- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1474-760X
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape ; volume:22 ; number:1 ; day:29 ; month:10 ; year:2021 ; pages:1-18 ; date:12.2021
Genome biology ; 22, Heft 1 (29.10.2021), 1-18, 12.2021
- Creator
-
Zappia, Luke
Theis, Fabian J.
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s13059-021-02519-4
- URN
-
urn:nbn:de:101:1-2022010819243813749466
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:22 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zappia, Luke
- Theis, Fabian J.
- SpringerLink (Online service)
Other Objects (12)
Current best practices in single‐cell RNA‐seq analysis: a tutorial
Single-cell RNA-seq denoising using a deep count autoencoder
Modeling fragment counts improves single-cell ATAC-seq analysis
Geometric source separation : algorithms and applications
Mathematics in independent component analysis
SCANPY: large-scale single-cell gene expression data analysis
Charting the endocrine cell system in health and diabetes using single-cell transcriptomics
Computational prediction of cellular states in time-lapse microscopy based on single-cell morphodynamics
Single-cell transcriptomics driven exploration of pathogenesis and impaired regeneration in chronic lung diseases
From RNA-sequencing to imaging flow cytometry: Profiling large single-cell datasets with machine learning
Quantification and analysis of single-cell protein dynamics in stem cells using time-lapse microscopy
MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
Current best practices in single‐cell RNA‐seq analysis: a tutorial
Single-cell RNA-seq denoising using a deep count autoencoder
Modeling fragment counts improves single-cell ATAC-seq analysis
Geometric source separation : algorithms and applications
Mathematics in independent component analysis
SCANPY: large-scale single-cell gene expression data analysis
Charting the endocrine cell system in health and diabetes using single-cell transcriptomics
Computational prediction of cellular states in time-lapse microscopy based on single-cell morphodynamics
Single-cell transcriptomics driven exploration of pathogenesis and impaired regeneration in chronic lung diseases
From RNA-sequencing to imaging flow cytometry: Profiling large single-cell datasets with machine learning
Quantification and analysis of single-cell protein dynamics in stem cells using time-lapse microscopy
MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
Current best practices in single‐cell RNA‐seq analysis: a tutorial
Single-cell RNA-seq denoising using a deep count autoencoder
Modeling fragment counts improves single-cell ATAC-seq analysis
Geometric source separation : algorithms and applications
Mathematics in independent component analysis
SCANPY: large-scale single-cell gene expression data analysis
Charting the endocrine cell system in health and diabetes using single-cell transcriptomics
Computational prediction of cellular states in time-lapse microscopy based on single-cell morphodynamics
Single-cell transcriptomics driven exploration of pathogenesis and impaired regeneration in chronic lung diseases
From RNA-sequencing to imaging flow cytometry: Profiling large single-cell datasets with machine learning
Quantification and analysis of single-cell protein dynamics in stem cells using time-lapse microscopy