CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data-Reference-Cited by-同舟云学术

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CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data

Published:2017-03-28 Issue:1 Volume:18 Page:
ISSN:1474-760X
Container-title:Genome Biology
language:en
Short-container-title:Genome Biol

Author:

Lin Peijie,Troup Michael,Ho Joshua W. K.

Funder

Department of Health, New South Wales Government (AU)

National Health and Medical Research Council (AU)

National Heart Foundation of Australia (AU)

Amazon Web Services

Publisher

Springer Science and Business Media LLC

Link

http://link.springer.com/content/pdf/10.1186/s13059-017-1188-0.pdf

Reference26 articles.

1. Pierson E, Yau C. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015; 16(1):1–10.

2. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11(7):740–2.

3. Scialdone A, Natarajan KN, Saraiva LR, Proserpio V, Teichmann SA, Stegle O, et al.Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015; 85:54–61.

4. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, et al.Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015; 33(2):155–60.

5. Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016; 17(1):1.

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