How does this skill help with large genomic datasets?

The skill provides implementation patterns for 'backed mode' and sparse matrix utilization, allowing developers to process and filter datasets that are significantly larger than the available system RAM.

Is this skill compatible with Scanpy and Muon?

Yes, AnnData is the core data structure for the scverse ecosystem. This skill provides specific patterns for integrating AnnData objects with Scanpy for analysis and Muon for multimodal data.

What is AnnData used for?

AnnData is a Python package specifically designed for handling annotated data matrices. It is the standard for single-cell genomics, storing expression data (X) alongside observations (cells) and variables (genes) metadata.

Can this skill help with batch correction?

While the core skill focuses on data structure management, it includes workflows for concatenating batches and preparing AnnData objects for integration tools like ComBat or scVI.

Which file formats does this skill support?

It provides comprehensive guidance for reading and writing h5ad, Zarr, Loom, CSV, MTX, and 10X Genomics HDF5 files, including compression and cloud-storage optimization.

AnnData Scientific Data Management

Name: AnnData Scientific Data Management
Author: Activer007

byActiver007

データサイエンスとML

Manages annotated data matrices and single-cell genomics datasets using the AnnData framework in Python.

概要

This skill provides specialized guidance for working with AnnData objects, the foundational data structure for single-cell genomics and large-scale biological measurements. It assists users in creating, manipulating, and storing complex datasets with multi-dimensional annotations (obs, var, obsm, layers), ensuring full compatibility with the scverse ecosystem tools like Scanpy and scvi-tools. Whether you are performing quality control, subsetting massive datasets in memory-efficient backed mode, or concatenating multiple experimental batches, this skill offers the implementation patterns and performance-optimized practices required for robust biological data analysis.

主な機能

Efficient data manipulation, subsetting, and filtering techniques
Advanced dataset concatenation and batch integration strategies
High-performance I/O for h5ad, zarr, loom, and 10X formats
Memory-efficient practices including sparse matrices and backed mode
Comprehensive AnnData object creation and metadata management
0 GitHub stars

ユースケース

Integrating multiple experimental batches into unified annotated datasets
Single-cell RNA-seq preprocessing, normalization, and quality control
Analyzing massive genomics datasets that exceed available system memory

概要

主な機能

Efficient data manipulation, subsetting, and filtering techniques
Advanced dataset concatenation and batch integration strategies
High-performance I/O for h5ad, zarr, loom, and 10X formats
Memory-efficient practices including sparse matrices and backed mode
Comprehensive AnnData object creation and metadata management
0 GitHub stars

ユースケース

Integrating multiple experimental batches into unified annotated datasets
Single-cell RNA-seq preprocessing, normalization, and quality control
Analyzing massive genomics datasets that exceed available system memory