver fuente ↗

Metadatos del archivo

name: alterlab-anndata
description: 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.
license: MIT
allowed-tools: Read Write Edit Bash(python:*) Bash(uv:*)
compatibility: "Runs under `uv run python` with `anndata` (>=0.11) installed in the project env; no API key or account required."
metadata:
    skill-author: AlterLab
    version: "1.0.0"

Ver texto original

---
name: alterlab-anndata
description: 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.
license: MIT
allowed-tools: Read Write Edit Bash(python:*) Bash(uv:*)
compatibility: "Runs under `uv run python` with `anndata` (>=0.11) installed in the project env; no API key or account required."
metadata:
    skill-author: AlterLab
    version: "1.0.0"
---

# 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

```bash
uv pip install anndata          # 0.11+ (the API namespaces below assume >= 0.11)

# Optional extra for Dask-backed lazy reads (ad.experimental.read_lazy)
uv pip install 'anndata[dask]'
```

## Quick Start

### Creating an AnnData object
```python
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
```python
import scanpy as sc  # 10x readers live in scanpy, not anndata

# 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 (these live under ad.io as of anndata 0.11)
adata = ad.io.read_csv('data.csv')
adata = ad.io.read_loom('data.loom')
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')
```

> **API namespaces (anndata >= 0.11)**: all format readers/writers moved to the
> `anndata.io` module (`ad.io.read_csv`, `ad.io.read_mtx`, `ad.io.read_loom`,
> `ad.io.read_elem`, ...). The top-level `ad.read_csv`-style aliases still work
> but emit a `DeprecationWarning`. **Exceptions**: `ad.read_h5ad`, `ad.read_zarr`,
> `adata.write_h5ad`, and `adata.write_zarr` stay top-level with no warning.
>
> **10x readers** (`read_10x_h5`, `read_10x_mtx`) live in **scanpy**
> (`sc.read_10x_h5`), not anndata — this skill defers analysis-specific I/O to scanpy.

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

# Read 10X data (10x readers live in scanpy, not anndata)
import scanpy as sc
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

# Read MTX format (.mtx is variables x observations; transpose so cells are rows)
adata = ad.io.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:
```python
# 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:
```python
# 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:
```python
# 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)
AnnData is scanpy's native object — once built/loaded, pass it straight in.
Preprocessing, dimensionality reduction, clustering, and plotting
(`sc.pp.normalize_total`, `sc.pp.highly_variable_genes`, `sc.pp.pca`,
`sc.pp.neighbors`, `sc.tl.umap`, `sc.tl.leiden`, `sc.pl.*`) are **scanpy's
job, not anndata's** — defer the analysis workflow there.
```python
import scanpy as sc

sc.pp.filter_cells(adata, min_genes=200)  # scanpy mutates the AnnData in place
# ... continue the analysis pipeline in scanpy
```

### Muon (Multimodal data)
```python
import muon as mu

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

### PyTorch integration
```python
from anndata.experimental import AnnLoader

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

for batch in dataloader:
    X = batch["X"]      # dict-style access; tensors, not attributes
    labels = batch["obs"]["cell_type"]
    # Train model
```

## Common Workflows

### Single-cell data lifecycle (the anndata-owned parts)
Load, compute simple QC metrics on `obs`/`var`, snapshot `raw`, subset, and write.
The normalize/log1p/HVG/cluster steps belong to scanpy — hand off there.
```python
import anndata as ad
import scanpy as sc

# 1. Load (10x readers live in scanpy)
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

# 2. Quick QC metrics on obs/var, then mask-subset (pure anndata wrangling)
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.obs['n_counts'] < 50000)].copy()

# 3. Snapshot raw before any gene filtering
adata.raw = adata.copy()

# 4. Hand off normalization / HVG / clustering to scanpy, then come back:
#    sc.pp.normalize_total / sc.pp.log1p / sc.pp.highly_variable_genes / ...
adata = adata[:, adata.var['highly_variable']].copy()  # subset is anndata's job

# 5. Save processed data
adata.write_h5ad('processed.h5ad', compression='gzip')
```

### Batch integration (concatenate, then defer correction)
```python
# Load and concatenate batches with source labels — this is anndata's job
adatas = [ad.read_h5ad(p) for p in ['batch1.h5ad', 'batch2.h5ad', 'batch3.h5ad']]
adata = ad.concat(
    adatas,
    label='batch',
    keys=['batch1', 'batch2', 'batch3'],
    join='inner',
)

# Batch correction and downstream analysis (combat / pca / neighbors / umap)
# are scanpy territory — pass `adata` to scanpy from here.
```

### Working with large datasets
```python
# 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:
```python
# 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:
```python
# 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:
```python
# 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.re

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La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Falta aprobación de revisión por IA
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 66 GitHub stars
Stars/forks activity: 66 stars, 13 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
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Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

IndexadoRevisión estática

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: AlterLab-IEU/AlterLab-Academic-Skills
Licencia: MIT
Versión: Unknown
Último push de GitHub: 4 sept 2026
Registro actualizado: 9 sept 2026
Ruta de instrucciones: skills/bioinformatics/alterlab-anndata/SKILL.md @ 4a5b75358026

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

57/100

Prometedor

Confianza

59/100

Do not auto-install

Auditoría

70/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
Falta aprobación de revisión por IA
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 66 GitHub stars
Stars/forks activity: 66 stars, 13 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

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Más detalles

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    "description": "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.",
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      {
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        "id": "claude-code",
        "label": "Claude Code",
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      "Dependency/runtime risk: command execution surface, credential or environment access",
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}

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Creador: AlterLab-IEU
Fuente: AlterLab-IEU/AlterLab-Academic-Skills
Indexado por: Índice comunitario de OpenAgentSkill

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Resumen

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 data lifecycle (the anndata-owned parts)

Batch integration (concatenate, then defer correction)

Working with large datasets

Troubleshooting

Out of memory errors

Slow file reading

Index alignment issues

Additional Resources

Revisar el código fuente

Precio y costes de ejecución

Empieza con una tarea pequeña

Fuente y notas de uso

Acceso para agentes

Skills relacionados

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