Can I use this skill with Scanpy?

Yes, this skill includes best practices for the scverse ecosystem, providing implementation patterns for Scanpy, Muon, and other related analysis tools.

What is AnnData used for?

AnnData is a Python package for handling annotated data matrices, essential for storing experimental measurements alongside metadata, primarily used in single-cell genomics.

How do I handle large datasets that exceed my RAM?

You can use the backed='r' parameter when reading h5ad files or convert your data to sparse matrices (CSR/CSC) to minimize memory footprint.

How does AnnData handle metadata?

It stores metadata in structured dataframes: 'obs' for observations (e.g., cell types), 'var' for variables (e.g., gene names), and additional fields like 'obsm' for multi-dimensional coordinates.

AnnData for Single-Cell Genomics

Name: AnnData for Single-Cell Genomics
Author: x-cmd

byx-cmd

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Data Science & ML

Manages annotated data matrices and metadata for single-cell genomics and large-scale biological datasets using the AnnData framework.

About

The AnnData skill equips Claude with specialized knowledge for handling annotated data matrices in Python, a foundational requirement for single-cell RNA-seq and other high-dimensional biological analyses. It provides optimized patterns for creating, reading, and writing h5ad and zarr files, managing complex metadata across observations and variables, and performing memory-efficient data manipulation. By integrating best practices for the scverse ecosystem, this skill helps developers and bioinformaticians streamline their data processing pipelines, handle large-scale datasets with backed mode, and ensure seamless interoperability between tools like Scanpy, Muon, and scvi-tools.

Key Features

Seamless integration with Scanpy and the broader scverse ecosystem for downstream analysis
Optimized I/O for h5ad, Zarr, Loom, and 10X Genomics formats
Advanced concatenation and merging strategies for multi-batch experimental data
Structured management of observations (obs), variables (var), and multi-dimensional annotations
8 GitHub stars
Memory-efficient handling of large datasets using sparse matrices and backed mode

Use Cases

Processing and quality control filtering of single-cell RNA-seq datasets
Integrating multiple experimental batches into a single unified data structure
Building scalable data pipelines for high-throughput genomics experiments

About

Key Features

Seamless integration with Scanpy and the broader scverse ecosystem for downstream analysis
Optimized I/O for h5ad, Zarr, Loom, and 10X Genomics formats
Advanced concatenation and merging strategies for multi-batch experimental data
Structured management of observations (obs), variables (var), and multi-dimensional annotations
8 GitHub stars
Memory-efficient handling of large datasets using sparse matrices and backed mode

Use Cases

Processing and quality control filtering of single-cell RNA-seq datasets
Integrating multiple experimental batches into a single unified data structure
Building scalable data pipelines for high-throughput genomics experiments