cellxgene-census

You can query gene expression data from the CELLxGENE Census programmatically by using an API that retrieves specific cell subsets, such as human B cells in lung tissue, without downloading the entire dataset locally.

Yes, you can use Scanpy and PyTorch for machine learning workflows with single-cell genomics data by retrieving standardized cells from the CELLxGENE Census API and integrating them directly into these frameworks for model training.

The best way to analyze millions of single-cell genomics cells without local storage is using the CELLxGENE Census API, which supports both in-memory analysis for smaller queries and out-of-core processing for large-scale datasets.

Yes, the CELLxGENE Census API supports cross-dataset comparisons for population-scale analyses by providing programmatic access to millions of standardized cells across diverse tissues, diseases, and cell types.

Yes, you need the cellxgene-census library to retrieve expression data and metadata, as it provides the necessary API to open the latest stable human census and query specific cell subsets efficiently.