Does this skill support large datasets?

Yes, it provides patterns for memory-efficient handling, including using sparse matrices and 'backed' mode for reading massive .h5ad files without loading them entirely into RAM.

When should I use this skill instead of Scanpy?

Use this skill for data structure tasks, file I/O, and data manipulation. For higher-level analysis like clustering, trajectory inference, or UMAP visualization, you should use the associated Scanpy skill.

Can I use this for non-genomic data?

While originally designed for genomics, AnnData is a general-purpose framework for any data requiring annotated matrices (observations vs. variables), making this skill useful for various multidimensional data tasks.

What is the AnnData skill for Claude Code?

It is a specialized capability that helps Claude understand and generate code for handling annotated data matrices, specifically optimized for single-cell genomics and the scverse Python ecosystem.

AnnData Genomics Data Management

Name: AnnData Genomics Data Management
Author: Sologa

bySologa

Data Science & ML

Manages annotated data matrices and single-cell genomics workflows using the AnnData framework and scverse ecosystem.

About

This skill enables Claude to efficiently handle AnnData objects, the industry-standard data structure for single-cell analysis and annotated matrices. It provides expert guidance on reading and writing complex formats like .h5ad and Zarr, performing data manipulations such as subsetting and concatenation, and implementing memory-efficient practices like backed mode and sparse matrices. It is an essential tool for researchers and developers working with Scanpy, scvi-tools, or other tools in the scverse ecosystem to maintain data integrity and optimize performance in large-scale biological datasets.

Key Features

Efficient data manipulation including complex subsetting and concatenation
Seamless integration patterns for Scanpy and scverse ecosystem tools
0 GitHub stars
Memory management via sparse matrices and backed mode operations
Expert handling of .h5ad, .zarr, and 10X Genomics file formats
Management of multi-dimensional annotations including obs, var, and obsm

Use Cases

Pre-processing single-cell RNA-seq datasets for downstream analysis
Managing large-scale genomic datasets on memory-constrained systems
Merging multiple experimental batches while preserving complex metadata

About

Key Features

Efficient data manipulation including complex subsetting and concatenation
Seamless integration patterns for Scanpy and scverse ecosystem tools
0 GitHub stars
Memory management via sparse matrices and backed mode operations
Expert handling of .h5ad, .zarr, and 10X Genomics file formats
Management of multi-dimensional annotations including obs, var, and obsm

Use Cases

Pre-processing single-cell RNA-seq datasets for downstream analysis
Managing large-scale genomic datasets on memory-constrained systems
Merging multiple experimental batches while preserving complex metadata