CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Abstract

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover(cellxgene.cziscience.com), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

Data-driven fine-grained region discovery in the mouse brain with transformers

“…Code will be publicly available at https://github.com/abbasilab/celltransformer. All data used for this publication is available either at the Allen Institute ABC-MWB data portal (https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas) or the CZI cellxgene portal 37 .…”

Scvi-hub: an actionable repository for model-driven single cell analysis

“…Recently, several large corpora of single cell omics datasets were collected to enable comprehensive view of cell types and states in various tissue contexts, thus starting to realize the potential of the cell atlases (Regev et al 2018). The largest of these efforts is the CELLxGENE census collection (CZI Single-Cell Biology Program et al 2023). It currently contains above 30 million human cells from a broad array of tissues, diseases and experimental settings.…”

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

“…The scTab data is available with instructions in the corresponding publication 4 . The smaller datasets are publicly available on CELLxGENE 48 and subsets of the scTab datasets (HLCA: Dataset ID 148, PBMC: Dataset ID 87, Tabula Sapiens: Dataset ID 41). The novel datasets are sourced from CELLxGENE 48 with instructions in the corresponding publications [49][50][51][52] .…”

“…The smaller datasets are publicly available on CELLxGENE 48 and subsets of the scTab datasets (HLCA: Dataset ID 148, PBMC: Dataset ID 87, Tabula Sapiens: Dataset ID 41). The novel datasets are sourced from CELLxGENE 48 with instructions in the corresponding publications [49][50][51][52] . The NeurIPS multiome dataset is publicly available from NCBI GEO under accession GSE194122 with instructions in the corresponding publication 54 .…”