CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data.

Autor:	Abdulla S; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK., Aevermann B; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Assis P; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Badajoz S; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Bell SM; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Bezzi E; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Cakir B; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK., Chaffer J; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Chambers S; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Cherry JM; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Chi T; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Chien J; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Dorman L; Chan Zuckerberg, Biohub, SF, 499 Illinois St, San Francisco, CA 94158, USA., Garcia-Nieto P; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Gloria N; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Hastie M; Clever Canary, 850 Front St. #1491, Santa Cruz, CA, USA., Hegeman D; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Hilton J; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Huang T; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Infeld A; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Istrate AM; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Jelic I; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Katsuya K; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Kim YJ; Chan Zuckerberg, Biohub, SF, 499 Illinois St, San Francisco, CA 94158, USA., Liang K; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Lin M; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Lombardo M; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Marshall B; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Martin B; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., McDade F; Clever Canary, 850 Front St. #1491, Santa Cruz, CA, USA., Megill C; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Patel N; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Predeus A; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK., Raymor B; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Robatmili B; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Rogers D; Clever Canary, 850 Front St. #1491, Santa Cruz, CA, USA., Rutherford E; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Sadgat D; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Shin A; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Small C; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Smith T; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Sridharan P; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Tarashansky A; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Tavares N; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Thomas H; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Tolopko A; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Urisko M; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Yan J; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Yeretssian G; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Zamanian J; Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA., Mani A; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Cool J; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA., Carr A; Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA.
Jazyk:	angličtina
Zdroj:	Nucleic acids research [Nucleic Acids Res] 2024 Nov 28. Date of Electronic Publication: 2024 Nov 28.
DOI:	10.1093/nar/gkae1142
Abstrakt:	Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells. (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Databáze:	MEDLINE
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