f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq-Reference-Cited by-同舟云学术

f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq

Published:2017-11-07 Issue:1 Volume:18 Page:
ISSN:1474-760X
Container-title:Genome Biology
language:en
Short-container-title:Genome Biol

Author:

Buettner Florian,Pratanwanich Naruemon,McCarthy Davis J.,Marioni John C.,Stegle Oliver^ORCID

Publisher

Springer Science and Business Media LLC

Link

http://link.springer.com/content/pdf/10.1186/s13059-017-1334-8.pdf

Reference43 articles.

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2. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, Stegle O. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015;33:155–60.

3. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, Slichter CK, Miller HW, McElrath MJ, Prlic M, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16:278.

4. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.

5. Hicks SC, Teng M, Irizarry RA. On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv. 2015:025528.

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