Accesses and analyzes over 61 million standardized single-cell genomics records from the CZ CELLxGENE Census.
This skill provides programmatic access to the CZ CELLxGENE Census, enabling researchers to query and analyze one of the world's largest collections of standardized single-cell genomics data. It allows for precise filtering by cell type, tissue, disease, and donor metadata across human and mouse organisms. With specialized patterns for out-of-core processing, integration with Scanpy for bioinformatics pipelines, and experimental PyTorch support for machine learning, this skill facilitates population-scale analysis and model training directly within genomic research workflows.
주요 기능
012,188 GitHub stars
02Query 61M+ cells across human and mouse organisms with standardized metadata
03Seamless integration with Scanpy for visualization and dimensionality reduction
04Experimental PyTorch data loaders for large-scale machine learning model training
05Efficient out-of-core processing for datasets that exceed available RAM
06Filter by cell type, tissue, disease state, and specific donor IDs
사용 사례
01Performing cross-tissue gene expression analysis to identify disease-specific markers
02Conducting meta-analyses of existing single-cell datasets without manual data curation
03Training deep learning models for automated cell type annotation using population-scale data
