arrow
Return

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

delete2024-11-28
delete4
delete
OA
AI
A
Abdulla, Shibla
B
Brian D. Aevermann
A
Assis, Pedro
B
Badajoz, Seve
S
Sidney M. Bell
E
Emanuele Bezzi
B
Batuhan Çakır
C
Chaffer, Jim
C
Chambers, Signe
J
J. Michael Cherry
C
Chi, Tiffany
J
Jennifer Chien
L
Leah C. Dorman
P
Pablo E. García-Nieto
G
Gloria, Nayib
H
Hastie, Mim
H
Hegeman, Daniel
J
Jason A. Hilton
H
Huang, Timmy
I
Infeld, Amanda
A
Ana-Maria Istrate
J
Jelic, Ivana
K
Katsuya, Kuni
Y
Yang Joon Kim
L
Liang, Karen
L
Lin, Mike
L
Lombardo, Maximilian
B
Bailey Marshall
M
Martin, Bruce
M
Mcdade, Fran
M
Megill, Colin
N
Nikhil Patel
A
Alexander V. Predeus
R
Raymor, Brian
R
Robatmili, Behnam
R
Rogers, Dave
E
Erica Rutherford
S
Sadgat, Dana
A
Andrew Shin
C
Corinn Small
T
Trent M. Smith
S
Sridharan, Prathap
A
Alexander J. Tarashansky
N
Norbert K. Tavares
T
Thomas, Harley
T
Tolopko, Andrew
U
Urisko, Meghan
Y
Yan, Joyce
G
Garabet Yeretssian
J
Jennifer Zamanian
M
Mani, Arathi
J
Jonah Cool
A
Ambrose Carr *
DOI:10.1093/nar/gkae1142delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
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.
Keywords:
ATLAS
LUNG
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Nucleic Acids Research cover
Nucleic Acids Research
IF:
13.1
Papers:
3.6W
Citations:
29.0W

Organization

S
Stanford University
Scholars:
9.6W
Papers: 8.2W
Citations: 17.0W
C
chan zuckerberg initiative (czi)
Scholars:
246
Papers: 73
Citations: 0
L
li ka shing center
Scholars:
31
Papers: 21
Citations: 0
W
wellcome trust sanger institute
Scholars:
6.9K
Papers: 4.3K
Citations: 17
researcher View more organizations