cellxgene-census

You can query single-cell transcriptomics data from the CZ CELLxGENE Census programmatically to filter by organism, tissue, disease, and cell type. This approach retrieves specific subsets directly, bypassing the need to download entire datasets.

Yes, spatial transcriptomics data queried from the CZ CELLxGENE Census integrates seamlessly with analysis tools like AnnData, Scanpy, and TileDB-SOMA. This allows you to perform statistics, embeddings, and spatial data analysis on the queried subsets.

To analyze single-cell data using the CZ CELLxGENE Census, you need Python >=3.10,<3.13 and the cellxgene-census library installed. This environment supports querying and integrating the data with analysis tools like Scanpy.

The best way to filter gene expression data across multiple datasets for a specific disease is to use programmatic querying against the CZ CELLxGENE Census. You can query relevant datasets by disease and perform analysis directly on the filtered results.

Yes, the CZ CELLxGENE Census supports querying single-cell data by both tissue and cell type. You can programmatically filter the Census data to retrieve specific subsets matching these biological metadata criteria.

You can access the latest stable Census release for human brain single-cell data by using the cellxgene-census library to programmatically query the Census. This allows you to target human organism and brain tissue directly without full dataset downloads.