anndata

Organize annotated single-cell data with a flexible AnnData structure.

321|26|Updated Mar 10, 2026
One-click install
npx skills add https://github.com/mkurman/tamux --skill anndata-mkurman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: anndata
Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/anndata
Command: npx skills add https://github.com/mkurman/tamux --skill anndata-mkurman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AnnData provides a flexible, memory-efficient framework for storing a data matrix X alongside rich metadata (obs, var, layers, obsm, varm, uns) to support scalable single-cell analysis workflows.

Core Features & Use Cases

  • Data model: X with aligned annotations (obs, var, layers, obsm, varm, uns, and raw) for end-to-end single-cell analyses.
  • Interoperability: built-in compatibility with Scanpy, Muon, and the broader scverse ecosystem for preprocessing, visualization, and modeling.
  • I/O and scalability: efficient reading/writing of dense and sparse data (h5ad, zarr, MTX, CSV) with backed mode to handle datasets larger than memory.

Quick Start

Create an AnnData object from a small dataset, then progressively attach metadata and embeddings, and persist to disk.

Frequently Asked Questions about anndata

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

FAQPage Schema
How do I store scRNA-seq data matrices with rich metadata in a single structure?▼

AnnData organizes scRNA-seq data matrices alongside aligned annotations like obs, var, layers, and uns, providing a memory-efficient framework for single-cell analysis workflows.

Can I use backed mode to process single-cell datasets larger than memory?▼

Backed mode enables handling large-scale single-cell datasets larger than memory by efficiently reading and writing dense or sparse data formats like h5ad and zarr.

Does the AnnData structure work with Scanpy and the scverse ecosystem?▼

AnnData offers built-in interoperability with Scanpy, Muon, and the broader scverse ecosystem, supporting preprocessing, visualization, and modeling for scRNA-seq workflows.

What is the best way to construct and filter annotated matrices for omics data?▼

Constructing and filtering annotated matrices for omics data uses a flexible structure with core components X, obs, and var, allowing progressive attachment of metadata and embeddings.

What file formats are supported for reading and writing single-cell data?▼

Supported I/O formats for single-cell data include h5ad, zarr, MTX, and CSV, accommodating both dense and sparse data structures for scalable analysis.

Why use a dedicated data structure for single-cell omics instead of a standard dataframe?▼

A dedicated single-cell structure aligns a data matrix X with multi-dimensional metadata like obsm and varm, which standard dataframes cannot natively support for omics workflows.