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Buettner, F. ; Natarajan, K.N.* ; Casale, F.P.* ; Proserpio, V.* ; Scialdone, A.* ; Theis, F.J. ; Teichmann, S.A.* ; Marioni, J.C.* ; Stegle, O.*

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

Nat. Biotechnol. 33, 155-160 (2015)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Embryonic Stem-cells; Gene-expression; Fate Decisions; Seq Analysis; Models; Noise; Transcriptomics; Mechanisms; Landscape; Cycle
Language english
Publication Year 2015
HGF-reported in Year 2015
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Journal Nature Biotechnology
Quellenangaben Volume: 33, Issue: 2, Pages: 155-160 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
PubMed ID 25599176
DOI 10.1038/nbt.3102
WOS ID WOS:000349198800020
Scopus ID 84923292191
Erfassungsdatum 2015-01-21
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