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anndata

Manipulate AnnData objects for scRNA-seq data with I/O and metadata management.

43|13|Updated Oct 10, 2025
Search Tags:#single-cell#data-structures#h5ad#scRNA-seq#AnnData#backed-mode
One-click install
npx skills add https://github.com/lifangda/claude-plugins --skill anndata-lifangda
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: anndata
Source: https://github.com/lifangda/claude-plugins/tree/main/cli-tool/skills-library/scientific-computing/bioinformatics/anndata
Command: npx skills add https://github.com/lifangda/claude-plugins --skill anndata-lifangda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AnnData provides a flexible container for annotated data matrices, enabling memory-efficient storage and manipulation of single-cell datasets across layers, embeddings, and metadata.

Core Features & Use Cases

  • Multi-dimensional annotations: obs, var, uns, obsm, varm, layers, and raw
  • Subsetting & views: efficient views, backed mode, and copy semantics
  • File I/O & transformations: handles .h5ad, .zarr, .loom, and concatenation of multiple datasets

Quick Start

Load a dataset with ad.read_h5ad, explore obs and var metadata, and perform a simple subset.

Frequently Asked Questions about anndata

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I load and explore single-cell RNA-seq data in h5ad format?▼

AnnData loads h5ad files via ad.read_h5ad(), providing immediate access to annotated matrices. Explore obs (cell metadata) and var (gene metadata) as DataFrames, and X contains the expression matrix—all structured for efficient single-cell workflows.

Can I work with single-cell data that's too large to fit in memory?▼

AnnData's backed mode enables disk-backed processing, storing data on disk while creating memory-efficient views. This allows manipulation of large scRNA-seq datasets without loading entire matrices into RAM.

What's the best way to combine multiple single-cell datasets?▼

AnnData concatenation supports join and merge strategies for combining datasets while managing obs/var metadata. This enables batch integration and multi-experiment analysis while preserving annotation structure across merged objects.

How do I store embeddings and dimensionality reduction results with my single-cell data?▼

AnnData organizes embeddings (X_pca, X_umap) and multi-dimensional annotations in obsm and varm slots, keeping projections aligned with cells and genes. This maintains computational results alongside raw data in a single structured container.

Does AnnData support sparse matrices for memory efficiency?▼

AnnData handles both dense and sparse matrices, with sparse storage significantly reducing memory for typical scRNA-seq data where most values are zeros. Seamless switching between formats supports scale-appropriate analysis.

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