Clues to quality of journals
Open Access Policy of Helmholtz Association 2016
CC Licencences
Publication Metrics
Home
Deutsch
New EVA Application
Advanced Search
Browse by ...
Publication overview
Statistics (last 5 years)
OA Publications
Submit Publication
Import publication from...
Report missing publication
Contact persons
Help
Text
Endnote (RIS)
BibTeX
Publ. Version/Full Text
DOI
PMC
 |
Open Access Gold |
|
as soon as is submitted to ZB.
scCODA is a Bayesian model for compositional single-cell data analysis.
Nat. Commun. 12:6876 (2021)
Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
14.919
3.055
2
23
Annotations
Special Publikation
Hide on homepage
Publication type
Article: Journal article
Document type
Scientific Article
Language
english
Publication Year
2021
HGF-reported in Year
2021
ISSN (print) / ISBN
2041-1723
e-ISSN
2041-1723
Journal
Nature Communications
Quellenangaben
Volume: 12,
Issue: 1,
Article Number: 6876
Publisher
Nature Publishing Group
Publishing Place
London
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503800-001
Grants
Projekt DEAL
WOS ID
WOS:000722866700018
Scopus ID
85119876768
PubMed ID
34824236
Erfassungsdatum
2021-12-10