Genomics

CELLxGENE Discover Census

CELLxGENE Discover (cellxgene.cziscience.com) is a free-to-use platform for the exploration, analysis, and retrieval of single-cell data. CELLxGENE Discover hosts the largest aggregation of standardized single-cell data from the major human and mouse tissues, with modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics. This year, CELLxGENE Discover has made available all of its human and mouse RNA single-cell data through Census (https://chanzuckerberg.github.io/cellxgene-census/) – a free-to-use service with an API and data that allows for querying its single-cell data...

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Data coverage

CELLxGENE Discover (cellxgene.cziscience.com) is a free-to-use platform for the exploration, analysis, and retrieval of single-cell data. CELLxGENE Discover hosts the largest aggregation of standardized single-cell data from the major human and mouse tissues, with modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics. This year, CELLxGENE Discover has made available all of its human and mouse RNA single-cell data through Census (https://chanzuckerberg.github.io/cellxgene-census/) – a free-to-use service with an API and data that allows for querying its single-cell data corpus directly from Python or R. The API uses a new technology, TileDB-SOMA, that allows for efficient and low-latency querying. The data are fully standardized and hosted publicly for free access, and they are composed by a count matrix of tens of millions of cells (observations) by >60 k genes (features) accompanied by standard cell metadata variables (e.g. cell type, tissue, sequencing technology, donor id, etc) and gene metadata that includes GENCODE-based IDs and gene names. While these data are built from hundreds of datasets, the APIs enable convenient cell- and gene-based filtering to obtain any slice of interest in a matter of seconds. All data can be quickly transformed to NumPy, Pandas, Anndata, Seurat, or R base objects. We created data loaders for the data to be directly used by PyTorch for modeling at scale. In addition, all the source dataset files in H5AD format are also available for retrieval.

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