- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2041-1723
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Detection and removal of barcode swapping in single-cell RNA-seq data ; volume:9 ; number:1 ; day:10 ; month:7 ; year:2018 ; pages:1-6 ; date:12.2018
Nature Communications ; 9, Heft 1 (10.7.2018), 1-6, 12.2018
- Classification
-
Biowissenschaften, Biologie
- Creator
-
Griffiths, Jonathan A.
- Contributor
- DOI
-
10.1038/s41467-018-05083-x
- URN
-
urn:nbn:de:101:1-2018091211165412081643
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:24 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
- Griffiths, Jonathan A.
- Richard, Arianne C.
- Bach, Karsten
- Lun, Aaron T. L.
- Marioni, John C.
- SpringerLink (Online service)
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Practical Compass of Single-Cell RNA-Seq Analysis
Phylogenetic inference from single-cell RNA-seq data
Improved bacterial single-cell RNA-seq through automated MATQ-seq and Cas9-based removal of rRNA reads
Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq
Correction: Practical Compass of Single-Cell RNA-Seq Analysis
Genotype-free demultiplexing of pooled single-cell RNA-seq
Evaluating imputation methods for single-cell RNA-seq data
Benchmarking UMI-based single-cell RNA-seq preprocessing workflows
scReadSim: a single-cell RNA-seq and ATAC-seq read simulator
SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
Single-cell RNA-seq: advances and future challenges
Practical Compass of Single-Cell RNA-Seq Analysis
Phylogenetic inference from single-cell RNA-seq data
Improved bacterial single-cell RNA-seq through automated MATQ-seq and Cas9-based removal of rRNA reads
Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq
Correction: Practical Compass of Single-Cell RNA-Seq Analysis
Genotype-free demultiplexing of pooled single-cell RNA-seq
Evaluating imputation methods for single-cell RNA-seq data
Benchmarking UMI-based single-cell RNA-seq preprocessing workflows