What problem does it solve?
This Skill helps you work with annotated data matrices by keeping measurements and rich metadata aligned, so you can reliably load, transform, and analyze large single-cell datasets.
Core Features & Use Cases
- AnnData object structure: Manage expression matrices (X) alongside observation metadata (obs), variable metadata (var), embeddings (obsm/varm), pairwise relationships (obsp/varp), and unstructured fields (uns).
- I/O for research workflows: Read and write common genomics formats (notably .h5ad and zarr), including backed mode for large-on-disk datasets.
- Common analysis operations: Subset, concatenate, transform, and follow best practices for memory efficiency and reproducible pipelines.
- Scverse ecosystem integration: Use as the foundational container for tools like scanpy and muon, and support downstream workflows such as QC, embedding, and batch handling.
Quick Start
Create an AnnData object from an expression matrix plus cell and gene metadata using the Python snippet described in the skill instructions.