Author Correction: Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Author Correction: Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets ; volume:15 ; number:1 ; day:4 ; month:6 ; year:2024 ; pages:1 ; date:12.2024
Nature Communications ; 15, Heft 1 (4.6.2024), 1, 12.2024
- Urheber
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41467-024-48978-8
- URN
-
urn:nbn:de:101:1-2408112116068.497888497267
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:57 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Eisele, A. S.
- Tarbier, M.
- Dormann, A. A.
- Pelechano, Vicent
- Suter, David
- SpringerLink (Online service)
Ähnliche Objekte (12)
Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets
Joint analysis of scATAC-seq datasets using epiConv
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets
Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets
scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
RNA-Seq analysis of blood meal induced gene-expression changes in Aedes aegypti ovaries
ScRNA-seq and ST-seq in liver research
scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
Validating LIGER Integration Using scNMT-seq and SNARE-seq
RNA splicing analysis using heterogeneous and large RNA-seq datasets
An interpretable framework for clustering single-cell RNA-Seq datasets
GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure
Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets
Joint analysis of scATAC-seq datasets using epiConv
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets
Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets
scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
RNA-Seq analysis of blood meal induced gene-expression changes in Aedes aegypti ovaries
ScRNA-seq and ST-seq in liver research
scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
Validating LIGER Integration Using scNMT-seq and SNARE-seq
RNA splicing analysis using heterogeneous and large RNA-seq datasets
An interpretable framework for clustering single-cell RNA-Seq datasets
GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure
Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets
Joint analysis of scATAC-seq datasets using epiConv
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets
Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets
scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
RNA-Seq analysis of blood meal induced gene-expression changes in Aedes aegypti ovaries
ScRNA-seq and ST-seq in liver research
scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously
Validating LIGER Integration Using scNMT-seq and SNARE-seq
RNA splicing analysis using heterogeneous and large RNA-seq datasets
An interpretable framework for clustering single-cell RNA-Seq datasets