CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data
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.
Authors
- Corinn Small (ORCID: https://orcid.org/0000-0002-2336-2552)
- Jennifer Zamanian (ORCID: https://orcid.org/0000-0003-1256-7496)
- Jennifer Chien (ORCID: https://orcid.org/0000-0003-4389-9821)
- Dave Rogers
- Prathap Sridharan
- Tiffany Chi
- Andrew Shin (ORCID: https://orcid.org/0000-0002-9323-189X)
- Alexander J. Tarashansky (ORCID: https://orcid.org/0000-0002-5185-6293)
- Trent M. Smith (ORCID: https://orcid.org/0000-0002-0474-9962)
- Jonah Cool (ORCID: https://orcid.org/0000-0002-0287-011X)
- Pablo E. García-Nieto (ORCID: https://orcid.org/0000-0001-8696-0923)
- Garabet Yeretssian (ORCID: https://orcid.org/0009-0006-2059-3810)
- Daniel Hegeman
- Leah C. Dorman (ORCID: https://orcid.org/0000-0002-5511-887X)
- Maximilian Lombardo
- Colin Megill
- Ana-Maria Istrate (ORCID: https://orcid.org/0000-0002-7953-5168)
- Erica Rutherford (ORCID: https://orcid.org/0000-0001-8134-3037)
- Alexander V. Predeus (ORCID: https://orcid.org/0000-0002-2750-1599)
- Jason A. Hilton (ORCID: https://orcid.org/0000-0002-1196-4871)
- Sidney M. Bell (ORCID: https://orcid.org/0000-0003-1933-6033)
- Brian D. Aevermann (ORCID: https://orcid.org/0000-0003-1346-1327)
- Batuhan Çakır (ORCID: https://orcid.org/0000-0003-4513-606X)
- J. Michael Cherry (ORCID: https://orcid.org/0000-0001-9163-5180)
- Mim Hastie
- Ambrose Carr (ORCID: https://orcid.org/0000-0002-8457-2836)
- Signe Chambers
- Yang Joon Kim (ORCID: https://orcid.org/0000-0003-1742-5657)
- Behnam Robatmili
- Fran McDade
- Bailey Marshall (ORCID: https://orcid.org/0009-0005-2344-2260)
- Norbert K. Tavares (ORCID: https://orcid.org/0000-0002-4989-1620)
- Shibla Abdulla
- Nikhil Patel (ORCID: https://orcid.org/0000-0001-6221-3843)
- Andrew Tolopko
- Seve Badajoz
- James Chaffer (ORCID: https://orcid.org/0000-0003-1653-8449)
- Meghan Urisko
- Kuni Katsuya
- Ivana Jelic
- Amanda Infeld
- Brian Raymor
- Emanuele Bezzi (ORCID: https://orcid.org/0009-0004-4116-448X)
- Dana Sadgat
- Pedro Assis
- Timmy Huang
- Joyce Yan
- Mike C. Lin
- Karen Liang
- Harley Thomas
- Bruce Martin
- Arathi Mani
- Nayib Gloria
Institutions
- Wellcome Sanger Institute (GB)
- Chan Zuckerberg Initiative (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- Nucleic Acids Research
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1093/nar/gkae1142
- Citations
- 257
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 59.62