TY - JOUR
T1 - CZ CELLxGENE Discover
T2 - A single-cell data platform for scalable exploration, analysis and modeling of aggregated data
AU - CZI Cell Science Program
AU - Abdulla, Shibla
AU - Aevermann, Brian
AU - Assis, Pedro
AU - Badajoz, Seve
AU - Bell, Sidney M.
AU - Bezzi, Emanuele
AU - Cakir, Batuhan
AU - Chaffer, Jim
AU - Chambers, Signe
AU - Cherry, J. Michael
AU - Chi, Tiffany
AU - Chien, Jennifer
AU - Dorman, Leah
AU - Garcia-Nieto, Pablo
AU - Gloria, Nayib
AU - Hastie, Mim
AU - Hegeman, Daniel
AU - Hilton, Jason
AU - Huang, Timmy
AU - Infeld, Amanda
AU - Istrate, Ana Maria
AU - Jelic, Ivana
AU - Katsuya, Kuni
AU - Kim, Yang Joon
AU - Liang, Karen
AU - Lin, Mike
AU - Lombardo, Maximilian
AU - Marshall, Bailey
AU - Martin, Bruce
AU - McDade, Fran
AU - Megill, Colin
AU - Patel, Nikhil
AU - Predeus, Alexander
AU - Raymor, Brian
AU - Robatmili, Behnam
AU - Rogers, Dave
AU - Rutherford, Erica
AU - Sadgat, Dana
AU - Shin, Andrew
AU - Small, Corinn
AU - Smith, Trent
AU - Sridharan, Prathap
AU - Tarashansky, Alexander
AU - Tavares, Norbert
AU - Thomas, Harley
AU - Tolopko, Andrew
AU - Urisko, Meghan
AU - Yan, Joyce
AU - Yeretssian, Garabet
AU - Zamanian, Jennifer
N1 - Publisher Copyright:
© 2025 The Author(s).
PY - 2025/1/6
Y1 - 2025/1/6
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85214287290
U2 - 10.1093/nar/gkae1142
DO - 10.1093/nar/gkae1142
M3 - Article
C2 - 39607691
AN - SCOPUS:85214287290
SN - 0305-1048
VL - 53
SP - D886-D900
JO - Nucleic Acids Research
JF - Nucleic Acids Research
IS - D1
ER -