cellxgene-census

You can query single-cell gene expression data across multiple datasets by accessing the CELLxGENE Census, a curated atlas of over 61 million cells. It allows efficient filtering and retrieval based on standardized cell types, tissues, and diseases.

Yes, you can use Scanpy and PyTorch for machine learning workflows with single-cell data. The CELLxGENE Census integration supports seamless analysis, allowing you to extract AnnData objects and feed them directly into your ML pipelines.

The best way to analyze B cells in lung tissue for COVID-19 immune responses is to programmatically query the CELLxGENE Census. It provides standardized metadata to filter over 61 million cells by specific diseases, tissues, and cell types.

Yes, querying large-scale single-cell atlases requires installing the cellxgene-census Python library, along with dependencies like tiledbsoma and pyarrow. These facilitate efficient data access and integration with analysis tools.

You can get AnnData for specific cell types from human single-cell data by applying filters to the CELLxGENE Census. This enables you to extract targeted subsets of cells, such as filtering for Homo sapiens B cells in lung tissue.

A limitation of using a curated single-cell atlas for comparative genomics is that analysis is constrained to the standardized metadata and the 61 million cells currently included, meaning non-standard annotations or extremely novel cell states might be absent.